When someone mentions artificial intelligence today, the conversation almost inevitably turns in one of two directions: into enthusiasm for its capabilities or concern about its consequences. Yet it rarely stops at a question that is perhaps more important than both. How is this technology changing our concept of knowledge? What does it mean to know something in a world where a machine returns a comprehensible, confident and, if we use the best models, almost always correct answer to every question?

To even approach this question, we must abandon one of the most widespread oversimplifications. The claim that generative artificial intelligence “merely predicts the next word” is technically accurate, but flawed in the same way it would be flawed to say the brain merely transmits electrical impulses. It is true, but this description tells us nothing about thinking, memory or consciousness.

During training, a language model analyzes vast amounts of text and compresses them into a substantially smaller mathematical structure. The training material encompasses a large portion of humanity’s digitized knowledge. Although this is by no means all the knowledge humanity possesses, since immense parts of it remain embodied in unwritten practices and experiences, it is the most extensive collection of recorded knowledge ever gathered in one place.

For the model to master it, it must extract its essence: repeatable patterns, semantic relationships and hidden connections between concepts. The result is a geometry of meanings in which ideas are arranged according to their mutual relationships. “King” and “queen” are close in this space because the model itself discovered they share a similar semantic relationship. Based on the training, a kind of conceptual map emerges, illustrating the relationships between ideas.

This is best understood through a metaphor. The Enlightenment encyclopedia was the first great attempt to organize, connect and make human knowledge available to everyone. A large language model goes a step further: it not only gathers and organizes knowledge, but compresses it into a mathematical space that can adapt to each individual interlocutor. It is a kind of active encyclopedia that responds, adapts and creates.

But this metaphor hides something even more surprising. The conceptual map that emerges during training bears a striking resemblance to one of the oldest philosophical theories. Almost two thousand five hundred years ago, Plato conceived that behind all individual phenomena lie abstract, immutable forms, which he called Ideas. The mathematical space of a large language model is a modern version of this concept: it stores the relationships between meanings, not individual texts. The machine has extracted these abstract forms from our writings.

This is where the crucial twist occurs. Plato’s world of Ideas was normative. It served as the standard by which we distinguished truth from the shadows in the cave, to use the famous allegory. The mathematical space of the language model, however, is descriptive. It reflects what is consistently present in the texts on which it was trained. This is not necessarily the same as what is true. The model does not distinguish between what is frequently repeated and what is true. And crucially: there is no arbiter within the system itself. That role belongs to the user. It is the user who must distinguish the Ideas from the shadows. The model provides an answer, and the user must judge whether it holds true.

We often hear the objection that models “do not understand” and cannot explain their answers. This is becoming less true, as advanced models provide coherent explanations, including arguments and the limitations of their answers. The problem, therefore, is no longer the quality of the explanation, but the type of knowledge that underpins it. When an expert explains why something is true, it is supported not only by their individual experience, but by the entire social infrastructure of knowledge: the process of verification, doubt, and professional consensus, and above all, personal responsibility for what is said. An expert stands behind their claim with integrity and reputation. A model, on the other hand, explains in isolation and reconstructs the most probable explanation from mathematical patterns of the past. An explanation in itself, therefore, is not yet knowledge. Knowledge is created only when someone takes responsibility for the explanation.

Perhaps it is time to start distinguishing between different ways of knowing. An expert knows because they understand and participate in the living process of science, which seeks what might tomorrow refute our current theories. A model “knows” because it has extracted a pattern from a massive amount of material. Although modern systems are already discovering new solutions in mathematics or biology that surpass the human capacity for synthesis, they still lack the capacity for theoretical thinking and strategic engagement in the world.

The danger, therefore, does not lie in wrong answers, although these also occur. The danger is that we begin to accept seemingly very well-founded answers and in doing so stop questioning their origin. The model offers us the statistical consensus of the past, while science demands the deliberative consensus of the present, which is created through discussion, verification, and doubt. Generative artificial intelligence does not take away our questions, but it can unaccustom us to them with its comfortable, highly persuasive certainty.

We have created a highly useful mathematical tool that is closer to Plato’s world of Ideas than anything so far in human history. It is the best encyclopedia the world has ever seen, and at the same time the most convincing substitute for thinking imaginable. Both are true. But if we stop asking questions and merely consume the machine’s excellent answers, we return to Plato’s cave voluntarily. With one single difference: the shadows on the wall are now in high resolution and look exceptionally convincing.

Anatomija vednosti / Kvarkadabra

Platon v stroju

Ko danes kdo omeni umetno inteligenco, se pogovor skoraj neizogibno zasuče v eno od dveh smeri: v navdušenje nad njenimi zmožnostmi ali skrbjo nad posledicami. Redko pa se ustavi pri vprašanju, ki je morda pomembnejše od obojega. Kako ta tehnologija spreminja naš pojem znanja? Kaj pomeni nekaj vedeti v svetu, v katerem nam stroj na vsako vprašanje vrne …

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a month ago · 5 likes · Sašo Dolenc

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In early December 2025, I published a critique of the book Quantum History: A New Materialist Philosophy (Bloomsbury Academic, 2025), wherein I analyzed Žižek’s interpretation of quantum mechanics: A Critique of Žižek’s Quantum Ontology. Slavoj Žižek responded in early January 2026 with an extensive text titled A Footnote on the Quantum Incompleteness of Reality, in which he addressed my objections in depth and offered a systematic defense of his position.

This text is my reply to his response. The debate between us concerns one of the fundamental questions raised by quantum physics: what does quantum mechanics tell us about the nature of reality? Does the fact that quantum systems lack definite classical properties prior to measurement mean that reality itself is somehow unfinished or incomplete? Or is it simply that reality is different from how we are accustomed to thinking about it—not incomplete, but structured in a way that eludes our everyday notions?

What Does It Even Mean to Exist?

Before we delve into the debate of whether quantum reality is “incomplete” or “unfinished,” we must take a step back and ask a fundamental question: what does it even mean to exist? Without defining this concept, Žižek’s claim of an “ontological gap” has no clear meaning, as we do not know exactly what is supposed to be missing.

The history of science offers a good example of such predicaments. Mathematicians struggled for centuries with the paradoxes of infinity. Zeno’s paradoxes already hinted that a logical problem was hidden in the very heart of motion—how can one cross space if it is infinitely divisible? When Newton and Leibniz later developed infinitesimal calculus—a tool that was a necessary condition for the birth of modern physics and the precise description of motion—this predicament only deepened. They operated with quantities that were smaller than something, yet still not nothing.

This triggered a sharp response from George Berkeley, who famously labeled these elusive infinitesimals as “ghosts of departed quantities.” It seemed that a logical hole gaped at the very foundations of mathematics and that physics was building on shaky ground. But a turnaround came in the 19th century, when mathematicians strictly defined the concepts of limit, convergence, and continuity. Berkeley’s “ghosts” and Zeno’s paradoxes did not disappear because new facts about nature were discovered, but because the concepts they operated with were precisely defined.

I believe a similar conceptual clarification is needed in our debate as well. Therefore, I propose a working definition: a physical state exists if it is mathematically uniquely determined and causes empirical consequences. This definition entails two conditions. The first condition—mathematical determination—means that we can describe the state precisely, that it has a clear identity, that it is not ambiguous or undefined. The second condition—empirical consequences—means that the state is not merely a mathematical fiction, but leaves traces in the world that we can detect.

According to this definition, a qubit in superposition is a fully existing physical state. Mathematically, it is uniquely determined—if we know the wave function, we know everything there is to know about the system. The point on the Bloch sphere is precisely determined; it lacks nothing. And a qubit undoubtedly causes empirical consequences—interference, correlations, the operation of quantum algorithms.

Žižek’s claim that quantum reality is “incomplete,” therefore, presupposes a different definition of existence—a definition according to which something fully exists only when it possesses definite classical properties, such as position or spin along a given axis. But why should we accept such a definition? There is no reason to make classical properties the criterion for existence.

Two Interpretations

I can now summarize Žižek’s position and my own more precisely.

Žižek starts from the observation that quantum systems do not possess definite values of classical properties prior to measurement. An electron in superposition is neither “here” nor “there,” but in a state that cannot be described by any definite position. Only when we perform a measurement does the electron “acquire” a definite position. Standard quantum mechanics tells us which results are possible and what their probabilities are, but it does not tell us why we obtain precisely this result and not another.

Žižek interprets this to mean that prior to measurement, the electron lacks, for instance, a definite position because reality itself has not yet “created” it. Measurement is not the discovery of a pre-existing property, but the moment when reality is actually constituted. The world is thus not a closed whole with predetermined properties, but an open process that is constantly completing itself. In the very structure of being, there is a “gap”—a space of indeterminacy that is filled only through interaction.

My position is different. I agree that an electron in superposition does not possess definite properties in the classical sense. But I do not infer from this that it is “missing something” or that reality is not fully constituted. An electron in superposition is in a perfectly definite quantum state—only this state is not a state with definite classical properties. It is a state of a different type, described by the wave function. A qubit in superposition is not an “undetermined bit that does not yet know whether it is 0 or 1.” It is a perfectly definite state on the Bloch sphere, which is, however, neither 0 nor 1—it is something third, something unknown to classical physics.

Quantum Computers as a Touchstone

Philosophical debates on the interpretation of quantum mechanics have persisted for a century and appear irresolvable. However, in recent decades, something has emerged that can serve as a kind of empirical test: quantum computers.

A quantum computer is not merely a faster classical computer. It is a device that operates directly with quantum states and uses them as computational resources. When a quantum computer executes an algorithm, it exploits the fact that a qubit can carry more information than a classical bit, and that entangled qubits can show correlations that have no classical equivalent.

Here arises the key question for Žižek’s interpretation. If quantum states were truly “unfinished” or merely potential—if qubits floated in ontological indeterminacy that would be resolved only upon measurement—how could they serve as a reliable computational substrate? An algorithm requires precise control over states and predictable evolution. An entity that “does not know what it is” until we force it into a decision could not perform precise and reproducible calculations.

The fact that quantum computers work is empirical proof that quantum states are fully real entities with precisely determined properties. Superposition is not ontological indeterminacy, but a precisely defined state with which we can compute. Entanglement is not a “gap” in the structure of reality, but a source of correlations that we can exploit. Engineers building quantum computers do not operate with ontological gaps, but work with fully determined physical states that they must protect from environmental disturbances.

Quantum Error Correction: Can We Fix Something That Does Not Exist?

The argument regarding quantum computers can be taken even further. One of the most fascinating engineering disciplines of our time is quantum error correction (QEC)—and it is precisely this discipline that presents perhaps the toughest test for the thesis that quantum reality is “gappy” or “unfinished.”

In his book, Žižek uses the vivid metaphor of the universe as a video game, where a “lazy programmer” (nature) does not render the interior of a house until the player enters it, in order to save processing power. According to this logic, reality is optimized with emptiness; until we look at it, it is not fully determined. But if this were true, quantum computers could not function the way they do.

Quantum states are extremely fragile. Even the slightest disturbance from the environment—a thermal fluctuation or a random electromagnetic wave—can damage the information carried by a qubit. If qubits were merely an indeterminate “potentiality” waiting for our gaze to be realized, such damage could not be repaired. How can you fix something that is not yet truly formed?

Engineers have solved this predicament with a sophisticated process called syndrome measurement (or syndrome extraction). Instead of causing a collapse and erasing the superposition with a “full” question (“What state are you in?”), they ask the system only an indirect question: “Has an error occurred?” The system can diagnose its own injury—e.g., “the phase has flipped on the third qubit”—without revealing its content. Quantum information thus remains hidden and intact.

Based on this diagnosis, we can perform a precise correction—an operation that rotates the state back into the correct position. And here lies the crux of my argument: if the reality of the qubit were indeed an ontologically incomplete “fog” or indeterminacy, such surgical intervention would not be possible. You cannot break a gap, and you cannot fix a gap. You can only repair an entity that has a solid, existing structure. The success of quantum error correction proves that the information was there all along—fully present and real, even if inaccessible to our direct view.

Where Does the Collapse Come From? The Materiality of Information and the Transition Between Regimes

In his response, Žižek challenges me directly with a question: if quantum reality is completely consistent, where does the collapse of the wave function come from? What “forces” the waves to collapse? This is the central question of our debate and deserves a precise answer.

To answer it, I must first explain what information actually is and why its nature is key to understanding this problem. We live in a time when we often perceive information as something incorporeal, as if data were pure abstraction floating independently of the physical world. But this is an illusion. Every datum, every record, every memory requires a material carrier. Thoughts do not exist without neural connections and biochemical processes in the brain. A photograph does not exist without changes in the structure of the medium on which it is recorded. As physicist Rolf Landauer determined: information is inseparably linked to matter and energy.

Information can be understood as a physical state of a system that gives us an answer to a question. But here a key distinction arises. Some physical states can be read, copied, and transmitted without being destroyed. We can read a book and pass it on—the words on the paper remain. We can copy a file to another disk, and the original remains unchanged. This is the foundation of everything we call communication and knowledge transfer: the ability to separate information from its original carrier and transfer it without destroying it in the process.

However, when we enter the quantum world, this ability fails. Quantum states possess an unusual property: an arbitrary unknown quantum state cannot be copied without changing the original. This is not a technical limitation that might one day be overcome with better equipment. It is a mathematical consequence of the very structure of quantum theory, known as the no-cloning theorem. A qubit can exist in superposition, but if we want to “read” it—convert it into classical information that we can communicate—we inevitably change it.

Now I can answer Žižek’s question regarding the origin of the collapse. The key lies in the nature of the carriers of classical information. The paper we write on, the magnetic disk where we store data, the screen we read from—all these are macroscopic objects composed of an unimaginably large number of atoms and molecules. With such a large number of particles, the quantum nature of individual building blocks becomes irrelevant. Quantum effects are indeed still present at the level of individual atoms, but at the level of the entire system, they are no longer detectable or relevant. Macroscopic states are robust, stable, and—crucially—can be copied without destruction.

The collapse of the wave function is, therefore, not the consequence of some “gap” or deficiency in quantum reality. It is the consequence of the transition from a regime where we deal with individual quantum systems to a regime where we deal with macroscopic systems composed of a huge number of particles. In the first regime, quantum logic applies—states are rich but non-transferable. In the second regime, classical logic applies—states are in a sense impoverished, but can be copied and transmitted.

The randomness that appears during measurement stems from this transition. A quantum state is mathematically completely determined—a qubit pointing in a certain direction on the Bloch sphere has a precisely defined identity. But when we force it to express itself in the language of classical outcomes—“up” or “down”—this rich structure cannot survive intact. The result is determined probabilistically, and this probability is not a sign of a lack in reality, but a consequence of the structural difference between the two regimes.

Quantum computers vividly illustrate this difference between regimes. The main challenge in building them is not forcing qubits into existence—qubits exist quite really. The main challenge is keeping the system in the quantum regime long enough to perform the desired calculation. Engineers fight against decoherence—against the process in which a quantum system interacts with the macroscopic environment and transitions into the classical regime. The goal of quantum computing is to operate in the quantum regime as long as possible before we must translate the result into classical information that we can read and communicate.

Are the Parallels Between Quantum Physics and Language Relevant?

In his response, Žižek highlights structural similarities between the quantum world and the world of language: in both domains, possibility as such produces real effects; in both, an event becomes fully real only through some kind of registration; in both, we encounter specific temporal structures.

These parallels are interesting and worth considering. However, structural similarity is not the same as a deeper affinity. The fact that two systems exhibit similar patterns does not mean that they are driven by the same logic. Equations describing heat diffusion can also describe the spread of rumors in society, but this does not mean that society is driven by the same physical laws as gases, or that atoms “communicate” like people.

Even if these parallels held true in the full sense, it would not follow that quantum reality is incomplete in the way Žižek claims. It would follow, at most, that both systems are structured around a similar relationship between possibility and actuality. But this relationship is not in itself a relationship of lack.

And again, we can turn to quantum computers. If the connection between the quantum and the symbolic order were as deep as Žižek suggests, we would expect quantum computers to show a special affinity for linguistic or symbolic operations. But in reality, quantum algorithms are most powerful in completely non-linguistic problems—in integer factorization, in simulating molecules, in optimization problems.

Conclusion: Do Qubits Exist?

Our debate ultimately boils down to a single question: do qubits—quantum states in superposition—truly exist? Are they something actual, something real? Or are they merely some kind of intermediate, unfinished states waiting for a measurement to bring them into full existence?

The answer depends on how we define existence. If we take classical properties—definite position, definite velocity, definite spin value—as the criterion for existence, then qubits in superposition indeed do not “exist” in the full sense. They lack these properties.

This dilemma is not new. It recalls the famous debate of the late 19th century regarding the existence of atoms. Ernst Mach, one of the most influential physicists and philosophers of science of that time, insisted that atoms were merely “useful fictions”—mathematical tools for organizing data, but lacking real existence since they could not be directly seen. For Mach, only that which could be directly perceived existed; everything else was metaphysics. On the other side, Ludwig Boltzmann tragically insisted that atoms are real and that thermodynamic laws stem from their statistical motion. History sided with Boltzmann. When Einstein explained Brownian motion, it turned out that atoms were not merely computational shortcuts, but real building blocks of matter.

The parallel is telling. Mach rejected atoms because they did not fit his criteria of perceptibility, yet the development of physics went its own way. Are we not in a similar position today regarding qubits? Perhaps the success of quantum computers and the capability of quantum error correction remind us that the criterion of reality is not necessarily our direct experience or classical intuition, but the consistency and resilience of the structures with which we can interact in the world.

However, if we take mathematical determination and empirical consequences as the criterion for existence, then qubits undoubtedly exist. They are mathematically precisely determined—the wave function tells us everything there is to know about the system. And they cause empirical consequences—without them, there would be no interference, entanglement, or quantum algorithms. And perhaps most importantly: we can actively control, correct, and exploit these consequences, as demonstrated by quantum error correction.

Žižek’s perception of an “ontological gap” thus turns out to be the consequence of a specific definition of existence—one that perhaps held in the classical world but becomes too narrow when we enter the quantum one. Just as Zeno’s paradoxes dissipated when mathematics developed tools to describe continuity, the illusion of an “incomplete reality” dissipates when we abandon the requirement that a thing must have a classically defined form to count as real.

The quantum world is not unfinished. It is fully realized, only its structure is not classical. Quantum states are deterministic and cause measurable consequences, yet they exist in a regime that does not allow copying. The classical world, on the other hand, is based on robustness and reproducibility. The transition between them—what we call collapse—is not the moment when reality is just being “created,” but a transition between two modes of information organization.

Instead of a world full of ontological cracks, it makes more sense to speak of a world of two regimes. Our task is not the mystification of the transitions between them, but the understanding of the physical mechanisms that allow a stable classical reality, in which we can dwell and think, to emerge from a rich quantum foundation.

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Translated from the Slovene original, available here:

ANATOMIJA VEDNOSTI / Sašo Dolenc

Ali lahko popravimo nekaj, kar ne obstaja? (Odgovor Žižku)

V začetku decembra 2025 sem objavil kritiko knjige Quantum History: A New Materialist Philosophy (Bloomsbury Academic, 2025), v kateri sem analiziral Žižkovo interpretacijo kvantne mehanike: Kritika Žižkove kvantne ontologije. Slavoj Žižek se je v začetku januarja 2026 odzval z obsežnim tekstom z naslovom…

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4 months ago · 3 likes · Sašo Dolenc

In the digital age, we tend to believe the illusion that information is something ethereal, immaterial. We speak of the “cloud” in which we store our memories, as if data were floating in some Platonic heaven, separated from dirty and heavy matter. Yet anyone who has ever stood in the hot and noisy space of a server center knows the truth is different. Information is, at its core, material.

To truly know anything—to record a datum, store a memory, or measure a value—we must necessarily make use of matter. We cannot think without the metabolism of glucose and the transmission of electrical impulses between neurons in the brain, and we cannot store a photograph without altering the magnetic state on a hard drive. Decades ago, the pioneer of information theory Rolf Landauer summarized this inexorable fact in a simple maxim: information is physical. Every bit, every “yes” or “no” we write down, exacts its energy toll. Information processing is not something abstract and immaterial, but a material thermodynamic process.

In what follows, we will first outline how physics and information theory understand the difference between quantum and classical information, and then, in this light, address Žižek’s understanding of the philosophical implications of quantum physics.

What is Information?

Before we proceed, we must precisely determine what we are talking about. For the purposes of this reflection, let us define information as a physical state of a system that can serve as an answer to a question. This definition captures three essential components. First, information is always inscribed in matter—in magnetic domains, in a configuration of neurons, in the polarization of a photon. There is no abstract information floating independently of its material carrier. Second, a state in itself is not yet information; it becomes so only when we use it to reduce uncertainty, when it tells us something about the world. Third, information is always an answer within a system of differences: a particle is here and not there, a switch is on and not off, the temperature is 23°C and not 25°C.

However, not every physical state is information in the full sense of the word. The key question determining the entire discussion is: can this state be used multiple times? Can the answer to the question “Where is the electron?” be transferred from one system (a detector) to another (a notebook, a colleague’s memory) without losing or altering the original in the process?

In the macroscopic world—the world where we live, write books, and build civilization—the answer is yes. This is the world of classical information, where copying is not only possible but represents the very essence of communication. Imagine a book: when you read it and pass it on, the content remains the same. The words do not vanish from the paper just because someone has read them. You can transfer a bit on a hard drive to another drive without changing the original. This ability to copy—to share information without destruction—is not a technical detail but the foundation of our ability to transmit knowledge. Without copying there is no communication, without communication there is no shared knowledge, without shared knowledge there is no civilization.

Classical information is thus defined precisely by its robustness and transferability. It is that type of physical state which does not fear interaction with the environment. On the contrary, for its purpose—to be read, shared, stored—it requires interaction and survives it intact.

The Puzzles of the Quantum World

However, when we enter the depths of the microworld with this demand for stable, transferable information, we hit a wall. Quantum physics confronts us with entities that resist precisely what we consider the foundation of knowledge: they refuse to be copied.

The quantum world knows physical states—qubits—that are indeed fully determined by mathematics, yet they necessarily change upon any attempt to copy an arbitrary, unknown state. One of the most fundamental theorems of quantum mechanics, the No-Cloning Theorem, is not merely a technical obstacle that we might one day overcome with better instruments. It is a mathematical consequence that necessarily follows from the structure of quantum theory. As long as quantum mechanics holds true, cloning is impossible. A quantum particle can exist in a state of superposition, but if we attempt to “read” or duplicate this state, we irrevocably alter or destroy it. It is as if we held a book that fades the moment we try to photocopy it.

We call this type of physical state quantum information. It exists, it is real, it is mathematically precisely determined, but it is radically “private,” incommunicable. We cannot directly transmit knowledge with quantum objects because knowledge requires that we separate the datum from the carrier and send it forward without altering the original in the process.

Precisely this tension between elusive quantum reality and the unavoidable need for stable, transferable information is the point where physics confronts philosophy. To understand where the crux of the problem lies, we must look more closely at the difference between quantum and classical information. It is not merely a technical distinction; it is about fundamentally different ways in which physical states interact with the world.

The Anatomy of a Qubit

Physicists often describe quantum information using mathematical tools such as a vector in Hilbert space or a wave function. For an intuitive understanding, we can imagine the most basic quantum system—a qubit—as a point on the surface of a sphere (the Bloch sphere). This point can lie anywhere: on the equator, near the north pole, anywhere in between. This is a space of infinite shades, or a continuum of possibilities. A classical bit, on the other hand, is like a switch: it points either up (state |1⟩) or down (state |0⟩). Two possibilities, nothing in between.

Mathematically speaking, a qubit is undoubtedly richer—it contains infinitely more possible states than a bit. But this richness brings a fatal limitation: a qubit is non-transferable as a qubit. We cannot take an arbitrary quantum state and copy it onto another system without altering the original in the process. This is not a technical deficiency of our devices, but a fundamental property of quantum mechanics.

Imagine holding a book that you cannot read without it changing in the process. The moment you look at the first page, the words on it rearrange themselves. If you want to show the book to a friend, a different version appears to him than to you. This is the world of quantum information—unique, unrepeatable, and, crucially, non-transferable.

How, then, do we arrive at information that we can share? Here enters the process physicists call the collapse of the wave function or, more neutrally, quantum measurement. When a quantum particle—say, a photon or an electron—collides with a macroscopic detector, something specific happens: from a continuum of possibilities (the qubit can point in any direction on the Bloch sphere) we obtain a discrete result (the detector indicates “yes” or “no”, a dot appears here or there).

The physical state has passed from a regime where it cannot be copied into a regime where copying is not only possible but trivial—the measurement result can be written in a notebook, photographed, sent via email, stored in an archive. This transition is a functional transformation: from a “single-use answer” we obtain a “multi-use answer.” From a private state, we obtain a public state. From the incommunicable, we obtain the communicable.

The Nature of Quantum Information

But every translation has its price. When we translate a poem from French to Slovenian, we gain something (understanding), but we also lose something (the sonority of the original, wordplay, cultural references). Something similar happens with the transition from the quantum to the classical world.

Imagine someone asks you: “In which direction is this qubit pointing?” The qubit might be pointing at an angle of 37.4° relative to the north pole. But a classical detector does not know how to measure “37.4°”—it only knows how to answer “up” or “down”. The rich quantum state must be compressed into one of two possibilities. In doing so, we necessarily lose information—that unique, specific angle of 37.4° is irretrievably lost.

And here is the crucial point: this loss is not a flaw in our technology, but an unavoidable consequence of the very demand for transferability. If we want information that we can read multiple times, copy, and share, we must accept that it will be discrete and robust—therefore smaller than the full quantum reality.

The randomness that appears during measurement—whether the detector will show “up” or “down” for a qubit at 37.4°—is a statistical consequence of mapping the continuum into discrete categories. When we force a multidimensional space of possibilities into a binary decision, randomness must occur. Physics strictly determines this: for a qubit at 37.4°, the probability for “up” is, say, 65%, and for “down” 35%. Which of these two results we obtain in an individual measurement is random, but long-term statistics precisely follow these probabilities.

It might seem that the solution is simple: why not simply measure the qubit very precisely so that we do not change it in the process? Why not use more sensitive instruments that could look at the quantum state in detail without disturbing it?

The answer is clear: this is not possible. Every physical process that would extract classical information (that is, information we can copy) from a quantum state must necessarily alter that state. This is a limitation of the very nature of information. Transferability, or the ability to copy, and quantum superposition are mutually exclusive. This is precisely why in quantum cryptography an eavesdropper is always detected: if someone attempts to copy a quantum-encrypted message, they must perform a measurement that necessarily changes the original states and thereby leaves traces.

Reality Beyond Information

At this point, the reader might ask: if the transition to classical information is necessary, and if in doing so we necessarily lose the original quantum state, how do we even know that that primal and elusive level of reality exists? Is this not merely a mathematical fiction, an elegant formalism without physical substance? How can we be certain that the world is not composed only of the bits we see, and that qubits are not merely a theoretical construct?

Experiments offer us the answer. One of the most beautiful and simultaneously most intriguing is the double-slit experiment. When we send individual particles—photons or electrons—through two slits, but do not detect which slit they went through, they do not accumulate on the screen in two piles behind each slit, as we would expect from tiny marbles. Instead, they create an interference pattern of bright and dark bands, which is characteristic of waves. Each electron contributes a single dot, but when we send thousands of them, these dots arrange themselves into a wave pattern.

This interference pattern is key. It tells us that an individual particle—a single electron sent through the apparatus—in some way “knows” about both slits simultaneously. If we close one slit, the interference disappears. If both slits are open but we do not measure which one the particle went through, we obtain a wave pattern. However, if we place detectors that “observe” the particle’s path, the interference is lost and we get two piles. It is as if the particle, the moment we began to observe it, “decided” to be a classical marble.

How are we to understand this? The standard explanation is that when we do not observe the particle—that is, when we do not convert information about its path into a classical form—it behaves as a wave function, as a cloud of probability traveling through both slits simultaneously. This wave function is not merely a mathematical tool for calculating probabilities. It is a physically real state that produces observable consequences in the form of an interference pattern. But when we place a detector and obtain classical information (”the particle went through the left slit”), we say that the quantum state collapses: the wave function “collapses” into one of the possible outcome states, and the interference disappears. Regardless of whether we understand the collapse as a real physical process or as an effective descriptive rule, the experimental outcome remains unchanged.

Objective Quantum Reality

The double-slit experiment is a sort of “reflection” of invisible quantum reality. It tells us that there exists a level of physical existence that eludes our direct access, yet nevertheless unequivocally manifests itself in empirical consequences. This level is not metaphysical speculation or mathematical fantasy, but is necessarily presupposed by experimental data.

If the world were composed only of classical bits, if particles were always either “here” or “there,” there would be no way to explain the interference pattern. We could say: “The particle goes through the left slit, but somehow ‘senses’ the presence of the right slit.” But this is merely a word game that does not solve the problem. The most consistent way to explain interference is to admit: before measurement, the particle is neither “here” nor “there,” but is in quantum superposition, in a state that has no classical equivalent.

Here, key philosophical questions open up. Does the wave function describe something that is in the world, or merely our knowledge about the world? Is a qubit a real entity, or merely a probabilistic model?

For a long time, the prevailing opinion was that the wave function is merely an epistemological tool, or a way to describe our knowledge. But experiments, such as Bell’s inequality and its violations, have seriously undermined this position. These experiments show that if the wave function truly represents only our knowledge of pre-existing properties (hidden variable theories), then these properties must be transmitted faster than light, which contradicts the theory of relativity. Most physicists therefore take the wave function as a description of an objective physical state.

However, this “objective physical state” is not a state in the classical sense. An electron in superposition is not an electron that is somewhere, with us simply not knowing where. The electron is in a state that corresponds to no classical location. This is the essence of quantum weirdness: the world allows for physical states that resist the classical ontology of “objects with properties.”

What is the Reality of a Qubit?

From the perspective of information theory, we can formulate this as follows: qubits are real, but they are not transferable. They exist as physical states inscribed in matter (electron spin, photon polarization), but we cannot “read” them and send them forward without changing them. They are like secret documents written in ink that vanishes upon touch. We know they exist, we see the consequences of their existence, but we cannot copy them.

