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.
President Trump launched a United States effort to use AI to speed up scientific breakthroughs.
Researchers built a model that integrates satellite imagery and other sensor readings across the entire surface of the Earth to reveal patterns of climate, land use, and other features.
Researchers used AI models to create novel viruses from scratch.
Google determined that its large language models have a smaller environmental footprint than previous estimates had led it to expect.
Researchers in China disguised a quadruped robot as a Tibetan antelope to help study the animals close-up.
The French AI company Mistral measured the environmental impacts of its flagship large language model.
An autonomous robot performed intricate surgical operations without human intervention.
In the era of reasoning models, delivering better answers to questions has an environmental cost. A new study quantifies the impact.
People who turn to chatbots for companionship show indications of lower self-reported well-being, researchers found.
An automated beehive uses computer vision and robotics to help keep bees healthy and crops pollinated.
The U.S. government is using AI to predict the paths of hurricanes.
An agent designed for broad biological research could accelerate the work of scientists in specialties from anatomy to zoology.
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