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An AI agent proposed new medical uses for established drugs nearly autonomously — uses that were supported by experiments on isolated human cells — with human input only to name diseases to be treated and run the AI-proposed lab experiments.
Imagine you are typing a sentence. But instead of a keyboard, joystick, or eye tracker, a device surrounding your head reads your intentions and generates that sentence on screen.
Google’s AlphaFold models pioneered the task of finding the shapes of biologically active molecules, opening new pathways for drug development.
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.
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