Thanks for the clarification.
Side question, but you had recently moved your AGI median from 2027 to 2028 after updating on Grok 3 and GPT-4.5. Has this changed, especially with Gemini 2.5 and o3/o4-mini + these new METR datapoints?
Person
Karma: 97
Google DeemMind’s recent FunSearch system seems pretty important, I’d really appreciate people with domain knowledge to disect this:
Large Language Models (LLMs) have demonstrated tremendous capabilities in solving complex tasks, from quantitative reasoning to understanding natural language. However, LLMs sometimes suffer from confabulations (or hallucinations) which can result in them making plausible but incorrect statements (Bang et al., 2023; Borji, 2023). This hinders the use of current large models in scientific discovery. Here we introduce FunSearch (short for searching in the function space), an evolutionary procedure based on pairing a pre-trained LLM with a systematic evaluator. We demonstrate the effectiveness of this approach to surpass the best known results in important problems, pushing the boundary of existing LLM-based approaches (Lehman et al., 2022). Applying FunSearch to a central problem in extremal combinatorics — the cap set problem — we discover new constructions of large cap sets going beyond the best known ones, both in finite dimensional and asymptotic cases. This represents the first discoveries made for established open problems using LLMs. We showcase the generality of FunSearch by applying it to an algorithmic problem, online bin packing, finding new heuristics that improve upon widely used baselines. In contrast to most computer search approaches, FunSearch searches for programs that describe how to solve a problem, rather than what the solution is. Beyond being an effective and scalable strategy, discovered programs tend to be more interpretable than raw solutions, enabling feedback loops between domain experts and FunSearch, and the deployment of such programs in real-world applications.
https://storage.googleapis.com/deepmind-media/AlphaCode2/AlphaCode2_Tech_Report.pdf
AlphaCode 2, which is powered by Gemini Pro, seems like a big deal.
AlphaCode (Li et al., 2022) was the first AI system to perform at the level of the median competitor in competitive programming, a difficult reasoning task involving advanced maths, logic and computer science. This paper introduces AlphaCode 2, a new and enhanced system with massively improved performance, powered by Gemini (Gemini Team, Google, 2023). AlphaCode 2 relies on the combination of powerful language models and a bespoke search and reranking mechanism. When evaluated on the same platform as the original AlphaCode, we found that AlphaCode 2 solved 1.7× more problems, and performed better than 85% of competition participants.
Seems important for speeding up coders or even model self-improvement, unless competitive coding benchmarks are deceptive for actual applications for ML training.
I also think the thing in question is not in fact an extremely important breakthrough that paves the path to imminent AGI anyway
Could you explain this assessment please? I am not knowledgeable at all on the subject, so I cannot intuit the validity of the breakthrough claim.
I couldn’t remember where from, but I know that Ilya Sutskever at least takes x-risk seriously. I remember him recently going public about how failing alignment would essentially mean doom. I think it was published as an article on a news site rather than an interview, which are what he usually does. Someone with a way better memory than me could find it.
How the AI can give new abilities to humans (the author of this post is incapable of writing novels or making paintings, yet here we are).
(Not a serious comment, just a passing remark)
At the point where the AI is making every step of it and the human has barely any actual contribution, I’m curious to see whether the standard for “artistic ability” will be loosened or if the pendulum will swing the other way, where artistic worth will have a bigger basis in craft, skill and effort, which (my intuition) seems like how artistic worth was determined back in the Renaissance for example.
I am trying to see if it is true. I need other people to help me alongside.
The whole thing generated enough buzz that Sam Altmann himself debunked it in a reddit comment (fitting, he was CEO of reddit at one point after all).
People say that he made correct predictions in the past.
His past predictions are either easily explained by a common trick used by sports fans on twitter, or have very shaky evidence for them since he keeps deleting his posts every few months, leaving us with 3rd party sources. Also, I wouldn’t a priori consider “GPT-5 finished training in October 2022 with 125T parameters” a correct prediction.
Or that he was genuinely just making things up and tricking us for fun, and a cryptic exit is a perfect way to leave the scene. I really think people are looking way too deep into him and ignoring the more outlandish predictions he’s made (125T GPT-4 and 5 in October 2022), along with the fact there is never actual evidence of his accurate ones, only 2nd hand very specific and selective archives.
Predicting the GPT-4 launch date can easily be disproven with the confidence game. It’s possible he just created a prediction for every day and deleted the ones that didn’t turn out right.
For the Gobi prediction it’s tricky. The only evidence is the Threadreader and a random screenshot from a guy who seems clearly related to jim. I am very suspicious of the Threadreader one. On one hand I don’t see a way it can be faked, but it’s very suspicious that the Gobi prediction is Jimmy’s only post that was saved there despite him making an even bigger bombshell “prediction”. It’s also possible, though unlikely, that the Information’s article somehow found his tweet and used it as a source for their article.
What kills Jimmy’s credibility for me is his prediction back in January (you can use the Wayback Machine to find it) that OAI had finished training GPT-5, no not a GPT-5 level system, the ACTUAL GPT-5 in October 2022 and that it was 125T parameters.
Also goes without saying, pruning his entire account is suspicious too.
