AI for Science¶

February 11, 2026
in Human-Centered AI, AI for Science, Knowledge Strategy
3 min read

A scientific companion should strengthen the evidence chain

Google DeepMind has described new results using Gemini Deep Think for mathematical and scientific discovery. The work is another indication that advanced models can contribute more than polished explanations: they can explore candidate approaches, connect ideas, and help experts work through difficult problems.

The most useful interpretation is not that the scientist is leaving the loop. It is that the loop itself can become richer—if the system preserves the evidence needed for expert challenge.

June 25, 2025
in AI for Science, Decision Systems, Trustworthy AI
3 min read

AlphaGenome and the discipline of decision support

Google DeepMind's June 25 introduction of AlphaGenome presents an artificial intelligence (AI) model that predicts how changes in deoxyribonucleic acid sequences may affect gene regulation across multiple molecular processes.

The scientific capability is impressive. The framing of its limits is equally instructive for anyone building high-stakes decision support.

May 5, 2025
in AI for Science, Knowledge Infrastructure, Trustworthy AI
3 min read

AI for science needs an evidence supply chain

Anthropic launches a program for using artificial intelligence (AI) in research—AI for Science—on May 5, offering application programming interface credits to researchers, with an initial emphasis on biology and the life sciences.

Access matters. Many scientific teams cannot afford sustained experimentation with frontier models. But access to a model is only one input to discovery. The harder work is building a trustworthy path from generated idea to scientific claim.

January 30, 2025
in AI for Science, National Security, Knowledge Infrastructure
3 min read

The national laboratories are an unusually good test for AI

OpenAI's January 30 announcement makes its reasoning models available to scientists at Los Alamos, Lawrence Livermore, and Sandia national laboratories through a partnership using the Venado supercomputer.

The announcement covers an extraordinary range of possibilities: materials, energy, medicine, cybersecurity, mathematics, and nuclear security. Yet the laboratories are interesting not only because their problems are difficult. They are interesting because their knowledge practices are demanding.