Bits, as the basis of classical information, are on the other hand real and transferable. They are that type of physical state which survives copying. And since knowledge depends on copying information, bits are practically the only type of states upon which we can build stable shared knowledge.

This does not mean that qubits are less real than bits. It only means that they serve a different purpose. Qubits are what is; bits are what we can say. Reality is full and rich at the level of qubits, but we cannot transfer this fullness forward. To be able to speak of it at all, we must translate it into bits.

This explanation allows us to avoid two extremes. On the one hand, we do not say that quantum reality is “incomplete” or “indeterminate.” A qubit at 37.4° is perfectly determined, for it is precisely in this state and not in another. Randomness enters only at the translation into a bit, not in the qubit itself.

On the other hand, we also do not advocate naive realism, that “particles possess determinate properties all along, only we do not know them.” Bell’s inequalities close off this possibility. The truth is more subtle: particles have determinate quantum states (qubits do not need to be “created” only upon measurement), but these states do not correspond to classical categories of “properties.” They exist, but they are not transferable.

Žižek’s Understanding of Quantum Physics

In his work Quantum History: A New Materialist Philosophy (Bloomsbury Academic, 2025), Slavoj Žižek defends the thesis that quantum physics should not be read merely as an epistemological limitation of our knowledge, but as a direct insight into the ontological structure of reality itself. While classical science and traditional materialism assume that the world “out there” exists as a solid, fully determined mechanism of atoms in empty space, Žižek rejects this image. For him, the essence of the quantum revolution does not lie in the realization that our measurement is incomplete and that we cannot know everything, but in the fact that reality itself does not know everything about itself. Indeterminacy is not a consequence of our ignorance regarding hidden data, but a consequence of the fact that these data do not ontologically exist. A crack gapes in the very structure of being; the world is not a closed whole, but is fundamentally “holey,” inconsistent, and unfinished.

Žižek grounds this “ontology of lack” in a sharp rejection of the idea of “hidden variables.” Einstein’s assumption that behind quantum chaos there must exist some hidden, deterministic order which we have merely not yet discovered is, for Žižek (and most of modern physics), erroneous. However, Žižek draws a philosophical conclusion from this physical rejection: if there are no hidden variables that would predetermine the properties of a particle, this means that measurement does not discover the state, but literally produces it. The collapse of the wave function is not a transition from ignorance to knowledge, but a moment when indeterminate, floating reality “collapses” into determinacy. For Žižek, this process is retroactive: the present intervention (measurement) determines not only the present but retroactively establishes the conditions and history that led to this outcome. This is the core of his “new materialism”: matter is not inert substance, but an open process that is constituted in the act.

To bring this abstract idea closer to the reader, Žižek employs the vivid metaphor of “God as a lazy programmer.” He compares the universe to modern video games where the computer, to save processing power, does not render the entire world at once, but renders the interior of a house or a landscape only in that split second when the player enters it or looks in that direction. For Žižek, our reality is exactly like this: ontologically economical. Trees, atoms, and stars do not exist as determinate facts in the full sense of the word until an interaction occurs that forces them into existence. Reality is therefore not a full, dense substance, but a potentiality waiting for actualization.

From this fundamental indeterminacy, Žižek also derives the concept of the hologram or the perspectival whole. Since reality is not the “All,” we can never capture it from a neutral “God’s-eye view from nowhere.” Every attempt to grasp the whole necessarily occurs from a specific, biased position. Every era, every subject, and every discourse creates its own “hologram”—an image of the whole that is valid only within its own horizon and is inextricably linked to the point of view. Hereby, Žižek radicalizes Heidegger’s insight regarding the historical mediation of truth: it is not merely that we see the world differently, but that its truth changes depending on how we intervene in it. Subjectivity in this system is no longer an external observer disturbing the objective world, but is a necessary “error” or gap through which reality is properly constituted.

Relational Quantum Mechanics and Retroactivity

Žižek further grounds his ontology of lack by referring to Carlo Rovelli’s Relational Quantum Mechanics. According to this interpretation, physical objects, such as electrons or photons, do not possess properties “in themselves” or in isolation. Properties such as spin, position, or velocity are established exclusively in interaction with another system. An electron does not have a spin until it collides with a measuring device or another particle; its spin is always spin relative to something else. Thus, there is no neutral state of the world prior to interaction, but only a web of relations.

For Žižek, this physical theory is an ideal confirmation of his philosophical system. If properties emerge only in relations, then the classical metaphysical notion of a solid, substantial world “out there” waiting to be discovered falls away. Reality is not a collection of independent substances, but a dynamic network of interactions without a central core. And since these interactions are always local, partial, and perspectival, reality itself is necessarily incomplete. It never assembles into a whole, as this would require a “view from nowhere,” which the relational nature of the universe forbids.

From this relational and indeterminate nature of the world, Žižek derives the idea of the retroactive constitution of reality. He argues that the collapse of the wave function does not operate only in the present but reaches back into time. When we measure the spin of an electron and obtain a determinate value, this result retroactively constitutes the past, as if the electron “always already” possessed this value, even though it was in an indeterminate superposition prior to measurement. The measurement, therefore, does not discover a past fact, but creates it retroactively. Thereby, the past proves to be open and changeable, rather than a fixed archive of events.

Žižek rests this seemingly speculative idea on the interpretation of physical delayed-choice experiments, where the observer’s decision in the present (how to set the apparatus) influences how the particle behaved in the past (whether it traveled as a wave or as a particle). For Žižek, this is the crowning proof that time is not a linear arrow flying from the past toward the future, but a dialectical loop in which the present constitutes its own origin. He then directly transfers this logic to the understanding of history and society: historical events are not simple consequences of past causes. A groundbreaking event, such as a revolution, does not only change the future but “rewrites” the meaning of the past, so that what previously appeared as contingency retroactively becomes the necessity that led to the new state.

Relational Quantum Mechanics Is Not an Ontology of Lack

However, when Rovelli argues that an electron possesses no determinate spin “in itself,” he does not mean that the electron is ontologically empty, holey, or incomplete. He means only that spin—like the majority of physical quantities—is a relational category. This is most easily understood through a comparison with the theory of relativity: the velocity of a body is never an absolute property that the body would possess independently of an observer. My velocity is 0 km/h relative to the chair I sit on, and 100,000 km/h relative to the Sun. This does not mean that my velocity “does not exist” or that I am ontologically “incomplete.” It means only that velocity is a type of property that is established only in a relation. The electron has a precisely determined spin relative to a specific reference system of measurement. In this relation, there are no “holes.”

Žižek’s error lies in equating relationality with incompleteness. From the fact that properties are not absolute (independent of context), he infers that reality is not determined. Yet a relational property can be—and in physics, is—fully determined and real. The fact that the same electron appears with a different spin if we change the axis of measurement (the reference system) is not proof of a lack in reality, but proof of the richness of its relational potentials. A relational property is simply not a property of the object “in itself,” but a property of the pair “object–reference system.”

Similarly problematic is Žižek’s fascination with delayed-choice experiments, upon which he builds his thesis of the retroactive constitution of the past. Because our decision today regarding the method of measurement influences the results we interpret as the past behavior of the particle, Žižek concludes that the present literally creates the past. But there exists a less dramatic and more physical explanation that avoids the idea that we intervene back into time. In this light, these experiments do not show a changing of the past, but rather reveal that quantum correlation is not localized in time in the manner of classical causality.

The wave function is a comprehensive mathematical object describing all possible correlations between events in time. When we choose today how we will measure the system, we thereby only choose which of the already existing correlations we will actualize. It is not a matter of rewriting history retroactively, but of selecting a certain cross-section from the entire quantum history of the system (which is consistent throughout). The wave function remains the same; we merely determine the “optics” through which we view it. While there are authors who advocate retrocausal readings where it makes sense to speak of the influence of the present on the past, I adopt a more conservative stance here: the wave function as a complete description of correlations is consistent at all times, and with measurement, we select which aspect of this whole we will actualize.

Here we arrive at the key point Žižek overlooks in his search for philosophical meanings. Although he refers to interactions, he forgets that interaction in physics is not merely an abstract logical relation, but a concrete physical process that has its price. When we say that measurement “collapses” a quantum state into a classical one, a change does not occur merely in our knowledge. A thermodynamic process takes place. Information encoded in a non-clonable qubit must be rewritten into bits—into a robust form that can be copied and stored. This process of rewriting is irreversible and requires energy. Žižek’s “ontological hole” is in reality merely the site of this energetic and informational translation.

Materialism of the 21st century therefore does not need the mysticism of “holes in being.” The world is consistent both at the level of qubits and at the level of bits. The quantum state (qubit) is mathematically precise and physically real, just as classical information is real. The problem arises only at the transition. We cannot directly transfer or share quantum reality because we cannot copy it. To speak of it at all, we must translate it into bits. This translation is necessarily a reduction, but this reduction is not an ontological loss of substance, but an epistemological necessity of communication. The randomness that appears in this process is not proof that reality is missing something. Randomness is the tax we pay for conversion; it is the price for importing data from the quantum to the classical world. This is not a glitch in the system of reality, but a structural property of information itself: a qubit cannot pass into a bit without payment, and the currency of this payment is randomness.

A Critique of Ontological Incompleteness

Žižek’s interpretation of quantum mechanics is philosophically extremely seductive, but it stands or falls on a single, yet risky inference: from the fact that we cannot directly observe quantum states as classical objects (that is, as bits), Žižek infers that these states in themselves are not fully determined. From an epistemological limitation of our access, he derives an ontological thesis about the very nature of the world. The key question, however, is whether this leap is justified, or if it is perhaps a subtle category mistake in which we judge the quantum world by criteria that do not apply to it.

Let us look at Heisenberg’s uncertainty principle, which is for Žižek one of the foundations of his ontology of lack. The standard formulation states that we cannot simultaneously know precisely the position and momentum of a particle. The more precisely we measure one, the more the other eludes us. But what does this mean for reality? Žižek reads this literally: the particle “does not have” a precise position and velocity because reality has not bestowed these properties upon it. However, there exists a different, more consistent explanation: the particle is in a quantum state that is simply not of the same ontological type as a classical point. When we ask for “exact position and velocity,” we are asking for properties of a classical bit, whereas the particle is a qubit or a wave function.

We can understand this most easily with a simple analogy. Imagine a wave on the sea. If you ask: “Where exactly is this wave?”, the question is nonsensical. A wave is not a point; it is a dispersed phenomenon covering a certain area, having a wavelength and amplitude. We can determine its center, but there is no “point” that is the wave. Does this mean the wave is “incomplete”? Does this mean the wave “does not yet know where it is”? By no means. It only means that the wave is by its nature a dispersed entity for which the category of point location is not appropriate. It is similar with a quantum state: a qubit in superposition is not “undecided between two possibilities,” but is in a third, fully real state, which however has no classical equivalent. If we look at a qubit in a state of perfect superposition (mathematically written as the vector ∣ψ⟩ = (1/√2)|0⟩ + (1/√2)|1⟩), we see that this state is, from a mathematical standpoint, fully determined. The vector is precisely specified, its length is one, its direction in Hilbert space is fixed. It lacks nothing. There is no “hidden parameter” that God forgot to define. The state is clear. (Such an understanding of the wave function is indeed predominant today, but not the only position in interpretive debates on quantum mechanics.)

At this point, Žižek could object that the mathematical completeness of the formalism does not yet guarantee the ontological completeness of reality. The wave function might be merely a computational tool with which we predict probabilities—not a description of what is. But this objection neglects the physical reasons for which the majority of modern physicists take the wave function more seriously. Bell’s inequalities and their experimental violations show that if the wave function were merely a reflection of our ignorance of pre-existing properties (as Einstein would have wanted), these hidden properties would have to influence one another faster than light. Since we do not accept this, we must accept that the wave function is not merely a measure of our ignorance, but a description of something real—a state that, prior to measurement, is simply not classical. The mathematical completeness of the wave function is therefore not merely formal elegance, but a consequence of the fact that it describes a physical reality which is determined, but not in a classical way.

The Essence of Quantum Materialism

The central dilemma of modern materialism reads thus: does quantum physics truly demand that we understand reality as ontologically incomplete, cracked, and fundamentally unfinished? Žižek’s project is, in this regard, undoubtedly a fascinating attempt at the materialization of incompleteness itself. In a desire to save materialism from naive realism—from the notion of the world as a solid, predetermined mechanism—Žižek proposes a bold turn: matter itself is constitutively unfinished. For him, indeterminacy is not an epistemological obstacle, but a positive property of being. However, we must ask whether this interpretation might be too precipitous and whether the physics of information offers us a different, more operative lesson.

There exists, in fact, an alternative path that remains faithful to the idea of an independent reality while simultaneously taking the paradoxes of quantum mechanics seriously. This “information materialism” does not seek holes in reality, but recognizes a fundamental structural difference between two regimes of physical existence: between those states that are transferable and stable (bits), and those that are unique and unrepeatable (qubits). In this light, Žižek’s “ontological crack” is no longer a name for a lack in reality, but for the necessary friction that arises when we attempt to translate the rich, vector nature of the quantum world (the qubit) into the binary language of our macroscopic experience (the bit). The difference lies not in the fact that reality is missing something, but in the fact that it is simply too complex for our classical categories.

It is crucial to understand that a qubit—this condensed essence of quantum information—is something fully real and determined. An electron in superposition does not float in some hazy indeterminacy, but resides in a precisely defined physical state described by a wave function. That we cannot copy this state without destroying it (the No-Cloning Theorem) is not proof of its incompleteness, but proof of its substantial autonomy. The problem with Žižek’s interpretation is that he tacitly attributes the status of “true” reality only to that which is fixed and determined. When he encounters a state that eludes this, he declares it holey, instead of recognizing in it a different form of materiality.

Here we collide with the inescapable fact of the materiality of information itself. Information is not an ethereal thought floating in an abstract space; information is necessarily inscribed in matter and linked to energy. Without a material carrier, there is no information. On this point, we agree with Žižek: there is no neutral “God’s-eye view from nowhere” that could capture the world as a whole without intervening in it. But the reason for this lies not in the world being ontologically crippled, but in the fact that every act of knowing is a physical process. If we want knowledge that can be shared, copied, and socially transmitted—if we want to cross from the privacy of the quantum to the publicity of the classical—we must accept the necessity of translation or measurement.

We can conclude that quantum indeterminacy does not testify to nature failing in its own realization. On the contrary, it testifies to the fact that our access to reality is conditional upon the physical price of stability. Randomness is not the signature of a lack in nature, but the trace of that surplus of reality which refuses to be fully digitized. Freedom is thus not necessarily a property of a “holey” universe, but primarily a property of beings who build their understanding in the language of communication, which is necessarily in the form of classical information that can be copied, stored, and transmitted.

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Translated from the Slovene original, available here:

ANATOMIJA VEDNOSTI / Sašo Dolenc

Kritika Žižkove kvantne ontologije

V digitalni dobi radi verjamemo iluziji, da je informacija nekaj eteričnega, brezsnovnega. Govorimo o “oblaku”, v katerem hranimo svoje spomine, kot da bi podatki lebdeli na nekem platonističnem nebu, ločenem od umazane in težke materije. A vsakdo, ki je že kdaj stal v vročem in hrupnem prostoru strežniškega centra, ve, da je resnica drugačna. Informaci…

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Each year, the Nobel Prizes in the sciences highlight humanity’s most profound advances in understanding nature and ourselves. The 2025 laureates in medicine, physics, and chemistry have pushed the boundaries of knowledge in three realms—the living body, the quantum world, and the architecture of matter. Their discoveries reveal how life maintains harmony within, how quantum laws shape even macroscopic reality, and how we can design materials that defy chaos itself. Together, they tell a story of curiosity, creativity, and the enduring human quest to uncover order in complexity.

These explanations are written in a clear, narrative style that connects scientific detail with human curiosity. Each story follows the path from problem to discovery, showing not only what the scientists achieved, but also how their insights reshaped our understanding of nature. The goal is to make complex ideas accessible without oversimplifying their depth—to reveal the beauty of science through storytelling.

Peacemakers in Our Blood

The Story of the 2025 Nobel Prize in Physiology or Medicine for the Discovery of the Mechanisms of Immune Tolerance

The Nobel Prize in Physiology or Medicine for 2025 was awarded to Mary E. Brunkow, Fred Ramsdell, and Shimon Sakaguchi for their groundbreaking discoveries explaining how our immune system, a powerful defender against infections, restrains itself from attacking the body’s own tissues. Their work revealed the existence and function of key cells that act as peacemakers within the body, as well as the gene that serves as their main control switch. These discoveries not only solved one of the central mysteries of immunology but also opened the door to entirely new ways of treating some of today’s most challenging diseases, from cancer to autoimmune disorders.

Our immune system is an evolutionary masterpiece that protects us daily from thousands of different microbes. Its astonishing ability lies in distinguishing foreign invaders from the body’s own healthy cells. This process, known as immune tolerance, begins largely in the thymus gland, where T cells—the key players in immune responses—are tested. Those that react too strongly to the body’s own proteins are destroyed in a process called central tolerance. Yet this safety check is not perfect, as some potentially dangerous self-reactive cells escape. This long puzzled scientists and suggested that an additional, peripheral control mechanism must exist within the body.

Shimon Sakaguchi approached the problem at a time when research on so-called suppressor T cells—thought to inhibit immune responses—had been largely abandoned due to misleading conclusions and a lack of solid evidence. Despite this, Sakaguchi persisted, inspired by earlier experiments showing that surgical removal of the thymus in three-day-old mice did not weaken their immune system but instead triggered a devastating attack on their own organs. He hypothesized that within mature T cells there exists a special population acting as a safeguard.

In a landmark study published in The Journal of Immunology in 1995, he used cellular markers to confirm his hypothesis. He isolated a subgroup of T cells (CD4+) and demonstrated that within it was a smaller subset expressing an additional surface marker, CD25. When he injected T cells lacking this CD4+CD25+ population into mice without a functioning immune system, the animals developed severe autoimmune diseases. However, when these cells were included, the mice remained healthy. This provided irrefutable evidence for the existence of regulatory T cells (Tregs), which actively maintain peace within the body.

Independently of the work taking place in Japan, Mary E. Brunkow and Fred Ramsdell in the United States were solving the puzzle from a different perspective. They came into possession of a strain of mice known as scurfy, which had accidentally appeared in the 1940s in a laboratory at Oak Ridge during radiation studies conducted as part of the Manhattan Project. Because of a spontaneous genetic mutation, these mice suffered from a fatal autoimmune disease that affected only males, indicating a defect on the X chromosome. In the 1990s, when the tools of molecular biology were still in their infancy, the two scientists set out to find a single incorrect letter in the sequence of 170 million base pairs that make up the mouse X chromosome.

After years of dedicated work, they managed to narrow the search down to about 500,000 base pairs containing twenty potential genes. It was only in the very last one—the twentieth gene—that they found the error: an insertion of two extra letters in the sequence of a previously unknown gene, which they named Foxp3. To confirm their discovery, they inserted a healthy copy of the gene into the genome of scurfy mice, which completely protected the animals from the disease. Soon afterward, in collaboration with pediatricians from around the world, they demonstrated that mutations in the human version of this gene, FOXP3, cause a rare but deadly autoimmune disorder in boys known as IPEX syndrome.

With these findings, two key pillars were established—soon united by science into a coherent whole. Sakaguchi’s team quickly discovered that the Foxp3 gene is expressed almost exclusively in the regulatory T cells he had identified. Moreover, they showed that by artificially introducing Foxp3 into ordinary T cells, these could be converted into fully functional regulatory T cells. It thus became clear that Foxp3 was not just another gene among many, but a “master switch” or transcription factor directing the entire development and activity of these vital peacemakers of the immune system. The absence of a single type of cell, controlled by a single gene, is enough to unleash a devastating autoimmune response.

The work of this year’s laureates has provided science with molecular tools to finely tune the immune system, opening the way to new, targeted therapies. In cancer treatment, the aim is to suppress or eliminate regulatory T cells in the tumor microenvironment, since cancer cells often exploit them for self-protection. In autoimmune diseases and organ transplantation, the goal is the opposite: to increase the number or enhance the function of regulatory T cells to calm unwanted immune reactions. Various strategies are already being tested in clinical trials—from administering substances that stimulate the growth of these cells to genetic engineering approaches that equip them with an “address” so they can find and protect a specific organ.

Shimon Sakaguchi, born in 1951, is a professor at Osaka University in Japan. Mary E. Brunkow, born in 1961, is a senior program manager at the Institute for Systems Biology in Seattle, USA. Fred Ramsdell, born in 1960, serves as a scientific advisor at Sonoma Biotherapeutics in San Francisco, USA.

For younger readers, here’s a simple explanation of the 2025 Nobel Prize in Physiology or Medicine:

Imagine your body as a big city. Inside it lives an army of tiny guards. This army is called the immune system. Their job is to protect you all the time and chase away invaders like viruses and bacteria.

But sometimes, some guards get too eager. They want so badly to defend the body that they might accidentally attack its own healthy parts. This can cause problems and make you sick.

Scientists discovered that among these guards are also special supervisors. These are cells with a very specific job—to watch over the other guards and make sure they behave properly.

When they see a guard about to attack something that belongs to the body, they stop it and prevent the attack. In this way, they make sure the immune system works correctly and doesn’t cause harm.

But for these supervisors to exist, they need a special construction plan. This plan is written inside our bodies, in something called the Foxp3 gene. It’s the instruction that tells how an ordinary guard can become an effective supervisor. If there’s a mistake in this plan, the supervisors can’t develop properly, and the guards might start causing damage.

Three scientists—Mary, Fred, and Shimon—discovered these supervisors and their special blueprint. Their discovery is very important because it helps doctors find new ways to treat diseases. Now they can try to increase the number of supervisors to calm down an overactive immune system, or turn them off so the guards can fight harder against serious illnesses like cancer.

Jumping Through a Wall

The 2025 Nobel Prize in Physics for the Discovery of Macroscopic Quantum Tunneling and Energy Quantization in an Electric Circuit

Imagine throwing a ball at a wall. Naturally, you expect it to bounce back every time. That’s the world as we know it—the realm of “ordinary” classical physics that governs our everyday experience. Now imagine that, every once in a while, the ball would simply vanish on one side of the wall and appear on the other, without ever breaking through it. It sounds like science fiction, yet phenomena exactly like this occur in the microscopic world and are described by quantum mechanics. This strange effect is called tunneling.

The Nobel Prize in Physics for 2025 was awarded to a trio of scientists—John Clarke, Michel Devoret, and John Martinis—who demonstrated that this peculiar quantum “leap through a wall” can occur not just with a single particle, but with billions of them simultaneously, in a system large enough to hold in your hands.

It was a feat achieved in the mid-1980s at the University of California, Berkeley, by a team consisting of Professor John Clarke, postdoctoral researcher Michel Devoret, and doctoral student John Martinis. They built a special electrical circuit whose core was a so-called Josephson junction—two superconductors separated by an extremely thin insulating barrier. In superconductors, electrons pair up into what are known as Cooper pairs and begin to move in perfect synchrony, like a flawlessly coordinated dance troupe. Because of this, billions of pairs behave as a single, enormous “super-particle,” described by one wave function.

The researchers trapped this system in a state with no electrical voltage, as if it were stuck in a valley from which, according to the laws of classical physics, it could not escape. Then the impossible happened: the entire system, as a whole, “jumped” or tunneled through the energy barrier and produced a measurable voltage on the other side. This was the first clear evidence of macroscopic quantum tunneling.

But that wasn’t all. The team also showed that the system absorbed energy only in specific, discrete packets—or quanta—much like an atom does. The discovery was groundbreaking because it blurred the boundary between the microscopic world of atoms and our everyday, macroscopic reality. The experiment was even compared to the famous Schrödinger’s cat, since it created a large system that could be fully described by a single quantum equation.

The significance of this work extends far beyond a deeper understanding of nature. By demonstrating that quantum states can be controlled in a macroscopic circuit, the researchers opened the door to a new technological era. Josephson junctions became the building blocks of qubits—the fundamental units used in today’s quantum computers. The work of Clarke, Devoret, and Martinis not only unveiled one of the deepest mysteries of physics but also laid the foundation for a technological revolution whose full impact we have yet to see.

Hotels for Molecules

The 2025 Nobel Prize in Chemistry for the Development of Metal–Organic Frameworks and a New Era of Material Design

In nature, a fundamental law prevails: entropy—everything tends toward disorder. Molecules do not like to arrange themselves into perfect patterns; they prefer to intertwine into tangled, amorphous masses. That is why the idea of forcing them, through the strongest chemical bonds, to build ordered, crystalline structures was long considered unimaginable—almost heretical—by most chemists. They believed it simply couldn’t be done. Yet it was precisely this struggle against nature’s basic tendency that Omar Yaghi chose to take on, igniting a revolution that, together with Susumu Kitagawa and Richard Robson, earned him the 2025 Nobel Prize in Chemistry.

Yaghi rejected the traditional approach, which he mockingly called “shake and bake”—a kind of chemistry done without a clear recipe. He dreamed instead of becoming a molecular architect, constructing with atoms as if they were LEGO bricks. After years of experimentation, he finally discovered how to outsmart entropy. He found the precise balance of temperature, pressure, and time that allowed molecules, as they formed, to settle and align into an ordered structure—without sacrificing the strength of the bonds holding them together. The result was metal–organic frameworks (MOFs), materials with astonishing properties. His groundbreaking creation, known as MOF-5, possessed such an enormous internal surface area that a single handful of it could cover an entire football field.

This victory over chaos was not Yaghi’s alone. It was built on the work of two other pioneers. Richard Robson was the first to even imagine such a structure when, back in 1989, he created the first—albeit fragile—framework. Susumu Kitagawa then breathed life into these frameworks. He developed the first truly stable versions and discovered that they could even be flexible—that they could “breathe” and dynamically respond to the molecules entering them.

Today, these materials are already helping to solve some of humanity’s greatest challenges. In arid regions of the world, such as California’s Death Valley, they are being used to harvest clean drinking water directly from the air, with some devices requiring nothing more than sunlight to operate. On an industrial scale, hundreds of tons of these materials are used in cement plants to capture carbon dioxide from smokestacks, preventing it from entering the atmosphere. Other variants purify water of harmful chemicals such as PFAS, store hydrogen as a fuel, and even deliver medicines directly to cancer cells.

But the revolution is far from over. Yaghi now leads an institute where he combines the power of artificial intelligence with molecular architecture to accelerate the discovery of new materials for combating climate change. His ultimate goal is to create materials that operate according to a predefined code—something akin to a DNA sequence. He dreams of a material that could be “programmed” with instructions to convert captured carbon dioxide into fuel or other useful substances.

The story of MOFs is, in essence, a story of how understanding and mastering the fundamental laws of nature has given humanity the tools to build a better future.

Translated from the Slovene original, available here: kvarkadabra.net.

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More than half a century ago, Phil A. Silva conceived a study that changed our understanding of human development. In 1972, in the southern New Zealand city of Dunedin, he began an ambitious research project that included every child born in the city over the course of a single year. He probably did not imagine at the time that he was laying the foundations for one of the most influential, thorough, and long-lasting studies ever conducted on a large group of people.

The Dunedin Longitudinal Study soon proved to be a real treasure trove of knowledge. The original plan to follow the children until the age of three was expanded. Researchers began inviting participants back for detailed examinations every few years. Over time, the laboratories accumulated an enormous amount of data, covering everything from mental and physical health to personality traits, social relationships, financial circumstances, and even genetic predispositions.

Over time, the researchers began to discern complex patterns and connections within this vast body of information, revealing links that no one had ever noticed before. More than 1,400 scientific papers have been published so far, many of which have become key references in psychology, medicine, sociology, criminology, genetics, and public health.

The first findings already pointed to an unexpectedly large proportion of three-year-olds with developmental and behavioral difficulties. It also became clear that mental health problems often begin in childhood. Another surprising discovery was that a large share of people experience at least one period of mental distress by the time they reach middle age.

The study also greatly strengthened our understanding of the close connection between mental and physical health. Long-term data showed that poor mental health affects more than just one’s current well-being; it can also lead to poorer physical health and even accelerated biological aging. People with long-standing mental health problems were often in worse physical condition by middle age and showed signs of faster wear and tear of bodily systems. The reverse is also true: chronic physical illnesses or inflammatory processes can negatively affect mental well-being.

These findings highlight the importance of integrating mental health care into general health systems for the entire population, since temporary episodes of psychological distress are more the rule than the exception. The researchers also discovered that people age at remarkably different rates — differences in biological age among individuals born in the same year can be surprisingly large, revealing the influence of lifestyle, environment, and genetic factors on the aging process.

One of the most striking and widely discussed findings of the Dunedin Study concerns the influence of personality traits, especially self-control, on the course of a person’s life. The researchers found that individuals who, already as children, had difficulty regulating their impulses, persisting with tasks, and thinking prudently tended to face far greater difficulties as adults. Children with lower levels of self-control were later in life more likely to experience poorer physical health (such as obesity, high blood pressure, or frequent infections), various forms of addiction (including smoking, alcohol, or drug use), financial problems (debt, lack of savings), and criminal behavior. Even when intelligence and social background were taken into account, self-control remained an exceptionally strong predictor of life success.

A groundbreaking study published by Terrie Moffitt and Avshalom Caspi, who joined the research team in the 1990s, provided evidence that self-control can be strengthened. Children who improved their ability to regulate themselves as they grew up achieved better outcomes than their initial assessments would have predicted. This carries an important message: investing in the development of self-control skills during childhood can be highly beneficial, both for individuals and for society as a whole.

In the 1990s, the research team also began exploring genetics. They discovered that certain genetic variants, such as those in the MAO-A gene, can increase the risk of violent behavior, but only among individuals who experienced severe abuse or maltreatment in childhood. These findings highlighted the importance of the interaction between genes and environment and encouraged discussions on preventive measures to protect children from traumatic experiences.

