Science

An AI-powered network of thermal sensors is helping ships avoid collisions with whales.

Introduced in 2020, Google’s AI system for detecting breast cancer in mammograms still hasn't been used to diagnose current patients.

Commitments by large AI companies to limit emissions of greenhouse gases are at risk as those companies pursue a massive build-out of data centers, many of which will be powered by fossil fuels in the near term and possibly beyond.

Generative AI has proven that it can produce text, images, audio, video, and code. The world’s most valuable pharmaceutical company is betting billions that it can produce drugs as well.

Simulating complex physical systems through traditional numerical methods is slow and expensive, and simulations based on machine learning are usually specialized for a specific type of system, such as water in a pipe or atmosphere surrounding a planet.

An open-weights model could help scientists compare the impact of genetic variations, identify mutations that cause diseases, and develop treatments.

LLMs have achieved gold-medal performance in math competitions. An agentic system showed strength in mathematical research as well.

Difficulty sleeping often precedes heart disease, psychiatric disorders, and many other illnesses. Researchers used data gathered during sleep studies to detect such conditions.

AI models that diagnose illnesses typically generate diagnoses based on descriptions of symptoms. In practice, though, doctors must be able to explain their reasoning and plan next steps. Researchers built a system that accomplishes these tasks.

An open protocol aims to enable AI agents to conduct scientific research autonomously across disciplinary and institutional boundaries.

Over the past few years, we have seen rapid progress in models that jointly reason over text, images, sequences, graphs, and time series. Yet in biomedical settings, these capabilities often remain fragmented, brittle, or difficult to interpret.

AI research in 2026 should confront a simple but transformative realization: Models that predict are not the same as systems that act. The latter is what we actually need.

In 2026, I hope AI will transition from being a tool for efficiency to a catalyst for scientific discovery.

Think step by step. Explain your reasoning. Work backwards from the answer. As 2025 began, models executed these reasoning strategies only when prompted. Now most new large language models do it as a matter of course, improving performance across a wide range of tasks.

Enabling a pretrained large language model to process a data type other than text (say, images), possibly in a specialized domain (say, radiology), typically requires thousands to millions of examples that pair the other data (perhaps x-rays) with text.