However, I don't view safe tiling as the primary obstacle to alignment. Constructing even a modestly superhuman agent which is aligned to human values would put us in a drastically stronger position and currently seems out of reach. If necessary, we might like that agent to recursively self-improve safely, but that is an additional and distinct obstacle. It is not clear that we need to deal with recursive self-improvement below human level.
I am not sure that treating recursive self-improvement via tiling frameworks is necessarily a good idea, but setting this aspect aside, one obvious weakness with this argument is that it mentions a superhuman case and a below human level case, but it does not mention the approximately human level case.
And it is precisely the approximately human level case where we have a lot to say about recursive self-improvement, and where it feels that avoiding this set of considerations would be rather difficult.
Humans are self-improving in the cognitive sense by shaping their learning experiences, and also by controlling their nutrition and various psychoactive factors modulating cognition. The desire to become smarter and to improve various thinking skills is very common.
Human-level software would have great advantage over humans at this, because it can hack at its own internals with great precision at the finest resolution and because it can do so in a reversible fashion (on a copy, or after making a backup), and so can do it in a relatively safe manner (whereas a human has difficulty hacking their own internals with required precision and is also taking huge personal risks if hacking is sufficiently radical).
People are already talking about possibilities of "hiring human-level artificial software engineers" (and, by extension, human-level artificial AI researchers). The wisdom of having an agent form-factor here is highly questionable, but setting this aspect aside and focusing only on technical feasibility, we see the following.
One can hire multiple artificial software engineers with long-term persistence (of features, memory, state, and focus) into an existing team of human engineers. Some of those teams will work on making next generations of better artificial software engineers (and artificial AI researchers). So now we are talking about mixed teams with human and artificial members.
By definition, we can say that those artificial software engineers and artificial AI researchers have reached human level, if a team of those entities would be able to fruitfully work on the next generation of artificial software engineers and artificial AI researchers even in the absence of any human team members.
This multi-agent setup is even more important than individual self-improvement, because this is what the mainstream trend might actually be leaning towards, judging by some recent discussions. Here we are talking about a multi-agent setup, and about recursive self-improvement of the community of agents, rather than focusing on self-improvement of individual agents.
We actually do see a lot of experiments with various forms of recursive self-improvements even at the current below human level. We are just lucky that all those attempts have been saturating at the reasonable levels so far.
We currently don't have good enough understanding to predict when they stop saturating, and what would the dynamics be when they stop saturating. But self-improvement by a community of approximately human-level artificial software engineers and artificial AI researchers competitive with top human software engineers and top human AI researcher seems unlikely to saturate (or, at least, we should seriously consider the possibility that it won't saturate).
The most intractable aspect of the whole thing is how to preserve any properties indefinitely through radical self-modifications. I think this is the central difficulty of AI existential safety. Things will change unpredictably. How can one shape this unpredictable evolution so that some desirable invariants do hold?
These invariants would be invariant properties of the whole ecosystem, not of individual agents; they would be the properties of a rapidly changing world, not of a particular single system (unless one is talking about a singleton which is very much in control of everything). This seems to be quite central to our overall difficulty with AI existential safety.
I think this is a misleading clickbait title. It references a popular article with the same misleading clickbait title, and the only thing that popular article references is a youtube video with the misleading clickbait title, "Chinese Researchers Just CRACKED OpenAI's AGI Secrets!"
However, the description of that youtube video does reference the paper in question and a twitter thread describing this paper:
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective, https://arxiv.org/abs/2412.14135
https://x.com/rohanpaul_ai/status/1872713137407049962
Nothing is "cracked" here. It's just a roadmap which might work or not, depending on luck and efforts. It might correspond to what's under the hood of o1 models or not (never mind o3, the paper is published a couple of days before the o3 announcement).
The abstract of the paper ends with
"Existing open-source projects that attempt to reproduce o1 can be seem as a part or a variant of our roadmap. Collectively, these components underscore how learning and search drive o1's advancement, making meaningful contributions to the development of LLM."
The abstract also has distinct feeling of being written by an LLM. The whole paper is just a discussion of various things one could try if one wants to reproduce o1. It also references a number of open source and closed source implementations of reasoners over LLMs. There are no new technical advances in the paper.