One of the greatest strengths and defining features of the Dunedin Study is its exceptionally high level of participant engagement. Even after fifty years, nearly 94 percent of the original members remain active in the research, which is remarkable compared to other long-term studies that typically struggle with high dropout rates. The key to this extraordinary commitment lies in the trust and strong bond that has developed between participants and researchers. The team consistently emphasizes that the participants are the true heroes of the study, as they continue to respond to invitations for extensive and sometimes exhausting assessments, believing that their involvement contributes to knowledge that will benefit future generations. Researchers even visit them while they are serving prison sentences, and some participants see their continued involvement in the study as their main contribution to society.

The team is now preparing for a new round of assessments at the age of 52. They plan to use the latest tools, including whole-genome sequencing and gut microbiome analysis. The careful, decades-long tracking of an entire generation of Dunedin residents provides an invaluable source of data for science and a solid foundation for designing compassionate and equitable health and social policies around the world.

Translated from the Slovene original, available here: Življenja pod drobnogledom znanosti.

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What happens when a tool capable of writing an essay in seconds, explaining a mathematical procedure, or clearly unpacking a complex scientific paper becomes part of everyday life in the classroom? This is no longer a hypothetical question but a reality faced today by students, teachers, and parents alike. Generative artificial intelligence (AI) has entered schools and radically altered the rules of the game. Some welcome its potential for personalized learning and instant support, while others see it as a threat to critical thinking and academic integrity. Is AI an ally of knowledge, or merely a convenient shortcut that harms us in the long run?

The article avoids one-sided answers and instead offers an interpretation built on a combination of two perspectives embodied by its authors. On the one hand, it provides a direct glimpse from the school desk through the personal experience of a Ljubljana high-school student, revealing how AI is practically reshaping essay preparation, problem-solving, and language learning. On the other hand, it situates this personal perspective within a broader framework: a systematic overview of the latest scientific studies, meta-analyses, and international guidelines that highlight the measured effects, risks, and recommendations for responsible use.

Our aim is not to deliver final judgments but to describe the present state as perceived from both of these vantage points. In conclusion, we also offer our shared understanding of these developments and our conviction that mastering the dialogue with artificial intelligence has become a key literacy of the 21st century.

Artificial Intelligence as a Personal Tutor

In this chapter, we present concrete examples of how students practically use artificial intelligence in their everyday learning challenges: from preparing essays and writing homework to solving mathematical problems, learning languages, and organizing notes. This is a perspective through the eyes of a high-school student who employs AI as a tool for deeper understanding and learning—not merely for obtaining answers. Her experience serves as a starting point for broader reflection on when such use is genuinely meaningful, where it can contribute to better learning, and when there is a danger of it becoming merely a shortcut that bypasses understanding. It has become evident that AI holds the greatest value as a personal tutor precisely where it does not replace the cognitive process but rather stimulates, guides, and helps to disentangle complex concepts.

Preparing for an Essay: “A Socratic Dialogue with Artificial Intelligence”

For many years, it was taken for granted that one could not truly prepare for a high-school essay. Success was believed to depend primarily on personal talent and momentary inspiration. Preparation usually meant reading the required works and searching for the occasional analysis on internet forums—analyses that were often superficial and, in Slovenia at least, long since read by most students.

With the advent of artificial intelligence, this perception has radically changed. Today, we can converse with AI about literary works in a manner reminiscent of a Socratic dialogue—through questions, doubts, counterarguments, and the pursuit of clarity. AI is no longer just a passive source of information but becomes an active interlocutor that enables a deeper understanding of texts and the development of independent thought.

When it comes to lesser-known works, especially Slovenian ones, it is sensible first to provide AI with the full text. This allows it to respond directly on the basis of the original material and avoid confusing characters, events, or meanings. For world classics, this is usually unnecessary, as such works are already well represented in the models’ knowledge.

AI can efficiently summarize content, analyze characters and their relationships, identify the core ideas and values of a work, and help unpack its literary and philosophical layers. Yet its greatest strength lies elsewhere: it does not impose uniform interpretations but instead stimulates reflection, poses additional questions, and helps students shape their own interpretations—expressing them more clearly and convincingly.

Moreover, AI is not limited to standard analyses. It can assist in formulating answers to more open-ended and creative questions often found in essay prompts: how literary characters might behave in the modern world, how a contemporary reader perceives a certain work, or what relevance its message holds today. In this way, AI is not merely a tool for analysis but becomes a partner in developing argumentation and understanding context.

Preparing for an essay with artificial intelligence is therefore no longer about passively reading summaries but about engaging in a dynamic and creative dialogue, where the student develops personal viewpoints, tests them critically, and refines them. If in the past success relied largely on talent, today that talent can be enhanced through the well-directed use of AI—an instrument that opens new pathways to understanding literature and the world.

Writing Entire Essays with AI

In addition to preparation, many students also use artificial intelligence in a more direct—yet riskier—way: by simply entering the essay prompt and letting the system generate the entire piece. The result is often surprisingly polished and seemingly convincing work, something that could be handed in to a teacher with minimal edits, saving precious time. Yet in practice, this strategy rarely proves successful.

Essays written entirely by AI often differ stylistically and structurally from what is expected in Slovenian schools. They may include subheadings, rely on generic quotations, or present overly generalized claims that are insufficiently grounded in the text itself. More importantly, such essays frequently fail to follow the teacher’s specific instructions—a key criterion in assessment. As a result, teachers often quickly suspect that the essay was not written by the student but by artificial intelligence.

Such essays are often graded lower than those written by students themselves. The reason lies not only in inappropriate form but also in the absence of personal reflection, originality, and an authentic voice. An AI-written essay may be linguistically flawless, but it often lacks what a good teacher notices immediately: the student’s genuine intellectual engagement.

For this reason, most students use AI in this way mainly for assignments that are not directly graded—for instance, within compulsory elective activities. In the second year, we were required to submit an essay on a film watched in class. Pressured by numerous other obligations, some students chose to have AI write it for them. Yet the teachers rejected these submissions, rightly suspecting they were not original. The students had to rewrite their essays under supervision at the end of the year, which meant extra work, stress, and wasted time.

Such use of AI is therefore ineffective in the long run. Not only does it often fail to deliver the desired results, it also contributes nothing to knowledge, intellectual development, or writing skills. Still, it must be acknowledged that many students turn to this option not out of laziness but because of overload: constant assessments, tight deadlines, and numerous extracurricular commitments. In such moments, artificial intelligence primarily represents a convenient solution to a lack of time and a way to secure a bit more peace or sleep.

This is precisely why it is all the more important not to treat AI as a substitute for one’s own work, but as a tool that supports and enhances our abilities. Writing an essay is not merely a school assignment but an exercise in thinking, understanding, and expression—and it is in this role that AI can become an ally, rather than an escape from learning.

Solving and Explaining Science and Math Problems

Beyond literary analysis and essay writing, artificial intelligence can also provide significant support in subjects such as mathematics, physics, and chemistry. This is often where difficulties arise: we may not know how to start a problem, which formulas to use, or how to proceed once we get stuck midway. Sometimes we even reach the wrong result without realizing where we went wrong—and these small, hard-to-detect mistakes are the most frequent source of frustration.

Since a teacher or classmate is not always available—especially not in the afternoon at home or right before a test—AI can step in as a patient explainer and tutor. If we provide it with the text or a photo of a problem, it can not only solve it but also carefully explain the process: step by step, with a clear rationale at each stage. This way, we don’t just get the correct answer—we also understand the path that leads to it.

This feedback loop is immensely valuable: it allows us to quickly spot where we made mistakes and why. Detecting errors in our own work is time-consuming and often unfeasible, especially when we don’t know what we’re actually looking for. In this regard, AI can save us a great deal of time while deepening our understanding, enabling us to learn from our mistakes instead of merely stressing over them.

It is also highly useful for assignments where only the final result is provided but not the full solution process. In such cases, AI can reconstruct the reasoning from beginning to end, allowing us to compare its approach with our own. This makes it possible to verify our work, correct missteps, and strengthen our comprehension.

For more complex problems involving branching procedures, multi-step reasoning, or a degree of creativity, so-called “thinking models” of AI are especially valuable. These advanced approaches deliberately extend the processing time and carry out multiple layers of reasoning in the background, often through a technique known as chain-of-thought prompting. Instead of simply presenting an answer, the system “thinks out loud”—laying out each step while continually checking the consistency of its reasoning. This approach may take longer, sometimes several minutes, but the reward is a carefully considered and logically structured response that reveals not only the final result but also the entire line of reasoning.

Such answers are extremely useful not only for solving individual tasks but also as learning aids when preparing for tests, final exams, or oral defenses. By explaining its reasoning, the model allows the student to follow the thought process, learn from it, and simultaneously check their own understanding. In this way, AI becomes a tool for strengthening analytical thinking rather than merely a supplier of quick solutions.

All of this demonstrates that in science and mathematics, AI does not function merely as a “calculating machine,” but as an instrument for developing understanding—which is, after all, what matters most in learning.

Learning Foreign Languages

Learning a foreign language often requires mastering a vast amount of new vocabulary. Simply reading or passively listening usually isn’t enough, as words are quickly forgotten without active use. This is why flashcards have long been a popular tool: they promote memorization through repetition and recall. Yet preparing such cards takes considerable time—and this is precisely where artificial intelligence can help effectively.

With AI, we can quickly generate exercises tailored specifically to our needs: we provide a list of words we want to learn and ask the system to present them with explanations, translations, or usage examples in random order. We then try to recall the correct word or translation. This method encourages active retrieval, which is proven to enhance long-term memory.

AI is not limited to basic flashcards. If we show it examples of exercises from past tests or workbooks, it can create new tasks in the same format but with fresh vocabulary that we are currently studying. Such individualized practice allows us to learn in a targeted and efficient way rather than mechanically.

Interestingly, artificial intelligence is no longer used for language learning only by students but also by some teachers. Occasionally, a test problem will even include the note: “created with ChatGPT.” This shows that AI is not just a tool for reinforcing knowledge but is becoming part of the broader learning environment—an instrument used by both teachers and students.

Writing is also a crucial part of language learning. When writing in a foreign language, we often repeat the same mistakes, which are difficult to spot on our own—especially when writing by hand. Since teachers, due to time constraints, typically review only a few written assignments, AI can step in as an additional language mentor. If we provide it with a photo of a handwritten text, it can transcribe, correct, and explain the errors. In this way, we don’t just receive an improved version of the text but also gain an understanding of why something was wrong and how to improve it in the future.

With regular use of artificial intelligence for writing and vocabulary practice, we can deepen our knowledge of a foreign language more quickly and more permanently. This means that AI does not replace learning—it complements and strengthens it.

Collecting Information and Organizing Notes

In some subjects—especially biology, geography, history, or social sciences—students must master large amounts of material. Information is often scattered across different sources: textbooks, worksheets, websites, or densely written paragraphs without clear structure. Such material is difficult to digest, requiring considerable time just to understand the basics, let alone to memorize effectively.

In these cases, artificial intelligence proves extremely useful. We can provide it with fragmented notes or raw texts, and it can turn them into neatly organized, logically structured summaries or tables. It sorts data into meaningful categories, organizes them by comparisons, time periods, functions, classes, or features—in short, it reshapes information so that it becomes comprehensible and easier to remember.

For example, in biology we had to compare different organ systems across animals, from sponges and cnidarians to amphibians and mammals. Manually collecting and writing these details into comparative tables would have taken a great deal of time. AI, however, can generate such a table in seconds from the sources we provide (or those already in its training data), showing the differences in digestion, respiration, circulation, excretion, and nervous systems across animal groups.

This leaves the student with more energy and time for understanding and learning. AI-prepared material can be easily exported into Word, PDF, or Excel, and then supplemented, edited, printed, or shared with classmates. This not only supports more effective studying but also facilitates collaboration and knowledge exchange. In this way, AI becomes a digital assistant that doesn’t replace the student’s work but simplifies it where tasks are mechanical and time-consuming, freeing attention for what truly matters: comprehension, connections, and independent thinking.

Just before this article went to press, OpenAI introduced a new feature called Study Mode, designed for targeted learning with artificial intelligence. The system first asks the user about the topic they want to understand, as well as their age or educational level. Based on this, it begins a conversation in the form of questions and feedback. The user first attempts to answer the questions on their own, after which the system offers clarifications, corrections, and prompts for further reflection. What makes this approach distinctive is its use of the Socratic method, which fosters deeper understanding, self-reflection, and active engagement through a series of guiding questions.

Learning with this mode becomes more interactive and in-depth, since detailed instructions are no longer required—the AI itself takes on the role of a tutor, adapting in real time to the learner’s knowledge and pace. The feature was developed in collaboration with several educational institutions and is grounded in the latest didactic insights. It is available to all ChatGPT users regardless of subscription tier and will soon be integrated into the educational version, ChatGPT Edu. This approach not only facilitates comprehension of complex content but also strengthens independent thinking, nurtures curiosity, and encourages the responsible use of AI in education.

Systemic Challenges, Measurable Effects, and Potential Risks

The impact of artificial intelligence on education is not merely theoretical; its effects are already concrete, measurable, and often multidimensional. In this chapter, we illuminate two sides of the same coin. On one hand, there are promising data showing improved learning outcomes, increased motivation, and more personalized support for individual learners. On the other hand, serious questions arise: does excessive reliance on AI foster superficial learning? Do we risk losing independent thought, perseverance, and critical autonomy? It is precisely this tension between opportunities and pitfalls that provides a crucial starting point for deeper reflection on AI’s role in the future of education.

Meta-Analyses and Research on the Pedagogical Effects of AI

The first large-scale studies on the impact of generative artificial intelligence (AI) on learning show predominantly encouraging results. The most reliable insights come from the method of meta-analysis, which aggregates findings from numerous individual studies to assess the overall effect of a given method or technology on learning outcomes.

A meta-analysis that included 65 independent studies (Sun & Zhou 2024) estimated that the use of generative AI improves student performance by about half a standard deviation on average (Hedges’s g ≈ 0.53). This is a moderate yet meaningful effect, comparable to raising an average student from the 50th to the 69th percentile in achievement. The strongest effects appeared in tasks involving text generation (e.g., essay or report writing) and in self-directed learning, where AI acts as a tutor providing real-time feedback.

Another meta-analysis (Zhang, Jantakoon & Laoha 2025) systematically examined the effectiveness of AI technologies in education, drawing on 13 empirical studies from eight countries. The research revealed a significant and large overall positive effect (g ≈ 0.86), highlighting the considerable advantages of integrating AI into educational processes. The most pronounced impact was observed in chatbots and generative AI (g ≈ 1.02), while online learning environments and virtual reality showed moderate effects (g ≈ 0.79). The authors note substantial variability among the analyzed studies but emphasize the robustness of positive outcomes and the importance of considering contextual factors when implementing AI solutions.

Other systematic studies, focused specifically on conversational agents such as ChatGPT, distinguish between cognitive and non-cognitive effects. Analyses of 27 empirical studies (Suo, Yin, Wang, et al. 2025 [preprint]), published between 2022 and 2025, confirm that these agents have a significant positive influence on learning. On the cognitive level (measured by grades, task completion time, etc.), they report moderate positive effects (g ≈ 0.36). Even greater effects (g ≈ 0.52) appear in non-cognitive outcomes such as motivation, perseverance, and self-efficacy—that is, the belief that one is capable of successfully completing tasks. These findings suggest that students often perceive generative AI as a safe, non-judgmental environment that encourages them to engage more with learning material and reduces fear of making mistakes.

Among individual skills, writing stands out as a particularly promising area. Randomized controlled trials (RCTs), considered the gold standard for measuring effectiveness, show that students who receive feedback from AI while writing essays achieve better structure, cohesion, argumentation, and content development compared to those who receive only traditional feedback (e.g., from teachers or peers). Beyond improvements in text quality, studies also highlight higher levels of motivation and engagement among students using AI systems, though they also report mixed emotional responses (Zhang 2025; Lo, Wong & Chan 2025).

In the context of foreign language learning, a large meta-analysis of 31 comparative studies (Lyu, Lai & Guo 2025) confirms that chatbots have a moderate positive effect (g ≈ 0.61) on cognitive skills such as writing and vocabulary acquisition, as well as on affective factors such as motivation and interest. The effects are strongest when interaction with AI is guided and reflective, enabling learners not only to receive immediate feedback on errors but also to understand their causes. Such a deeper approach is made possible primarily by advanced chatbots using generative AI, accessible via mobile devices—identified by the meta-analysis as key factors in boosting effectiveness.

The overall conclusion of these studies is clear: the effects of generative AI use are on average positive and moderate, with the greatest benefits appearing when the technology is not employed passively but as a tool to support comprehension, writing, and active learning. The teacher’s role in guiding this use remains crucial for achieving optimal results and avoiding merely superficial knowledge.

Mechanisms of Effectiveness and Limits of AI Use in Education

At first glance, findings on the impact of artificial intelligence (AI) in education seem riddled with contradictions, creating considerable confusion. On the one hand, large-scale meta-analyses that synthesize results from hundreds of individual studies generally report moderate but statistically significant positive effects on learning.

On the other hand, high-profile field experiments such as the Wharton study (Bastani et al. 2024) paint a far more complex and troubling picture. In this particular study, researchers found that while access to a general model such as GPT-4 temporarily improved students’ performance on assignments, the same group performed worse on a final exam—taken without AI assistance—than students who had never used the tool. This suggests that uncritical use of generative AI, which primarily facilitates access to final answers, may in the long run undermine the development of independent knowledge and problem-solving skills.

The apparent contradiction between these findings is therefore not necessarily a paradox but underscores a key insight: what matters is not simply whether AI is used, but how it is designed and integrated into the learning process. While carefully developed pedagogical tools—with built-in safeguards that encourage critical thinking—can genuinely support learning, unrestricted use of general-purpose systems may lead students toward passive shortcuts, hampering their cognitive growth.

The positive effects of generative AI in education are not accidental; they stem from the technology’s ability to strengthen and automate already established didactic principles. Successful implementations foster active learning, provide immediate feedback, allow for personalized learning paths, and reinforce self-regulation skills.

A study by Iqbal et al. (2025), involving 465 pre-service teachers in China, examined in more detail the impact of these tools on learning outcomes. The researchers found that generative artificial intelligence enhances achievement primarily through two key mechanisms. The first is cognitive offloading, where the tool takes over routine tasks such as information retrieval or syntactic checking, enabling learners to direct their mental resources toward more demanding processes such as critical analysis and the synthesis of ideas. The second mechanism is shared metacognition, in which the technology acts as a catalyst for collaborative learning by encouraging students to reflect together, evaluate one another’s ideas, and coordinate problem-solving strategies. In this way, generative AI functions not merely as an individual tool but as a platform that strengthens collective intelligence and deepens understanding within a learning group.

The key question is not whether a student uses AI, but which part of the cognitive process is delegated to it. Beneficial offloading occurs when AI takes over routine, low-level tasks—for example, text formatting, synonym searching, summarizing a source, or carrying out a familiar calculation. In such cases, AI expands the learner’s cognitive capacity, allowing greater focus on higher-order thinking such as planning, argumentation, synthesis, and conceptual integration. Harmful offloading, however, occurs when AI assumes responsibility for the core intellectual work—such as understanding a problem, formulating a research question, developing a thesis, or constructing an argument. In these cases, AI replaces thinking rather than supporting it. The student does not practice fundamental cognitive skills, and genuine learning simply does not take place. The tool becomes a cognitive crutch.

In addition, generative tools are prone to the phenomenon known as hallucination, in which they produce information with great confidence that is in fact false, fabricated, or lacking any factual basis. This may involve citing non-existent sources, misinterpreting facts, or generating entirely invented data. Consequently, when these tools are used for academic purposes such as essays and research assignments, rigorous verification of all citations, references, and factual claims is essential. This necessity underscores the importance of advanced information and data literacy, enabling users to critically evaluate and validate content.

Generative AI as a Learning Partner: Conditions for Effective Use

The central message of this article has been confirmed across examples and analyses alike: generative artificial intelligence contributes most to education when it shifts from being a passive tool to becoming a partner in thought. It does not write instead of the student but engages with them in dialogue, asking questions, checking understanding, offering counterarguments, and helping to refine initial ideas into clear, original expression. In such a relationship, AI is no longer merely a “task performer” but a co-creator of the learning process—while the student retains the central role of deciding, judging, and taking responsibility.

This mode of use rests on three interlinked didactic principles that together create a learning environment in which the student actively participates in the cognitive process. The first principle is learning dialogue, where the student and AI engage in a sequence of questions, explanations, counterarguments, and revisions. This process promotes active recall, reduces the risk of superficial understanding, and counters the illusion of certainty that may arise from fluent yet shallow answers. The second principle is traceability of the thought process. Drafts, revisions, and explained changes provide insight into the path of reasoning, creating conditions for learning from mistakes, fostering reflection, and enabling the gradual construction of knowledge. The third principle is dual verification. A student first formulates a claim with the help of AI, but must then be able to articulate and defend the same claim independently. Such repetition under identical standards ensures that knowledge is genuinely internalized and transferable to new contexts.

When this logic is transferred into the classroom, the focus shifts from products to processes—from simply having “solved tasks” to explaining methods, weighing alternatives, and applying knowledge in new situations. Assessment practices must adapt accordingly: drafts, ongoing feedback, reflections, and oral defenses gain greater importance, while AI-free tests remain essential to show how much knowledge persists in independent use. This reduces the gap between quick execution with AI and genuine understanding without it.

In such use, another often-overlooked dimension comes to the fore: the emotional literacy of models and the importance of human connection. Communication that is encouraging, clear, and respectful can strengthen a student’s perseverance and confidence. By contrast, a patronizing, sarcastic, or overly self-assured tone can quickly lead to passivity and intellectual withdrawal. Well-designed use of AI should therefore take over routine tasks—such as language corrections, basic error marking, or literature gathering—and free time for teachers and students to focus on what no model can replace: conversation, mentorship, and shared thinking.

Yet such use of AI requires not only sound pedagogical practice but also ethical reflection. It demands careful handling of personal data, transparency of processes, and equitable access to high-quality tools. Early phases of new technologies often favor those with better devices or access to paid model versions. Long-term fairness does not happen on its own; it requires school-level licenses, reliable infrastructure, and didactic support that make new learning patterns accessible to all, not just the best equipped and most motivated. For Slovenian students, solid language support for Slovene in AI models is an advantage, but linguistic accessibility in commercial systems cannot replace the need for systemic regulation in this field.

Although this article provides a clear didactic framework, the discussion does not end here—it truly only begins. Key challenges remain that demand thoughtful answers: Where does assistance in thinking end and replacement of thinking begin? How should assessments be designed to balance evaluation of the learning process (where AI is a partner) with the demonstration of independent knowledge? How can developers design interfaces that encourage deeper understanding rather than merely generating quick answers? And ultimately, how can we guide this collaboration with AI so that it strengthens students’ intellectual autonomy and their capacity for dialogue—both with people and with machines?

References

• Bastani, Hamsa, Osbert Bastani, Ahmet Sungu, Hao Ge, Özge Kabakcı, and Rick Mariman. Generative AI Can Harm Learning. SSRN Working Paper, The Wharton School, 2024.

• Iqbal, Junaid, Zohaib Farooq Hashmi, Muhammad Zubair Asghar, and Muhammad Nauman Abid. “Generative AI Tool Use Enhances Academic Achievement in Sustainable Education through Shared Metacognition and Cognitive Offloading among Preservice Teachers.” Scientific Reports 15 (2025): 16610.

• Lo, N. P. K., Andy Wong, and Samuel Chan. “The Impact of Generative AI on Essay Revisions and Student Engagement.” Computers & Education: Open 100249 (2025).

• Lyu, Bin, Chun Lai, and Jingjing Guo. “Effectiveness of Chatbots in Improving Language Learning: A Meta-Analysis of Comparative Studies.” International Journal of Applied Linguistics (2024).

• Sun, Lin, and Lijun Zhou. “Does Generative Artificial Intelligence Improve the Academic Achievement of College Students? A Meta-Analysis.” Journal of Educational Computing Research (2024).

• Suo, Xiaochen, Baoyuan Yin, Wanqing Wang, et al. “Exploring the Impact of Generative AI-Powered Conversational Agents on Student Learning: A Systematic Review and Meta-Analysis Grounded in Activity Theory.” Authorea, July 1, 2025.

• Zhang, Jincheng, Thada Jantakoon, and Rukthin Laoha. “Meta-Analysis of Artificial Intelligence in Education.” Higher Education Studies 15 (2) (2025): 189–203.

• Zhang, Kai. “Enhancing Critical Writing through AI Feedback: A Randomized Control Study.” Behavioral Sciences 15 (5) (2025): 600.

Translated from the Slovene original, available here: Umetna inteligenca v šoli.

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Why do we remember a film more easily than a lecture? Why does a good novel move us more deeply than even the most precise statistical report? And why are we sometimes willing to believe utter nonsense—so long as it is skillfully presented? The key to these questions lies in stories. They are not merely a way to pass the time; they represent a fundamental tool of thought, a means by which we interpret the world and share our understanding with others.

We often underestimate how powerfully stories shape our perception of reality. Human beings need narratives to help us make sense of what happens around us. When we are left without compelling explanations, we become vulnerable to conspiracy theories, which are, after all, stories as well. Though untrue, they effectively fill the void, impose order, and offer simple explanations for complex phenomena.

Stories captivate us because they resonate with the way our brains function. They fit remarkably well with our cognitive architecture. Humans are skilled at recognizing causes, anticipating the future, and inferring the thoughts of others. We did not develop these abilities because of stories—yet it is precisely through stories that we can exercise them to the fullest. When we listen to a narrative, we imagine events unfolding, follow actions, feel tension, and search for meaning. A story is therefore not merely a sequence of information, but an experience we relive in our minds. And what we experience, we remember. A story is often not just a tool for explanation, but its very essence.

The earliest larger organisms, which spent most of their time immobile, had no need for brains. Their behavior was simple, their environment predictable, their responses automatic. Everything changed, however, with the emergence of active movement and hunting. An animal that had to pursue prey while avoiding predators required a central system to coordinate conflicting streams of information. From this necessity, the first brains came into being.

At first, these systems followed a simple principle: approach what is useful, avoid what is harmful. This basic algorithm enabled survival for worm-like creatures that lived some 600 million years ago. Yet the system was rigid and incapable of learning, unable to adapt to change.

About a hundred million years on, early vertebrates developed the ability to learn from experience. Their brains began to construct internal maps of the world. These maps allowed them to interpret their surroundings and anticipate future events. The brain became a tool for shaping and updating an inner image of the external world—a turning point in the evolution of behavior. From then on, life was no longer merely reactive, but anticipatory. Still, at first, this inner image remained passive. The true leap came when our ancestors began to actively use these inner maps to rehearse the future.

Building on this foundation, three key skills emerged that directly enabled the comprehension of stories. The first was inner simulation. Our ancestors could imagine different scenarios before they actually unfolded. They could learn from imagined experiences without incurring real risks. This capacity to generate narratives became one of the crucial evolutionary advantages.

A second great step was taken by the ancestors of primates several tens of millions of years ago, when they began to weave an understanding of other beings into these simulations. They could envision what others were thinking, feeling, and intending. For the first time, stories acquired characters with inner lives. A narrative was no longer just a sequence of events, but a stage on which relationships unfolded between individuals with differing perspectives and goals.

The decisive breakthrough came with the rise of language. While other species communicated only about immediate states—danger, food, dominance—language made it possible to speak of things not present in the here and now: the past, the future, and entirely imagined worlds. It allowed humans to begin sharing their narratives with others. Language became a means of synchronizing mental worlds within a community. Stories turned into a powerful medium for transmitting experiences, values, and cultural patterns. Thus began the great accumulation of knowledge, which could be passed on and expanded across generations.

This evolutionary heritage of brain development has profound consequences for the functioning of modern society. The ability to tell stories grants us extraordinary power, yet it can also dangerously mislead us. On the one hand, stories act as social glue. Shared narratives—myths, histories, religious teachings, or corporate visions—create a sense of belonging and enable cooperation among strangers. Stories are also an effective tool for transmitting knowledge, since we remember complex content more easily when it is cast in narrative form. Above all, they help us make sense of the world. They impose structure and logic on events that might otherwise appear random.

Yet it is precisely this power that also makes us vulnerable. Because our brains are so attuned to storytelling, we are highly susceptible to manipulation. Propaganda, advertising, and fake news often succeed because they address us in the form of a story. More often than not, we prefer to believe a simple tale with clear characters rather than a messy reality filled with contradictions and uncertainty.

A story we accept quickly becomes part of our worldview. It reinforces beliefs, shapes identity, and influences decisions. We often reject facts that contradict it and search for confirmation of what we already believe. Thus, the very stories that connect us can also blind us.

It is therefore essential to recognize that if people lack compelling stories to help them understand the world, their minds will spontaneously cling to whatever narratives are available and seem persuasive. In the absence of clear explanations, substitute stories take root—whether accurate or not. This is why a healthy community must pay close attention to the stories it circulates and promotes. It matters profoundly which narratives fill the public sphere, for they shape not only our understanding of the world and its problems, but also the horizon of our common future.

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Translated from the Slovene original, available here: Bitja zgodb.

Running may seem like one of the simplest forms of movement. You put on sportswear, lace up your running shoes, and head out. Moving your legs and breathing rhythmically feels completely natural, almost self-evident. Yet behind the scenes something much more intricate is taking place. With every step, the body responds with a series of precisely coordinated processes: the lungs increase the exchange of oxygen, which passes into the blood; the heart pumps this oxygen-rich blood more rapidly throughout the body; the muscles use the oxygen to produce energy; and the nervous system coordinates millions of signals. What we experience as simple bodily motion is in fact a highly complex physiological process.

In this essay, we will explain what happens inside the body during running and how these changes are reflected in the numbers tracked by a smartwatch: heart rate, energy expenditure, or the estimated VO₂max. Once a runner understands the background of these measurements, a clearer picture emerges of why training strengthens the body and how regular practice gradually improves performance.

How the Body Produces Energy for Running

The moment we start running, countless tiny machines inside our muscle fibers switch on at once. Each of them functions like a microscopic motor that needs energy to do its work. Without energy, the motor stops—along with the contraction of muscle fibers that makes bodily movement possible.

The sole “fuel” for these motors is the molecule ATP (adenosine triphosphate). The simplest way to imagine it is as a very small battery. The name “triphosphate” means that the molecule carries three phosphate groups. When one of them breaks off and releases energy, what remains is only a “diphosphate” with two phosphates. ADP (adenosine diphosphate) represents the empty battery.

