Neural networks

Optical neural networks, which use photons instead of electrons, have advantages over traditional systems. They also face major obstacles.

Researchers are drawing on ideas from game theory to improve large language models and make them more consistent.

The computer scientist Ellie Pavlick is translating philosophical concepts such as “meaning” into concrete, testable ideas.

Using machine learning, string theorists are finally showing how microscopic configurations of extra dimensions translate into sets of elementary particles — though not yet those of our universe.

By apparently overtraining them, researchers have seen neural networks discover novel solutions to problems.

Large language models do better at solving problems when they show their work. Researchers are beginning to understand why.

Erasing key information during training results in machine learning models that can learn new languages faster and more easily.

A new study suggests that so-called emergent abilities actually develop gradually and predictably, depending on how you measure them.

Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.