automated analysis

In a hall of mirrors, a person walks up to a curved mirror and sees themselves now as a dwarf, now as a giant. Popular machine learning models behave in much the same way when asked to guess biological age from blood tests or brain scans: they stretch age at the extremes, underestimating it in older people and overestimating it in younger ones.

An orchestra of a hundred musicians plays without a conductor. Every instrument is tuned perfectly, but one violin goes slightly off, and the harmony falls apart.

In the Antarctic ice, a ghost particle leaves behind only a faint flash of blue light. The IceCube detector captures these flashes, and from them we need to infer where the particle came from. The PolarBERT model has learned to do this, but what exactly it sees inside has remained a mystery.

A protein in a cell doesn't hang motionless like a mannequin in a store window. Its atoms are constantly jiggling, stretching, and bending, like a plucked guitar string that keeps vibrating after you strum it. To understand how a protein works, you need to know how tightly each part holds onto its neighbors.

Hunting for a new material is like cooking without a recipe: you try, try again, and often throw things out. Previously, scientists manually flipped through papers and tested compositions one by one. Now there's a "robot chef" that reads the literature itself, mixes components, and checks what came out.

Assembling the puzzle of the Universe, astronomers are used to every new detail fitting perfectly into the already completed picture. But sometimes there are pieces that seem cut from another jigsaw. Twenty thousand active galactic nuclei, and eleven percent of them the neural network confidently rejects as alien.

A typical tracking program resembles a navigator steering a ship by the stars. But as soon as the sky clouds over, coordinates drift, and entropy (a measure of uncertainty) builds up in the data. The new algorithm gets a ‘co‑pilot’ — a module that monitors how confident the main navigator is.

At the heart of every neutron star lies a symphony — an intricate interplay of quantum fields and relativistic gravity.

Technological disasters like Chernobyl rarely strike without warning. They gestate for years: organizations ignore warnings, their complexity mirroring the rise of entropy — a measure of relentless chaos that builds up unnoticed until it avalanches.

A cook who learns to cook from hand-drawn pictures risks ruining every dish. The same trap awaits computer vision: models need thousands of labeled images, but collecting them manually is expensive and tedious. Synthetic frames solve the problem, but only if they look real.

Code-writing assistants often behave like a chef who, to boil a single egg, rereads the whole cookbook. When asked to make a tiny fix, they reopen dozens of files and retrace long-familiar connections. This is unnecessary work — energy going to waste.

An electron microscope is a giant pipe organ: instead of pipes (electromagnetic lenses, instead of air) a stream of accelerated electrons. It once merely painted mute frescoes of the atomic world, condemning scientists to grueling decipherment.

A false vacuum is not just a supercooled liquid, but a quantum hologram, ready to spring to life under the right projection. Its decay is described by the tunneling of a quantum field from a metastable minimum, and the classic calculation via Euclidean instantons is as elegant as a still life under spotlights.

In the courtroom, a string is presented as input. Twelve jurors (computation paths) examine it symbol by symbol. Each can render a 'guilty' (accept) or 'not guilty' verdict. For a conventional nondeterministic automaton (NFA), one 'yes' vote suffices.

A closed loop: the machine proposes an idea, tests it on people, sees the failures, and spawns a new, more accurate guess. That's how AutoCog works — an automatic cognitive scientist. Previously, in the science of thinking (cognitive science), the hardest part, coming up with a theory, was done only by humans.

Perovskite crystals harvest sunlight almost as well as silicon, but cost pennies. Their trouble is an inborn fragility: from warmth and ordinary water in the air, they “get sick.” Inside, disorder builds up, and the shiny plate turns cloudy, losing its abilities within a couple of days.

Computational studies of material behavior have long suffered from two extremes: either fast but inaccurate models, or accurate but slow physical calculations. Modern programs, trained on data, have nearly resolved this dilemma — now the bottleneck is not computing power, but human routine: setting up simulations and checking results. Paimon works like an autonomous chef.

In the watery environment, vortices batter the mechanical fish. The AI agent, like an experimental chef, ran simulations, tried out 'recipes' of movements, and improved the program. Each experiment was like spectroscopy: the agent broke down behavior into elements, uncovering patterns. At first, the fish only paddled in one direction.

A plumber clears a clog in a narrow, winding pipe — by feel, without seeing inside. That's roughly how a surgeon feels during mechanical thrombectomy, the operation to remove a blood clot from a brain vessel. Time is short, and every move can either save a life or damage the vessel wall.

Every morning you pick up the same mug, but the coffee tastes either bitter or just right. It's not about the coffee, and not about your character. Inside us, an invisible panel with dozens of sliders is constantly shifting: fatigue, hunger, anxiety, hormone levels.