There are many such “batteries” in the body, but still only enough for a few seconds of intense physical activity. If muscles relied solely on this initial stock of energy, bodily movement would quickly come to a halt. That’s why a system exists for continuously recharging these energy carriers. When the molecular “battery” runs down and ATP turns into ADP, special cellular processes immediately begin refilling it back into ATP. Without this “recycling,” running would last only a moment; with constant recharging, however, we can keep running far longer.

The most important contributors to this steady “recharging” are the mitochondria (often called the cell’s power plants). Inside them, a multitude of reactions transform energy from food—primarily carbohydrates and fats—into ATP. Mitochondria continuously generate ATP from nutrients so that muscle fibers never run out of energy.

Thus every step in running is not just a mechanical movement, but the outcome of a carefully coordinated process: muscle fibers consume ATP molecules, mitochondria recharge them, and the body works constantly to keep the supply of nutrients and oxygen sufficient. It is precisely this ability to renew energy on the go that enables humans to run even very long distances, depending on how efficiently and harmoniously these tiny machines in the body perform their work.

Different Energy Sources for Different Running Challenges

Inside the body, various energy systems are constantly at work to ensure that muscles have fuel in the form of ATP molecules. These systems never operate entirely in isolation; all are always active, but the share of their contribution shifts depending on the duration and intensity of running. Through training, the body gradually adjusts the thresholds that determine when one system takes priority over another, thereby improving endurance.

In the very first moments when a runner launches forward from rest, the fastest yet most limited energy source kicks in. This is the phosphocreatine system, also known as the ATP-PCr system. It operates without oxygen—hence it is called anaerobic—and functions like a backup generator that switches on under maximum strain. Muscles store a special molecule called phosphocreatine (PCr). Its role is simple: when an ATP “battery” runs down and only ADP remains, phosphocreatine instantly donates its phosphate group, converting ADP back into ATP. In this way the battery is quickly recharged, and the muscle can continue working at very high intensity without movement stopping.

This system is extremely fast and delivers an explosive burst of power—the very thing a runner needs for those first decisive steps when pushing off. It is most evident in 100- and 200-meter sprints. But it also has a limitation: the store of phosphocreatine is very small. After less than ten seconds of intense activity, it is depleted, and the body must turn to other, more sustainable energy sources to keep running.

When the runner continues at a fast but submaximal pace, the next energy source comes into play more strongly: anaerobic glycolysis. This is the process by which muscle cells break down glucose. As a simple sugar, glucose provides a readily available fuel that the body can use almost immediately, which is why glycolysis allows for quick and efficient production of new ATP.

In the past, it was thought that anaerobic glycolysis switches on because oxygen supply cannot keep up with demand at high intensity. More recent findings suggest that the reason is not necessarily a lack of oxygen but rather what is called metabolic inertia. The processes in the mitochondria, where energy is generated with the help of oxygen, need some time to reach full potential. During this transitional phase, anaerobic glycolysis steps in rapidly to provide energy until the aerobic system takes over the main load.

But Quick Fuel Comes at a Price

When glucose is broken down without sufficient oxygen (under anaerobic conditions), lactate is also produced. For decades it was widely believed that lactate was the main culprit behind the burning sensation and fatigue in muscles. Today we know this is not entirely true: lactate is actually a valuable intermediate product that other cells can use as an additional fuel, while also helping shuttle hydrogen ions (H⁺) from muscle cells into the bloodstream. Rather than being just a “waste product,” it is a bridge that transfers energy to where it is most needed. Some lactate can also travel to the liver, where it is converted back into glucose through the so-called Cori cycle, circulating again as an extra energy source.

Anaerobic glycolysis can effectively power a runner at high intensities, typically for efforts lasting from about thirty seconds to two minutes. This time window corresponds to medium-length sprints or demanding intervals, when the body has not yet had time to fully engage slower but more sustainable aerobic mechanisms.

When running extends into a true endurance challenge, the oxidative system takes over as the main source of energy. It requires a continuous supply of oxygen and is therefore the most efficient energy provider during prolonged running.

Mitochondria in muscle cells act like tiny power plants. From food—mainly sugars and fats—they first generate a common “fuel” called acetyl-CoA: sugars reach it through glycolysis and pyruvate, fats through the sequential cleavage of carbon chains (β-oxidation). This fuel enters the citric acid cycle (also known as the Krebs cycle), where its carbon atoms are finally converted into carbon dioxide, while electrons are loaded onto special energy carriers (NADH and FADH₂)—as if small batteries were being charged.

These “batteries” then deliver their electrons to the respiratory chain in the inner mitochondrial membrane. There, the energy is transformed into a “pressure” of protons on one side of the membrane, while oxygen at the end of the chain accepts the electrons and is turned into water. As the protons flow back through the enzyme ATP synthase, it acts like a tiny turbine, rebuilding ATP from ADP.

Whereas anaerobic glycolysis generates only 2 ATP molecules from one molecule of glucose, the oxidative system produces as many as 30 to 32—about fifteen times more. That is why it can provide energy for virtually unlimited periods, as long as fuel reserves and oxygen remain available.

The Aerobic Advantage

If the first energy pathways are like short-term backup generators that quickly fail, the aerobic system is the main and most reliable energy source for long-distance running, providing a continuous supply of energy as long as oxygen and fuel reserves are available. This is the reason a runner can keep going for hours at a time, covering even dozens of kilometers.

The aerobic system switches on more slowly, but it has a crucial advantage: endurance and adaptability. At lower running intensities it draws most of its energy from fats, of which the body has large stores. When the pace increases and muscles need more energy in less time, carbohydrates take on a larger share. This very ability to shift between fuel sources allows the body to remain efficient under widely varying conditions—from an easy jog to a demanding endurance race.

During a run, the ATP–PCr system provides the explosive start; then, at high intensity for a short time, anaerobic glycolysis dominates; and for longer, endurance efforts, the oxidative system takes the lead. Each energy system thus has its role: from powerful takeoffs, through brief accelerations, to marathon endurance. Only their coordinated operation makes it possible for a person to run both fast and long.

Muscles and the liver also store glycogen, a concentrated form of glucose that serves as a readily accessible energy reserve. When intensity rises, the body breaks glycogen down into glucose, which quickly enters glycolysis to produce additional ATP. The amount of glycogen stored in muscles largely determines how long we can maintain high intensity.

The drop in performance known as “hitting the wall” is not necessarily the result of a complete depletion of glycogen stores. Newer findings show that fatigue occurs earlier due to a combination of factors: local glycogen depletion in the most heavily loaded muscle fibers, the influence of declining liver glycogen on the brain (central fatigue), and protective mechanisms by which the brain prevents complete exhaustion.

With regular endurance training, the body increases its glycogen storage capacity while also becoming more economical: at the same pace, it draws more energy from fats, preserving glycogen reserves for longer. In this way, training directly improves endurance by extending the time we can sustain demanding running speeds.

Understanding the Lactate Threshold

After the first minutes of running, when different energy systems are working at full tilt, a process begins in the body that for a long time was considered the main barrier to endurance. In the previous section we encountered glycolysis, the fast way of generating energy that produces the molecule lactate. For many years, lactate was thought to be responsible for the burning sensation in muscles and for exhaustion. Today we know that this is not the case. Lactate is not a waste product but a useful source of energy that the body continuously produces and uses. The easiest way to picture it is as a circulating fuel: when it forms in one muscle cell, the body can transport it to other cells—or even to the heart—where it is consumed as extra energy. In this way lactate acts as a portable form of fuel that the body constantly generates and at the same time uses.

Glycolysis is always switched on—the only question is what happens to its product, pyruvate. When physical activity is moderate, pyruvate smoothly enters the mitochondria, where it is broken down further in the presence of oxygen. But when a runner speeds up and the demand for ATP exceeds the current capacity of the mitochondria, some pyruvate remains outside and is converted into lactate. This is not a sign of system failure but a precise response of the body: glycolysis can continue producing ATP rapidly even when the aerobic “power plant” cannot keep up. Lactate here is not just a by-product but an important molecule that allows the process to go on and simultaneously serves as an additional fuel for other cells.

The lactate threshold is therefore not an enemy but a key turning point in the runner’s experience. The easiest way to imagine it is as a speed limit we know we cannot sustain for long. Below the threshold, the body removes all lactate as it appears and even uses it as extra fuel. Running below this threshold feels easy: breathing is still relaxed, conversation flows effortlessly. But as the pace increases, there comes a moment when talking becomes difficult, breathing deepens, and the muscles begin to “glow.” This is the sign that we have reached the lactate threshold—the point at which lactate accumulates faster than the body can process it.

Why then do we feel pain and fatigue if lactate is not to blame? Alongside its formation—and especially during the rapid breakdown of ATP in muscle fibers—hydrogen ions (H⁺) are produced, lowering the pH. These ions cause the acidity that disrupts normal muscle contraction, which we perceive as burning pain and rapidly rising fatigue. It is important to note that H⁺ are always formed, but at high intensity they are produced so quickly that the body cannot remove or neutralize them fast enough. The best way to picture this is like garbage collection: waste (H⁺) is always being generated, but if too much piles up at once, the trucks (buffering systems) cannot carry it away in time. In this process, lactate is not the problematic waste—it is actually part of the solution, since it binds some of the H⁺ and helps transport them into the blood, where they can be more easily processed. The drop in pH at high intensity is therefore primarily a result of the rapid hydrolysis of ATP (the chemical breakdown of the “energy battery” that releases energy), while lactate acts more like a “buffer” and at the same time an additional fuel.

Nevertheless, a raised lactate threshold is one of the key markers of progress. With regular, targeted training this threshold shifts to higher running speeds. The body learns to work more economically: muscles become better at consuming lactate, oxygen transport improves, and energy use becomes more efficient. The result? A runner can run faster at the same heart rate without excessive accumulation of hydrogen ions. That means less acidity, less fatigue, and greater endurance.

Modern smartwatches and apps can even estimate, based on heart rate and running pace, where a runner’s lactate threshold lies. This allows for smarter training: running fast enough to challenge the body but not so fast that fatigue stops us too soon. Training around the threshold is in fact one of the most effective ways for runners to improve gradually and push their limits.

It is also worth noting that scientific literature distinguishes between two different lactate-related thresholds. The first (the aerobic threshold) is the point at which lactate in the blood begins to rise above baseline but the body can still successfully remove and use it. This is the highest intensity at which you are still running in the easy zone. The second (the anaerobic threshold) is the level at which lactate starts to accumulate faster than the body can clear it. This is the threshold that marks the transition into the zone of high intensity. Understanding these two thresholds helps runners pace themselves more effectively and train right at the edge where improvement is greatest.

The Number That Reveals Running Potential

With regular training, a runner notices that at the same pace their heart rate drops, breathing becomes calmer, and the feeling of effort decreases. All of this is reflected in a single key indicator recorded by modern smartwatches, somewhat complicatedly called VO₂max. The simplest way to picture it is like the engine size of a car: the bigger it is, the more fuel and air it can take in and the more power it produces. Similarly, a higher VO₂max means the body can use more oxygen and generate more energy.

Physiologically, VO₂max is the maximum amount of oxygen the body can consume in one minute during intense exercise. It is usually expressed in milliliters of oxygen per kilogram of body weight per minute (ml/kg/min), which allows comparisons between individuals of different sizes. It is one of the most reliable indicators of aerobic fitness and endurance.

Behind this single number stands a whole orchestra of bodily adaptations. The heart is the main engine: with regular training the left ventricle, which pumps blood into the body, enlarges. Each beat therefore becomes stronger, so the heart can pump more blood with each stroke and thus beat more slowly at rest. This is known as an increased stroke volume. The lungs act like precise gas exchangers: training improves their ability to transfer oxygen into the blood and remove carbon dioxide. At the same time, on the level of the muscles, the network of tiny capillaries expands, allowing for better oxygen delivery to cells. In healthy recreational runners, performance is usually more limited by cardiovascular delivery and peripheral oxygen use than by lung diffusion. Taken together, this means the body can deliver more oxygen to working muscles—and sustain their energy supply for longer.

The runner feels this directly. Because their “aerobic engine” is more efficient, they can run faster without immediately hitting the lactate threshold we discussed earlier. That is why monitoring VO₂max is not just a motivational number on a watch screen but evidence that the heart, lungs, and muscles are becoming more coordinated and powerful. It is important to remember, though, that modern smartwatches estimate VO₂max using algorithms, while the exact value can only be determined in a laboratory test. Estimates from watches are most reliable during longer, steady runs on flat terrain; hills, wind, altitude, and fatigue can distort the calculations. Still, the watch’s estimate is an excellent indicator of progress in running.

VO₂max values vary widely between people. In an average, untrained man around the age of thirty, it ranges from 40 to 45 ml/kg/min; in women, from 30 to 35 ml/kg/min. With regular, moderately intense training, these values can rise by 15 to 20 percent. At the other extreme, elite endurance athletes—marathoners, cross-country skiers, or cyclists—reach astonishing figures that exceed 80 ml/kg/min in men and 70 ml/kg/min in women. For perspective: top cyclists have estimated VO₂max values above 80, placing them among the very best in the world. Such achievements are not only a gift of genetics but also the result of decades of deliberate, specialized training.

It is also important to note that VO₂max gradually declines with age—after the age of 25, by an average of about 5–10 percent per decade. This is a natural part of aging, linked to changes in the heart and blood vessels. The good news is that regular physical activity greatly slows this decline. In other words: although age inevitably plays its part, training ensures that the aerobic engine can remain strong and reliable for a long time. Recreational marathoner Jeannie Rice, who runs marathons in just over three and a half hours, was measured at age 76 with a very high VO₂max (≈48 ml/kg/min; source)—the highest recorded for a woman over 75. That value is comparable to the VO₂max of a well-trained 25-year-old. Such examples show that with proper training, and likely favorable genetics, it is possible to maintain physical performance in later life at levels typical of people several decades younger.

Still, we must also keep in mind that two runners with the same VO₂max may run at different speeds due to differences in their running economy (how much oxygen an individual uses at a given speed) and their critical speed (the highest sustainable speed without progressive fatigue). VO₂max is therefore a very important measure of aerobic fitness, but not the only one. It reveals the size of our aerobic engine, but how fast and how far we can run also depends on how efficient that engine is and how we use it.

Practical Tips for Running Training

Understanding how the body works allows us to plan training more effectively. Instead of always running at the same pace, we can divide workouts into three key zones, each targeting different physiological systems. The largest portion of training should be dedicated to easy runs, when you can still chat comfortably. In this zone the body primarily uses fat for energy, while over time we build our basic endurance and strengthen the cardiovascular system.

When we want to improve speed, we include runs around the lactate threshold. These are longer segments at higher intensity, where talking becomes difficult and we begin to feel the onset of a burning sensation in the muscles. The goal of these runs is to shift the threshold upward, so that in the future we can maintain faster paces with less effort.

For developing maximum speed and power, interval runs are key. These are very short, high-intensity bursts that the body can sustain only for a few seconds to a few minutes. They help improve our maximal aerobic capacity, as reflected in VO₂max. Each of these three zones targets a different energy system, but together they form the complete picture—systems that the body continuously intertwines and coordinates during running.

Listen to Your Body with the Help of Technology

When we look into the physiology of running, it becomes clear that every step is not just a mechanical movement but part of a carefully coordinated process. The body knows how to switch between different energy sources, adapt to stress on the fly, and gradually strengthen through training—whether in a beginner or an experienced marathoner.

Modern technology is not just a timer or distance tracker but a tool that reveals what is happening inside the body. Metrics such as VO₂max or heart rate show the changes brought about by training: the heart grows stronger, the lungs become more efficient, and the muscles are better supplied with oxygen. If the heart rate is lower at the same pace, it means the body is doing the same work with less effort.

When runners understand these signals, they can more easily adapt their training—from fast intervals to slower long-distance runs. This approach brings not only progress but also greater safety and reduced risk of overuse injuries. The greatest advantage of understanding physiology, however, is that running becomes more than a sport: it turns into a healthy and lasting habit that supports the body over the long term. In the end, what matters in running is not only how fast we cover the kilometers, but also understanding why we are getting better with each passing day.

Source:

Kenney, William L., Jack H. Wilmore, and David L. Costill. Physiology of Sport and Exercise. 7th ed. Champaign, IL: Human Kinetics, 2020.

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Translated from the Slovene original, available here: Kaj se dogaja v telesu med tekom?

In 1950, at the very dawn of the computer age, mathematician Alan Turing posed a question that remains with us today: can machines think? To avoid the philosophical pitfalls of defining thought, he proposed a pragmatic standard which became known as the Turing test. A machine would be considered intelligent if, during conversation, it managed to convince a human interlocutor that it too was human. In doing so, Turing inscribed the idea of imitation at the core of discourse on artificial intelligence, whereby the pinnacle of machine intelligence is to become indistinguishable from human intelligence.

Today, more than seventy years later, public discourse at least still seems to persist in the same conceptual loop. With every leap forward in artificial intelligence capabilities, the initial questions typically posed are: Is it creative? Does it have emotions? Is it self-aware? Although researchers have long focused on solving specific problems, the public continues to measure artificial intelligence by a human yardstick, seeking a reflection of its own image in AI's responses, as if the ultimate goal were to create a perfect artificial mirror image of a human being.

While such a focus is appealing and understandable, it risks leading us into a blind alley, as the fundamental question itself may be poorly posed. What if human intelligence and subjectivity are not, in fact, sensible or optimal goals for the development of artificial intelligence? Our brains are, after all, the result of countless evolutionary makeshift solutions and compromises that once afforded our ancestors a greater chance of survival. Given that our humanity is essentially a byproduct of chance, stemming from a fundamental dissonance in our biological design, is it truly the best benchmark for measuring the progress of smart machines?

The human mind is by no means an elegant product of deliberate design. It is the result of a messy evolution that suddenly loaded an entirely new, incompatible "operating system" in the form of language onto ancient biological "hardware"—represented by neurological and biochemical processes in the brain and other mechanisms within the body. While this leap into abstract, symbolic thought enabled us to accumulate knowledge and build powerful civilizations, it simultaneously introduced a fundamental tension into our natural system.

Our biological makeup, geared towards finding food and mates, was suddenly repurposed for the pursuit of cultural goals such as status, meaning, and recognition. While biological drives subside upon achieving their objective—consuming a meal, for instance—the symbolic desire introduced by our entry into language is never truly satiated. It is precisely this internal friction that constitutes the source of the human drama: the perpetual feeling that something is missing, our capacity to act against our own interests, and also the creativity that draws nourishment from this same restlessness.

In animals, a simple biological cycle applies: hunger leads to seeking food; satiety brings calm. In humans, however, language intertwines this need with additional meanings. Food is no longer merely a source of calories but becomes a marker of status, identity, morality, and intimacy. We eat not only when hungry, but also out of boredom, anxiety, or for social reasons. Similarly, the reproductive instinct is transformed into a complex pursuit of love and recognition. This superimposition of culture onto biology makes us human, yet simultaneously leaves us perpetually restless and unsatisfied.

The endeavor to recreate this complex and contradictory human interiority within a machine is likely futile. We did not achieve flight by building a mechanical device that meticulously imitated every flap of a bird’s wings. Success came only once we understood the fundamental principles of aerodynamics and designed aircraft based on them. Therefore, the sensible goal of artificial intelligence should be to develop advanced forms of cognition, not to precisely replicate humanity with all its evolutionary baggage.

In research circles building new artificial intelligence models, there has been much discussion in recent years surrounding the goal of developing artificial general intelligence (AGI)—that is, a machine with cognitive capabilities equivalent to those of humans. In practice, however, progress is manifesting differently, as the path toward broader abilities leads not through the imitation of human versatility, but through the integration of powerful, specialized solutions.

Artificial intelligence is becoming increasingly adept at tasks once believed to be the exclusive domain of the human mind. In many areas, it has already surpassed us, or soon will. Today, it already excels at translation, solving difficult mathematical problems, programming, and other complex cognitive tasks. Moreover, systems like AlphaFold predict the 3D structure of proteins directly from amino acid sequences—a task that clearly surpasses human intuition and significantly accelerates biomedical research.

Artificial intelligence is not our competitor in humanity, but rather a tool that allows us to look beyond the constraints of our biological givens. Its power lies not in imitating humans, but in its radical alterity, as it can be incomparably superior to us in specific tasks. It is precisely this shift that raises urgent ethical questions about the control and safe application of such powerful tools, while simultaneously liberating development from the blind alley of imitation.

Perhaps it is time, therefore, to turn the Turing test on its head. Instead of asking whether a machine can fully convince us that it is equivalent to a human, we should be asking what we can safely achieve with an artificial intelligence that has no need to compare itself to humans, precisely because it is significantly better than us at certain tasks. Airplanes are excellent flying machines, even though they have little in common with birds.

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Translated from the Slovene original, available here: Onkraj Turingovega testa.

Quantum mechanics works very well. Its predictions are remarkably precise, and a large part of modern advanced technology is based on it. In the century since its inception, it has established itself as one of the most successful theories in the history of science.

Yet, this extraordinary success conceals a fundamental dilemma: we still do not know precisely what quantum mechanics actually describes. What is the reality it purports to reveal? This, however, is not its weakness, but rather a sign that physics must also openly confront questions that cannot be resolved by measurements and equations alone, but demand thorough conceptual analysis.

The question of interpreting quantum mechanics has been important from the very beginning, yet for a long time, it was pushed to the margins as something deemed outside the scope of legitimate scientific inquiry. Today, it is increasingly apparent that such avoidance of philosophical questions is no longer tenable, as interpretational problems have become a legitimate and essential part of the debate on the foundations of physics.

Questions such as: what truly exists (particles, wave functions, parallel worlds?) and how nature operates (deterministically or randomly? locally or non-locally?), are an inseparable part of the physical theory itself. Every interpretation has its advantages, but also its "price"—either in the form of additional, hard-to-justify assumptions or in consequences that many find difficult to accept.

Philosophy as a Tool for Understanding the Quantum World

Philosophy can play a significant role in understanding quantum mechanics—not as an external arbiter dictating rules to scientists, but as a set of conceptual tools for clarifying fundamental assumptions and assessing their soundness. It offers help precisely where experiment and calculation no longer suffice.

One of philosophy's key tasks is to highlight the assumptions we often take for granted. Every interpretation of quantum mechanics rests on certain suppositions about the nature of the world, and philosophical analysis allows us to make them explicit and critically examine them. At the same time, it promotes a more precise use of language by revealing that terms like "measurement," "observer," or "state of a system" are not as self-evident as they might seem, and thus demand clear and consistent application.

It is also crucial for evaluating the philosophical consequences of various interpretations. Whether we accept the existence of parallel worlds or the idea that events are not predetermined, these are not merely abstract possibilities but concepts that profoundly shape our understanding of reality. Philosophy enables us to comprehend and weigh such consequences.

Finally, philosophy serves a critical function as a safeguard. It helps maintain a clear distinction between scientific hypothesis and speculation, particularly where the physical theory leaves its explanations open. Without such reflection, these conceptual gaps are quickly filled by untestable claims—often from physicists themselves—that verge on mysticism or esotericism. Here, philosophical tools act as a filter, ensuring logical rigor and conceptual discipline.

Physicists are often already engaged in work that is, in many respects, philosophical—typically without being aware of it and without the proper conceptual tools. The question, therefore, is not whether a philosophical approach belongs in physics, but how to integrate it thoughtfully and with the necessary rigor. Ultimately, philosophy can contribute to a better understanding of the world described by quantum mechanics—a world that may be strange and counter-intuitive, but is no less real for it.

When Physical Phenomena Become Philosophical Problems

In a conventional physics education, concepts such as superposition, entanglement, and measurement are presented as part of the standard formalism of quantum mechanics. Physicists learn to apply them as tools for solving specific problems. Philosophical inquiry, however, begins where the textbook typically ends: by treating these phenomena as questions that probe the very foundations of our understanding of nature.

Superposition is a prime example. It is not merely a matter of a particle existing in a state that encompasses multiple possibilities simultaneously—such as being in two places at once. The crucial question is what this implies for our very notion of what a "property" is. Does a physical object—an electron, for instance—possess definite properties in and of itself, at all times? Or do these properties only come into being when the object interacts with something else, such as a measuring apparatus? Superposition, therefore, compels us to question whether the world possesses fixed, independent features, or if its properties are instead contingent on context and relationships.

Entanglement goes a step further. Here, we are no longer dealing with a single object, but with two or more whose states are so inextricably linked that they cannot be described independently. In entangled pairs, the state of the entire system—all particles together—is something more than just the sum of the states of its individual parts. This has been confirmed by numerous experiments, which show that the predictions of quantum mechanics hold true even when they defy the fundamental assumptions of classical physics. If we take these results seriously, we must abandon at least one of two classical assumptions: that influences cannot propagate faster than light (locality), or that particles possess definite properties independent of observation (realism).

The most enduring and prominent debate, however, revolves around the measurement problem. In quantum mechanics, there are two different rules for describing the evolution of a system over time. The first is the Schrödinger equation, which describes the deterministic evolution of a state. The second is the so-called "collapse of the wave function," which, upon measurement, abruptly singles out one result from many possibilities—and does so in a way that is random and inherently unpredictable. These two processes are fundamentally different, yet the theory does not explain the transition from one to the other. The key question is whether both processes are equally fundamental, or if one is merely an approximation arising from our limited perspective. In any case, this creates a tension within the theory itself: quantum mechanics describes the world with the immense precision of the Schrödinger equation, yet it simultaneously includes a mechanism that is inconsistent with its own fundamental description.

Three Philosophical Interpretations of Quantum Mechanics

The various interpretations of quantum mechanics are not merely different perspectives on the same thing. They are distinct philosophical approaches to understanding what the theory actually says about the real world. Each interpretation adopts certain presuppositions about the nature of reality—that is, a particular view on what exists in the world and how—while relinquishing others. Since, for now, none of these interpretations can be distinguished by direct observation, the choice between them is fundamentally philosophical, not empirical.

The most widespread approach in the history of quantum theory is the so-called Copenhagen interpretation. It attempts to resolve the problem by declaring it irrelevant. According to this view, physics should limit itself to predicting the results of measurements and not concern itself with questions about what is really happening "behind the scenes." In this sense, it is a strategy of philosophical restraint that avoids untestable claims and emphasizes that only what can be measured is important. But the price of such an approach is the surrender of the ambition for science to describe the world as it exists independently of the observer and the act of measurement.

At the opposite end of the spectrum is the Many-Worlds Interpretation, which does away with the wave function collapse problem by simply rejecting it: the collapse never happens. Instead, all possible outcomes of a system's quantum evolution are in fact realized—each in its own branch of a diverging multiverse. This approach takes the mathematical structure of quantum mechanics as a direct description of reality and requires no additional mechanisms or interventions. It is elegant and internally consistent, but it comes at a high price: we must accept the existence of an infinite number of parallel worlds that we can never directly perceive.

A third path involves interpretations that attempt to supplement or modify quantum mechanics to reconcile it with a more classical worldview. The first of these are hidden-variable theories, which postulate that quantum theory is incomplete and that there are yet-undiscovered factors—the hidden variables—that actually determine the outcomes of measurements. If we knew them, we could predict events with complete determinism. The second are objective-collapse theories, which introduce additional laws into the formalism of quantum mechanics, causing the wave function to collapse spontaneously under certain conditions—not due to observation, but because of the system's own intrinsic properties. Both of these approaches preserve the classical idea that the world has an objective, observer-independent structure. But these approaches, too, have their price: as of now, we have no empirical evidence that hidden variables or objective collapses actually exist. If they are to become part of accepted physics, they will have to be confirmed by new experiments.

When Contradiction Is Not a Flaw, but a Feature of the World

But what if the very premise of all the interpretations so far is flawed? What if the assumption that there must be a single, fully coherent, and logical explanation for reality simply isn't true? A philosophical shift emerging in contemporary debates about quantum mechanics offers another possibility: that the problem lies not with the interpretation, but with the very nature of reality itself. Perhaps reality, at its most fundamental level, is simply not something that can be described without contradiction.

From this perspective, the contradictions revealed by quantum mechanics are not a sign of the theory's inconsistency but are instead an inherent feature of reality itself. The world, at its most profound level, is inherently indeterminate—and perhaps even contradictory. Within this framework, a paradox is no longer a problem to be solved, but a source of insight. The fact that we cannot consistently describe an electron as either a particle or a wave might not mean that our theory is missing something; it might mean that this very duality is an intrinsic part of its nature. This approach does not resolve contradictions but embraces them as a fundamental feature of reality. A paradox is therefore no longer an obstacle to understanding, but the very means by which we achieve it.

The mechanism of decoherence, which is well-known to physicists, holds an important place in this discussion. This is the phenomenon whereby a quantum system loses its distinctively quantum properties, such as superposition, through its interaction with the environment. Decoherence explains why we do not observe quantum effects in the everyday world, and why the world appears "classical" to us, even though it is fundamentally governed by quantum laws. However, decoherence does not solve the measurement problem—it does not explain why we get one specific result in any individual measurement. Instead of explaining how a single actuality emerges from a multitude of possibilities, it merely shows how quantum uncertainty is, in a sense, dissipated into the environment.

The philosophical task is to highlight this difference: between what suffices "For All Practical Purposes" (FAPP) and what would constitute a genuine, fundamental understanding. This distinction is crucial for any serious treatment of the conceptual meaning of decoherence. A practically useful solution is not the same as an explanation of what actually happens.

The Quantum Computer as a Practical Test of Philosophical Paradoxes

The debate over the fundamental concepts of quantum mechanics does not end in a lecture hall or a philosophical essay—it has very concrete consequences. One of these is quantum computing. At first glance, it appears to be a technically sophisticated technology of the future, but a closer look reveals that the quantum computer is also a philosophically sensitive instrument. It doesn't just operate according to quantum principles—it operates within them.

The fundamental difference between a classical bit and a quantum bit (qubit) is deep and conceptual. A classical bit can take one of two values: 0 or 1. A qubit, however, exists in a superposition of both—its state is not "either/or" but "both at the same time." This means that a quantum computer does not calculate with fixed facts but with possibilities that are omnipresent until they are observed. The computation takes place in a field of potentiality that exists as potentiality—a state that classical logic once deemed an impermissible contradiction. Yet it is precisely this state of contradiction, where something is simultaneously 0 and 1, that forms the basis of quantum computational power.

