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François Chollet: The Arc Prize and How We Get to AGI [video]

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• qoez 1y ago
  I feel like I'm the only one who isn't convinced getting a high score on the ARC eval test means we have AGI. It's mostly about pattern matching (and some of it ambiguous even for humans what the actual true response aught to be). It's like how in humans there's lots of different 'types' of intelligence, and just overfitting on IQ tests doesn't in my mind convince me a person is actually that smart.
  • avmich 1y ago
    Roughly speaking, the job of a medical doctor is to diagnose the patient - and then, after the diagnosis is made, to apply the healing from the book, corresponding to the diagnosis. The diagnosis is pattern matching (again, roughly). It kinda suggests that a lot of "intelligent" problems are focused on pattern matching, and (relatively straightforward) application of "previous experience". So, pattern matching can bring us a great deal towards AGI.
    • AnimalMuppet 1y ago
      Pattern matching is instinct. (Or at least, instinct is a kind of pattern matching. And once you learn the patterns, pattern matching can become almost instinctual). And that's fine, for things that fit the pattern. But a human-level intelligence can also deal with problems for which there is no pattern. (I mean, not always successfully - finding a correct solution to a novel problem is difficult. But it is within the capability of at least some humans.)
  • yorwba 1y ago
    I think the people behind the ARC Prize agree that getting a high score doesn't mean we have AGI. (They already updated the benchmark once to make it harder.) But an AGI should get a similarly high score as humans do. So current models that get very low scores are definitely not AGI, and likely quite far away from it.
    • cubefox 1y ago
      > I think the people behind the ARC Prize agree that getting a high score doesn't mean we have AGI The benchmark was literally called ARC-AGI. Only after OpenAI cracked it, they started backtracking and saying that it doesn't test for true AGI. Which undermines the whole premise of a benchmark.
      • yorwba 1y ago
        It does test for AGI. For its absence.
        • cubefox 1y ago
          I would call something as intelligent as an animal an AGI. They are very general (as opposed to narrow) intelligences, just not necessarily very smart ones. ARC-AGI arguably conflates these two dimensions.
  • whiplash451 1y ago
    You're not the only one. ARC-AGI is a laudable effort, but its fundamental premise is indeed debatable: "We argue that human cognition follows strictly the same pattern as human physical capabilities: both emerged as evolutionary solutions to specific problems in specific evironments" (from page 22 of On the Measure of Intelligence) https://arxiv.org/pdf/1911.01547 https://arxiv.org/pdf/1911.01547
    • Davidzheng 1y ago
      But I believe that because of this "even edge" thing which people call of AI weakenesses being not necessarily same of humans, once we run out of these tests which AI is worse than humans it will actually in effect be very much superhuman. My main evidence for this is leela-zero the Go AI who struggled with ladders and some other aspects of Go play well into the superhuman regime (in go it's easier to see when it's superhuman bc you can have elos and play win-rates etc and there's less room for debates)
  • energy123 1y ago
    https://en.m.wikipedia.org/wiki/AI_effect https://en.m.wikipedia.org/wiki/AI_effect But on a serious note, I don't think Chollet would disagree. ARC is a necessary but not sufficient condition, and he says that, despite the unfortunate attention-grabbing name choice of the benchmark. I like Chollet's view that we will know that AGI is here when we can't come up with new benchmarks that separate humans from AI.
  • loki_ikol 1y ago
    Well for most, the next steps are probably towards removing the highly deterministic and discrete characteristics of current approaches (we certainly don't think in lock steps). Those have no measures. Even the creative aspect is undermined by those characteristics.
  • kubb 1y ago
    AGI isn't defined anywhere, so it can be anything you want.
    • FrustratedMonky 1y ago
      Yes. And a lot of humans also don't pass for having AGI.
    • mindcrime 1y ago
      Oh, it's defined in lots of places. The problem is.. it's defined in lots of places!
  • oldge 1y ago
    Today’s llms are fancy autocomplete but lack test time self learning or persistent drive. By contrast, an AGI would require: – A goal-generation mechanism (G) that can propose objectives without external prompts – A utility function (U) and policy π(a│s) enabling action selection and hierarchy formation over extended horizons – Stateful memory (M) + feedback integration to evaluate outcomes, revise plans, and execute real-world interventions autonomously Without G, U, π, and M operating llms remain reactive statistical predictors, not human level intelligence.
    • KoolKat23 1y ago
      I'd say we're not far off. Looking at the human side, it takes a while to actually learn something. If you've recently read something it remains in your "context window". You need to dream about it, to think about, to revisit and repeat until you actually learn it and "update your internal model". We need a mechanism for continuous weight updating. Goal-generation is pretty much covered by your body constantly drip-feeding your brain various hormones "ongoing input prompts".
