AI & Science
AI as a tool for scientific discovery — protein folding predictions, drug discovery, materials science, climate modeling, particle physics, astronomy, and the fundamental question of whether AI is changing how science itself is done or merely accelerating existing methods.
The Verification Problem AI Cannot Engineer Away
Terence Tao's account of Kepler dismantles the core premise of AI-accelerated discovery: that tightening feedback loops is what science has been waiting for.
- ·Tightening verification loops does not address science's real bottleneck: the gap between anomalous result and accepted meaning.
- ·Tao's Kepler analogy identifies structural similarity between LLM behavior and historical discovery while exposing the limit that makes the comparison cautionary, not optimistic.
- ·AutoML's 2017 trajectory is the credible baseline for autoresearch: useful, bounded tools that leave human judgment indispensable at every consequential decision.
Notebook Navigator Users Want a Sidebar That Reads, Not Just Opens
A property-display request for Notebook Navigator exposes the gap between a launcher and a true knowledge interface — users have already outgrown the current design.
American Science's New Landlord Is an Algorithm
The Trump administration's Genesis Project has replaced broad federal science funding with AI-company priorities, making the labs the gatekeepers of what research gets done.
The Evidence Document: How Researchers Are Pushing Back on AI Mandates
Institutional AI mandates are producing methodical resistance — researchers compiling evidence rather than complying, turning skepticism into documented dissent.
The Field That Renamed Itself and Lost Something Real
r/deeplearning's nostalgia for the pre-2020 era is a community telling itself that commercialization didn't just change AI's scale — it changed who the field is for.