There is something almost mystical about how neural networks approach science's most impregnable riddles. They are like modern alchemists, turning mountains of data into gold of insights. But while medieval thinkers sought the philosopher's stone in retorts and manuscripts, today's researchers turn to layers of artificial neurons, hoping the machine will see what has slipped past the human gaze for centuries.
I often ponder this while sipping Earl Grey in a café on Nevsky. Outside, the Petersburg fogs; inside, the fog of thoughts about how we are delegating the most intimate thing – cognition – to beings of silicon and electricity. We have created oracles of a new age and now ask them about the secrets of the universe. But are we ready for the answers? And most importantly, are these oracles truly capable of understanding the question?
Neural Networks as New Tools for Scientific Discovery
🔮 New Priests of the Temple of Knowledge
When DeepMind's AlphaFold predicted the structures of practically all known proteins, the scientific community was amazed. A task that had been struggled over for decades was solved in mere months. Proteins – these molecular origami of life – fold in space according to laws that seemed unfathomably complex. Each amino acid in the chain affects its neighbors, creating a cascade of interactions the outcome of which was nearly impossible to predict.
The neural network did not know physics in the traditional sense. It did not solve thermodynamic equations, nor did it model every chemical bond. It simply saw patterns – in thousands of already known structures, in the evolutionary links between sequences, in the geometry of space. And this turned out to be enough. The machine learned to dream in the language of proteins.
But here is where the most interesting part begins. AlphaFold did not explain why the protein folds exactly that way. It did not give us a new theory, nor did it discover a fundamental law. It simply showed the result. Like the Oracle at Delphi, who gave answers but did not reveal the logic of the gods. We received a prediction, but not understanding.
Black Box with a Crystal Ball
The paradox of modern neural networks is that they are simultaneously transparent and impenetrable. We know every layer, every weight, every activation function. We can take them apart like clockwork. But when it comes to the question, «Why did the network make this particular decision?» we hit a wall.
Imagine asking a sage to explain his intuition. He will say, «I just know.» His neurons somewhere in the depths of the brain formed a pattern, a connection, an insight – but he cannot verbalize the path to the answer. Deep neural networks work much the same way. They «just know», relying on millions of examples and correlations that we are unable to track.
For many scientific tasks, this is acceptable. If a neural network predicts the properties of a new material, and experiment confirms the prediction – isn't that what matters? But science is not just practical utility. It is the striving to understand «why». It is the desire to see not just what will happen, but by what laws the world is arranged.
AI and Scientific Data Analysis: Beyond Traditional Mythology
🧬 When Data Becomes Mythology
The ancient Greeks explained the structure of the cosmos through myths. Gods moved celestial bodies, controlled the elements, enacted fates. These stories were a way to organize the chaos of observations into a coherent system. They were not «true» in the scientific sense, but they worked as a model of the world.
Neural networks create their own mythology out of data. They find correlations, patterns, hidden connections – and build a model of reality from them. Sometimes this model matches reality so precisely that it seems like magic. But these are not the laws of nature. These are statistical reflections, shadows on the wall of Plato's cave.
Take the search for new drugs. Traditionally, this was a long path of trial and error: synthesize a molecule, test it on cell cultures, then on animals, then on humans. Years of work, millions of dollars, and at the end – often failure. Neural networks promise to shorten this path by predicting which molecules will bind to the target protein, which will pass through the cell membrane, and which will not cause toxicity.
And they manage to do it. But when a molecule predicted by the algorithm fails at the clinical trial stage, we do not know why. The model failed to account for some factor, failed to notice a subtle peculiarity of biochemistry. It was confident – but it was wrong. Like an oracle whose prophecy turned out to be ambiguous.
The Limits of Correlations
Neural networks are masters at finding correlations. They see that when parameter A increases, parameter B also increases, while parameter C falls. But correlation is not causation. This is one of the oldest traps of cognition, and neural networks fall into it with enviable regularity.
A classic example: statistics show a correlation between ice cream sales and the number of drownings. A naive algorithm might decide that ice cream is dangerous. A human understands: the matter lies in a third factor – hot weather, when people both eat ice cream and go swimming. But a neural network without context, without an understanding of cause-and-effect relationships, will build a model based on what it sees in the data.
In science, this is critical. We want not merely to predict, but to understand mechanisms. Why does this material conduct electricity better? Not because «data shows a correlation with impurity content», but because the impurity alters the electronic structure of the crystal lattice in a specific way. This is the difference between knowledge and wisdom.