      • onemoresoop 1y ago
        > I'd say we're not far off. How are we not far off? How can LLMs generate goals and based on what?
        • NetRunnerSu 1y ago
          Minimize prediction errors.
          • tsurba 1y ago
            But are we close to doing that in real-time on any reasonably large model? I don’t think so.
            • NetRunnerSu 1y ago
              This is not about reasoning , this is about continuous learning and perpetual learning . https://github.com/dmf-archive/PILF https://github.com/dmf-archive/PILF https://dmf-archive.github.io/docs/posts/beyond-snn-plausible-sparsity/ https://dmf-archive.github.io/docs/posts/beyond-snn-plausibl...
  • TheAceOfHearts 1y ago
    Getting a high score on ARC doesn't mean we have AGI and Chollet has always said as much AFAIK, it's meant to push the AI research space in a positive direction. Being able to solve ARC problems is probably a pre-requisite to AGI. It's a directional push into the fog of war, with the claim being that we should explore that area because we expect it's relevant to building AGI.
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    • lostphilosopher 1y ago
      We don't really have a true test that means "if we pass this test we have AGI" but we have a variety of tests (like ARC) that we believe any true AGI would be able to pass. It's a "necessary but not sufficient" situation. Also ties directly to the challenge in defining what AGI really means. You see a lot of discussions of "moving the goal posts" around AGI, but as I see it we've never had goal posts, we've just got a bunch of lines we'd expect to cross before reaching them.
      • MPSimmons 1y ago
        I don't think we actually even have a good definition of "This is what AGI is, and here are the stationary goal posts that, when these thresholds are met, then we will have AGI". If you judged human intelligence by our AI standards, then would humans even pass as Natural General Intelligence? Human intelligence tests are constantly changing, being invalidated, and rerolled as well. I maintain that today's modern LLMs would pass sufficiently for AGI and is also very close to passing a Turing Test, if measured in 1950 when the test was proposed.
        • fvdessen 1y ago
  • ben_w 1y ago
    You're not alone in this; I expect us to have not yet enumerated all the things that we ourselves mean by "intelligence". But conversely, not passing this test is a proof of not being as general as a human's intelligence.
    • NetRunnerSu 1y ago
      Unfortunately, we did it. All that is left is to assemble the parts. https://news.ycombinator.com/item?id=44488126 https://news.ycombinator.com/item?id=44488126
    • kypro 1y ago
      I find the "what is intelligence?" discussion a little pointless if I'm honest. It's similar to asking a question like does it mean to be a "good person" and would we know whether an AI or person is really "good"? While understanding why a person or AI is doing what it's doing can be important (perhaps specifically in safety contexts) at the end of the day all that's really going to matter to most people is the outcomes. So if an AI can use what appears to be intelligence to solve general problems and can act in ways that are broadly good for society, whether or not it meets some philosophical definition of "intelligent" or "good" doesn't matter much – at least in most contexts. That said, my own opinion on this is that the truth is likely in between. LLMs today seem extremely good at being glorified auto-completes, and I suspect most (95%+) of what they do is just recalling patterns in their weights. But unlike traditional auto-completes they do seem to have some ability to reason and solve truly novel problems. As it stands I'd argue that ability is fairly poor, but this might only represent 1-2% of what we use intelligence for. If I were to guess why this is I suspect it's not that LLM architecture today is completely wrong, but that the way LLMs are trained means that in general knowledge recall is rewarded more than reasoning. This is similar to the trade-off we humans have with education – do you prioritise the acquisition of knowledge or critical thinking? Maybe believe critical thinking is more important and should be prioritised more, but I suspect for the vast majority of tasks we're interested in solving knowledge storage and recall is actually more important.
      • ben_w 1y ago
  • OtomotO 1y ago
    You're not alone in this, no. My definition of AGI is the one I was brought up with, not an ever moving goal post (to the "easier" side). And no, I also don't buy that we are just stochastic parrots. But whatever. I've seen many hypes and if I don't die and the world doesn't go to shit, I'll see a few more in the next couple of decades
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  • NetRunnerSu 1y ago
    To pass Arc, you need a living model with sentient abilities, not the dead frog now. https://news.ycombinator.com/item?id=44488126 https://news.ycombinator.com/item?id=44488126
  • nxobject 1y ago
    I understand Chollet is transparent that the "branding" of the ARC-AGI-n suites is meant to be suggestive of its purpose, than substantial. However, it does rub me the wrong way - as someone who's cynical of how branding can enable breathless AI hype by bad journalism. A hypothetical comparison would be labelling SHRDLU's (1968) performance on Block World planning tasks as "ARC-AGI-(-1)".[0] A less loaded name like (bad strawman option) "ARC-VeryToughSymbolicReasoning" should capture how the ARC-AGI-n suite is genuinely and intrinsically very hard for current AIs, and what progress satisfactory performance on the benchmark suite would represent. Which Chollet has done, and has grounded him throughout! [1] [0] https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU [1] https://arxiv.org/abs/1911.01547 https://arxiv.org/abs/1911.01547
    • heymijo 1y ago
      I get what you're saying about perception being reality and that ARC-AGI suggests beating it means AGI has been achieved. In practice when I have seen ARC brought up, it has more nuance than any of the other benchmarks. Unlike, Humanity's Last Exam, which is the most egregious example I have seen in naming and when it is referenced in terms of an LLMs capability.
