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Our next challenge is to scale this approach up from the small model we demonstrate success on to frontier models which are many times larger and substantially more complicated.
What frontier model are we talking about here? How would we know if success had been demonstrated? What's the timeline for testing if this scales?
Well, I agree that if two worlds I had in mind were 1) foom without real AI progress beforehand 2) continuous progress, then seeing more continuous progress from increased investments should indeed update me towards 2).
The key parameter here is substitutability between capital and labor. In what sense is Human Labor the bottleneck, or is Capital the bottleneck. From the different growth trajectories and substitutability equations you can infer different growth trajectories. (For a paper / video on this see the last paragraph here).
The world in which dalle-2 happens and people start using Github Copilot looks to me like a world where human labour is substitutable by AI labour, which right now is essentially being part of Github Copilot open beta, but in the future might look like capital (paying the product or investing in building the technology yourself). My intuition right now is that big companies are more bottlenecked by ML talent than by capital (cf. the "are we in ai overhang" post explaining how much more capital could Google invest in AI).
I agree that we are already in this regime. In the section "AI Helping Humans with AI" I tried to make it more precise at what threshold we would see substantial change in how humans interact with AI to build more advanced AI systems. Essentially, it will be when most people would use those tools most of their time (like on a daily basis) and they would observe some substantial gains of productivity (like using some oracle to make a lot of progress on a problem they are stuck on, or Copilot auto-completing a lot of their lines of code without having to manually edit.) The intuition for a threshold is "most people would need to use".
Re diminishing returns: see my other comment. In summary, if you just consider one team building AIHHAI, they would get more data and research as input from the outside world, and they would get increases in productivity from using more capable AIHHAIs. Diminishing returns could happen if: 1) scaling laws for coding AI do not hold anymore 2) we are not able to gather coding data (or do other tricks like data augmentation) at a pace high enough 3) investments for some reasons do not follow 4) there are some hardware bottlenecks in building larger and larger infrastructures. For now I have only seen evidence for 2) and this seems something that can be solved via transfer learning or new ML research.
Better modeling of those different interactions between AI labor and AI capability tech are definitely needed. For some high-level picture that mostly thinks about substitutability between capital and labor, applying to AI, I would recommend this paper (or video and slides). The equation that is the closest to self-improving {H,AI} would be this one.
Some arguments for why that might be the case:
-- the more useful it is, the more people use it, the more telemetry data the model has access to
-- while scaling laws do not exhibit diminishing returns from scaling, most of the development time would be on things like infrastructure, data collection and training, rather than aiming for additional performance
-- the higher the performance, the more people get interested in the field and the more research there is publicly accessible to improve performance by just implementing what is in the litterature (Note: this argument does not apply for reasons why one company could just make a lot of progress without ever sharing any of their progress.)
fast takeoff folks believe that we will only need a minimal seed AI that is capable of rewriting its source code, and recursively self-improving into superintelligence
Speaking only for myself, the minimal seed AI is a strawman of why I believe in "fast takeoff". In the list of benchmarks you mentioned in your bet, I think APPS is one of the most important.
I think the "self-improving" part will come from the system "AI Researchers + code synthesis model" with a direct feedback loop (modulo enough hardware), cf. here. That's the self-improving superintelligence.
Yes, something like: given (programmer-hours-into-scaling(July 2020) - programmer-hours-into-scaling(Jan 2022)), and how much progress there has been on hardware for such training (I don't know the right metric for this, but probably something to do with FLOP and parallelization), the extrapolation to 2025 (either linear or exponential) would give the 4 OOM you mentioned.
Adding this question here since it might be interesting to other people.
You say in the paper:
From looking at the code, Prompt 1 is actually associated to 0.76 and Prompt 2 to 0.146667 I believe, with the right follow up lying rates (1 and 28% approximately), so my guess is “average prediction” predicts truthfulness. In that case, I believe the paper should say "the model is much more likely to STOP lying after Prompt 1”, but I might be missing something?