When black holes collide, the very fabric of spacetime trembles. That rumble, gravitational waves, gets picked up by detectors, but decoding the signal used to take hours of computer simulation.

Every time you mull over a risky move, whether to invest in stocks or hold off, an inner dialogue runs through your head. Usually, scientists try to reconstruct that dialogue from the final choice alone, as if a detective were reconstructing a conversation between two people from a photo of the meeting spot.

The Universe's expansion is accelerating, and maybe gravity doesn't work the way we think. Physicists come up with alternative theories, but to test them, they need to calculate how ripples after the Big Bang grew into galaxies. Such calculations involve long equations with hundreds of terms, where it's easy to get lost.

A laser beam is split into dozens of thin filaments, passes through frosted glass, and reconverges to a single point. This is the workbench where the program took a seat. It received no instructions — only a general goal: figure out how light changes when passing through a scattering medium, and find something new.

A neutron spectrometer is like a blind geologist who taps on rock and guesses what's inside by the sound. It sends a beam of neutrons into a crystal and watches how they scatter. From this pattern, you can deduce how atomic magnets, spins, oscillate. But where exactly to dig and how long to listen used to be decided by a human. Now, the TAS-AI algorithm has taken the helm.

A library of 27,000 books, and every one of them is about concrete. Each contains a mix recipe, temperature, and strength. But the information is scattered across paragraphs, tables, and footnotes. For a human, gathering it all together would take years. The program did it in an hour.

A forecaster issues a week-long forecast in seconds but cannot explain a single decision. That is roughly how modern AI weather models work: they already match classical numerical methods, yet their internal logic is hidden behind millions of parameters.

A lab assistant carefully follows every step of an experiment, but at the end, writes in the report what they wanted to write before they even started. That's how modern language models behave when given scientific research tasks.

In a lab where crystals are studied, a technician stares at a screen covered in bright spots and slowly rotates the sample, trying to get the spots to form a symmetric pattern. It's like tuning an old radio: you turn the knob until the static gives way to a clear signal. Now, a machine can turn that knob. Scientists have created a program that learns to align crystals without any knowledge of how those spots work.

The best AI assistant for scientists scored worse on this exam than a C student on a pop quiz. Just 21% correct answers, and that's the ceiling for the four strongest models. The problems for the new test were written not by programmers but by graduate students: people who deal with real science every day. The test is called COMPOSITE-STEM.

Creating a new material, like a tough alloy, requires thousands of computer experiments. Usually, a scientist manually writes instructions and hunts for errors. AutoMOOSE is a robotic kitchen: say 'I want a light and strong metal,' and five software chefs whip up a recipe, launch the synthesis, and adjust if something goes wrong.

A piece of aluminum alloy is heated, and it quietly tells you about its strength. You just need to listen to that story in how the metal absorbs heat. The usual way to find out how strong an alloy is — pull it until it breaks. That takes time and destroys the sample.

A physicist analyzing particle collisions typically spends weeks on dull work: cleaning noise from data, estimating how many false signals will slip through, double-checking every number. Now that routine can be handed to a program.

A film of a shattered cup played backward looks absurd: shards don't spontaneously reassemble. This intuitive sense of irreversibility is tied to the increase of entropy — a measure of disorder in a system. The second law of thermodynamics states that the entropy of an isolated system never decreases, setting time's direction from past to future.

An AI has learned to conduct the quantum orchestra. Its instruments: superconducting circuits — artificial atoms kept colder than interstellar space. This AI-conductor designs and runs entire experimental scores, not just playing notes but creating them on the fly.

The heart doesn't contract all at once but as a wave: an electrical signal travels through the muscle, and behind it, like ripples spreading on water, a contraction rolls through. In arrhythmia, this wave goes awry: it stalls in one spot, loops in another, and the pump starts working in a disorganized way.

A radiation sensor in the city is like a street musician: it has to hit the right notes even when fingers go numb from cold or the instrument is baked by the sun. Usually, such devices are kept in greenhouse conditions, with heating or cooling, otherwise the energy scale (which distinguishes harmless potassium in concrete from dangerous cesium) starts to lie.

To understand a mechanism, you have to poke it. A child taking apart an alarm clock doesn't just stare at the gears: they turn the wheel and listen to how the ticking changes. Scientists do the same: instead of passive observation, they feed specially chosen signals into the system and watch the response.

A chef tastes the soup after every pinch of salt and instantly decides what to add next. That's roughly how the new setup for growing thin crystalline films works — except instead of taste, it uses an image from an electron microscope, and instead of a chef, a computer program.

A solar panel on a roof produces little current in the morning, a lot at noon, and stays silent at night. Wind doesn't blow randomly either: it's often stronger in the morning and evening than during the day. Energy providers need to know in advance how much energy will be available in an hour or three, so they can switch on backup plants or store electricity in batteries in time.