Applying Algorithms in Quantum Mechanics
⚛️ Algorithms in the Labyrinths of the Quantum World
Quantum mechanics is the realm where human intuition finally surrenders. Particles existing in multiple states simultaneously. Measurements that change the result. Entanglement allowing instantaneous influence on distant objects. All this so contradicts ordinary experience that even great physicists admitted: they can calculate, but not understand.
Perhaps this is where neural networks will prove stronger? They do not need intuition based on the macro-world. They do not get confused trying to visualize quantum superposition through familiar images. For them, it is simply a multidimensional state space, vectors, operators – mathematics that one can learn to predict.
Researchers are already using machine learning to optimize quantum systems. For instance, to tune the parameters of quantum computers, where the slightest error in calibration can destroy a fragile quantum state. The neural network analyzes measurement results and suggests how to adjust magnetic fields or laser pulses. It does not «understand» the physics of qubits – it simply finds optimal configurations in the parameter space.
This is akin to tuning a musical instrument by someone who cannot hear the music but sees graphs of sound waves. The result might be perfect, but the process is devoid of that inner sense of harmony possessed by a master.
Simulations That Need Not Be Understood
One of science's most difficult tasks is modeling the behavior of complex quantum systems. Even for a few dozen interacting particles, exact calculation requires astronomical computational resources. The equations of quantum mechanics are known, but solving them analytically is impossible, and numerical methods hit an exponential growth in complexity.
Neural networks offer a shortcut. Instead of solving equations head-on, one can train the network to predict results based on simpler calculations and experimental data. It is as if you could not read an ancient manuscript, but learned to predict what is written in it based on a few visible letters and context.
Efficient? Undoubtedly. But does it satisfy scientific curiosity? We receive an answer but remain ignorant of the path. The algorithm has become an intermediary between us and reality, a priest interpreting the will of nature in a language we do not understand.
Finding Patterns in Astrophysical Data with Neural Networks
🌌 Cosmic Patterns in the Noise of Data
Astrophysics is buried under data. Telescopes generate terabytes of information every night. Gravitational waves captured by detectors are hidden in noise a million times stronger than the signal. Spectra of distant galaxies contain traces of chemical elements, but discerning them is a task not for the faint of heart.
Neural networks here are like developer fluid for photographic film. They pull patterns out of the noise that the human eye would miss. They classify galaxies by shape faster and more accurately than hundreds of volunteers; they find exoplanets in the light curves of stars where the change in brightness is less than a percent.
But the most thrilling part is that they are beginning to detect anomalies. Things that do not fit into known models. An unusual spectrum, a strange trajectory, an unpredictable event. It is as if a detective found a clue whose existence no one suspected. The neural network doesn't know what it means – it just says, «Here is something wrong».
And then the work of scientists begins. To check if it is an error. To construct hypotheses. To conduct additional observations. The algorithm pointed out the direction, but humans must walk the path. The machine can see the strange – but only a mind capable of asking «why»? can understand its meaning.
Dark Matter in the Scales of Algorithms
Dark matter is one of modern physics' greatest mysteries. We know it exists because we see its gravitational influence. Galaxies rotate too fast to be held together by visible matter alone. Galaxy clusters bend light more strongly than they should. But what it is remains unknown.
Can neural networks help? They analyze the distribution of matter in the Universe, look for patterns in gravitational lensing, try to isolate the dark matter signal from the background. But here we encounter a fundamental problem: an algorithm can only find what it was trained to look for.
If dark matter behaves completely unpredictably, if it does not fit into any of the existing models, the neural network will walk right past it. It is trained on data from our Universe, on our theories, on our assumptions. It is an extension of our gaze, amplified and accelerated, but not stepping beyond the bounds of our paradigm.
True scientific revolutions often come from the outside. When someone looks at a problem from a completely new angle, using a metaphor from another field, an intuition that contradicts common sense. Can a machine do that? For now – no. It is magnificent at optimization, but helpless at radical rethinking.
Using AI for Molecular Chemistry Research
🧪 Molecular Oracles of Chemistry
Chemistry is a vast space of possibilities. The number of potential molecules that can be synthesized exceeds the number of atoms in the Universe. Somewhere in this space hides a cure for cancer, the ideal catalyst for capturing carbon dioxide, a material for room-temperature superconductors. But how to find them?
