Terence Tao on frontier AI: like a knowledgeable, slightly drunk mathematician
gerardsans · x · 2026-09-07
In Big Think's "The paradox at the heart of AI and science", mathematician Terence Tao describes how frontier AI tackles math problems:
- Asking LLMs for proofs often yields complete rubbish, but looping with enough checks can produce a positive success rate
- It's "completely orthogonal" to grounded, first-principles human thinking — like "someone who knows a lot and is slightly drunk throwing out ideas", from whom useful output can be extracted with enough guidance
- It's not the most advanced mathematics, but with lots of data, time, and band-aids, it works pretty well
The quoting poster adds a critical take: AI samples the solution space via corpus-based sampling, needs external verifiers, and offers no first-principles insights.
More from AGI Musings
- Geoffrey Hinton admits he was wrong about AI replacing radiologists — and explains why — Afinetheorem · 2026-09-07
- Open Offices Were Onto Something — But They Need Mature Ambient Compute to Work — curious_vii · 2026-09-07
- Ex-xAI researcher Ethan He: finding the right axis to scale matters more than raw compute — ricklamers · 2026-09-07
- Seth Lazar: Social sciences must self-critique before asking labs for funding — sethlazar · 2026-09-07
- As AI drives creation costs to zero, the 'handmade' premium on creative work erodes — joonasvirtanen · 2026-09-07
- Does simulationism dissolve AGI risk? Researchers clash over Yampolskiy's stance — teortaxesTex · 2026-09-07