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ai-for-science
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The AI Revolution in Math Has Arrived
AlphaEvolve found hypercube structures in permutation groups that mathematicians hadn't noticed in 50 years: not by answering the question posed, but by surfacing a pattern nobody thought to look for. The real capability shift isn't AI proving things faster; it's AI scanning combinatorial spaces too large for human intuition and returning structures that reframe entire research programs. Discovery is being commoditized; the scarce resource is now verification infrastructure and the human judgment to recognize which discoveries matter.
Why It's Crucial We Understand How A.I. 'Thinks'
Interpretability's real breakthrough isn't cracking the black box: it's using imperfect understanding to extract hypotheses humans missed. Goodfire and Prima Mente's Alzheimer's biomarker discovery reframes the field from safety obligation to discovery engine. The commercial signal matters more than the methodology debates: $1.25B for a standalone interpretability lab means enterprises will pay for explanation scoped to specific use cases, not universal model transparency.
AI and scientific paperwork: productivity boon or slop flood
Grant writing is the case with no checker at all. A typical application takes about 34 days and succeeds about 20% of the time, so one researcher cutting a day a week to 45 minutes still loses most rounds once rivals do the same. Fabricated citations across 2.5m biomedical papers rose 12-fold to about 0.5%, and ICML submissions doubled to 24,000. The cost lands on reviewers, and effort stops working as a signal of seriousness.
Can Theoretical Physics Research Benefit from Language Agents?
The physics-agents paper names its promising uses (quantum error-correcting code search, Mathematica-verified constructions, proof checking), and each one has an external checker. The weak spots, taste and choice of approximation, have none, and the paper's two worked failures, reusing 'H' after a Jordan-Wigner transform and picking a path at random on a fork, are ambiguity errors. Rowspace shows the checker can be built by hand; this paper says where to build the next one.
Four Direct, Reasonable, and Important AI Questions (Which the AI People Will Ignore)
deBoer holds AI maximalists to their own yardstick and finds daily life in 2026 barely distinguishable from 2021. His best argument is that cancer is limited by tumor recurrence, toxicity and patient dropout, and curing every cancer would add only about three years of life expectancy. That sets the ceiling for Schmidt's claim that disease could be cured in 15-20 years: where the constraint is physical, faster thinking buys little.
AI and scientific paperwork: productivity boon or slop flood
Grants are the version of this problem with no checker at all, which is why the Economist piece matters beside the code stories. A typical application takes about 34 days and succeeds about 20% of the time, so a researcher cutting a day a week to 45 minutes still loses most rounds once rivals do the same. Fabricated citations in 2.5m biomedical papers rose 12-fold to about 0.5%, and ICML submissions doubled to 24,000, with the cost landing on reviewers. Where Postgres has committers and PHP has maintainers, science has reader attention, and no mechanical test to ration it. Triage and digest agents start to look less like conveniences and more like core infrastructure.