Occasionally reading what OSS AI gurus say, they definitely overhype their stuff constantly. The ones who make big claims and try to hype people up are often venture entrepreneur guys rather than actual ML engineers.
Because of LW, I genuinely get frustrated when other forums I browse don’t just copy the UI. It’s just too good.
It’s the 25th installment of weekly update posts that go over all the important news of the week, to which Zvi adds his own thoughts. They’re honestly amazing sources of information and it helps that I love Zvi’s writing style.
Suleyman’s statements are either very specific capabilities predictions or incredibly vague statements like the one you brought up that don’t really inform us much. His interviews often revolve around talking about how big and smart their future models will be while also spending time putting in a good word for their financial backers (mainly NVIDIA). I find myself frustrated at seeing this company with a lot of compute and potential impact on timelines, but whose CEO and main spokesperson seems very out-of-touch with the domain he does business in.
You’re right, I completely misread it. I’ll edit my comment with that in mind.
I have an intuition about Suleyman, that being that his marketing background make him an incredibly unreliable source of actual information. He makes a lot of big predictions on future AI capabilities, like for hallucinations as a recent example I can think of and engages heavily in hype drumming in his interviews and social media. The untrustworthy aura I feel around the company extends to their products. Inflection-1′s technical paper (I can’t find a potential longer version) is very short compared to GPT-4 or PALM-2 and is entirely pictures of condensed benchmark results with a few paragraphs of explanations.
I expect my views to be wrong, but for now while inflection definitely has the compute, I have a feeling there’s a lot more limits and smoke involved that wouldn’t quite put them up with OpenAI, DeepMind and Meta in terms of impact on both the market and AGI timelines.
Edit: Originally misread the computer calculations from the post and used my mistake as evidence of my first point. Though the evidence is no longer there, it was confirming an intuition I already had and still stand by.
The post reads to me as the author trying to unilaterally impose their personal ideal of perfection on other people.
I can’t say I had the same observation regarding the post, but I just wanted to agree with the problem you describe. It irks me when people attempt to categorize flaws and build up a model of biological perfection, not realizing all the consequences, psychological and social alike, that it entails. It seems like a very naïve endeavor rooted in personal biases more than an objective assessment.
Provided the paper is legit, what are the implications on AI timelines? Would compute-intensive paradigms like WBE suddenly become feasible?
I’ve always wondered, how do you retain agency if you embed a vastly smarter and faster mind to yours, when they would theoretically make better decisions than you would have 90% of the time. Scaling this intuition, turning humanity into a hive-mind does not strike me as a valuable future world.
Edit: I’ve also given more thought to the idea of BCIs allowing us to ‘download’ skills, and I’d really like someone to engage with the following. If we agree we derive value and meaning from effort we put into learning and the satisfaction we get from it, essentially specializing ourselves depending on our tastes, how do we find value in a world where anyone can instantly know anything? It’s a question I’ve been ruminating on for a few hours.
Thanks for engaging with people’s comments here.
is whether a smart agent could sneak a deceptive malicious artifact (e.g. some code)
I have a somewhat related question on the subject of malicious elements in models. Does OAI’s Superalignment effort also intend to cover and defend from cases of s-risks? A famous path for hyperexistential risk for example is sign-flipping, where in some variations, a 3rd party actor (often unwillingly) inverts an AGI’s goal. Seems it’s already happened with a GPT-2 instance too! The usual proposed solutions are building more and more defenses and safety procedures both inside and outside. Could you tell me how you guys view these risks and if/how the effort intends to investigate and cover them?
Heads up: I am not an AI researcher or even an academic, just someone who keeps up with AI
But I do have quick thoughts as well;
Kernel optimization (which they claim is what resulted in the 1% decrease in training time) is something we know AI models are great at (see RE-Bench and the multiple arXiv papers on the matter, including from DeepSeek).
It seems to me like AlphaEvolve is more-or-less an improvement over previous models that also claimed to make novel algorithmic and mathematical discoveries (FunSearch, AlphaTensor) notably by using better base Gemini models and a better agentic framework. We also know that AI models already contribute to the improvement of AI hardware. What AlphaEvolve seems to do is to unify all of that into a superhuman model for those multiple uses. In the accompanying podcast they give us some further information:
The rate of improvement is still moderate, and the process still takes months. They phrase it as an interesting and promising area of progress for the future, not as a current large improvement.
They have not tried to distill all that data into a new model yet, which seems strange to me considering they’ve had it for a year now.
They say that a lot of improvements come from the base model’s quality.
They do present the whole thing as part of research rather than a product
So yeah I can definitely see a path for large gains in the future, thought for now those are still on similar timetables as per their own admission. They expect further improvements when base models improve and are hoping that future versions of AlphaEvolve can in turn shorten the training time for models, the hardware pipeline, and improve models in other ways. And for your point about novel discoveries, previous Alpha models seemed to already be able to do the same categories of research back in 2023, on mathematics and algorithmic optimization. We need more knowledgeable people to weight in, especially to compare with previous models of the same classification.
This is also a very small thing to keep in mind, but GDM models don’t often share the actual results of their models’ work as usable/replicable papers, which has caused experts to cast some doubts on results in the past. It’s hard to verify their results, since they’ll be keeping them close to their chests.