Researchers have applied AI to every step of the drug development process. But this might not be enough to design safe and effective drugs.

Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.

MIT engineers developed AI frameworks to identify evidence-driven hypotheses that could advance biologically inspired materials.

With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.

The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.

A narrower, more personalized ‘normal range’ could help doctors better diagnose and treat disease in individual patients.

A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.

Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data.

A detailed new look at dopamine signaling suggests neuroscientists’ model of reinforcement learning may need to be revised.

AI shopping agents are coming. They could hunt for deals, find the right items and even make purchases – if you trust them to ‘get’ you, protect your privacy and not coax you into buying too much.