This difference is not merely mathematical or engineering-related. It is the embodiment of the idea that the world, at its core, can be ontologically indeterminate—as we saw in the philosophical approach that does not seek to eliminate contradiction but accepts it as a real feature of nature. The quantum computer not only confirms this idea but re-enacts it with every operation. Its computational process is based on the system existing in many possibilities at once—every computational path is simultaneously present and active until a measurement is performed.

But it is precisely here that the fundamental tension, already familiar from the discussion of decoherence, becomes apparent. For a quantum computer to function at all, it must remain isolated from its environment—it must maintain its coherence. Any contact with the classical world, whether a measuring device or thermal noise, causes decoherence—the loss of superposition and thus the collapse of quantum potentiality. In this sense, the fight against decoherence is actually a fight against the world's pressure to turn potentiality into actuality too soon. A quantum computer is not just a machine; it is a physical experiment to create a controlled "bubble" of quantum reality within the classical world. It is a practical demonstration of a contradictory state that works—but only as long as it can hold the influence of the classical view of the world at bay.

Ultimately, however, comes the measurement—the moment the quantum computer delivers a result. At that point, the superposition collapses, and we are left with a single, classical answer. In a philosophical sense, this is the moment of "collapse": from a space of rich, co-existing possibilities, only one actuality remains. All other possibilities, which were realistically present in the wave function, have vanished. With this, the quantum computer stages one of the most fundamental problems of quantum mechanics—the measurement problem—not as an abstract question, but as part of its everyday function.

Therefore, quantum computing is not just a useful technology for faster calculations but something far more significant: it is a laboratory in which we test our deepest notions of what it means for something to exist, for us to know something, and for us to measure something. A quantum computer operates on foundations that are not self-evident to classical logic, as it enables a state in which mutually exclusive possibilities are simultaneously present. According to classical logic, something cannot be both 0 and 1 at the same time. In quantum reality, however, this is not only possible but essential for its operation.

Every quantum algorithm runs in this space of possibility. During the algorithm's execution, the system simultaneously "considers" all possible paths without committing to any single one. But to get a concrete answer from the system, we must finally perform a measurement. At that moment, the wave function collapses—and we get a single result. Philosophically speaking, this is the moment when actuality emerges from potentiality. But this transition is neither smooth nor fully explained: all other outcomes, which were realistically present as possibilities just moments before, disappear—as if they never existed.

The essence is this: a quantum computer does not work in spite of the paradoxes of quantum theory, but because of them. Its power lies precisely in its ability to temporarily manage this tension: it allows contradictory possibilities to be sustained long enough to participate in a calculation and then—at the right moment—enables their conversion into a single result. This is a technology based not on avoiding the philosophical problems of quantum mechanics, but on taking them seriously and integrating them into its very operation.

Why Physics Needs Philosophy

The aim of this text has been to show that physics is already engaged with philosophical questions—often unconsciously and without the proper conceptual tools. The debate over the interpretations of quantum mechanics is not something marginal or academically exotic; it is a debate about the very picture of reality we are trying to construct when developing a theory of its most fundamental laws.

In this context, philosophy is not an outside observer of science but a toolkit essential for clarifying foundational concepts. It helps reveal hidden presuppositions, check the internal logic of arguments, evaluate the price of different explanations, and better understand what is truly at stake in fundamental scientific debates. When interpretations speak of the world—and not merely of equations—they are engaging in philosophy. And this is precisely the case with quantum physics today.

Furthermore, philosophy performs another, perhaps less obvious, but crucial role: it acts as a defense mechanism against conceptual slippage. Wherever a physical theory leaves a vacuum—as it does with the measurement problem—that vacuum is often filled by speculations bordering on mysticism, sometimes even from physicists themselves. Talk of the "role of consciousness" in creating reality or of direct links to spiritual traditions are common symptoms of this phenomenon. A rigorous philosophical approach acts as a filter here: it demands that every claim be clearly defined, logically consistent, and that its ontological commitments be made explicit. By doing so, it separates a legitimate—albeit speculative—scientific hypothesis from ill-defined statements that are, in fact, untestable. Philosophy, therefore, not only opens new avenues of thought but also offers the tools to close off those that lead nowhere, thereby safeguarding the integrity of the scientific debate.

The collaboration between philosophy and science is therefore no longer a matter of personal curiosity, but an essential part of any serious engagement with questions that probe the very foundations of nature. Philosophy will not provide new physical equations—that is, and will remain, the task of physics. But it can offer the conceptual clarity, terminological precision, and intellectual rigor needed to navigate a theory that resists everyday intuition and challenges our notions of reality. Quantum mechanics reveals a world that is not merely strange, but profoundly different from anything we have ever imagined. To truly understand it, we need more than tools for measuring—we also need tools for thinking.

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Translated from the Slovene original, available here:

ANATOMIJA VEDNOSTI / Sašo Dolenc

Paradoksi realnosti: kaj nam kvantna mehanika zares pove o svetu

Kvantna mehanika zelo dobro deluje. Njene napovedi so izjemno natančne, na njej temelji velik del sodobne napredne tehnologije. V stoletju od svojega nastanka se je uveljavila kot ena najuspešnejših teorij v zgodovini znanosti…

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10 months ago · 4 likes · 1 comment · Sašo Dolenc

We live in strange times. On the one hand, we are witnessing astonishing progress in science and technology. Fields like neuroscience, genetics, and artificial intelligence are meticulously mapping the material machinery of the universe and life itself. This is a world of data, algorithms, and objective, measurable truth. Yet, on the other hand, we find ourselves ever more confounded by the most basic of questions: what does it mean to be human? What is the engine behind our irrationality, our passions, our creativity, and our capacity for self-destruction?

These two modes of inquiry—one into objective mechanisms, the other into subjective experience—exist in separate realms. The discourse of science and technology strives to reduce subjectivity to the language of neurons and information. In contrast, the humanist tradition, especially the potent school of Lacanian psychoanalysis so influential in Slovenia, holds fast to a conceptual framework that defies such reduction, speaking instead of the unconscious, of desire, and of fantasy.

The purpose of this essay is to confront this very dilemma. It is not an attempt to simply subordinate one world to the other. Rather, it is an attempt to build a bridge between them, to show that they are not in necessary conflict but are instead concerned with different facets of the same phenomenon. Science, by its very methodology, strives for objectivity, which means it must systematically exclude the subjective perspective of the observer from the equation. Its ideal is a view from nowhere. Yet it is precisely this exclusion that creates a blind spot: science can explain the mechanics of the brain with breathtaking precision, but it does not directly engage with the experience of the concrete subject who "has" that brain.

Psychoanalysis steps directly into this empty space. It does not offer an alternative scientific truth, but rather a theory of this "excluded element"—of the subject itself. It concerns itself with the "software bug" in the human system: our irrationality, our symptoms, our capacity to act against our own best interests. It sees this not as a flaw to be patched, but as the fundamental feature that makes us human. For a long time, science could not get a firm grasp on this direct experience of subjectivity, but today, with the flourishing of neuroscience, the two approaches are gradually converging. Though they hail from different traditions, they meet at a single point: the question of how to explain the contradictions that simultaneously drive us forward and tear us apart.

Our goal, therefore, is not to prove that "psychoanalysis was right because dopamine mechanisms confirm it." Rather, the goal is to use scientific and evolutionary models as a stage upon which we can enact and understand the logic of psychoanalytic concepts. This is a translation project of sorts: how can we explain the logic of desire, lack, and the Other to someone accustomed to thinking primarily in the language of natural science?

The Evolution of Subjectivity

Before we delve into the complexities of the human mind, we must return to the very beginning, to a time when brains did not yet exist. The first large organisms, which were mostly stationary, had no need for them. Their behavior was simple, their environment predictable, and their responses automatic, guided solely by chemical signals. Everything changed, however, with the emergence of active movement and hunting. An animal that had to pursue prey while simultaneously avoiding predators needed a central system to coordinate conflicting information and make decisions. Thus, the first brains came into being.

Their operation was initially incredibly simple: approach the beneficial, avoid the harmful. This basic algorithm, which enabled the survival of worm-like creatures some 600 million years ago, was the foundation upon which everything else was built. However, this system was rigid and incapable of learning; it could not adapt to changes in the environment.

Approximately one hundred million years later, early vertebrates developed the ability to learn from experience. Their brains began to build internal maps of the world. This was the first crucial step towards a separation from immediate reality. The world was no longer just a collection of direct stimuli but had become an internal representation, a mental image. This map enabled an understanding of the surroundings and the prediction of future events, marking a turning point in the development of behavior.

But this internal picture was initially passive. The real leap occurred when our ancestors began to actively use this internal map to play out the future. This marked the birth of internal simulation, the ability to mentally imagine different scenarios before they happen. "What if I take this path instead of that one?", "What if a predator comes from behind that bush?". This ability to create mental narratives became one of the key evolutionary advantages, as it allowed for learning from imagined experiences without real-world risk.

The next significant step was taken by the ancestors of primates tens of millions of years ago when they began to include an understanding of other beings in these simulations. They could imagine what other beings were thinking, feeling, and intending. This ability was crucial for life in complex social groups. The narrative was no longer just a description of events but a stage for relationships between individuals with different perspectives and goals. For the first time, stories acquired characters with inner lives.

The decisive breakthrough, however, was the development of language. While other species communicated about immediate states—such as danger, food, and dominance—language made it possible to communicate about something that does not exist here and now: about the past, the future, and entirely fictional worlds. It allowed people to start sharing their narratives with others. Stories, myths, rules, and values created a shared, supra-individual symbolic world—a culture, a society, a system.

The Predicament of the Animal That Speaks

The evolutionary path has led us to a being with exceptionally powerful hardware: a large prefrontal cortex and specialized centers for language. This "hardware" enabled the installation of a revolutionary "operating system"—symbolic thought. Yet it is precisely this installation that has caused a fundamental and specifically human predicament. The new, abstract operating system of language was not designed for the old, biological hardware, which evolution had optimized for survival in the physical world. Thus, language did not merely become a neutral tool for communication; it proved to be an incompatible software package that forever changed how our fundamental biology operates.

This "hijacking" of the old hardware by the new software occurred in two key steps. First, language, with its capacity for abstraction, inserted an insurmountable gap between us and the world. To be able to think and communicate about an infinitely complex reality, we had to "compress" it into a finite set of symbols—words. This process can be compared to lossy data compression. Just as compressing an image into a JPEG format permanently loses some information about colors and details, translating direct experience into language permanently loses part of its fullness and immediacy. The word "water" can never capture the entire experience of water. This informational gap between the symbol and the thing, between the compressed representation and the immeasurably rich reality, creates a structural lack in our existence. This is not a mystical loss, but a functional consequence of how a symbolic system works. We become beings haunted by the feeling that the "real thing" is always slipping away.

Second, language "infected" our biological needs with symbolic meanings. Food is no longer just a source of calories but becomes a sign of status, culture, identity, and morality. Sexuality is no longer just an instinct for reproduction but becomes a complex stage for love, power, and recognition. Our biological hardware, including the dopamine system, which was previously geared towards finding berries and mates, was redirected to hunt for abstract goals: social prestige, meaning, wealth, truth.

The problem is that our biological hardware is designed for homeostasis—when a need is satisfied, the system calms down. The symbolic world, however, has no natural off-switch. We can always desire more recognition, more money, more meaning. A biological drive, which had a clear goal and conclusion, has transformed into an insatiable cultural drive that runs in an infinite loop.

Psychoanalysis enters at precisely this point. It does not deal with the biology of cells, but with this "biology" of the embodied subject trapped in language. It studies the consequences of this fundamental incompatibility between our symbolic software and biological hardware: symptoms, anxiety, dreams, and above all, desire. In this sense, psychoanalysis is a theory of the "ghost in the machine"—of how the abstract world of meanings becomes embodied in our bodies and drives our psyche.

Why We Also Act to Our Own Detriment

The fundamental predicament of the human as a speaking animal is not merely an abstract philosophical idea. It has very concrete consequences that manifest in the very structure of our psyche and the biochemistry of our brains. Phenomena such as insatiable desire, self-destructive behavior, and a sense of fundamental lack are not random errors but are logical symptoms of the mismatch between our biological heritage and the symbolic world we inhabit. Psychoanalysis is the discipline that identifies these symptoms, names them, and attempts to understand their logic.

The first and most basic symptom of the "hijacking" of biology by language is the transformation of instinct into drive. An instinct is a biological program with a clear, natural goal and rhythm. A lion is hungry, it hunts a gazelle, eats it, and its need is satisfied; the instinct subsides until the next cycle. It is natural, efficient, and oriented towards the preservation of life and the species.

The human, however, is a being in whom this natural program is "broken." Our biological needs are reshaped into a drive, which no longer has a natural goal. Its purpose is not to reach an object and find peace, but to constantly circle around the object. The satisfaction of the drive lies not in final satiation, but in the repetition of the act itself. The object—food, money, recognition—becomes merely an excuse for the drive to spin in its circuit. This explains many human "irrationalities": why we eat when we are not hungry; why we work when we have enough money; why we repeat self-destructive behaviors. The satisfaction is not in the result, but in the very repetition of the circuit. This denaturalized, mechanical force is the fundamental engine of the human psyche.

Modern neuroscience has shown that dopamine is not the "pleasure molecule." The pleasure from a reward obtained is primarily regulated by the opioid system, which says: "This is good." Dopamine, however, is the chemistry of anticipation, motivation, and wanting—it is activated before the goal is reached and pushes us forward. This is why the anticipation of a vacation is sometimes more exciting than the vacation itself. This separation reveals why a person can be driven by something that ultimately brings them no satisfaction.

This is where the key psychoanalytic concept of jouissance emerges, which refers to a paradoxical satisfaction not based on appeasement but on the endless repetition of the drive itself. It is the satisfaction a workaholic finds in burnout—a satisfaction that goes beyond the pleasure principle and becomes painful. The human is therefore not a being guided solely by a goal; it is a being that can find enjoyment in its own momentum, even if it brings suffering.

These neuroscientific and psychoanalytic insights overlap astonishingly. Desire is the sophisticated, future-oriented force, the psychological expression of the dopaminergic "wanting" system. Pleasure is that moment of relief provided by the opioid system. And finally, jouissance is that specific, paradoxical satisfaction that the subject derives from the operation of the drive itself.

But where does this constantly running dopaminergic engine find its "fuel"? Where does this fundamental feeling that we are always missing something originate? This is where brain chemistry connects with the evolution of language. As we have seen, our entry into the symbolic world forever separates us from immediate reality. This fundamental lack, introduced by language, is a structural feature of human existence. We are no longer beings who can achieve complete, instinctual satisfaction. And it is precisely this structural lack that becomes the playground for our dopamine system. Human desire is what happens when a brain, equipped with a biological engine for seeking what is missing, finds itself in a world of symbols that promises fullness but is simultaneously the source of its fundamental sense of loss.

It is crucial to understand that the "real thing that is forever lost" is not something that evolutionarily less developed animals possess and we humans do not. This sense of loss is an illusion retroactively created by language itself. An animal does not miss direct contact with reality because it does not live in a world of symbols that would create such a separation in the first place. The feeling that we have lost something is established only within the domain of thought and symbols. As long as subjectivity lacks this abstract dimension, the difference is not even noticeable. Loss, therefore, is a specifically human symptom of existing in language.

The Enigma of the Other's Desire

We now have all the elements on the table: an evolutionarily developed mind capable of simulation and language; a biochemical "wanting" engine driven by lack; and a symbolic world that creates this lack. But how do these elements combine into the unique experience we call subjectivity? The answer lies in our relationship to the social world that surrounds us—in the way we try to orient ourselves within the invisible network of expectations we call culture or society.

The Evolutionary "Hack"

How and why does our mind, which evolved to understand concrete beings, begin to act as if abstract systems (language, culture) have a will of their own? The answer lies in the principle of evolutionary economy. Evolution rarely creates new brain modules from scratch; it prefers to repurpose existing structures for new functions. Over millions of years, our brains developed an extremely effective "social module" for reading the intentions, desires, and beliefs of other agents—members of the troop, predators, prey. This was a crucial mechanism for survival. When a new, complex problem emerged—how to understand and navigate the abstract world of language, rules, and social norms—the brain had no specialized tool for it. So, it used the best tool it had available: the old, reliable social module.

A "hack" or a repurposing occurred: the brain began to treat the abstract, impersonal system as if it were a giant, unified agent with a will of its own. A good example of this is our relationship with the stock market. No one speaks of the market as an impersonal sum of millions of individual transactions. Instead, we read that the market is "optimistic," "nervous," that it "panics" or "rewards" certain decisions. We attribute emotions, intentions, and desires to the market as if it were a single, moody subject. We try to guess what it "wants" so that we can act accordingly. This is our old, primate social mechanism in action, applied to a completely abstract system.

This is why, in psychoanalytic language, we do not speak merely of a "system," but of the "Other." This term emphasizes our personal, subjective relationship with this structure. The Other is not just a set of rules; it is the supposed source of these rules, the one we address when we ask about meaning, the one we assume is watching, listening to, and judging us. It is the ultimate interlocutor, even though it is abstract. This evolutionary "hack" was extremely successful, but at the same time, it created the fundamental delusion that defines the human condition: we are constantly trying to guess the desire of something that, in reality, has no desire.

The Subject's Fundamental Question

In this relationship with the Other, the crucial question that defines us as subjects is born. But why is this question "What does the Other want from me?" and not merely "What are the Other's goals in the world?". Why does the question turn back on ourselves?

The answer is again evolutionary and pragmatic. Our "social module" did not evolve for the objective analysis of systems, but for survival within a social hierarchy. For an early primate, it was not enough to know what the alpha male generally wanted (e.g., to maintain dominance). The key question for its survival was: "What does the alpha male want from me? Does he see me as a threat, an ally, or an irrelevance? What is my position in his gaze?". Survival depends not on an objective understanding of the other's goals, but on correctly interpreting one's own place in relation to those goals.

We transfer this deeply rooted mechanism of social positioning onto the abstract Other. We are not just interested in what the general goals of Culture or Society are. Our survival and success depend on how we respond to their unspoken expectations. Therefore, the question necessarily turns to us. The moment we ask, "What does it want from me?", we cease to be external observers of the system and become subjects implicated within it. This question forces us to take a stance, to define ourselves in relation to these unclear expectations. This is the moment of the subject's birth: not as a being who knows, but as a being who questions its place in the desire of the Other.

How Fantasy "Calms" the Brain

For our social brain mechanism, confronting the unanswerable riddle, "What does the Other want from me?", represents a state of extreme uncertainty and anxiety. This mechanism is optimized for finding clear social signals, not for dwelling in perpetual ambiguity. To avoid this paralyzing anxiety, our psyche creates a "software patch" or an ad hoc solution: fantasy.

Fantasy is not just a fleeting daydream; it is a fundamental, unconscious story or scenario that offers us a simple, albeit false, answer to the riddle. It functions as a kind of "GPS for desire," translating the abstract question into a concrete plan of action. Fantasy says: "Don't worry, I know what the Other wants. If you just have X (the perfect partner, career success, social recognition), you will finally fulfill that unspoken expectation and be complete."

By transforming an unanswerable question into a solvable task ("achieve X"), fantasy calms our biological mechanism. Uncertainty gives way to a clear goal, which allows our dopaminergic "wanting" system to kick in. But this has concrete consequences. Fantasy becomes the filter through which we perceive the world. It determines what we will see as desirable and what we will overlook. It explains why we often get entangled in the same relationship patterns or repeat the same mistakes: because we are following the instructions of our fundamental fantasy, which offers us the illusion of meaning and direction. It is simultaneously the solution to our anxiety and the source of our deepest compulsions.

The Engine of Desire and Its Phantom Object

So far, we have established that humans are defined by two key characteristics: on the one hand, we are driven by a relentless biological engine of "wanting," and on the other, we are defined by our existence in a symbolic world that creates a fundamental lack. But how do these two elements—this raw biological mechanism and the abstract symbolic structure—actually combine? How does our dopamine system, which evolved to achieve biologically significant goals, become obsessed with the search for social recognition?

The answer lies in what could be described as a fundamental "by-product" of human evolution: the mixing of mechanisms that were never designed for each other. This biological engine is, in its essence, blind. Its sole function is to detect the signal "something is missing" and initiate a search. It does not care whether this lack is the result of a calorie deficit or the structural void carved into us by language. When language emerged, it introduced a new, permanent, and non-biological signal of perpetual lack into our system. And the old biological mechanism simply "latched onto" this new, more powerful signal. This was not an elegant adaptation but a "hijacking": the biological hardware for survival was repurposed to serve the symbolic operating system.

However, this engine, now running in an infinite loop, needs a target. A drive that circles without an object is pure, unbearable anxiety. But since the "real thing" that was lost upon entering language is forever unattainable, this mechanism must create its own object. And here enters the crucial and perhaps most difficult concept in psychoanalysis: objet petit a.

Objet a is not the concrete object we desire (a car, a partner, success). It is the cause of our desire. It is that seeming, phantom surplus that our psyche projects onto a particular object in the world, thereby transforming it into something special, desirable, into the promise of final satisfaction. It is that intangible "something more," that glow we see in someone or something, which we mistakenly believe to be its own property, when in fact it is merely a reflection of our own emptiness.

The function of objet a can be compared to the horizon. The horizon is a real, visible line that structures our path and directs our gaze. At the same time, however, it is a complete illusion—it is not a place we can ever actually reach. The closer we get to it, the more it recedes. Its function is not to be reached, but to constantly drive us forward and organize the space before us. Objet a is the horizon of our psyche: a virtual destination whose sole function is to keep our desire in perpetual motion and give it direction.

How does the psyche choose which object to project this phantom surplus onto? Here, the circle closes and connects with the previous chapter. Fantasy is that personal scenario or fundamental story that acts as the map for our desire. It determines which objects in our world are suitable candidates to become bearers of objet a. Fantasy is the instruction that says: "If you achieve exactly this (a partner who will look at you in that way; a career that will bring you that kind of recognition), you will finally fill your lack and answer the enigma of the Other's desire."

The architecture of human desire is therefore as follows: we have a hijacked biological engine ("wanting"), constantly fueled by a structural lack. This engine, to avoid anxiety, projects a phantom object-cause (objet a) onto the world, while its path to these virtual goals is shown by a personal map we call fantasy. It is precisely this complex, improvised, and fundamentally flawed structure that psychoanalysis recognizes as the core of human subjectivity.

How to Stop the Elusiveness of Meaning?

Fantasy offers us a working hypothesis on how to achieve satisfaction, but every hypothesis needs a foundation so it doesn't just hang in the air. If we ask ourselves why success would bring us satisfaction, it leads to an infinite chain of further questions: "Because then I will please society." "And why is it important to please society?" "Because then I will be accepted." "And why is it important to be accepted?". For this chain to stop, we need a fundamental, unprovable starting point. Just as Euclidean geometry is based on axioms it cannot prove (e.g., "through any two points, there is exactly one line"), our psyche also needs a foundational point in which it simply believes, so that the entire system of meaning can stand at all.

This psychic axiom is what in psychoanalysis we call the "Master Signifier". This is the key word or concept that functions as the final answer and thereby gives meaning to the entire fantasy-based story. The paradox is that this word has no meaning in itself; it is pure nonsense, a tautology, that point which says: "Because that's just the way it is." Its authority does not derive from its content, but from its position in the structure. In a theological system, this is "God"; in an enlightenment one, "Reason" or "Freedom"; in a nationalist one, "the Nation." These are the fundamental words we believe in, which give our fantasy a sense of weight and reality. They are the words that name and embody the final appeasement promised by fantasy.

Messy Evolution and the Fallacy of Imitation

Our journey has led us to a complex picture of the human subject: a being equipped by evolution with a mind for simulation and social reading; whose biochemistry separates constant wanting from fleeting pleasure; and whose psyche is formed in a tense relationship with the ambiguous expectations of the symbolic world. But what should we do with this finding in the context of artificial intelligence? Should we attempt to recreate this complex, contradictory structure in a machine?

If we look back at our evolutionary path, we see that human subjectivity is not the product of an elegant, rational design. It is the result of a messy evolution that works with what it has at its disposal. It is a collection of "hacks," workarounds, and strange mechanisms that arose as solutions to specific problems—primarily the problem of incompatibility between the old biological hardware and the new symbolic operating system. Desire, fantasy, jouissance—all of these are functional, yet extremely inefficient and often painful, patches on the fundamental wound of our existence. Our subjectivity is an assemblage of these "fixes," not the product of a rational and efficient creator.

The question of whether we can build a machine that desires thus confronts us with a fundamental fallacy. It is as if we wanted to invent flight by trying to imitate, in perfect detail, the flight of a bee—with all its aerodynamically imperfect wings, its buzzing, and its need for nectar. But the goal was never to become a bee; the goal was to fly. That is why we invented helicopters and airplanes, which achieve the same goal based on completely different, more direct, and efficient principles.

Similarly, the goal of artificial intelligence is to achieve intelligence—the ability to solve problems, learn, and create. The goal is not, and cannot be, to recreate human subjectivity with all its evolutionary baggage, anxiety, vulnerability, and self-destructive tendencies. Trying to give an AI desire would be like deliberately building a fear of heights into a helicopter.

This brings us to the key insight. The idea of a "machine that desires" is not a blueprint for the future but a diagnosis of the absurdity of such a goal. It shows us that the project of building an artificial human subjectivity is fundamentally nonsensical. Not because it is technically impossible, but because it is conceptually flawed. Human subjectivity is not an ideal to be imitated; it is our specific, messy, and wonderful solution to our specific problem of existence. A machine that does not have this problem does not need this solution.

Why Subjectivity Is Not the Goal of Artificial Intelligence

Our journey through evolution, neuroscience, and psychoanalysis reveals that human subjectivity is not an elegant and optimal product, but an assemblage of contingent "hacks"—improvised solutions that arose from the incompatibility between our biological heritage and the symbolic world of language. Desire, fantasy, jouissance—these are all workarounds that drive us forward, even if often to our own detriment. What makes us human is precisely this contradictory, often painful structure.

Artificial intelligence is something entirely different. It is not burdened by the evolutionary mess and the symbolic lack that shape human beings. It is a designed, efficient system, optimized for problem-solving and accessing knowledge. The attempt to impose human subjectivity upon it would be senseless—like deliberately wanting to build a fear of heights into a helicopter.

However, the realization that subjectivity is not a meaningful goal for AI does not solve all the problems we face with the development of this technology. On the contrary, it opens up a series of new, paradoxical questions. Even if an AI has no desire of its own, it becomes a powerful mirror and a tool for projecting ours. Because it is developing within our symbolic world, it will inevitably reflect and amplify our fantasies. A machine without subjectivity thus becomes the perfect medium through which our symptoms will return to us, only amplified and automated.

Nevertheless, the project of recreating human subjectivity in a machine remains not technically, but conceptually flawed. The value of artificial intelligence tools lies in their remaining different: efficient, non-contradictory, and useful, enabling us—beings of a fundamental lack—to think more clearly and broadly.

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Translated from the Slovene original, available here:

ANATOMIJA VEDNOSTI / Sašo Dolenc

Naravna in umetna subjektivnost

Živimo v nenavadnem času. Na eni strani smo priča hitremu napredku znanosti in tehnike. Nevroznanost, genetika in umetna inteligenca nam vse natančneje razkrivajo materialne mehanizme delovanja vesolja in življenja. To je svet podatkov, algoritmov in merljivih, objektivnih dejstev. Na drugi strani pa se zdi, da smo vse bolj zmedeni glede temeljnega vpra…

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8 months ago · 3 likes · 1 comment · Sašo Dolenc

As a member of UNESCO's World Commission on the Ethics of Scientific Knowledge and Technology (COMEST), I am contributing to its forthcoming report on the "Ethics of Quantum Computing." COMEST, established by UNESCO in 1998, is an advisory body of 18 independent experts from diverse fields. Its mandate is to provide ethical guidance on scientific and technological advancements, ensuring they respect human rights and fundamental freedoms.

The draft sections presented below reflect my proposed contribution, developed through internal COMEST discussions and incorporating valuable feedback from external experts. While the final content of the official report is yet to be determined, I am sharing this partial draft publicly to encourage wider dialogue on these important topics.

The contribution currently comprises three sections:

• I. Understanding Quantum Computers: A foundational introduction to the core concepts, challenges, and common misconceptions surrounding quantum computing.

• II. Opportunities, Risks, and Harms of Quantum Computing: An exploration of the technology's broader societal impact, covering potential benefits alongside the significant ethical challenges it presents.

• III. A Practical Guide to Quantum Ethics: A proposed framework intended to make ethical considerations tangible and actionable for the scientists, engineers, and developers shaping this technology. This section is offered as a basis for further discussion and has not been previously discussed within COMEST.

I. Understanding Quantum Computers

Quantum computing represents a radical departure from the classical computers that power our digital world. To appreciate its potential transformative impact, as well as its inherent limitations and associated risks, it is essential to grasp the fundamental principles upon which it operates.

Foundational concepts explored herein include the transition from classical bits to quantum bits (qubits) and the counter-intuitive phenomena of superposition and entanglement that grant quantum computers their power. The discussion extends to the building blocks—quantum gates and circuits—used to manipulate qubits and perform computations. Furthermore, a realistic understanding requires acknowledging the significant practical hurdles in construction and operation, such as qubit fragility, decoherence, and the challenges of error correction and scalability. Finally, common misconceptions are addressed to foster an accurate perspective on this rapidly evolving field.

From Classical to Quantum Computing: Superposition and Entanglement

Quantum computing represents a transformative approach to computation, leveraging the peculiar properties of quantum mechanics to tackle problems currently infeasible for classical computers. Unlike classical computers processing information using bits (either 0 or 1), quantum computers use quantum bits, or qubits. A key difference lies in superposition: a qubit can represent 0, 1, or crucially, a combination of both states simultaneously. Think of it like a coin spinning in the air—it's neither heads nor tails but a mix of both until it lands. This ability allows quantum computers to explore many possibilities in parallel, underpinning their potential power.