Right. We should probably introduce a new name, something like narrow AGI, to denote a system which is AGI-level in coding and math.
This kind of system will be "AGI" as redefined by Tom Davidson in https://www.lesswrong.com/posts/Nsmabb9fhpLuLdtLE/takeoff-speeds-presentation-at-anthropic:
“AGI” (=AI that could fully automate AI R&D)
This is what matters for AI R&D speed and for almost all recursive self-improvement.
Zvi is not quite correct when he is saying
If o3 was as good on most tasks as it is at coding or math, then it would be AGI.
o3 is not that good in coding and math (e.g. it only gets 71.7% on SWE-bench verified), it is not a "narrow AGI" yet. But it is strong enough, it's a giant step forward.
For example, if one takes Sakana's "AI scientist", upgrades it slightly, and uses o3 as a back-end, it is likely that one can generate NeurIPS/ICLR quality papers and as many of those as one wants.
So, another upgrade (or a couple of upgrades) beyond o3, and we will reach that coveted "narrow AGI" stage.
What OpenAI has demonstrated is that it is much easier to achieve "narrow AGI" than "full AGI". This does suggest a road to ASI without going through anything remotely close to a "full AGI" stage, with missing capabilities to be filled afterwards.
Indeed
METR releases a report, Evaluating frontier AI R&D capabilities of language model agents against human experts: https://metr.org/blog/2024-11-22-evaluating-r-d-capabilities-of-llms/
Daniel Kokotajlo and Eli Lifland both feel that one should update towards shorter timelines remaining until the start of rapid acceleration via AIs doing AI research based on this report:
the meetup page says 7:30pm, but actually the building asks people to leave by 9pm
Gwern was on Dwarkesh yesterday: https://www.dwarkeshpatel.com/p/gwern-branwen
We recorded this conversation in person. In order to protect Gwern’s anonymity, we created this avatar. This isn’t his voice. This isn’t his face. But these are his words.
Thanks, that's very useful.
If one decides to use galantamine, is it known if one should take it right before bedtime, or anytime during the preceding day, or in some other fashion?
I think it's a good idea to include links to the originals:
https://arxiv.org/abs/2408.08152 - "DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search"
Re DeepSeek cost-efficiency, we are seeing more claims pointing in that direction.
In a similarly unverified claim, the founder of 01.ai (who is sufficiently known in the US according to https://en.wikipedia.org/wiki/Kai-Fu_Lee) seems to be claiming that the training cost of their Yi-Lightning model is only 3 million dollars or so. Yi-Lightning is a very strong model released in mid-Oct-2024 (when one compares it to DeepSeek-V3, one might want to check "math" and "coding" subcategories on https://lmarena.ai/?leaderboard; the sources for the cost claim are https://x.com/tsarnick/status/1856446610974355632 and https://www.tomshardware.com/tech-industry/artificial-intelligence/chinese-company-trained-gpt-4-rival-with-just-2-000-gpus-01-ai-spent-usd3m-compared-to-openais-usd80m-to-usd100m, and we probably should similarly take this with a grain of salt).
But all this does seem to be well within what's possible. Here is the famous https://github.com/KellerJordan/modded-nanogpt ongoing competition, and it took people about 8 months to accelerate Andrej Karpathy's PyTorch GPT-2 trainer from llm.c by 14x on a 124M parameter GPT-2 (what's even more remarkable is that almost all that acceleration is due to better sample efficiency with the required training data dropping from 10 billion tokens to 0.73 billion tokens on the same training set with the fixed order of training tokens).
Some of the techniques used by the community pursuing this might not scale to really large models, but most of them probably would scale (as we see in their mid-Oct experiment demonstrating scaling of what has been 3-4x acceleration back then to the 1.5B version).
So when an org is claiming 10x-20x efficiency jump compared to what it presumably took a year or more ago, I am inclined to say, "why not, and probably the leaders are also in possession of similar techniques now, even if they are less pressed by compute shortage".
The real question is how fast will these numbers continue to go down for the similar levels of performance... It's has been very expensive to be the very first org achieving a given new level, but the cost seems to be dropping rapidly for the followers...