@garrytan That reduces to a single criterion that it’s on the frontier of capability vs cost. The absolute best is automatically on that frontier because no other model can get that score regardless of the cost
If you loop enough times and fix the bugs found before the next search, it does eventually find no more bugs
I’ve done this. Expect runs to take 2-15 hours depending on the size of change, if you’ve built up the repo this way.
If you’ve built the repo without ruthlessly eliminating bugs, it’ll take longer and you should definitely get it to drop any bugs it finds that are outside the scope of your intended change. Otherwise it’ll keep pulling more of the repo in, the run could take 30+ hours and produce a PR upwards of 50k lines changed
You want confidence that pros outweigh cons. You have to base your confidence on something. Options include:
- A human doctor/mathematician. Even if you don’t know them, your trust is based on society cultivating good experts (eg, it takes a long track record of excellence and integrity to become a reputable expert due to feedback loops from medical/academic associations)
- A separate AI report. Trust can draw from your experience with that model, concrete benchmarks on similar tasks, etc.
- Interactively digging into it with the help of AI. Again you need to have some confidence in the model you’re using. Additionally you’re trusting that you’re able to ask sufficiently thorough questions to properly evaluate the treatment
You can build confidence from multiple sources, and long term, requiring human-based trust is not necessary. In the short term, we as society don’t have the mechanisms in place to accurately gauge the proper amount of trust in AI for high risk decisions.
For this scenario, the risk of this treatment vs nothing may be low enough, and your trust in the model may be high enough, that getting additional trust via human experts may not be necessary
This is a slide from Terence Tao's 'Math 2.0' public lecture yesterday evening at Caltech. I am highly confident that, if the cure actually worked, almost any Stage 4 cancer patient would tell you that human comprehension of its mechanism is completely irrelevant to them.
When building with agents and quality matters and you’re not sure how big a single task should be:
1. do the task twice
2. if the results differ by more than your error threshold, you’re not meeting your quality bar. So throw away the work, split the task, and repeat this process on each subtask
> brand new benchmark to probe something frontier models still suck at vs human
> gpt-6 scores 50%+ on release
at this point i actually doubt you can, at all, make a fair benchmark where humans get >80% and the best ai model <20%
man if only there were a term for this