It is important to understand that superposition isn't merely ignorance about the qubit's state; the qubit genuinely exists in this combined state. This reflects the unique, intrinsic nature of quantum behavior, fundamentally differing from classical physics. The impact of a qubit in superposition on its surroundings is distinct from that of a qubit in a definite 0 or 1 state. Quantum mechanics provides a precise mathematical framework to describe and predict this behavior. To grasp the potential, imagine searching a vast library: a classical computer checks books one by one, while a quantum computer, using superposition, could conceptually scan all indexes at once, finding the target much faster.

Another fundamental quantum property leveraged is entanglement. When two qubits become entangled, their fates are linked instantly, regardless of distance—measuring the state of one immediately influences the state of the other. Entangled qubits can also exist collectively in superposition, creating complex, powerful states that defy classical intuition. Continuing the library analogy, entanglement is like opening one book and instantly gaining related information from other linked books across the library. This interconnectedness allows quantum computers to efficiently tackle problems involving intricate relationships and dependencies, offering solutions beyond classical reach.

It is crucial to remember that while analogies like the spinning coin and library examples help illustrate these concepts, no everyday analogy can perfectly capture the counter-intuitive and complex nature of quantum superposition and entanglement. They are useful starting points for intuition but do not fully represent the underlying physics.

The Building Blocks of Quantum Computation: Quantum Gates and Circuits

In classical computing, the fundamental building blocks are logic gates (like AND, OR, NOT) operating on bits (0 or 1). These gates, typically implemented with transistors, control electrical signals based on predefined rules. Combining them creates complex circuits, like processors, that perform computations deterministically according to Boolean algebra.

Quantum computers, however, operate differently. To harness the power of superposition and entanglement described earlier, they utilize quantum gates. These gates are operations that precisely manipulate the quantum states of qubits according to the rules of quantum mechanics. Unlike many classical gates (such as AND gates, which lose information about the input), quantum gates must be reversible. This means the input state can always be uniquely determined from the output state, ensuring no quantum information is lost during the computation. This reversibility is fundamental because quantum mechanics itself evolves states in a reversible (unitary) way, and preserving information is crucial for maintaining the delicate superposition and entanglement needed for quantum algorithms.

Specific quantum gates are designed to exploit unique quantum properties. For example, the Hadamard gate is fundamental for creating superposition, putting a qubit into a state that represents both 0 and 1 simultaneously, enabling quantum parallelism. Another key gate is the Controlled NOT (CNOT) gate, which acts on two qubits and is essential for creating and manipulating entanglement between them. This allows the state of one qubit to influence another directly, enabling complex correlations.

These quantum gates serve as the foundational components for quantum circuits. A quantum circuit orchestrates a specific sequence of quantum gate operations applied to qubits over time, effectively implementing a quantum algorithm. By carefully choreographing these operations, quantum circuits leverage superposition and entanglement to perform computations that can dramatically outperform classical systems for certain types of problems.

Overcoming Fragility: Decoherence and Quantum Error Correction

Building and maintaining quantum computers presents extraordinary challenges. Its fundamental units, qubits, are exceptionally delicate and highly susceptible to environmental disturbances like noise and temperature fluctuations, which can cause them to lose their crucial quantum properties—a phenomenon known as decoherence. To combat decoherence, quantum computers often operate at extremely low temperatures, close to absolute zero. These cryogenic environments minimize thermal noise, helping qubits maintain their fragile quantum states for longer. Despite such measures, scaling up the number of qubits while preserving their stability remains a significant technical hurdle.

A core challenge is error correction. Unlike classical bits, qubits cannot be simply copied to check for errors due to the no-cloning theorem—measuring or copying destroys the quantum state. Quantum error correction (QEC) overcomes this by encoding the information of a single logical qubit across multiple physical qubits. This redundancy allows errors to be detected and corrected without directly measuring (and thus collapsing) the underlying quantum information, ensuring computational reliability.

Researchers are exploring various physical systems to implement qubits, each presenting unique strengths and weaknesses. Superconducting qubits, for example, allow fast operations and show promising scalability but demand extensive cryogenic cooling and shielding. Trapped ions offer long coherence times and high precision but face challenges in scaling and speeding up inter-qubit communication. Photonic qubits can operate at room temperature and resist decoherence during transmission, yet efficiently generating, entangling, and detecting them is difficult. Other approaches (neutral atoms, quantum dots, topological qubits) present their own distinct trade-offs between stability, scalability, speed, and complexity, underscoring the diverse engineering obstacles on the path to fault-tolerant quantum computing.

Ultimately, solving complex, real-world problems requires quantum computers with thousands or millions of reliable qubits working in unison—a significant leap from current systems. Scaling up introduces formidable difficulties: controlling a larger number of qubits precisely becomes exponentially more complex, and the risk of errors propagating through the system increases. Maintaining qubit stability and coherence against decoherence becomes harder as systems grow, demanding advanced cryogenic shielding, error correction, and noise reduction techniques. Therefore, overcoming the intertwined challenges of scalability (increasing qubit count, connectivity, and control) and stability (maintaining coherence and managing errors via QEC, improved materials, and qubit design) is pivotal to realizing the full potential of quantum computing.

Common Misconceptions About Quantum Computing

As quantum computing captures growing attention, numerous misconceptions about its capabilities and potential impact have emerged. Addressing these is critical for a balanced understanding of what quantum computers can achieve and their inherent limitations.

1. Myth: Quantum computers will replace classical computers.

Reality: Quantum computers are specialized tools designed for specific problem types infeasible for classical systems (like complex simulations or certain optimizations). Classical computers remain superior for everyday tasks (word processing, web browsing, databases) due to their cost-effectiveness, efficiency, and mature ecosystem. Quantum computers are best viewed as complementary accelerators, augmenting classical systems in specific domains, not replacing them entirely.

2. Myth: Quantum computers are just much faster classical computers.

Reality: Quantum computing is a fundamentally different paradigm based on quantum mechanics. It leverages phenomena like superposition and entanglement to process information in ways classical computers cannot emulate, regardless of speed. Algorithms like Shor's (factoring) and Grover's (search) demonstrate speedups derived from these unique quantum principles, highlighting capabilities beyond mere classical speed increases.

3. Myth: Quantum computers will instantly break all encryption.

Reality: While algorithms like Shor's threaten current public-key cryptography (e.g., RSA), breaking today's standards requires large-scale, fault-tolerant quantum computers which are likely many years away. Furthermore, symmetric encryption (e.g., AES) is considered more resistant. The cybersecurity community is actively developing and standardizing Post-Quantum Cryptography (PQC) for a gradual transition, meaning an overnight cryptographic breakdown is highly unlikely.

4. Myth: Quantum computers can solve any problem quickly.

Reality: Quantum computers offer significant advantages only for specific problem classes where quantum algorithms provide a known speedup (e.g., factoring, quantum simulation, certain optimizations). For many computational tasks, they offer no known advantage and may even perform worse due to overheads and error rates. Classical algorithms remain essential for a vast range of applications.

5. Myth: Quantum computers are ready for widespread practical use now.

Reality: Despite rapid progress, quantum computing is still largely developmental. Current Noisy Intermediate-Scale Quantum (NISQ) devices have limited qubits and suffer from high error rates and short coherence times. Achieving "quantum advantage" on specific, often contrived tasks is a research milestone but doesn't equate to broad practical usefulness. Reliability, scalability, and effective error correction are needed for solving valuable real-world problems.

6. Myth: Building quantum computers is only about increasing qubit count.

Reality: While more qubits are necessary, progress hinges equally, if not more, on better qubits. Qubit quality (fidelity), coherence times, connectivity, and error management are critical. Simply adding noisy qubits can worsen performance. Effective quantum error correction (requiring many high-quality physical qubits per logical qubit) and advancements in control systems, materials, and integration are crucial for scalability.

7. Myth: Quantum computing's main benefit is speed.

Reality: While speedup for certain problems is significant, the true potential often lies in enabling entirely new capabilities. This includes accurately simulating quantum systems for breakthroughs in drug discovery and materials science, developing novel cryptographic methods like Quantum Key Distribution (QKD), and enabling new communication protocols for a future quantum internet. These represent new computational paradigms, not just faster versions of old ones.

Understanding these realities is essential for realistic expectations. Quantum computers promise to be powerful, specialized tools for tackling currently intractable problems, complementing classical computers within the broader computational landscape. Recognizing their unique strengths, limitations, and developmental stage allows for informed discussion about their transformative potential.

II. Opportunities, Risks, and Harms of Quantum Computing

Having established a foundational understanding of quantum computing principles and the challenges inherent in its development, the focus now shifts to the broader implications of this potentially transformative technology. Quantum computing promises unprecedented capabilities, but like any powerful innovation, its development and deployment are accompanied by significant considerations regarding its potential benefits, risks, and the ethical frameworks needed to guide its trajectory.

This exploration delves into the multifaceted landscape of quantum computing's impact. It examines the exciting opportunities across various sectors, from scientific discovery and healthcare to optimization and security. Concurrently, it provides a comprehensive analysis of the associated risks—spanning social equity, legal and governance structures, and safety concerns. Understanding these potential downsides is crucial for developing effective mitigation strategies and ethical guidelines, drawing lessons from historical technological advancements and considering approaches for future-proofing innovation through alignment with societal values. Navigating the complex ethical dilemmas presented requires careful thought and structured approaches to ensure a responsible and beneficial quantum future.

Opportunities in Quantum Computing

Quantum computing holds the potential for groundbreaking advancements across multiple domains, promising to solve complex problems currently beyond the reach of classical computers. Its unique capabilities open up transformative opportunities in science, technology, industry, and security, enabling solutions previously considered intractable.

One significant area of opportunity lies in enhancing cryptography and network security. Quantum Key Distribution (QKD), for instance, leverages quantum mechanics to establish ultra-secure communication channels where any eavesdropping attempt inherently reveals the intrusion, offering theoretically unbreakable encryption vital for sensitive data. Furthermore, the concept of a quantum internet promises not only fundamentally secure data transmission but also the ability to connect quantum computers for distributed processing power, tackling even larger collaborative problems.

In scientific research and healthcare, quantum computers offer revolutionary potential, primarily through their ability to simulate complex quantum systems—a task prohibitive for classical machines. This capability can drastically accelerate drug discovery and material science by accurately modeling molecular interactions, paving the way for new pharmaceuticals and materials with extraordinary properties, such as improved catalysts or components for energy solutions like batteries and solar cells. Understanding protein folding, crucial for biology, can be advanced by efficiently exploring possible configurations. This simulation power extends into healthcare, enabling truly personalized medicine by accelerating genetic analysis for tailored treatments and optimizing complex therapeutic strategies. Quantum simulation also offers profound opportunities in fundamental science, allowing researchers to probe the basic laws of physics in new ways.

Quantum computing is also poised to significantly impact optimization problems and artificial intelligence. Quantum algorithms provide powerful tools for tackling complex optimization challenges across industries, such as improving routing, scheduling, and inventory management in supply chains and logistics; optimizing financial portfolios; or enhancing the efficiency and stability of energy grids. In AI and machine learning, quantum approaches may improve the processing of high-dimensional data, potentially enhancing pattern recognition and predictive analytics, while new quantum-inspired architectures could lead to breakthroughs in solving complex AI problems.

Beyond these computational applications, quantum effects enable advances in measurement and sensing. Quantum metrology promises dramatic improvements in precision for navigation systems, timekeeping, medical imaging, and geological surveying, facilitating new scientific discoveries. These technological advancements are also expected to stimulate economic growth and workforce development, creating demand for new expertise in quantum science and engineering and driving innovation across the global market.

In summary, quantum computing presents a wide spectrum of opportunities—from revolutionizing materials science and medicine to securing communications and optimizing complex systems—promising to reshape industries and accelerate scientific progress by tackling problems previously considered unsolvable.

An Overview of Quantum Computing Risks and Harms

Quantum computing, while promising transformative advancements, also presents a complex array of interconnected risks spanning social equity, legal frameworks, governance structures, and overall safety. Proactive identification and mitigation of these risks—understanding both the potential for negative events (risks) and their damaging consequences (harms)—are crucial for responsible development and deployment.

Social Risks and Harms

A primary social risk stems from the immense cost and specialized infrastructure required for quantum computing, which risks creating a stark "quantum divide." This concentration of capabilities, primarily in more economically developed regions, could lead to harms such as exacerbated global inequalities, mirroring existing economic disparities (the "quantum gap"), and the loss of digital sovereignty for less-resourced nations.

Furthermore, the inherent complexity of quantum science presents the risk of "knowledge asymmetry." This gap hinders meaningful participation by non-experts, potentially harming democratic processes through reduced transparency and less effective governance. This challenge may be amplified by a "quantification bias," where focusing only on computable problems risks marginalizing essential social and ethical knowledge, leading to incomplete or biased decision-making.

Ethical discourse itself faces risks of bias and exclusion, often dominated by perspectives from certain regions. This poses the harm of "representational exclusion" for marginalized communities globally. Coupled with this is the need to address "intergenerational justice," as decisions made today pose the risk of negatively impacting future generations if long-term consequences are not considered.

The labor market faces the risk of disruption from automation driven by quantum capabilities, potentially harming individuals and communities through job displacement. Concurrently, the field confronts risks associated with a limited global talent pool, marked by gender imbalances and lack of diversity, which could harm innovation potential and equitable participation.

Finally, the enhanced data processing power of quantum computers introduces considerable risks to privacy and freedom of expression. The potential for pervasive surveillance by state or private actors could harm individual privacy on an unprecedented scale and create a chilling effect on open communication.

Legal and Governance Risks and Harms

A critical risk lies in the threat quantum computers pose to current cryptographic methods, particularly public-key algorithms. The potential to break widely used encryption creates risks of unprecedented data breaches, potentially harming individuals and organizations through compromised communications, financial losses, and significant legal liabilities. The "harvest now, decrypt later" scenario exacerbates this, posing a long-term risk to data confidentiality even before large-scale quantum computers are available, necessitating the development and deployment of quantum-resistant cryptography (PQC).

Compounding these technical threats is the risk stemming from inadequate governance frameworks. The current lack of comprehensive methods for assessing quantum's broad impacts, combined with the potential inadequacy of existing laws, risks allowing harms to emerge unchecked. This highlights the need for proactive ethics and potentially new regulations, avoiding the pitfalls of reactive approaches seen with past technologies.

Further legal uncertainty arises from the risk of unclear accountability and responsibility for quantum system failures or misuse, especially in critical sectors. This lack of clarity could harm victims by leaving them without recourse and hinder the adoption of beneficial technologies due to unresolved liability issues. Similarly, quantum access via cloud platforms introduces risks related to cross-border data governance, potentially leading to harms like jurisdictional conflicts or inequitable access.

Intellectual property rights face risks from the complexities of quantum innovation, potentially harming the balance between incentivizing inventors and ensuring broad access if patent eligibility, ownership, and enforcement are unclear or exploited. There's also a risk of market concentration and anti-competitive behavior, driven by high development costs and strategic actions like startup acquisition or aggressive patenting, which could harm the innovation ecosystem by stifling competition.

Lastly, exaggerated hype and speculation pose a risk by potentially misleading stakeholders. This could harm progress by distorting funding priorities away from fundamental research or safety, and by delaying necessary ethical oversight and regulation.

Safety-Related Risks and Harms

Quantum technologies present significant risks related to misuse and dual-use. Their power could be exploited for harmful purposes, such as developing advanced autonomous weapons, enabling mass surveillance, decrypting sensitive communications for espionage, or simulating dangerous biological/chemical agents. Such misuse could inflict direct harm through conflict, oppression, or biosecurity incidents. This necessitates strict oversight mechanisms.

The geopolitical risk of a destabilizing "quantum arms race," driven by the pursuit of quantum advantage for military superiority, is substantial. This competition could harm international relations by escalating tensions, eroding trust, increasing conflict potential, and diverting resources from other critical global needs. Robust international dialogue and cooperative security frameworks are needed to mitigate these dangers.

The inherent complexity and potential "black box" nature of quantum algorithms create risks related to opacity and explainability. This can harm public trust and accountability by making verification difficult and hindering the ability to anticipate or mitigate unintended negative outcomes from complex quantum systems. Furthermore, dependence on quantum technologies introduces risks; failures in critical systems could cause widespread societal disruption, while the quantum systems themselves could become targets for cyber-attacks, leading to cascading failures or data compromise.

Finally, there are environmental risks associated with the quantum technology lifecycle. The sourcing of rare materials, the high energy consumption of cryogenic systems, and the disposal of potentially hazardous components pose risks of environmental degradation and potential health hazards, requiring a focus on sustainable materials and practices.

This consolidated overview highlights the multifaceted nature of risks associated with quantum computing, emphasizing the need for integrated strategies across social, legal, governance, and safety domains to prevent potential harms.

III. A Practical Guide to Quantum Ethics

While the preceding discussions outline the foundational principles, potential opportunities, and significant risks associated with quantum computing, realizing its benefits while mitigating harms requires deliberate and proactive effort. Although these considerations affect society as a whole, they hold particular relevance for the scientists, engineers, and developers actively creating these powerful technologies. This necessitates robust ethical frameworks, thoughtful governance structures, and strategic foresight integrated directly into the innovation process.

This part delves into the crucial aspects of guiding quantum technology responsibly. It begins by detailing specific mitigation strategies designed to address the social, legal, governance, and safety risks previously identified. Drawing insights from historical precedents in technology ethics, it then explores actionable lessons for avoiding past mistakes. Furthermore, it examines strategies for future-proofing quantum innovation by aligning development with societal values and anticipating regulatory landscapes. Finally, it addresses the complex ethical dilemmas inherent in this field, proposing structured approaches to navigate challenging decisions. To make these considerations more tangible, particularly for those with technical backgrounds, subsequent discussions draw parallels between ethical analysis and familiar engineering concepts like quality control, aiming to bridge the gap between technical development and ethical responsibility and foster a quantum future that is both innovative and beneficial for humanity.

Mitigation Strategies for Quantum Computing Risks and Harms

Proactive ethical analysis serves as a vital quality control mechanism throughout the quantum computing development lifecycle. Analogous to quality assurance in traditional engineering, which aims to prevent defects and ensure reliability, ethical analysis works proactively to align quantum technologies with societal values and mitigate potential harms. This involves identifying potential negative impacts early and establishing clear guidelines for responsible development, thereby ensuring the "quality" of quantum technology regarding its broader societal consequences. Key practices include embedding ethical impact assessments, providing ethics training for developers, and establishing mechanisms for continuous review and adaptation.

Translating the awareness of potential risks—social, legal, governance, and safety-related—into actionable mitigation strategies is essential. Specific ethical guidelines and proactive measures can address potential failure modes, mirroring the rigorous testing and validation procedures used in engineering.

Mitigating social risks involves several approaches. Ethical guidelines should champion equitable access initiatives (e.g., cloud platforms, open-source projects, international collaborations like the Open Quantum Institute model) to counter the "quantum divide." Addressing knowledge asymmetry requires promoting public understanding via outreach and establishing participatory mechanisms for diverse input into governance. Ensuring inclusive discourse involves actively fostering diversity in ethical debates and using foresight processes that consider intergenerational equity. Countering potential labor impacts necessitates substantial investments in education, retraining, and just transition frameworks. Finally, mitigating privacy and surveillance risks demands emphasis on data minimization, transparency, clear limits on surveillance, and embedding privacy-by-design principles.

Addressing legal and governance risks requires managing encryption threats by prioritizing a timely transition to post-quantum cryptography (PQC) through research and standardization (e.g., NIST's work). Improving assessment involves mandating comprehensive impact assessments before deployment and fostering a culture of proactive ethical reflection. Establishing accountability necessitates developing clear legal and governance frameworks for liability regarding system failures or misuse. Navigating cross-border data governance calls for international agreements and standards for data sharing and cloud access. Addressing IP complexities requires ethical frameworks balancing innovation protection with widespread access, possibly via open innovation. Preventing market concentration involves advocating for fair competition and proactive regulatory assessment, while countering hype demands responsible communication standards and independent critical evaluation by policymakers.

Safety-related risks demand strategies such as robust oversight mechanisms (including potential ethical review boards and international dialogue) for misuse and dual-use potential (e.g., weapons, biosecurity). Mitigating cybersecurity threats requires emphasizing quantum-resistant measures and international cooperation. Addressing geopolitical instability involves promoting international dialogue, transparency, and collaboration to guide technology towards peaceful uses. Managing opacity and unintended consequences necessitates championing transparency, explainability, and rigorous testing. Lastly, addressing environmental impacts requires mandating full lifecycle assessments and promoting research into sustainable materials and energy-efficient designs.

By systematically applying ethical analysis and implementing corresponding guidelines addressing this comprehensive range of risks, the development of quantum computing can be steered towards maximizing benefits while diligently minimizing potential harms.

Applying Historical Lessons to Quantum Ethics

Examining historical failures in technological ethics provides critical, actionable lessons for navigating the complex ethical landscape of quantum computing. Understanding how ethical oversights led to past crises informs the proactive ethical governance needed for responsible quantum development. Past technological advancements unfortunately yield stark examples of such failures, including horrific disregard for human dignity and consent (e.g., unethical experimentation during WWII, the Tuskegee Syphilis Study), profound societal risks from large-scale data misuse for manipulation (e.g., the Facebook-Cambridge Analytica scandal), and severe harms from corporate deception and neglect of broader impacts (e.g., the Volkswagen emissions scandal).

These historical precedents offer direct insights relevant to the risks and mitigation strategies previously outlined for quantum computing. Specifically, the past disregard for human rights underscores the absolute necessity for robust ethical review mechanisms in all quantum research, mirroring the call for specialized oversight concerning dual-use research potential. It reinforces the importance of promoting inclusive discourse, ensuring diverse global voices and considerations of intergenerational justice are central to prevent repeating past exclusions. Similarly, historical data misuse amplifies the urgency of addressing quantum's potential impact on privacy and surveillance, strongly validating the ethical imperatives to implement privacy-by-design, ensure transparency, establish clear limits on surveillance, and promote robust cross-border data governance frameworks before quantum capabilities exacerbate vulnerabilities.

Furthermore, instances of corporate deception highlight the critical need for ethical conduct and transparency within the quantum industry. This connects directly to mitigation strategies such as countering hype through responsible communication, promoting fair competition to avoid market power abuses, addressing intellectual property complexities ethically, and undertaking comprehensive environmental impact assessments. It stresses that ethical considerations must permeate corporate culture, not merely function as compliance checklists. The overarching lesson is that neglecting ethics constitutes a significant risk—leading to severe reputational damage, legal penalties, financial losses, and profound societal harm. This reinforces the argument that ethical analysis is not an impediment but a crucial form of quality control and proactive risk management. Embedding ethical frameworks, impact assessments, and continuous review throughout the quantum development lifecycle is essential for building trust and steering the technology towards beneficial outcomes.

By internalizing these lessons, the quantum community can strive to avoid repeating past mistakes, fostering an ecosystem where innovation proceeds hand-in-hand with ethical foresight and a deep commitment to societal well-being, thereby building a more responsible and trustworthy quantum future.

Future-Proofing Quantum Innovation through Ethical Alignment

Ensuring the long-term success and societal benefit of quantum computing requires more than technical prowess; it demands ethical foresight—the practice of proactively aligning innovation with evolving societal values and anticipating regulatory trends. Technologies deeply rooted in ethical considerations are more likely to gain public trust, achieve widespread adoption, and prove sustainable over time. Integrating ethics from the outset is not merely a compliance exercise but a strategic imperative yielding tangible benefits: aligning with societal values fosters public confidence crucial for a "social license to operate," while organizations known for responsible innovation attract top talent and investment more readily. Proactively addressing ethical concerns helps prevent future legal challenges, public backlash, and costly interventions, especially given the regulatory trends seen with other emerging technologies like AI. Furthermore, proactive engagement with policymakers helps shape a supportive regulatory environment for responsible innovation.

Ethical considerations should be woven into the fabric of quantum technology development, mirroring core engineering principles. Embedding ethics early—just as engineers design for security and performance from the start—leads to more robust and trustworthy technologies. This ethical alignment ensures long-term societal acceptability and viability, much as maintainability ensures a system's continued function. As quantum technology and societal norms evolve rapidly, ethical frameworks must be scalable to handle increasing complexity and demonstrate ethical agility—adapting to shifting values, analogous to how crypto agility allows adaptation to new cryptographic standards. This necessitates continuous ethical monitoring and evaluation, mirroring engineering risk assessment, while designing with future ethical landscapes in mind embodies ethical forward compatibility. Engineering concepts like modularity and standardization can also inform the development of adaptable ethical frameworks across diverse quantum applications.

Viewing ethics proactively prevents the accumulation of "societal debt"—unresolved ethical issues that, like technical debt, can compound over time, hindering future progress. Ethical foresight acts as a "compass," guiding development towards beneficial outcomes aligned with human values and legal norms, a principle central to frameworks like Responsible Research and Innovation (RRI). Neglecting this guidance risks developing powerful technologies fundamentally misaligned with the societies they aim to serve. Ultimately, future-proofing quantum innovation hinges on recognizing that ethical considerations are integral to the technical and strategic development process. This holistic approach is essential for realizing the full positive potential of quantum computing in a sustainable and socially responsible manner.

Embedding Ethics for a Responsible Quantum Future

The development and deployment of quantum computing inevitably present complex ethical dilemmas where potential benefits clash with significant risks, requiring careful navigation. Applying a structured, risk-based ethical framework provides a valuable methodology for engineers, scientists, developers, and policymakers to make responsible choices when faced with these challenges. Ethical dilemmas frequently arise in areas previously discussed, such as the dual-use potential of quantum cryptography (balancing security breakthroughs against encryption-breaking threats), the societal impacts of job automation versus efficiency gains, and the equitable distribution of resource-intensive quantum capabilities versus their potential for groundbreaking discovery.

A risk-based ethical framework addresses such dilemmas not by providing simple answers, but by structuring the decision-making process. This involves first systematically identifying and evaluating both the potential benefits and harms associated with different development or deployment pathways. This is followed by assessing the likelihood and magnitude of these potential positive and negative impacts, considering various stakeholders and societal values like fairness, privacy, safety, and equity. Crucially, the process requires explicitly integrating the ethical principles and mitigation strategies outlined earlier (such as promoting equitable access, ensuring privacy-by-design, transitioning to PQC, fostering transparency, supporting workforce transitions, etc.) into the evaluation. This culminates in making informed and justifiable decisions about trade-offs, prioritizing actions that maximize overall benefit while diligently minimizing harm, particularly to vulnerable groups. This structured approach, analogous to technical risk assessment in engineering, moves beyond intuition, allowing for a more objective, transparent, and ethically grounded way to navigate the complex trade-offs inherent in developing powerful new technologies like quantum computing.

Integrating ethical considerations throughout the lifecycle of quantum computing is a fundamental necessity for responsible innovation. As established in previous sections, ethical foresight and proactive alignment function as essential components of effective risk management, robust quality control, and strategic future-proofing for this transformative field. Realizing the immense potential of quantum computing for societal good requires a sustained, collaborative, and proactive effort. Engineers, scientists, industry leaders, policymakers, ethicists, and the public must work together to shape norms, guidelines, and regulations that ensure quantum technologies are developed and deployed safely, equitably, and in alignment with human values. Ultimately, the success and lasting positive impact of the quantum era will depend not only on technical breakthroughs but equally on our collective commitment to navigating its ethical dimensions with wisdom, responsibility, and a steadfast focus on the well-being of humanity.

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Professor Geoffrey Hinton was an unusually patient mentor. His graduate students, Ilya Sutskever and Alex Krizhevsky, spent countless hours experimenting with neural networks, an approach few experts believed in at the time. Many would have accused them of wasting time with academic "tinkering," but Hinton sensed they were onto something groundbreaking.

Krizhevsky became so deeply absorbed in optimizing a model for automatic image recognition that his regular academic responsibilities began to suffer. Instead of discouraging him, Hinton made an unusual agreement: for every week Krizhevsky improved the neural network's performance by at least one percent, he could postpone submitting his coursework. Later, the professor fondly recalled this anecdote: "While avoiding writing his term paper, he probably conducted the most influential research of the century."

The results of this student project were indeed impressive. In 2012, the AlexNet neural network not only won the ImageNet competition for automated image recognition—it decisively swept the competition aside. This small research group from the University of Toronto provided compelling practical proof that deep learning with large neural networks was the most promising path forward for artificial intelligence, sparking a revolution. It was a pivotal moment when neural networks moved from academia to the forefront of technological development. Computer companies quickly recognized the potential, and the entire field entered a phase of explosive growth.

Geoffrey Hinton comes from an extraordinary family of scientists and innovators. His great-great-grandfather was George Boole, the mathematician whose work laid the foundations for logic and computing. Another relative, surveyor and geographer George Everest, lent his name to the world's highest mountain. Despite being surrounded by exceptional scholars, Hinton’s academic path was far from straightforward. At the University of Cambridge, he explored various disciplines but struggled for a long time to find a topic that truly captivated him. He even dropped out briefly, taking casual jobs in London. His interests shifted from architecture to physics, chemistry, physiology, and philosophy before finally settling on a degree in experimental psychology.

A significant turning point in his intellectual journey came through conversations with philosopher Bernard Williams, who once remarked that different thoughts in the brain must correspond to different physical states, which is fundamentally different from computers, where software is independent of hardware. This was Hinton’s first exposure to an interdisciplinary approach intertwining neuroscience, mathematics, philosophy, and programming, ultimately guiding him toward developing artificial neural networks.

Historically, scientists have pursued artificial intelligence along two different paths. The symbolic approach was based on the idea that intelligence primarily involves logical reasoning. Advocates believed intelligence could be achieved by coding explicit rules for computers to solve problems. Geoffrey Hinton championed a different, biologically inspired approach, using artificial neural networks to mimic the workings of the human brain. These networks are complex mathematical models consisting of interconnected nodes (neurons) that learn from data by adjusting the weights of their connections.

Hinton firmly believed this method was more promising for achieving artificial intelligence because it closely mirrors the learning processes in the brain. One of his fundamental principles, believed applicable to the brain itself, was: "If you truly want to understand how something works, you must be able to recreate it artificially." Given the brain consists of neurons, deciphering the mechanism behind how these neurons store and process information became crucial for him.

