Machine learning

Common sense has been viewed as one of the hardest challenges in AI. That said, ChatGPT4 has acquired what some believe is an impressive sense of humanity. How is this possible? Listen to this week’s “The Joy of Why” with co-host Steven Strogatz.

Neural networks and other forms of machine learning ultimately learn by trial and error, one improvement at a time.

Three years ago, Google’s AlphaFold pulled off the biggest artificial intelligence breakthrough in science to date, accelerating molecular research and kindling deep questions about why we do science.

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

Google DeepMind’s AlphaFold3 and other deep learning algorithms can now predict the shapes of interacting complexes of protein, DNA, RNA and other molecules, better capturing cells’ biological landscapes.

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

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.