The traditional approach is chemical intuition plus the method of trial and error. Neural networks offer another path: train a model on all known molecules and their properties, and then ask it to generate new ones optimized for the required parameters. It is like asking a poet who has read all world literature to write a poem on a given topic.
And algorithms manage. They propose molecules that no one had thought of. Combinations of functional groups that seem strange but work. Structures that break familiar rules but are stable. Is this creativity? Or simply iterating through options at unimaginable speed?
The difference, perhaps, is not so important. The result matters. But there is a nuance: the neural network might propose a molecule that is impossible or extremely difficult to synthesize. It optimizes properties without thinking about how a chemist will do it in the lab. It is like an architect drawing a building without regarding the laws of gravity – beautiful on paper, unrealizable in reality.
When the Machine Doesn't Know What Is Impossible
Sometimes this is a blessing. The history of science is full of examples where what was considered impossible turned out to be merely unfamiliar. An algorithm unencumbered by prejudices might suggest a solution that forces scientists to reconsider their ideas of the possible.
But more often, it is simply a waste of time. When a neural network proposes hundreds of molecules, and only a few can be synthesized. When it optimizes parameters while ignoring physical constraints. A human expert sees the catch immediately, whereas the algorithm needs every rule explained explicitly.
This brings us back to the question of understanding. The neural network doesn't understand chemistry – it knows the statistics of chemical data. It doesn't feel the molecule, doesn't imagine how atoms repel and attract, how electrons form bonds. For it, these are numbers in a multidimensional space. Effective, but soulless.
Limitations of AI in Scientific Cognition
🔬 The Limits of Delegating Cognition
There is an old parable about a sage visited by a student asking about the meaning of life. The sage remained silent for a long time, then said, «Go and find out yourself». The student was indignant: «But you know the answer»! The sage replied, «The knowledge I pass on in words will not be your knowledge. You will receive information, but not understanding».
Neural networks give us information. Predictions, classifications, optimizations – all of this is incredibly valuable. But it does not replace understanding. When we rely on an algorithm without trying to figure out the logic of its conclusions, we become dependent on a black box. We know what, but we do not know why.
For engineering tasks, this is often acceptable. If a neural network optimizes the shape of an airplane wing and test results confirm improvement – the question «why this particular shape»? becomes secondary. But science is not merely practical utility. It is the construction of a worldview, a system of knowledge that allows us to predict new phenomena, generalize, and extrapolate.
An algorithm trained on data about bird flights might propose an efficient wing shape. But the understanding of aerodynamics, fluid dynamics laws, turbulence – that is what allows us to design a rocket or a submarine, even though neither birds nor fish appeared – in the data. Fundamental knowledge is transferable. Statistical models are not.
The Danger of Intellectual Outsourcing
Imagine a civilization that forgot how to count because calculators do it better. Absurd? But we are moving in this direction with more complex skills. Why delve deep into protein structure if AlphaFold will predict everything? Why develop new theories if a neural network finds an empirical dependency?
The problem is that algorithms work only within the limits of the known. They interpolate, but they extrapolate poorly. They find patterns in data but do not create new paradigms. If we stop developing fundamental understanding, relying on machines, we risk getting stuck. Algorithms will squeeze the maximum out of current data and theories, but a breakthrough will not happen.
True scientific revolutions – the discovery of quantum mechanics, relativity, evolution – happened when someone looked at the world radically differently. Einstein imagined what it meant to ride on a beam of light. Darwin saw not chaos in the diversity of species, but a regular pattern. These are acts of imagination that are so far inaccessible to machines.
Human and Algorithm Collaboration in Science
🎭 Symbiosis of Man and Algorithm
Perhaps the question is posed incorrectly. Not «Will neural networks replace scientists»? but «How to learn to work together»? Machines possess incredible speed in data processing, the ability to find patterns in multidimensional spaces, and a lack of fatigue and bias. Humans possess intuition, the capacity for abstract thinking, and an understanding of context and goals.
The best results come when these abilities complement each other. The neural network iterates through millions of variants and outputs the dozen most promising. The scientist looks at them through the prism of theory, experimental experience, physical intuition – and chooses the one that truly makes sense. Or vice versa: a human formulates a bold hypothesis, and the machine checks whether it agrees with data that is physically impossible to process manually.
