AI for Science Struggles with Hard-to-Verify Tasks
GaelVaroquaux · x · 2026-07-11
This reply mentions that AI for Science is particularly difficult for problems where answers are hard to verify, such as economics or health-related tasks.
The context relates to OpenAI's @joyjiao12 discussing the challenges of AI for science at the ICML GenBio workshop:
- When the "correct answer" for a task is difficult to verify quickly and reliably, it becomes harder to empirically judge the model's performance.
- Economics and health are cited as prime examples, as these issues are often both complex and hard to validate directly.
Related event: OpenAI Outlines Challenges in AI for Science(3 posts)→
More from Research
- MaP-WAM tackles non-Markovian robot manipulation with memory-grounded planning — Sizhe Zhao · 2026-09-11
- Negative Self-Distillation improves LLM reasoning by avoiding flawed reasoning paths — Rongcan Pei · 2026-09-11
- DeepMind-led paper makes design docs the source of truth, code disposable — SMART regenerates in 1.5-3h for ~$100 — Roger_M_Taylor · 2026-09-11
- GameWorld wins Best Paper Runner-Up at ECCV 2026 Multimodal Digital Agents Workshop — MikeShou1 · 2026-09-11
- Yann LeCun live at ECCV on World Models — Weak_Assistance_5261 · 2026-09-11
- 3D ResNet Paper Crosses 3,000 Citations Eight Years After CVPR 2018 — HirokatuKataoka · 2026-09-11