Category Technology/Electronics

Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed machine learning-based methods to predict material properties even with limited data. This can aid in the discovery of materials with desired properties, such as semiconductors.

In recent years, materials engineers have turned to machine learning models to predict which types of materials can possess specific properties such as electronic band gaps, formation energies, and mechanical properties, in order to design new materials. However, data on material properties—which is needed to train these models—is limited because testing materials is expensive and time consuming.

This prompted researchers led by Sai Gautam Gopalakrishnan, Assistant Professor at the D...

Students learning quantum mechanics are taught the Schrodinger equation and how to solve it to obtain a wave function. But a crucial step is skipped because it has puzzled scientists since the earliest days—how does the real, classical world emerge from, often, a large number of solutions for the wave functions?

Each of these wave functions has its individual shape and associated energy level, but how does the wave function “collapse” into what we see as the classical world—atoms, cats and the pool noodles floating in the tepid swimming pool of a seedy hotel in Las Vegas hosting a convention of hungover businessmen trying to sell the world a better mousetrap?

At a high level, this is handled by the “Born rule”—the postulate that the probability density for finding an object ...

You’re running late at the airport and need to urgently access your account, only to be greeted by one of those frustrating tests—”Select all images with traffic lights” or “Type the letters you see in this box.” You squint, you guess, but somehow you’re wrong. You complete another test but still the site isn’t satisfied.

“Your flight is boarding now,” the tannoy announces as the website gives you yet another puzzle. You swear at the screen, close your laptop and rush towards the gate.

Now, here’s a thought to cheer you up: Bots are now solving these puzzles in milliseconds using artificial intelligence (AI). How ironic. The tools designed to prove we’re human are now obstructing us more than the machines they’re supposed to be keeping at bay.

Welcome to the strange battle ...

A new artificial intelligence (AI) model has just achieved human-level results on a test designed to measure “general intelligence.”

On December 20, OpenAI’s o3 system scored 85% on the ARC-AGI benchmark, well above the previous AI best score of 55% and on par with the average human score. It also scored well on a very difficult mathematics test.

Creating artificial general intelligence, or AGI, is the stated goal of all the major AI research labs. At first glance, OpenAI appears to have at least made a significant step towards this goal.

While skepticism remains, many AI researchers and developers feel something just changed. For many, the prospect of AGI now seems more real, urgent and closer than anticipated. Are they right?

Generalization and intelligence
To understand ...