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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?
With the size of the project and commitment, along with OAI acknowledging they might hit walls and will try different courses, one can hope investigating better behavioral systems for an AGI will be one of them.
Are you really doubting that 225 000 people flew to Liberia just to vote for him?
Jokes aside, yes, it was historically way easier back then to sabotage or trick elections. Liberia is a special case, where the human rights abuses there which included suspicions of practicing slavery very nearly resulted in it being placed as a Polish protectorate (yes, Poland). Flow of information being slow and mostly conveyed through newspapers or newsreel at the theater really gave anyone in charge who had decent executive power a better hand in manipulating outcomes of elections. Right now, we have increased scrutiny making it way harder to successfully pull it out, which is why states that do it tend not to even bother trying to hide it (Belarus, Russia's referendums in Ukraine, Algeria, etc.)
The assumption goes that after ingesting human data, it can remix it (like humans do for art, for example) and create its own synthetic data it can then train on. The go-to example is AlphaGo, which after playing a ton of simulated games against itself, became great at Go. I am not qualified enough to give my informed opinion or predictions, but that's what I know.
If AGI happens this decade the risks are very much real and valid and should not be dismissed, certainly not for such a flimsy reason.
Especially considering what people consider the near-term risks, which we can expect to become more and more visible and present, will likely shift the landscape in regards to taking AI x-risk seriously. I posit x-risk won't remain speculative for long with roughly the same timeline you gave.
I have read your comments on the EA forum and the points do resonate with me.
As a layman, I do have a personal distrust with the (what I'd call) anti-human ideologies driving the actors you refer to and agree that a majority of people do as well. It is hard to feel much joy in being extinct and replaced by synthetic beings in probably a way most would characterize as dumb (clippy being the extreme)
I also believe that fundamental changing of the human subjective experience (radical bioengineering or uploading to an extent) in order to erase the ability to suffer in general (not just medical cases like depression) as I have seen being brought up by futurist circles is also akin to death. I think it could possibly be a somewhat literal death, where my conscious experience actually stops if radical changes would occur, but I am completely uneducated and unqualified on how consciousness works.
I think that a hypothetical me, even with my memories, who is physically unable to experience any negative emotions would be philosophically dead. It would be unable to learn nor reflect and its fundamentally different subjective experience would be so radically different from me, and any future biological me should I grow older naturally, that I do not think memories alone would be enough to keep my identity. To my awareness, the majority of people would think similarly and that there is value ascribed to our human nature, including limitations, which has been reinforced by our media and culture. Though whether this attachment is purely a product of coping, I do not know. What I do know is that it is the current reality for every functional human being now and has been for thousands of years. I believe people would prefer sticking with it than relinquishing it for vague promises of ascended consciousness. I think this is somewhat supported by my subjective observation that to a lot of people who want a posthuman existence and what it entails, their end goal seems to often come back to creating simulations they themselves can live in normally.
I'm curious though if you have any hopes for the situation regarding the nebulous motivations of some AGI researchers, especially as AI and its risks have recently started becoming "mainstream". Do you expect to see changes and their views challenged? My question is loaded, but it seems you are already invested in its answer.