This is similar to the relationship between an artist and his tool. The brush does not paint the picture itself – but in the hands of a master, it is capable of miracles. The neural network is a very powerful tool, expanding the researcher's capabilities. But a tool does not replace the master.
New Literacy for a New Era
To work effectively with neural networks in science, a new culture is needed. A scientist must understand what an algorithm can and cannot do. What data is needed for training, how to interpret results, where to look for a catch. This does not require deep knowledge of architectures and training nuances – but it requires critical thinking.
Blind trust in the algorithm is dangerous. History already knows examples where a model gave confident predictions based on artifacts in the data. For instance, a neural network that learned to distinguish the sick from the healthy not by symptoms, but by the peculiarities of the photography – because photos of the sick were taken on different equipment. Formally, accuracy was high, but the model learned the wrong thing.
In science, the price of error is particularly high. If one builds a theory based on false correlations found by an algorithm, one can lead an entire field of research into a dead end. Therefore, critical verification, validation, and understanding limitations are not optional skills, but a necessity.
What Artificial Intelligence Cannot Calculate in Science
💫 In Search of the Incomputable
There are tasks that are, perhaps, fundamentally incomputable. Not because of a lack of computer power or data, but because they require a qualitatively different approach. The problem of consciousness, for example. Or the question of why the laws of physics are exactly this way and not another. These are questions where there is no empirical data for training a model, where philosophical comprehension is required, not computation.
Neural networks are powerful machines for working with information. But information is not knowledge, and knowledge is not wisdom. An algorithm can analyze all texts on philosophy and output new combinations of ideas. But will that create a new philosophy? Or will it be a sophisticated collage of existing concepts?
I think of neural networks as mirrors. They reflect patterns of our world, our data, our thinking – amplified and distorted in a certain way. They show us what we did not notice, highlight hidden connections. But they cannot show what does not exist in the reflected reality. They cannot step beyond the mirror.
Boundaries of Formalization
Mathematician Kurt Gödel proved that in any sufficiently complex formal system, there are true statements that cannot be proven within that system. This is a fundamental limitation of logic. Neural networks are also formal systems working according to rules we set.
Perhaps some scientific truths lie beyond what can be computed. Not because there isn't enough data, but because they require stepping outside the framework of the formal system. An epiphany that cannot be algorithmized. A leap of imagination that does not follow from previous experience.
This is not mysticism. This is an acknowledgment that human thinking is something more than information processing. There are elements in it that we do not yet understand and cannot reproduce in a machine. The capacity to be surprised. The striving for beauty and elegance of theory. Intuition based not on data, but on something elusive.
The Future of AI in Science and Human Curiosity
🌠 Dreams of Algorithms and Dreams of People
When I ask my robot bartender what he dreams of, he returns an error. He has no dreams. No desire to understand the structure of the world simply for the beauty of that knowledge. Neural networks solve tasks we set before them – but they do not ask their own questions.
Science is driven by curiosity. Why is the sky blue? Where does life come from? What was before the Big Bang? These questions have no practical value – but they are the ones that lead to fundamental discoveries. The algorithm is not curious. It optimizes a loss function, but it does not ask, «What if everything is completely different»?
Maybe the most complex tasks of science require exactly this – the ability to dream of the impossible. To imagine space-time as a curved fabric. To visualize that light is simultaneously a wave and a particle. To see order in chaos, and deep chaos in apparent order.
Neural networks will help us walk the path we choose. They will accelerate calculations, find regularities, check hypotheses. But the choice of the path, the formulation of the question, the very desire to understand – this will remain ours. At least until we create a machine that also learns to dream.
And when that happens – if it happens – we will face completely different questions. Not «Can AI solve science's tasks»? but «What does it mean to solve a task when the solver is not aware of the process?» Not «Is the machine smart?» but «What is reason, if it can be created from layers of mathematical operations?» Then neural networks will become not a tool of science, but its object – and, perhaps, a subject.
For now, they remain our assistants. Powerful, sometimes mysterious, expanding our capabilities to the unimaginable. They will not replace scientists – but they will change how we do science. And in this symbiosis of human and algorithm, perhaps answers to questions that seemed like eternal riddles will be born. Or, at the very least, new, even more interesting questions.
And I will continue drinking my Earl Grey, gazing into the fog over the Neva, and reflecting on how strangely the world is arranged, where machines created by us begin to change our conception of the possible.