  • maaaaattttt 1y ago
    I've said this somewhere else, but we have the perfect test for AGI in the form of any open world game. Give the instructions to the AGI that it should finish the game and how to control it. Give the frames as input and wait. When I think of the latest Zelda games and especially how the Shrine chanllenges are desgined they especially feel like the perfect environement for an AGI test.
    • Lerc 1y ago
      And if someone makes a machine that does all that and another person says "That's not really AGI because xyz" What then? The difficulty in coming up with a test for AGI is coming up with something that people will accept a passing grade as AGI. In many respects I feel like all of the claims that models don't really understand or have internal representation or whatever tend to lean on nebulous or circular definitions of the properties in question. Trying to pin the arguments down usually end up with dualism and/or religion. Doing what Chollet has done is infinitely better, if a person can easily do something and a model cannot then there is clearly something significant missing It doesn't matter what the property is or what it is called. Such tests might even help us see what those properties are. Anyone who wants to claim the fundamental inability of these models should be able to provide a task that it is clearly possible to tell when it has been succeeded, and to show that humans can do it (if that's the bar we are claiming can't be met). If they are right, then no future model should be able to solve that class of problems.
      • maaaaattttt 1y ago
        Given your premise (which I agree with) I think the issue in general comes from the lack of a good, broadly accepted definition of what AGI is. My initial comment originates from the fact that in my internal definition, an AGI would have a de facto understanding of the physics of "our world". Or better, could infer them by trial and error. But, indeed, it doesn't have to be the case. (The other advantage of the Zelda games is that they introduce new abilities that don't exist in our world, and for which most children -I've seen- understand the mechanisms and how they could be applied to solve a problem quite naturaly even they've never had that ability before).
  • davidclark 1y ago
    In the video, François Chollet, creator of the ARC benchmarks, says that beating ARC does not equate to AGI. He specifically says they will be able to be beaten without AGI.
    • cubefox 1y ago
      He only says this because otherwise he would have to say that - OpenAI's o3 counts as "AGI" when it did unexpectedly beat the ARC-AGI benchmark or - Explicitly admit that he was wrong when assuming that ARC-AGI would test for AGI
      • sweezyjeezy 1y ago
        FWIW the original ARC was published in 2019, just after GPT-2 but a while before GPT-3. I work in the field, I think that discussing AGI seriously is actually kind of a recent thing (I'm not sure I ever heard the term 'AGI' until a few years ago). I'm not saying I know he didn't feel that, but he doesn't talk in such terms in the original paper.
        • cubefox 1y ago
          > We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans. https://arxiv.org/abs/1911.01547 https://arxiv.org/abs/1911.01547
          • davidclark 1y ago
            > It is important to note that ARC is a work in progress, not a definitive solution; it does not fit all of the requirements listed in II.3.2, and it features a number of key weaknesses… Page 53 > The study of general artificial intelligence is a field still in its infancy, and we do not wish to convey the impression that we have provided a definitive solution to the problem of characterizing and measuring the intelligence held by an AI system. Page 56
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  • aaron695 1y ago
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  • cainxinth 1y ago
    > It's mostly about pattern matching... For all we know, human intelligence is just an emergent property of really good pattern matching.
  • CamperBob2 1y ago
    If you can write code to solve ARC by "overfitting," then give it a shot! There's prize money to be won, as long as your model does a good job on the hidden test set. Zuckerberg is said to be throwing around 8-figure signing bonuses for talent like that. But then, I guess it wouldn't be "overfitting" after all, would it?
  • cttet 1y ago
    The point is not that having a high score -> AGI, their ideas are more of having a low score -> we don't have AGI yet.
  • gonzobonzo 1y ago
    I agree with you but I'll go a step further - these benchmarks are a good example of how far we are from AGI. A good base test would be to give a manager a mixed team of remote workers, half being human and half being AI, and seeing if the manager or any of the coworkers would be able to tell the difference. We wouldn't be able to say that AI that passed that test would necessarily be AGI, since we would have to test it in other situations. But we could say that AI that couldn't pass that test wouldn't qualify, since it wouldn't be able to successfully accomplish some tasks that humans are able to. But of course, current AI is nowhere near that level yet. We're left with benchmarks, because we all know how far away we are from actual AGI.