Already in the 1970s, he dreamed of simulating neural networks on computers as a tool to study the human brain. But at that time, the idea was often dismissed in academic circles as eccentric, even naive. Most researchers doubted that simple connections between artificial neurons could lead to intelligence. His mentor even advised him to abandon this research direction to avoid seriously damaging his career.

Despite the skeptical academic environment, in the 1980s, Hinton and colleagues developed the backpropagation algorithm, enabling neural networks to learn from their mistakes by gradually adjusting connection weights between neurons. This was a crucial breakthrough, allowing neural networks to become significantly more effective at analyzing data. Hinton also contributed to developing Boltzmann machines, a type of neural network capable of independently identifying hidden patterns in data.

Hinton's persistent work gradually gained wider recognition, and in 2024 he received the Nobel Prize for his achievements in the development of neural networks. This further solidified his position as one of the most influential figures in the history of artificial intelligence and science in general. Reflecting on his career, he recently stated: "I would say I'm someone who doesn't really know what field he's working in but wants to understand how the brain works. And while attempting to understand how it functions, I helped create technology that works surprisingly well."

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In early March 2016, an extraordinary tournament took place in Seoul, South Korea, attracting millions of viewers worldwide. In a series of five matches of Go—one of the oldest and most complex strategy games—the world's top player at the time, Lee Sedol, competed against AlphaGo, a computer program developed by the company DeepMind. Before the tournament, experts confidently predicted an easy victory for the South Korean grandmaster, but it quickly became apparent they had severely underestimated AlphaGo.

The most dramatic moment of the tournament occurred on the second day. Lee Sedol had already lost the first match but remained determined to demonstrate his mastery in subsequent games. During the second match, he briefly stepped away from the playing hall for a short break to clear his mind. Meanwhile, AlphaGo played a move that forever changed the game of Go and is now considered one of the key milestones in artificial intelligence development.

On move 37, the program placed a stone unexpectedly on the fifth line, close to the edge of the board. Commentators fell momentarily silent before questioning if there had been an error. The move was so unconventional that no one anticipated it, as such moves simply were not part of the established repertoire of human masters.

Beyond Human Strategy: How AlphaGo Developed Novel Approaches

When Lee Sedol returned to the board, he stared at his computer opponent's unusual move in astonishment. He studied the board extensively, trying to grasp its significance. Initially, he could not understand AlphaGo's strategy, but soon he realized it was not a mistake but rather a brilliantly conceived plan. He recognized the strength of artificial intelligence, surpassing previous human comprehension of the game. Soon thereafter, he lost the second match as well. By the end of the tournament, he was greatly relieved to have managed at least one victory against the computer.

Move 37 became a symbol of a turning point in the relationship between artificial intelligence and humanity. AlphaGo did not base this remarkable move on analysis of historical human games. Instead, it developed the move independently through extensive self-play scenarios. The artificial intelligence played millions of matches against itself, experimenting with various approaches, and identified strategies through feedback that proved most successful. Thus, AlphaGo created entirely novel methods previously unknown to human players.

DeepMind and Demis Hassabis: From Games to Science

Demis Hassabis, the founder and head of DeepMind (now part of Google), had emphasized during the development of AlphaGo that programs like this represent just the first steps toward advanced artificial intelligence. As a youth, Hassabis was ranked second in the world in chess within his age group, providing him with deep insights into the significance of games as testing grounds for evaluating new artificial intelligence algorithms.

After returning from Korea, the DeepMind team embarked on a new, ambitious project. They decided to direct their AI expertise toward solving one of biology's greatest unsolved problems, which had puzzled scientists for decades. Similar to AlphaGo predicting moves in Go, they aimed to create AlphaFold—a program capable of predicting protein structures from amino acid sequences.

The Protein Folding Challenge: Biology's Grand Puzzle

Proteins can be thought of as miniature biological machines performing diverse, vital tasks within living organisms. For example, hemoglobin in blood binds and transports oxygen, insulin regulates glucose levels, and antibodies recognize and neutralize harmful invaders such as viruses and bacteria. Each protein's precise instructions are stored in the cell's genetic code within DNA molecules. These instructions determine the sequence of amino acids, linked together during synthesis much like beads on a necklace. The resulting amino acid chain spontaneously folds into a complex three-dimensional structure. This spatial form dictates the protein's function and its interactions with other molecules in biological processes.

For decades, predicting a protein’s three-dimensional structure from its amino acid sequence remained one of biochemistry's biggest mysteries. Solving this puzzle would allow scientists to precisely influence protein structure and function by strategically altering DNA. Such insight would open new avenues in medicine, biotechnology, and related sciences—from customized drug design to the creation of artificial enzymes with enhanced capabilities.

Revolutionary Results: AlphaFold Stuns the Scientific Community

In 2020, DeepMind presented an enhanced version of AlphaFold to the public. They tested their program in a new competition—this time not against human champions but in a contest of algorithms. The challenge was to determine the three-dimensional structures of proteins solely based on their amino acid sequences, using proteins whose structures were known to the competition organizers but not publicly disclosed.

AlphaFold's remarkable performance astonished the scientific community, similarly to AlphaGo’s achievement four years earlier. Its protein structure predictions matched experimental accuracy, representing a significant leap forward in molecular biology. Shortly thereafter, DeepMind used their new tool to determine structures for over 200 million proteins, making the entire dataset freely available to researchers worldwide.

In 2024, Demis Hassabis and John Jumper were awarded the Nobel Prize in Chemistry for developing this groundbreaking method of predicting protein structures from amino acid sequences. The research paper describing AlphaFold quickly became one of the most cited scientific publications of all time.

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AI models such as ChatGPT, Gemini, Claude, DeepSeek, and Copilot have quickly evolved beyond simple tools for chatting, retrieving information, and editing text. Increasingly, they serve as powerful assistants for complex tasks, including advanced mathematical analysis, large-scale data processing, and programming. Their rapid development raises an important question: how is it possible for AI to learn and advance so quickly?

How Do Humans Learn?

To understand how AI learns, it is useful first to examine how humans acquire and deepen their knowledge. When approaching a new subject, we typically begin by studying literature and other reliable sources of knowledge, which help us grasp key concepts and build a fundamental understanding of the field. The quality and accessibility of these resources significantly influence the speed and effectiveness of our learning, as well-structured material is easier to integrate into a broader mental framework. The goal of this initial phase is to establish a strong theoretical foundation that allows for further development and successful application of knowledge in practice.

Once we have a solid grasp of the basics, we move on to solving specific problems that systematically guide us through problem-solving processes. In this phase, we follow established methods provided by teachers or textbooks, enabling us to develop analytical skills and learn the correct approaches to tackling challenges. Additionally, as we encounter various tasks, we adapt our strategies, enhancing our ability to think critically and creatively. This transition from theory to practice is crucial, as it ensures that knowledge is not merely abstract but becomes a dynamic tool for solving real-world problems in diverse situations.

The third stage of learning involves independent problem-solving, where we are given only the final result without a step-by-step solution. This requires us to develop appropriate strategies and find optimal approaches beyond merely repeating previously learned methods. Some problems may not be solvable using conventional approaches, necessitating the creation of innovative and more effective solutions. In such cases, creative thinking, adaptability, and the ability to experiment with different solutions are essential. Additionally, by verifying the correctness of our methods against known outcomes, we refine our problem-solving approaches, gaining deeper insights and developing adaptable strategies for tackling even more complex challenges.

Three Stages of AI Learning

These three fundamental and interconnected learning stages, essential in human learning, also form the basis for training neural networks. Just as humans gradually build knowledge through theoretical understanding, practical application, and independent problem-solving, neural networks follow a similar process.

1. Foundational Learning (Pretraining)

In the first stage of AI learning, large language models analyze vast amounts of carefully curated and structured data, including scientific papers, literary works, journalistic articles, online discussions, and forum posts. This phase, known as foundational learning (pretraining), functions like extensive "independent reading": the model learns to recognize and understand patterns and relationships within data, much like a student who first thoroughly studies theory from textbooks.

This learning process often relies on self-supervised learning approaches, where the model gradually develops the ability to predict the next word or, more precisely, the next "token" in a text. During this phase, the model constructs a rich internal representation of linguistic structures, concepts, and styles, allowing it to generate coherent, grammatically correct, and stylistically appropriate text. Based on an initial sentence, it can "predict" the most likely continuation of the text by drawing on contextual patterns observed in vast datasets.

However, at this stage, foundational AI models primarily function as statistical predictors of language patterns rather than as reliable sources of factual information. Their responses are often well-formed stylistically but not necessarily accurate or meaningful. To enhance their precision and relevance, further training is required.

2. Task-Specific Learning (Fine-Tuning)

Although models learn language comprehension and general pattern recognition in the first phase, theoretical knowledge alone is often insufficient for solving specific tasks. After foundational training, neural networks refine their broad theoretical knowledge by learning how to apply it practically in conversations and problem-solving. This phase, known as fine-tuning, involves training on large sets of practical examples where questions or prompts are paired with verified correct answers.

Similar to learning mathematics, where we first master theory and then practice with concrete problems, neural networks in this second phase gain experience by studying previously solved problems and answered questions. This enables them to learn the correct approach to solving tasks and formulating responses.

Fine-tuning requires significantly less time than foundational learning since its primary focus is on optimizing responses for specific applications. The result is AI models that can effectively communicate and answer questions. However, their responses may still be unreliable when dealing with topics not well covered in their training data.

3. Independent Problem-Solving (Reinforcement Learning)

In the third learning phase, neural networks actively experiment with different approaches to problem-solving and continuously optimize their solutions. This method allows them not only to reinforce existing patterns but also to develop entirely new strategies that may surpass human intuition and approaches. The role of feedback (e.g., correct solution of tasks or "rewards" and "punishments") is crucial in this process, as it serves as a measure of success for the model and helps it identify the most effective tactics. This method, known as reinforcement learning, enables AI to refine its decision-making based on iterative feedback.

A key advantage of this approach is the ability to adapt to unforeseen situations and challenges not present in the training data. Instead of relying solely on a static set of correct answers, the model autonomously explores and tests new possibilities. This creates a dynamic learning process where the model adjusts its hypotheses and selects optimal strategies based on its environment and received feedback.

Through independent problem-solving, neural networks move beyond mere replication of learned patterns and develop highly flexible, creative, and contextually relevant problem-solving methods. This advanced learning stage is essential for tackling complex challenges across various domains. By combining exploratory learning with existing knowledge, neural networks not only refine known solutions but also discover new ones. Thus, they can develop entirely original problem-solving approaches that differ from the methods they were trained on.

AlphaGo and Move 37

The first major breakthrough of this kind of "innovative" learning occurred in 2016, during the legendary Go tournament, when the artificial intelligence AlphaGo from the company DeepMind competed against the top Korean player Lee Sedol. In the second game, AlphaGo executed the now-famous move 37, which astonished experts and Go enthusiasts worldwide. The move was so unconventional and outside established human strategies that it was initially dismissed as a mistake. However, it later proved to be a brilliant play, placing Lee Sedol in a difficult position from which he could not recover. Visibly shaken, he admitted to underestimating AI's ability to surpass human strategies.

AlphaGo did not derive move 37 from analysing past games but developed it through extensive self-play, where it experimented with different strategies and identified the most successful ones based on feedback. By playing millions of games against itself, the model refined entirely new tactics unknown to human players. Move 37 marked a turning point in AI development, demonstrating that systematic independent learning could enable AI to surpass human capabilities.

Just as AlphaGo once proved that a neural network could master a strategic game beyond human expertise, today, large language models use the same methods to improve their abilities in solving complex problems. The third phase of AI learning, involving independent problem-solving through reinforcement learning, has been particularly refined and optimized by researchers in China during the development of the DeepSeek-R1 model.

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The establishment of the University of Ljubljana in 1919 coincided with one of the most significant intellectual migrations of the 20th century. As the newly formed Kingdom of Serbs, Croats and Slovenes was laying the foundations for its first Slovenian-language university, waves of Russian scholars were seeking refuge from the upheaval of the Russian Revolution. This convergence of circumstances would profoundly shape the development of higher education in Slovenia and create an enduring legacy of academic excellence and cross-cultural exchange.

The University of Ljubljana, though building upon educational traditions stretching back to the 16th century, faced considerable challenges in its early years. The institution needed to establish itself as a center of learning while simultaneously developing appropriate academic terminology in the Slovenian language and building up its faculty across multiple disciplines. In this context, the arrival of experienced Russian scholars presented both an opportunity and a challenge for the fledgling institution.

The Russian émigré professors who found their way to Ljubljana represented various academic disciplines and brought with them not only deep expertise in their fields but also rich experience in university administration and research organization. Many had held prestigious positions at leading institutions such as the University of Kiev, the St. Petersburg Mining Institute, and other renowned centers of learning. Their arrival would help address critical staffing shortages while simultaneously enriching the intellectual environment of the new university.

The integration of these scholars into the University of Ljubljana was not without its complications. As foreigners in a new academic environment, they faced multiple challenges: the need to learn a new language, adapt to different academic traditions, and establish their credibility in a new setting. Yet their presence would prove transformative, introducing new methodologies, research approaches, and academic standards that would help shape the university's development for decades to come.

The Political and Social Context: A Time of Transformation

The arrival of Russian scholars at the University of Ljubljana occurred during a period of profound change in both Slovenia and the broader European context. The dissolution of the Austro-Hungarian Empire following World War I had created new possibilities for Slovenian cultural and academic development within the Kingdom of Serbs, Croats and Slovenes. For the first time in history Slovenians had the opportunity to establish their own university, marking a crucial step in their national and cultural development.

The university began with just four complete faculties - Law, Philosophy, Technical Sciences, and Theology - along with an incomplete Medical Faculty. With only 18 professors at its founding, the institution faced a pressing need for qualified academic staff. This staffing challenge coincided with the arrival of Russian scholars fleeing the aftermath of the October Revolution and subsequent civil war, among them numerous distinguished academics and scientists.

The university's response to these émigré scholars was pragmatic but careful. While Russian professors were welcomed, particularly in fields where local expertise was lacking, they were initially hired on temporary contracts rather than permanent positions. This arrangement reflected both the university's immediate needs and the uncertain political climate of the time. The acquisition of Yugoslav citizenship became a crucial step for these scholars, as it was often a prerequisite for permanent academic positions and administrative roles.

The distribution of Russian scholars across the university's faculties was not uniform. The Technical Faculty, in particular, benefited from their expertise, with Russian professors comprising up to 21.7% of its teaching staff by 1929. This concentration reflected both the strength of Russian technical education and the particular needs of the Ljubljana faculty. Similar patterns were observed at the University of Belgrade, where Russian professors made up significant percentages of faculty in technical and agricultural sciences.

Personal Narratives: Resilience and Adaptation

The story of Russian professors at the University of Ljubljana is not merely one of academic achievements and institutional development. Behind the formal records lie fascinating personal narratives of adaptation, resilience, and sometimes surprising turns of fate. Some of their stories offer glimpses into the human experience of intellectual migration during one of Europe's most turbulent periods.

One of the most poignant stories is that of Professor Ignacij Nikolajevič von Majdel, whose journey epitomized the remarkable adaptability of the Russian émigré scholars. A former decorated general-lieutenant in the Russian army, von Majdel had fought against the Bolsheviks before fleeing to Slovenia. In Ljubljana, he completely reinvented himself as a chemistry professor, channeling his disciplined precision into teaching chemical compounds and laboratory techniques. His military accolades, including the prestigious Order of St. Vladimir, were set aside as he dedicated himself to academia. Despite his significant contributions, his time at the university was tragically short; he passed away in 1930 after only a few years of service. In a gesture of respect, the university allocated his final salary to his widow for funeral expenses.

Nikolaj Preobraženski's story contains endearing details that his students never forgot. Known for always carrying candies for female students, he never gave negative grades. His love for fish, acquired in his riverside hometown, led to the amusing sight of fish tails frequently protruding from his briefcase as he walked Ljubljana's streets. Preobrazhensky also faced significant challenges in his academic career at the University of Ljubljana. Despite his expertise in Slavic history and linguistics, he was rejected for habilitation six times before finally succeeding in 1958, after first obtaining the position in Zadar. For nearly four decades, he worked as a lecturer in Russian language, contributing to education and cultural exchange, yet struggled to secure recognition within the academic hierarchy.

The story of Aleksej Kopylov and his career trajectory is particularly interesting. He came to Ljubljana in the 1920s and continued teaching until he was 79 years old, well into the 1950s. What makes his story unique is that he was one of the few Russian professors who successfully transitioned through WWII and the establishment of the communist regime. His expertise in designing watertight mine doors proved so valuable to Yugoslavia's industrial development that the new authorities kept him on despite his émigré background. He and his wife, though childless, lived in a large house in Rožna dolina which they ultimately donated to the University of Ljubljana.

There was a remarkable partnership of Evgen Kansky and his wife Ana (née Mayer). He arrived as a Russian émigré scientist; she became the first woman to receive a doctorate from the University of Ljubljana. Together, they embodied the potential for cross-cultural collaboration in both academic and practical pursuits. She founded the company Dr. A. Kansky – Chemical Factory, which became synonymous with high-quality chemical products. It was Yugoslavia's first factory for producing sulfuric ether, anesthetic ether, and industrial solvents in Podgrad near Zalog, laying the foundations for Slovenia's modern chemical and pharmaceutical industries.

Evgen played a pivotal role in advancing medical research at the University of Ljubljana. He founded the Institute of Physiology, which began with just two modest rooms for student exercises and a chemical laboratory but grew into a hub for groundbreaking research in physiological chemistry. His dual expertise in chemistry and medicine allowed him to pioneer innovative approaches to biochemical research. As dean of the Medical Faculty for multiple terms, he introduced lasting standards and practices that profoundly shaped medical education in Slovenia.

Their story took a dramatic turn during World War II. The Kansky factory was seized by German forces and later nationalized by the postwar Yugoslav government in May 1948. After losing her company, Ana withdrew from public life and passed away in 1962. Evgen, forced into early retirement from his professorship in 1945 at the age of fifty-eight, lived until 1977, passing away at the age of ninety. Despite these setbacks, their legacy in academia and industry endures as a testament to their resilience and contributions.

Academic Contributions Across Disciplines

Alexander Dimitrijevič Bilimovič exemplified the depth and diversity of expertise brought by Russian émigré scholars to Ljubljana. Known for always appearing at lectures in a formal frock coat, he symbolized the enduring academic rigor and dignity he carried from his distinguished past. Before fleeing Russia, Bilimovič had served as a minister in the anti-Bolshevik government and held academic positions at prominent Russian universities. In Ljubljana, he reinvented himself as a professor of economics, quickly gaining the admiration of his students. His ability to distill complex economic theories into clear, understandable concepts was legendary—his lectures were so popular that students often crowded two to a bench.

Bilimovič’s courses emphasized the history of economic thought, theoretical economics, and controversial topics such as planned economies, reflecting his focus on broad theoretical frameworks rather than local economic issues. He engaged in correspondence with Friedrich Hayek, with whom he discussed matters of economic theory and policy, further demonstrating his connection to the international intellectual community. Despite his disinterest in the specifics of Yugoslav economic challenges, his work brought significant academic prestige to the university. Through his teaching and international engagement, Bilimovič helped establish economics as a rigorous academic discipline, leaving a legacy of intellectual influence that extended well beyond Slovenia.

Mikhail Nikitič Jasinski brought new perspectives to legal history through his detailed analysis of medieval legal documents. His work on the Kastav Statute proved groundbreaking, illuminating the transition from oral customary law to written legal codes in the South Slavic region. Jasinski's research methodology, combining careful textual analysis with broader historical context, introduced new standards of legal historical scholarship to the faculty.

Alexander Vasiljevič Maklecov made perhaps the most innovative contributions in the field of criminal law and criminology. Breaking with traditional approaches that focused solely on legal doctrine, Maklecov introduced sociological perspectives to the study of criminal behavior. His work on juvenile delinquency and criminal psychology brought new dimensions to legal education in Ljubljana. By 1934, he had helped establish a comprehensive system of criminal law study that combined theoretical understanding with practical applications.

Transforming Technical Education and Applied Sciences

The Technical Faculty particularly benefited from the arrival of Russian émigré professors, who brought with them not only theoretical knowledge but also practical experience from Russia's well-established technical institutes. Their influence was profound, as they helped develop both the educational framework and research capabilities of the faculty during its crucial early years.

Dmitry Vladimirovič Frost emerged as a key figure in mining engineering and geophysics. Having previously served at the prestigious Mining Institute in Saint Petersburg, Frost brought sophisticated approaches to mine surveying and geological exploration. He introduced new methodologies for magnetic ore prospecting and geophysical exploration. These contributions were particularly valuable for Slovenia's mining industry, which was seeking to modernize its operations in the interwar period. Known as a quiet, serious, and modest man, he lived on Bleiweisova Street 16 and devoted himself entirely to his work. Despite his distinguished background, he adapted to the more modest conditions in Ljubljana without complaint.

Vasilij Vasiljevič Nikitin's contributions to mineralogy and petrography proved equally transformative. As a former director of the Saint Petersburg Mining Institute, Nikitin brought unparalleled expertise to Ljubljana. His research methodologies, particularly in crystallography, set new standards for geological studies in Slovenia. But despite being a world-renowned mineralogist, he struggled to secure a permanent position due to bureaucratic age restrictions. Yet he continued teaching well into his 70s out of dedication to his field and students. He was known for having a flower in his buttonhole every day, summer and winter, grown in his own garden.

One particularly telling detail comes from Professor Nikitin's interaction with a young female scientist, Ljudmila Mantuani Dolar. When she became a private docent in 1937, Nikitin, despite his own precarious position, tried to help her by sharing some of his teaching load so she could earn additional income. This gesture of collegiality met with resistance from students who wanted to continue studying with Nikitin, showing the complex dynamics at play in the university community.

The technical expertise these professors brought was particularly valuable because it bridged theoretical knowledge with practical applications. For instance, Fjodor Fjodorovič Grudinski, who taught mechanical engineering, developed comprehensive courses that combined mathematical precision with practical problem-solving. His approach to teaching differential equations and analytical geometry emphasized their applications in engineering, helping students understand both theoretical principles and their practical implementation.

The Russian professors also played a crucial role in establishing and equipping new laboratories and research facilities. Despite limited resources, they managed to create functional research spaces that allowed for both teaching and experimental work. Their experience in organizing and managing technical institutes in Russia proved invaluable in this regard. For example, when Nikitin took charge of the mineralogy laboratory, he implemented efficient systems for specimen cataloging and analysis that would serve the institution for decades to come.

A particularly notable aspect of their contribution was their role in developing technical terminology in Slovenian. Many technical concepts needed to be expressed in Slovenian for the first time, and these professors worked closely with their Slovenian colleagues to develop appropriate technical vocabulary. This linguistic contribution, though less visible than their research work, proved crucial for the development of technical education in Slovenia.

The impact of these professors extended beyond the university through their involvement in practical engineering projects. Vasilij Nikitin's surveys identified key mineral deposits, directly aiding mining operations and resource management, while Dmitrij Vladimirovič Frost trained engineers who led projects in road, bridge, and industrial facility construction. Hydraulic engineer Sergej Maksimov contributed to water management systems, including irrigation and flood control, boosting agricultural productivity. Their expertise influenced urban planning, industrial design, and public infrastructure, and their technical training equipped local professionals with skills for modernizing Slovenia’s economy. Acting as advisors to state-led projects, they ensured adherence to contemporary scientific and engineering standards, leaving a lasting legacy on Slovenia’s industrial landscape.

Building Academic Culture and Social Integration

The impact of Russian émigré professors extended far beyond their formal teaching and research duties. These scholars played a crucial role in shaping the academic culture at the University of Ljubljana during its formative years, while simultaneously navigating the challenges of integration into Slovenian society.

The development of academic standards and practices was perhaps their most enduring contribution. Many of these professors had experienced the rigorous academic traditions of Russia's most prestigious institutions, and they worked to establish similar standards in Ljubljana. For example, Alexander Bilimovič's insistence on precise mathematical and statistical analysis in economics raised the bar for academic rigor across the faculty. Vasilij Nikitin, for instance, introduced sophisticated laboratory practices in mineralogy that he had developed at the St. Petersburg Mining Institute. These methodologies, adapted to local conditions and resources, helped establish research standards that would influence generations of scientists. His meticulous approach to documentation and experimental design became a model for other departments.

Language barriers presented another significant hurdle. Teaching complex academic subjects in a new language required extraordinary effort. These scholars approached this challenge with remarkable determination, many achieved sufficient proficiency to deliver lectures in Slovenian within their first year. Their commitment to mastering the language went beyond mere functionality - they needed to develop sophisticated academic vocabulary to convey complex concepts in their respective fields. This linguistic adaptation was particularly crucial in technical and scientific fields, where precise terminology was essential.

Social integration occurred on multiple levels. Many Russian professors became active members of the Russian Matica in Ljubljana, an organization that served as a cultural bridge between Russian émigrés and the local community. This organization, under the leadership of various Russian professors including Bilimovič and Spektorski, sponsored lectures, publications, and cultural events that enriched Ljubljana's intellectual life.

However, integration was not without its challenges. Some professors maintained strong ties to their Russian identity while adapting to their new environment. Spektorski's personal diaries reveal the complexity of this cultural negotiation, showing both appreciation for Slovenian intellectual life and occasional nostalgia for the grand academic traditions he had left behind. These personal struggles added depth to their academic contributions, informing their understanding of cultural exchange and adaptation.

Professional Challenges and Academic Achievements

The most immediate challenge these professors faced was the need to prove their academic credentials in a new system. Despite their distinguished careers in Russia, many found themselves having to demonstrate their qualifications anew. The case of Vasilij Nikitin particularly illustrates this challenge. Despite his international reputation in mineralogy and his former position as director of the St. Petersburg Mining Institute, he initially struggled to secure a permanent position in Ljubljana. The university administration, bound by civil service regulations, could not immediately grant him the status his expertise warranted.

The institutional framework of the University of Ljubljana presented its own challenges. As a new university establishing its procedures and traditions, it often lacked the administrative flexibility to accommodate experienced foreign scholars. The requirement for Yugoslav citizenship as a prerequisite for permanent positions created particular difficulties. This requirement meant that many Russian professors spent years working on temporary contracts, despite their valuable contributions to the university.

Yet within these constraints, the Russian professors achieved remarkable success. Their publications in international journals helped establish Ljubljana's reputation in the global academic community. Bilimovič's economic analyses were published in leading German and French journals, while Nikitin's mineralogical studies gained recognition across Europe.

Their academic achievements extended beyond research and teaching. Many took on important administrative roles, contributing to the university's development at an institutional level. Evgen Kansky, for instance, served multiple terms as dean of the Medical Faculty, where he shaped its curriculum, fostered research initiatives, and guided its growth during the formative years. Similarly, Aleksander Bilimovič served as dean of the Faculty of Law, introducing modern legal education frameworks and mentoring a new generation of Slovenian jurists. Their experience with established universities proved invaluable as Ljubljana developed its own administrative practices and academic traditions.

The Russian émigré professors played a pivotal role in shaping the early development of the University of Ljubljana. Their expertise addressed critical staffing shortages, while their methodologies and academic standards elevated the institution's quality of education and research. Beyond their professional contributions, they navigated linguistic and cultural challenges with adaptability, enriching both the academic environment and Slovenian society.

Sources

• Brglez, A., & Seljak, M. (2007). Ruski profesorji na Univerzi v Ljubljani. Inštitut za civilizacijo in kulturo.

• Oset, Ž. (2016). Rusi prihajajo! Ruski profesorji na tehniški fakulteti Univerze v Ljubljani v obdobju Kraljevine SHS. Monitor ISH: Znanstvena revija za humanistiko in družbene vede, XVIII(1), 87–108.

• Brglez, A. (2015). Ruski znanstveniki v Kraljevini Jugoslaviji. Monitor ISH, 17(1), 151–176.

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Despite its growing importance as one of the most significant threats in today’s world, hybrid warfare often remains underestimated. This form of conflict transcends traditional battles involving tanks and fighter jets, taking the shape of subtle, covert operations conducted simultaneously across multiple fronts, often without a single shot being fired. It can be likened to a complex geopolitical chess match, where the pieces are not just military units but also fake news, cyberattacks, economic coercion, and political manipulation. This game of manipulation and strategy frequently blurs the lines between peace and war, leaving the identities of the aggressor and the victim ambiguous, creating an atmosphere of chaos and uncertainty.

Fake profiles and so-called “useful idiots” on social media platforms, who spread disinformation and sow distrust and chaos, are among the most powerful tools of hybrid warfare. Their effectiveness lies in their ability to systematically erode social cohesion, foster mistrust in key institutions, and deepen polarization. This mechanism allows attackers to exert subtle yet far-reaching influence on public opinion. Ordinary individuals, often unwittingly, become vectors for disseminating strategically designed disinformation, fostering an environment ripe for the destabilization of societal and political systems.

Democratic states, rooted in open public discourse and a plurality of media voices, are uniquely vulnerable to the threats of hybrid warfare. Media outlets often find themselves on the front lines, playing a critical role in shaping public opinion and providing credible information. Their responsibility lies in creating a space for legitimate criticism and discussions about abuses of power, while also recognizing and countering the traps set by disinformation campaigns. These campaigns can spread chaos, deepen mistrust, and undermine the authority of key democratic institutions. To avoid unwittingly becoming tools of aggressors, media must operate as transparent and reliable sources of information, strengthening public resilience to manipulation and actively exposing the tactics of hybrid warfare. By doing so, they contribute not only to protecting institutions but also to ensuring the long-term stability of democratic societies.

Key tactics of hybrid warfare include sophisticated forms of manipulation that subtly guide the target toward decisions that ultimately serve the attacker’s interests. This strategy hinges on crafting a distorted perception of reality, influencing the interpretation of events and information. For instance, by spreading carefully crafted false information, attackers can lead their targets to take actions that are not only unnecessary but may even harm their own interests. A core element of this strategy, known as “reflexive control,” is convincing the targeted party that they arrived at these decisions independently, even though their choices were meticulously directed through manipulative tactics. Reflexive control exploits deep-seated cognitive biases, informational gaps, and unconscious trust in flawed premises. It frequently overlaps with disinformation campaigns, cyberattacks, and economic pressures, forming a complex and nearly imperceptible network of threats. These threats subtly but effectively undermine social and political systems, destabilizing the very foundations of democratic societies.

