DeepMind

A reinforcement learning model maintains cooler temperatures in large data centers and other buildings.

Where spores from DeepMind scatter, startups blossom. Nearly 200 former employees of Google’s elite AI research lab have gone on to found or join startups.

Reinforcement learning agents have mastered games like Go that provide complete information about the state of the game to players. They’ve also excelled at Texas Hold ’Em poker, which provides incomplete information, as few cards are revealed.

Matrix multiplication is executed so often in deep learning, video games, and scientific computing that even a slight acceleration can save substantial amounts of processing time. New work finds ways to speed up this crucial operation.

Amid speculation about the threat posed by OpenAI’s ChatGPT chatbot to Google’s search business, a paper shows how the search giant might address the tendency of such models to produce offensive, incoherent, or untruthful dialog.

Behind schedule on a software project? There’s an app for that. Language models fine-tuned on computer code proved capable of generating software routines similar to the work of experienced developers — though the results can be hit-or-miss.

The route to improving transformer-based language models like GPT-3 and Gopher, which are trained on immense quantities of text scraped from the web, has been to increase their size. But research shows that, given a processing budget, bigger doesn’t necessarily mean better.

A proposed European Union law that seeks to control AI is raising questions about what kinds of systems it would regulate. Experts at a roundtable staged by the Center for Data Innovation debated the implications of limitations in the EU’s forthcoming Artificial Intelligence Act.

Machine learning is making wind power more predictable. Engie SA, a multinational energy utility based in France, is the first customer for an AI-powered tool from Google that predicts the energy output of wind farms.

Engineers who design aircraft, aqueducts, and other objects that interact with air and water use numerical simulations to test potential shapes, but they rely on trial and error to improve their designs. A neural simulator can optimize the shape itself.

Researchers took a step toward achieving a longstanding goal: One model that performs a whole lot of very different tasks. Scott Reed, Konrad Żołna, Emilio Parisotto and a team at DeepMind announced Gato.

Nuclear fusion technology, long touted as an unlimited source of safe, clean energy, took a step toward reality with a machine learning algorithm that molds the fuel in a reactor’s core.

Let’s say you have a pretrained language model and a small amount of data to fine-tune it to answer yes-or-no questions. Should you fine-tune it to classify yes/no or to fill in missing words — both viable approaches that are likely to yield different results?

Programming is hard. Programming competitions are harder. Yet transformers proved themselves up to the task.

Large models pretrained on immense quantities of text have been proven to provide strong foundations for solving specialized language tasks. My biggest hope for AI in 2022 is...