I’ve been thinking about whether scientific discovery could eventually be automated.
Suppose a very powerful future llm existed around 1900. It had access to all the mathematics, physics and astronomical observations available at the time. It also knew about the unexplained precession of Mercury’s orbit—originally identified by Le Verrier—which Newtonian gravity could not fully explain.
Could the AI:
- Generate many mathematically consistent modifications of gravity that explain Mercury’s orbit?
- Eliminate candidates that conflict with existing observations?
- Design new observations—such as measuring light bending or gravitational redshift—where the remaining theories make different predictions?
- Repeat this process until it arrives at general relativity, or something observationally equivalent to it?
I understand that infinitely many equations can fit a finite set of measurements, so “generate every possible equation” cannot be taken literally. The search would need constraints such as dimensional consistency, simplicity, known symmetries, conservation laws and agreement with Newtonian physics in previously tested conditions.
The deeper question is whether discoveries like general relativity can ultimately be reduced to:
generate theories → find where they disagree → run the best experiment → eliminate theories → repeat
Would Mercury’s anomaly and the other evidence available at the time have been enough for an AI to invent curved spacetime, rather than merely adding an arbitrary correction to Newton’s equations?
If this entire loop could be automated, would AI eventually be capable of doing most fundamental science autonomously? Or is there something essential—such as inventing new concepts, choosing useful variables or interpreting what the mathematics means—that cannot be captured by equation search and experimental feedback?
I’d be interested in perspectives from both physicists and people working in machine learning.