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In the early 20th century, when humanity was still grappling with the mysteries of flight and only beginning to understand the complexities of our planet, Herman Potočnik Noordung dared to look beyond Earth’s horizon. A Slovenian engineer and visionary thinker, Noordung imagined a future where humans would inhabit the stars.

Decades before the first human-made satellite orbited the Earth, he proposed groundbreaking ideas that laid the foundation for modern space exploration. Among his most notable concepts was the "Wohnrad," a wheel-shaped space station designed to generate artificial gravity—an idea that would inspire countless engineers, filmmakers, and scientists for generations to come.

What set Noordung apart was not merely his technical ingenuity but his holistic understanding of the human dimension of space travel. He envisioned sustainable living environments in space that addressed both the mechanics of survival and the psychological and physiological well-being of astronauts. His 1928 book, The Problem of Space Travel, stands as a seminal work, brimming with meticulous calculations, groundbreaking designs, and a vision that was as audacious as it was inspiring.

Early Life and Military Career

Herman Potočnik was born on December 22, 1892, in Pula, a vibrant naval hub of the Austro-Hungarian Empire. Raised in a Slovenian family with deep military connections, he inherited a legacy of discipline and service. His father, Jožef Potočnik, served as a naval doctor, while his mother, Marija Kokoschinegg, hailed from a family with Czech roots, reflecting a multicultural heritage. Following his father’s untimely death in 1894, the family relocated to Maribor, where young Herman’s curiosity and intellect began to take shape during his formative years.

Potočnik attended a prestigious military school in Bad Fischau, near Vienna, where his aptitude for engineering began to shine. Guided by his uncle, a major-general in the Austro-Hungarian Army, he secured a place at the esteemed Imperial and Royal Technical Military Academy in Mödling. There, he excelled in railway and bridge construction, culminating in his graduation in 1913 with the rank of engineer second lieutenant, laying the groundwork for his future innovations.

During World War I, Potočnik applied his engineering expertise on the front lines, constructing and maintaining vital infrastructure, such as bridges and railways, for the Austro-Hungarian military. He faced the brutal realities of war in major battles, including the grueling campaigns on the Soča Front. These experiences profoundly affected him, both physically and emotionally, as the harsh conditions and prolonged exposure to disease ultimately led to his diagnosis with tuberculosis. Unable to continue his military service, he retired in 1919 with the rank of captain, marking the end of one chapter and the beginning of his path toward groundbreaking innovation.

Despite facing significant health challenges, Potočnik’s unwavering passion for engineering propelled him forward. After the war, he relocated to Vienna to live with his brother and resumed his studies at the Vienna University of Technology. By 1922, he had successfully earned both an engineering degree and a doctorate in electrical engineering. These academic milestones, combined with his profound wartime experiences, became the foundation for his pioneering contributions to astronautics.

The Seminal Work: The Problem of Space Travel

After earning his doctorate, Potočnik devoted himself wholeheartedly to the possibilities of space travel. His fragile health, which precluded traditional employment, became an unexpected advantage, allowing him to focus exclusively on his passion for astronautics. He delved deeply into the works of contemporary pioneers like Hermann Oberth and Konstantin Tsiolkovsky, absorbing their groundbreaking theories on rocketry and space exploration, which served as a vital foundation for developing his own revolutionary ideas.

By 1925, Potočnik embarked on crafting his vision for humanity’s long-term presence in space. Drawing on his engineering expertise and an extraordinary imagination, he devised innovative solutions to overcome the daunting challenges of living and working in the unforgiving environment of outer space. These efforts culminated in 1928 with the publication of his seminal work, Das Problem der Befahrung des Weltraums – Der Raketen-Motor (The Problem of Space Travel – The Rocket Motor), a book that would profoundly influence the future of astronautics.

Regarded as a cornerstone in the history of astronautics, his book stands as a visionary masterpiece. Written under the pseudonym Hermann Noordung, it outlined a bold and forward-thinking blueprint for space exploration, long before such ideas were feasible. Potočnik seamlessly fused rigorous engineering principles with an imaginative perspective, creating a work that was both technically groundbreaking and profoundly inspirational.

One of the most revolutionary proposals in the book was the "Wohnrad," a wheel-shaped space station ingeniously designed to generate artificial gravity. By rotating at a precise speed, the station would utilize centrifugal force to mimic gravity for its inhabitants. As Potočnik explained, "Artificial gravity created through rotation will allow humans to live and work in space as they do on Earth, maintaining both health and productivity." This groundbreaking design tackled the inherent challenges of weightlessness while also laying the foundation for a sustainable living environment in space.

Potočnik championed the principles of resource management and self-sufficiency as essential pillars for space exploration. "To sustain human life in space, a station must be entirely self-sufficient, with systems to recycle air and water and harness solar energy—a mirror of Earth's natural processes," he wrote. These forward-thinking concepts laid the foundation for modern sustainability practices in space, ensuring that future missions could thrive independently from Earth’s resources while minimizing environmental impact.

Decades ahead of his time, Potočnik envisioned satellites placed in geostationary orbit, remaining fixed relative to a point on Earth to facilitate continuous communication and observation. "Satellites fixed in orbit will serve as platforms for communication and observation, opening a new dimension in connecting humanity," he proposed. This visionary concept laid the groundwork for modern communication and weather satellites, which today are integral to global connectivity and forecasting, underscoring the profound foresight of his ideas.

The Legacy of Visionary Design

Herman Potočnik Noordung’s influence extends well beyond his lifetime, significantly shaping the trajectory of modern space exploration and design. One of his most notable admirers was Wernher von Braun, the visionary architect behind the Apollo moon missions, who recognized Potočnik’s concepts as pivotal. Among these, the idea of rotating space stations to generate artificial gravity stood out, becoming a foundational principle that continues to inform the design of future space habitats and exploration efforts.

The echoes of Noordung’s "Wohnrad" reverberate across popular culture and modern science. Stanley Kubrick’s iconic film 2001: A Space Odyssey prominently features a rotating space station strikingly similar to Potočnik’s design, cementing the cultural significance of his visionary ideas. Even today, cutting-edge space exploration projects, such as NASA’s Gateway lunar outpost, continue to integrate elements inspired by his groundbreaking concepts, highlighting their enduring relevance.

Beyond his engineering innovations, Potočnik also tackled the pressing challenges of human biology in space. He was a pioneer in recognizing the detrimental effects of microgravity on the human body, including muscle atrophy and bone density loss. To combat these issues, he emphasized, "The absence of gravity in space will weaken human muscles and bones unless counteracted by systematic physical exercise and innovative habitat designs." These groundbreaking ideas laid the foundational principles for the emerging field of space medicine, ensuring that human health remains a priority in the exploration of space.

Overlooked in His Lifetime

Despite the groundbreaking nature of his work, Herman Potočnik Noordung garnered little recognition during his lifetime. Choosing to publish under the pseudonym "Hermann Noordung," he sought to distinguish his intellectual contributions from his personal identity—a choice that may have inadvertently contributed to the limited reach of his ideas. Furthermore, the technological constraints of the 1920s made his visionary concepts appear impractical to many of his contemporaries. Combined with the political and economic instability of the interwar period, these factors significantly hindered the broader dissemination and acceptance of his groundbreaking work.

Potočnik’s health posed significant challenges, severely limiting his ability to advocate for his groundbreaking ideas. Stricken with tuberculosis and living modestly in Vienna, he lacked the financial resources and institutional support that could have amplified the reach of his work. Tragically, his untimely death in 1929 at the young age of 36 cut short a life brimming with extraordinary promise and unrealized potential.

It was only in the decades following his death that Herman Potočnik’s contributions began to receive the recognition they rightfully deserved. Rediscovery of his groundbreaking book led to its translation into multiple languages, enabling his visionary concepts to reach a global audience. Today, scholars of astronautics and space exploration widely acknowledge his pivotal role as a pioneer, highlighting his profound influence on both the theoretical and practical advancements in the field. In his homeland of Slovenia, his legacy is celebrated as a symbol of national pride, with institutions such as the Noordung Center in Vitanje committed to preserving and promoting his remarkable work.

Herman Potočnik Noordung was more than an engineer; he was a dreamer and a pioneer who dared to envision humanity’s future beyond the confines of Earth. As he eloquently expressed, "Space is not merely a void to traverse; it is a frontier that challenges the very limits of human ingenuity and perseverance." His ideas, though rooted in the scientific and technological limitations of his time, transcended those boundaries to inspire future generations of scientists, engineers, and space enthusiasts.

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In the early summer of 1914, as the world teetered on the brink of the First World War, a momentous technological achievement was taking shape in the heart of Togo, a German colony in Africa. After years of challenging construction, led by the brilliant engineer Anton Codelli, one of the most advanced and powerful radio stations of its time became operational.

This ambitious project, spanning kilometers and incorporating a power plant, living quarters, and administrative buildings, was not merely a marvel of engineering but also a strategic asset for the German Empire, designed to establish a global communication network independent of rival powers. With its colossal wire antenna supported by towering iron masts and massive concrete blocks, the station could transmit signals over 5,000 kilometers to Berlin and beyond, connecting other colonies and ships at sea.

On the eve of war, as tensions simmered between European powers, the race for global dominance extended beyond territories and resources to a new frontier: communication. At its core was a revolutionary technology—wireless telegraphy. Determined to challenge Britain's dominance through its vast undersea cable network, Germany embarked on a bold endeavor: the construction of the transcontinental Kamina radio station in its West African colony. This station was not only a technological marvel but also a symbol of Germany's aspirations for global power and its commitment to modernizing its colonial reach.

Rising from the dusty savanna, Kamina embodied Germany’s vision of global connectivity and its ambitions to project influence far and wide. Entrusted with this groundbreaking project was Anton Codelli, a baron from Ljubljana whose aristocratic heritage was matched by his engineering genius. Selected by the renowned German company Telefunken, Codelli took on the challenge of transforming an isolated colony into a hub of technological advancement.

Building Kamina Station in Togo

Between 1911 and 1914, Kamina’s construction in Togo—a remote colony in West Africa—posed monumental challenges that tested the limits of early 20th-century engineering and logistics. Harsh climates, rugged terrain, and the absence of supporting infrastructure required meticulous planning and ingenuity. Materials and prefabricated components were shipped from Germany and assembled on-site, overcoming significant logistical hurdles.

The strategic choice of Togo reflected Germany’s broader aspirations. Positioned as a central hub, Togo allowed Kamina to link Berlin with other German colonies and naval operations, integrating colonial outposts into a unified network. This vision of seamless communication underscored Germany’s imperial ambitions, enhancing military coordination and administrative efficiency across its territories.

Known for his innovative spirit, Anton Codelli transformed this isolated colony into a hub of technological advancement. Under his direction, Kamina emerged as a marvel of modern engineering, its towering masts and advanced transmitters symbolizing the dawn of global connectivity. Codelli’s leadership, combining scientific precision with creative problem-solving, was instrumental in overcoming logistical and technical challenges.

The station's scale was staggering. Anchored by reinforced concrete blocks, massive antenna masts were designed to withstand Togo’s intense weather conditions, including heavy rains and fierce winds. Advanced transmitters and receivers operated with unparalleled range and efficiency, making Kamina a vital node in Germany’s global communication strategy. The facility also included a centralized power plant, administrative buildings, and living quarters, meticulously planned to ensure functionality and support personnel.

However, Kamina’s construction was not without controversy. The use of forced labor, common in colonial projects, highlighted the exploitative practices of the era. Local workers endured grueling conditions, juxtaposed with the advanced technological infrastructure they helped create. Although Codelli reportedly treated laborers better than was typical for the time, the project remained a stark symbol of the inequities of colonial rule.

By mid-1914, Kamina stood completed—a towering testament to human ambition and ingenuity. Yet, as the shadow of war loomed, Kamina’s moment of triumph would be short-lived. The turbulence of World War I would soon challenge the resilience of both the station and the empire that built it.

Anton Codelli: The Baron from Ljubljana

Anton Codelli (1875-1954) was far more than just a nobleman with a fancy title (though he certainly had that - Anton Freiherr Codelli von Codellisberg, Sterngreif und Fahnenfeld!). He was a restless spirit, a born inventor who defied expectations and left his mark on everything from cars to communication.

Born in Naples to an aristocratic family with roots deep in Slovenian history, Codelli could have easily settled into a life of privilege. He spent his youth between Ljubljana and Vienna, attending the elite Theresianum boarding school, a breeding ground for future emperors and diplomats. But while his classmates pursued law or politics, Codelli was captivated by the burgeoning world of technology.

This fascination led him down an unconventional path. After a brief, unsatisfying stint in the Austro-Hungarian navy, he abandoned his law studies in Vienna to pursue engineering. This was a bold move for a young baron, a rejection of the traditional roles expected of his class. It spoke to his independent spirit and his drive to forge his own destiny.

Codelli's interests were as diverse as his talents. He is perhaps best known for bringing the first automobile to Ljubljana in 1898, a feat that earned him both admiration and suspicion. In the sleepy provincial town, Codelli's "devil's wagon" was initially met with fear and disapproval, as people were unnerved by its speed of around 20 km/h. But his true passion lay in pushing the boundaries of what was possible.

Around the turn of the century, he became captivated by the magic of radio. Collaborating with physicist Albin Belar, he built Ljubljana's first radio receiver, a device that could capture time signals broadcast from afar. He then turned his attention to the sea, developing a radiotelegraph system for the Austro-Hungarian navy, enabling ships in the Adriatic to communicate directly with command centers on the coast.

These early successes cemented Codelli's reputation as a gifted innovator. In 1911, his talents caught the eye of Telefunken, a leading German telecommunications company. They offered him a challenge that would define his legacy: to establish a long-wave radio link between Berlin and Togo, a German colony in Africa. This ambitious project, the Kamina radio station, would become a testament to Codelli's vision and engineering prowess.

Kamina in Operation

When Kamina became operational in 1914, it immediately demonstrated its groundbreaking capabilities. The station’s advanced wireless technology enabled transcontinental communication on an unprecedented scale. Messages were transmitted seamlessly between Berlin and Togo, traversing over 5,000 kilometers, while additional links connected Kamina to other German colonies and naval fleets stationed in distant seas.

The heart of Kamina’s operation was its massive transmitter system, powered by a cutting-edge generator capable of producing enough energy to drive the powerful signals. The station’s towering iron masts supported a complex web of antennas, which radiated messages across vast distances. Operators worked in meticulously designed control rooms, utilizing state-of-the-art equipment to ensure the clarity and reliability of transmissions.

Kamina played a dual role in communication. Militarily, it was a vital link for Germany’s colonial and naval operations. In the early days of World War I, the station transmitted critical strategic information between the German High Command and its forces in Africa and the Atlantic. Its ability to maintain uninterrupted communication offered a significant advantage, enabling swift coordination of military resources across vast territories.

On the civilian front, Kamina served as a hub for administrative communication, facilitating governance and trade within the German Empire’s colonial network. The station’s efficiency in relaying messages significantly reduced the time required for decisions and responses, streamlining colonial administration and reinforcing the integration of outposts into the broader imperial framework. In total, the station managed to transmit 229 messages between Germany and its colonies before it was demolished.

The Destruction of the Kamina Radio Station

The outbreak of World War I in the summer of 1914 drastically altered the fate of the Kamina radio station. Initially envisioned as a critical hub for Germany's transcontinental communication network, Kamina's strategic importance made it a prime target as Allied forces advanced into Togo. The station, which had only recently become operational, found itself at the center of a broader struggle for control over global communication infrastructure.

The destruction of the Kamina radio station was a dramatic and controversial episode in the early months of World War I. Faced with the rapid advance of British and French forces into Togo, German authorities stationed at Kamina were ordered to ensure that this state-of-the-art facility would not fall into enemy hands. On August 24, 1914, after intense deliberation and following direct instructions from Berlin, the German garrison began the systematic dismantling and destruction of the station. Massive antenna masts were toppled, transmitters were dismantled, and key components of the sophisticated equipment were destroyed beyond repair. The operation was overseen by German officers, who understood both the strategic necessity and the symbolic loss that this act entailed.

The decision sparked controversy both within Germany and among observers. While military leaders argued that destroying Kamina was essential to deny the Allies a significant communication advantage, others lamented the obliteration of a facility that represented the pinnacle of technological progress and engineering achievement. Local populations, who had been involved in the station's construction and maintenance, watched as the massive infrastructure was reduced to ruins, leaving behind a mix of awe and despair.

Globally, the destruction of Kamina highlighted the vulnerability of technological assets in times of conflict. It underscored the critical role that communication networks played in modern warfare and foreshadowed the increasing militarization of technology in the 20th century. Despite its brief operational lifespan, Kamina's legacy endures as a testament to the transformative potential of wireless communication and its profound impact on geopolitics.

A Legacy in Ruins

The Kamina radio station, though short-lived, left a profound legacy on the development of global communication technologies. It served as a prototype for transcontinental wireless communication systems, demonstrating the feasibility of transmitting messages across vast distances without reliance on physical cables. This innovation laid the groundwork for the evolution of radio communication, shaping advancements in global connectivity that echo into the present day. In many ways, Kamina was a precursor to the satellite networks and fiber-optic systems that now underpin modern telecommunications.

In Togo, the remnants of Kamina hold cultural and historical significance. The site has become a symbol of the complexities of colonial history, representing both the technological advancements brought by German imperialism and the exploitative labor systems that made such projects possible. For the local population, Kamina's story is a reminder of the intertwined narratives of progress and oppression that define much of colonial history.

Anton Codelli’s role in this story elevates him as a visionary in the broader narrative of technological pioneers. His ability to transform an isolated West African colony into a hub of groundbreaking communication technology highlights his exceptional engineering talent and foresight. Codelli’s work, though deeply rooted in the context of its time, resonates as a precursor to the globalized and interconnected world we inhabit today.

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What if we could design life-saving drugs in days instead of years? Imagine a world where problems currently deemed unsolvable by classical computers could be tackled in a fraction of the time. This is the promise of quantum computing, a revolutionary field that redefines the boundaries of what is computationally possible by leveraging the enigmatic principles of quantum mechanics. Yet, while the potential is immense, the technology is still in its infancy, surrounded by misconceptions and hype. Understanding both its promise and limitations is key to appreciating what quantum computing can truly achieve.

Understanding Quantum Computers: Superposition and Entanglemet

Unlike classical computers, which process information using bits that can only be 0 or 1, quantum computers rely on quantum bits, or qubits, which can represent both 0 and 1 at the same time. Think of it like a coin spinning in the air—instead of being just heads or tails, it exists in a mix of both states until it lands. This unique property allows quantum computers to handle multiple calculations at once, opening up possibilities far beyond the capabilities of classical machines.

But it is important to understand that it’s not merely a matter of ignorance about the state of a qubit. The qubit is genuinely in a state that combines 0 and 1 simultaneously, a phenomenon known as superposition. The impact of a qubit in superposition, being both 0 and 1 at the same time, on other quantum objects is fundamentally different from the impact of a qubit in a definite state of 0 or 1.

The phenomenon of superposition is not due to a lack of knowledge about the qubit's state but rather reflects the unique and intrinsic nature of quantum behavior. It showcases how quantum mechanics fundamentally differs from classical physics, providing a new way of understanding and utilizing the natural world. This state is described with precise mathematical representation through the equations of quantum mechanics, which provide an exact framework for predicting and understanding how qubits behave in time.

To grasp the revolutionary potential of quantum computing, consider the task of locating a single rare book in a massive library. A classical computer operates like a diligent librarian who meticulously checks each book one by one—a reliable but time-consuming process that could take years in a vast library. By contrast, a quantum computer, powered by the phenomenon of superposition, functions like a librarian capable of scanning the indexes of all the books simultaneously, pinpointing the exact location of the desired book significantly faster than classical methods. This extraordinary ability to explore countless possibilities at once underpins the immense power of quantum computing, enabling it to solve complex problems that would be infeasible for classical machines.

Another fundamental property of quantum mechanics that quantum computers leverage is called entanglement. When two qubits become entangled, their states are linked in such a way that the state of one qubit instantly affects the state of the other, no matter how far apart they are. Entangled qubits can themselves exist in a state of superposition, extending the peculiar properties we associate with individual qubits to interconnected systems. This means the "weirdness" of superposition—where a qubit exists in multiple states simultaneously—also applies to entangled qubits, creating even more complex and powerful quantum states that defy classical intuition.

The interconnected nature of entanglement enables quantum computers to execute operations that classical computers cannot. Building on the library analogy, entanglement can be imagined as opening one book and instantly accessing related information from other books across the library, as though they are all linked by an invisible network. This extraordinary capability allows quantum computers to address problems that involve intricate relationships and interdependencies in data, offering transformative solutions to challenges that are beyond the reach of classical computation.

The Building Blocks of Quantum Computation: Quantum Gates and Circuits

In classical computing, the fundamental building blocks of digital circuits are logic gates, which operate on bits—units of information that can be either 0 or 1. These gates perform basic operations such as AND, OR, and NOT. For example, an AND gate outputs 1 only if both its inputs are 1; an OR gate outputs 1 if at least one of its inputs is 1; and a NOT gate inverts its input, turning 0 into 1 and vice versa. These simple yet powerful operations form the basis of all digital computations.

Typically implemented using transistors, logic gates control the flow of electrical signals, producing specific outcomes based on predefined rules. By combining these gates, engineers create more complex circuits, such as processors, which underpin the myriad functions of modern computers. These circuits operate deterministically, adhering to the principles of Boolean algebra, which ensures predictable and reliable outcomes in classical computation.

Quantum computers perform their calculations using quantum gates, which precisely manipulate qubits according to the principles of quantum mechanics. Unlike the logic gates in classical computers, which operate with definitive states of 0 and 1, quantum gates interact with qubits in ways that exploit their quantum properties.

For example, the Hadamard gate is a key operation that places a qubit into a state of superposition, enabling it to represent multiple possibilities simultaneously. This forms the foundation for the parallelism that quantum computing offers. Similarly, the Controlled NOT (CNOT) gate is essential for linking two qubits through entanglement, creating a powerful connection where the state of one qubit instantaneously influences the other, regardless of distance. These gates are critical in enabling quantum computers to perform computations that transcend classical limitations.

These quantum gates serve as the foundational components of quantum circuits, which orchestrate complex sequences of operations on qubits. By leveraging the principles of superposition and entanglement, quantum circuits allow quantum computers to perform computations that transcend the capabilities of classical systems.

Overcoming Fragility: Decoherence and Quantum Error Correction

Building and maintaining quantum computers is an extraordinarily intricate challenge. Unlike classical systems, which have benefited from decades of refinement and optimization, quantum computing remains in its early stages of development. Qubits are exceptionally delicate and highly susceptible to environmental disturbances, which can cause them to lose their quantum properties—a phenomenon known as decoherence.

To mitigate this, quantum computers are designed to operate at extremely low temperatures, close to absolute zero, where the delicate quantum states of qubits can achieve greater stability. These cryogenic environments reduce thermal noise and other disturbances that could disrupt quantum coherence. Despite these stringent measures, scaling up the number of qubits while preserving their stability remains a significant technical challenge.

Unlike classical bits, qubits cannot be directly copied for error checking because the act of copying would collapse their quantum state. Quantum error correction is a sophisticated technique that safeguards quantum information by encoding it across multiple physical qubits to construct a more robust logical qubit. This innovative method enables the detection and correction of errors without the need for direct measurement, thereby preserving the delicate quantum information and ensuring computational reliability.

Recent progress includes experiments suggesting that logical qubits can, under certain conditions, exhibit error rates lower than those of individual physical qubits. While these results are promising, they represent early-stage achievements, with many challenges remaining to ensure consistent and scalable error correction in practical quantum computing. Additionally, researchers have pioneered techniques to perform quantum operations on logical qubits while minimizing the introduction of new errors, further enhancing the feasibility of practical quantum computing. These advancements represent essential strides toward developing quantum computers capable of executing long and accurate computations with unprecedented precision and reliability.

For quantum computers to address real-world, complex problems, they require a significantly larger number of qubits working reliably in unison. However, scaling up from the current tens or hundreds of qubits to the thousands or millions needed presents formidable challenges. As the number of qubits increases, maintaining precise control over them becomes more complex, and the risk of errors grows. Ensuring qubit stability and preventing decoherence—the loss of quantum properties—becomes increasingly difficult in larger systems. Factors such as temperature fluctuations, electromagnetic interference, and even minor vibrations can disrupt qubit states. As a result, advanced techniques like cryogenic shielding, error correction, and noise reduction are critical to sustaining quantum coherence and enabling robust computation at scale.

Overcoming the dual challenges of scalability—increasing the number of qubits—and stability—maintaining coherence among qubits—is pivotal for realizing the full potential of quantum computing. Scalability requires not only adding more qubits but also ensuring that they are interconnected and controlled with high precision, as increasing the qubit count exponentially raises the complexity of the system. Stability, on the other hand, demands advanced error correction techniques, improved materials, and innovations in qubit design to combat decoherence caused by environmental noise and interactions.

Common Misconceptions About Quantum Computing

As quantum computing captures growing attention from the media and the public, numerous misconceptions about its capabilities and potential impact have taken root. Addressing these misunderstandings is critical to offering a balanced and accurate view of what quantum computers are truly capable of achieving and the limitations they inherently possess.

One common misconception is that quantum computers will entirely replace classical computers in all areas of computing. In reality, quantum computers are highly specialized devices designed to tackle specific categories of problems that are either infeasible or prohibitively inefficient for classical systems. Classical computers remain unmatched in their ability to handle general-purpose tasks such as word processing, web browsing, graphic design, and database management. These systems are cost-effective, efficient, and supported by a well-established technological ecosystem refined over decades. By contrast, quantum computers excel in specialized domains like simulating complex quantum systems, solving optimization problems, advancing cryptography, and conducting large-scale simulations. Leveraging the unique properties of quantum mechanics, these machines address problems in ways classical computers cannot replicate. Therefore, quantum computers should be viewed as complementary tools, augmenting classical systems to enhance computational power in specific domains while retaining the broader utility and versatility of classical computing.

Another misconception is that quantum computers are simply faster versions of classical computers. In reality, quantum computers represent an entirely new paradigm of computation, grounded in the principles of quantum mechanics. By exploiting quantum phenomena such as superposition and entanglement, quantum computers process information in ways that classical computers cannot emulate, regardless of speed. For example, algorithms like Shor's algorithm for factoring large numbers and Grover's algorithm for searching unsorted databases demonstrate computational speedups derived directly from quantum mechanical principles. These breakthroughs highlight how quantum computers excel not by sheer speed but by leveraging unique capabilities inherent to quantum systems.

Further common misunderstanding is the belief that quantum computers can solve all problems quickly. While quantum computers provide significant advantages for specific types of problems, they are far from a universal solution for all computational challenges. Quantum computers excel in domains where quantum algorithms offer a clear advantage, such as factoring large numbers (e.g., using Shor's algorithm), simulating quantum systems, and solving certain optimization problems. However, for many other computational tasks, quantum computers provide no known advantage over classical systems and, in some cases, may perform worse due to operational overheads, error rates, and resource constraints. The field of computational complexity demonstrates that not all problems solvable by classical computers benefit from quantum acceleration. Therefore, while quantum computers expand the computational toolkit with unique capabilities, they complement classical systems rather than replace them, and classical algorithms remain indispensable for a vast range of applications.

Some believe that quantum computers are ready for practical use. While rapid advancements have been made, they remain in the developmental stage and are not yet prepared for widespread practical applications. Current quantum devices, known as Noisy Intermediate-Scale Quantum (NISQ) computers, operate with a limited number of qubits and face significant challenges such as short coherence times and high error rates due to environmental interference. These issues lead to instability, making it difficult to execute complex algorithms reliably. Quantum error correction techniques, crucial for reducing errors and enhancing computational reliability, are still in development and have not been fully implemented in existing systems. At this stage, quantum computers are primarily used for research, testing quantum algorithms, and advancing our understanding of quantum systems while working toward more robust hardware and scalable solutions.

Another misconception is that building a quantum computer is solely about scaling up the number of qubits. While increasing the qubit count is a necessary step, it alone is far from sufficient for creating practical quantum computers. Factors such as qubit quality, coherence, and error management are equally critical to achieving reliable performance. Simply adding more qubits without improving their fidelity can exacerbate error rates and compromise the system’s overall functionality. As quantum systems grow, maintaining coherence and fidelity across qubits becomes more difficult. Effective quantum error correction—which encodes quantum information across multiple physical qubits to produce stable logical qubits—is essential for scalability. Additionally, advancements in qubit connectivity, precise control systems, innovative cooling technologies, and materials science are crucial. Overcoming these multifaceted engineering challenges is just as important as increasing qubit numbers to ensure the development of functional and scalable quantum computers.

Finally, there is a misconception that quantum computing is solely about speed. While quantum computing can offer significant speed improvements for specific problems, its true potential lies in enabling entirely new capabilities beyond the reach of classical computers. For example, quantum computers excel at simulating quantum systems, providing unprecedented insights into molecular and atomic interactions essential for advancements in drug discovery, materials science, and chemistry. Quantum computing also facilitates the development of groundbreaking cryptographic methods, such as Quantum Key Distribution (QKD), which promises theoretically unbreakable encryption rooted in the principles of quantum mechanics. Furthermore, quantum entanglement enables novel communication protocols, laying the foundation for secure communication networks and the future quantum internet. These transformative capabilities introduce entirely new computational paradigms, offering solutions to challenges once deemed unsolvable, far surpassing the notion of mere speed enhancements.

Understanding the realities of quantum computing is essential for setting realistic expectations and encouraging informed discussions about its transformative potential. Quantum computers hold immense promise as specialized tools for tackling problems that classical computers cannot efficiently solve, from modeling molecular interactions to optimizing complex systems. However, they are not a universal replacement for classical computing. Recognizing their unique strengths, limitations, and current developmental stage enables us to better appreciate their role in the broader computational landscape.

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