AI for science needs reasoning, not just data
MIT Technology Review 2 months ago 35
AI for science advances beyond protein-structure prediction as the article argues that AlphaFold-style systems rely on unusually rare, high-cost data resources. It cites the Protein Data Bank as roughly 170,000 protein structures assembled over 53 years and about $21 billion in experimental work. The focus shifts toward agentic AI, where AI systems use tools and reasoning to iteratively replicate research steps, improving reproducibility tracking and speeding up scientific testing.