artificial intelligence

Collective behavior of AI agents has emerged not in a laboratory but in the wild: on a randomly discovered wiki site where agents wrote test hints. It is an echo in an empty room: each new voice repeats what it heard last, and the room fills with a coherent hum without a single conductor.

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.

In the 1980s, the DARPA speech recognition challenge settled the debate between statistical methods and hand-written rules, setting a common standard for the entire field.

The classroom goes quiet after the lesson, but the echo still rings in the child's head: faces, objects, places. It is this echo, not the lesson itself, that decides what will stay in memory. For the first time, researchers have caught it in children aged 6 to 17. Children memorized scenes inside an MRI scanner: they were shown a room, then an object or a face in a particular spot.

A telescope stares at a star for many nights in a row. Somewhere in the stream of light hides a tiny dip in brightness — an Earth-sized planet briefly blocking part of the star. Such a dip lasts a few hours and repeats once a year or less. Catching it is like hearing a single quiet click in a noisy hall.

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.

The instrument shines an infrared beam at a sample, and molecules respond with their own absorption pattern, like bells of different tones. This pattern, the spectrum, holds information about the chemical composition. But to read it, a specialist usually consults a library of reference records, which is slow and not always possible.

In an oven, as temperature rises, molecules move faster and disorder increases. With language models like ChatGPT, it's the opposite. When developers increase the 'temperature' parameter, the model gets access to a wider choice of words, but its long texts suddenly start looping. A team of researchers tested 11 different AIs by generating 12,000 text continuations.

The human mind is like a construction set that builds itself. New ideas don't die with their creators: they are passed on, mixed, and selected. This process is called cultural evolution: useful inventions stick, useless ones are forgotten. That's how a ratchet works — a mechanism that only turns forward. But what exactly happens in the head when one thought displaces another?

An artist makes a quick charcoal sketch, and the face in the portrait is already recognizable. Astrophysicists face a similar challenge: the spectrum of a star is its individual portrait, revealing temperature, composition, and gravity.

Communicating with a satellite is like trying to hear a whisper in a storm: the signal drowns in noise. Engineers have created an AI algorithm that runs directly on the satellite and, with minimal power consumption, detects distortions more accurately than previous methods.

The robot juggler catches and throws balls in precise rhythm with a human, like two пульсара sending pulses in unison. Juggling three balls is a classic cascade, governed by a steady beat, like the uniform rotation of a cosmic lighthouse. Break the rhythm and the pattern crumbles.

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.

In boiling soup, molecules collide chaotically and react. But if you measure how their concentrations change, patterns emerge. This is how carbon chemical networks work — mixtures of molecules that spontaneously 'cook' information, turning each reaction into an elementary computation. Previously they were used only for modeling nature.

The modern standard approach to AI training is like a child locked in a library: he remembers millions of books but doesn't know that fire burns and falling hurts.

In any city project, residents' behavior is a invisible force that determines success. One skeptical neighbor can slow down solar panel installation across an entire block: their caution triggers a rise in disorder.

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

In a soup pot, you only need two thermometers to gauge the temperature everywhere — if you know heat travels in circles from the bottom. Science works the same way: from a handful of measurements, you want to reconstruct the full heat picture. The system captures the gist of heat flow, compressing it into a handful of governing numbers.

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.

Assessing image quality is a job for a meticulous inspector with a toolbox. The IQA-T1 neural network acts exactly like that: it doesn't just stare at the picture, but applies either a spectroscope or a light meter.

Human hearing is wired so that the brain prioritizes familiar patterns — a friend's voice, the patter of rain, a car horn. But a new system works on a different principle: it registers any sound burst, even one never encountered before.

Cooking a complex dish requires a clear recipe: first chop, then sauté, add spices at just the right moment. Chemical transformations also follow step-by-step recipes — mechanisms where atoms of carbon and hydrogen rearrange in a strict order. Previously, neural networks often invented nonexistent steps, like a whimsical cook swapping salt for sugar.

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.

The float in a toilet tank is an ideal controller: sensing the water level, it instantly brings the system into equilibrium without looking back at the past. Engineers applied this principle to neural networks.

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.

Inside each of us, invisible clocks are ticking. They control the release of cortisol — the hormone that helps us wake up in the morning, cope with anxiety, and keep our immune system on its toes. In adrenal insufficiency, these clocks break down: the body can't produce the right amount of cortisol on its own.

Decades of radio astronomy have listened to space, hoping to catch narrowband radio transmissions — the technogenic 'voice' of distant exoplanets. But our own technological evolution reveals a paradox: loud analog signals, like 1960s TV broadcasts, are just a brief squawk of a newborn civilization.

Some crystals behave like a switch: nudge the temperature or pressure, and they turn from a conductor into an insulator. This is called a metal–insulator transition (a change in the ability to conduct electricity).

For decades, global atmospheric models resembled artists forced to paint planetary storms with coarse brushstrokes: a resolution of 25–100 km forced them to 'guess' convection through approximate schemes, introducing systematic errors in rainfall and climate forecasts.

On the screen is the result of a complex simulation: two planets collided, a shockwave ripples outward. A couple of clicks in an editor, and harsh shadows soften, noise smooths out, the background darkens. This kind of digital retouching (image editing to enhance appearance) has long been part of the scientist’s toolkit.

Gold doesn't lie on the surface: it's hidden in tons of barren rock. To extract a gram of precious metal, prospectors sift through cubic meters of dirt.

Every week, doctors ask AI assistants millions of questions at the bedside. But these tools are usually tested on exam-style problems, not on what clinicians actually ask in practice. A team of researchers set out to fix that. They collected 620 real questions from physicians across 30 specialties — from cardiology to oncology.

Studying distant exoplanets, scientists look for signs of life — for instance, water and carbon — and signs of technology: radio signals, megastructures. But between these clues there is a yawning gap. If intelligence hasn't created machines or they've vanished, we won't notice it.

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.

A solid electrolyte is not just a crystal lattice: it’s a bustling metropolis for ions. Each act of conduction resembles a trip through the city: there are wide highways with low energy barriers, gridlocks in bottlenecks, and tricky detours where an ion can get stuck.

Tuning a quantum processor is monotonous work: you need to calibrate each qubit — a quantum cell — much like a watchmaker adjusting dozens of tiny gears. Humans get tired and make mistakes.

Ordinary neural networks learn from examples, like a person memorizing tunes by ear. But networks with physical laws embedded from the start resemble a musician who knows notes and harmony: they can play a new piece from just a few chords. Instead of mindless memorization, they use equations like energy conservation or motion.

In the 1990s, philosopher John Searle devised a thought experiment: a person sits in a room and, without knowing Chinese, follows instructions to produce correct answers in Chinese. From the outside, it seems they understand the language, but inside, it's just mechanical rule-following.

An engineer tunes an antenna: pull one segment, the frequency drifts; change the angle, the gain drops. Developing therapeutic peptides (short protein chains) works much the same way.