    • criddell 1y ago
      The AGI test I think makes sense is to put it in a robot body and let it navigate the world. Can I take the robot to my back yard and have it weed my vegetable garden? Can I show it how to fold my laundry? Can I take it to the grocery store and tell it "go pick up 4 yellow bananas and two avocados that will be ready to eat in the next day or two, and then meet me in dairy"? Can I ask it to dice an onion for me during meal prep? These are all things my kids would do when they were pretty young.
      • gonzobonzo 1y ago
        I agree, I think of that as the next level beyond the digital assistant test - a physical assistant test. Once there are sufficiently capable robots, hook one up to the AI. Tell it to mow your lawn, drive your car to the mechanic and have the mechanic to get checked, box up an item, take it to the post office, and have it shiped, pick up your dry cleaning, buy ingredients from a grocery store, cook dinner, etc. Basic tasks an low-skilled worker would do as someone's assistant.
      • bumby 1y ago
        I think the next harder level in AGI testing would be “convince my kids to weed the garden and fold the laundry” :-)
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  • SubiculumCode 1y ago
    [1]https://app.rescript.info/public/share/W_T7E1OC2Wj49ccqlIOOztg2MJWaaVbovTeyxcFEQdU https://app.rescript.info/public/share/W_T7E1OC2Wj49ccqlIOOz... Perhaps it's because the representations are fractured. The link above is to the transcript of an episode of Machine Learning Street Talk with Kenneth O. Stanleyabout The Fractured Entangled Representation Hypothesis[1]
  • crazylogger 1y ago
    I think next year's AI benchmarks are going to be like this project: https://www.anthropic.com/research/project-vend-1 https://www.anthropic.com/research/project-vend-1 Give the AI tools and let it do real stuff in the world: "FounderBench": Ask the AI to build a successful business, whatever that business may be - the AI decides. Maybe try to get funded by YC - hiring a human presenter for Demo Day is allowed. They will be graded on profit / loss, and valuation. Testing plain LLM on whiteboard-style question is meaningless now. Going forward, it will all be multi-agent systems with computer use, long-term memory & goals, and delegation.
    • Miraltar 1y ago
      This sounds like a terrible idea to me, you're training intelligent computer to aim for power. It's fine as long as they're bad but if they get good then we have a problem
  • mindcrime 1y ago
    > I feel like I'm the only one who isn't convinced getting a high score on the ARC eval test means we have AGI. Wait, what? Approximately nobody is claiming that "getting a high score on the ARC eval test means we have AGI". It's a useful eval for measuring progress along the way, but I don't think anybody considers it the final word.
  • andoando 1y ago
    Who says intelligence is anything more than "pattern matching"? Everything is patterns
  • sva_ 1y ago
    It is a necessary condition, but not a sufficient one.
  • tippytippytango 1y ago
    He’s playing the game. You have to say AGI is your goal to get attention. It’s just like the YouTube thumbnail game. You can hate it, but you still have to play if you want people to pay attention.
  • cess11 1y ago
    Much like other forms of psychometry, especially related to so called intelligence, it's mainly about stratification and discrimination for ideological purposes.
  • yunwal 1y ago
    The author of the test agrees with you.
  • dfilppi 1y ago
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  • jxjnskkzxxhx 1y ago
    > I feel like I'm the only one who isn't convinced getting a high score on the ARC eval test means we have AGI Francois explicitly says that's not how ARC is supposed to be interpreted.
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• hackinthebochs 1y ago
  Has Chollet ever talked about his change of heart regarding AGI? It wasn't that long ago when he was one of the loudest voices decrying even the concept of AGI, let alone us being on the path to creating it. Now he's an advocate and has his own prize dataset? Seems rather convenient to change your tune once hundreds of billions are being thrown at AGI (not that I would blame him).
  • zamderax 1y ago
    People are allowed to evolve opinions. It seems to me he believes that a combination of transformer and program synthesis are key. The big unknown at the moment is how to do program search.
    • hackinthebochs 1y ago
      Absolutely. Presumably there is some specific considerations or evidence that helped him evolve his opinion. I would be interested in seeing a writeup about it. With him having been a very public advocate against AGI, a writeup of his evolution seems appropriate and would be very edifying for a lot of people.
      • blibble 1y ago
        > Presumably there is some specific considerations or evidence that helped him evolve his opinion. suitcases full of money?
      • Bjorkbat 1y ago
        I recall it as less an evolution and more a complete tonal shift the moment o3 was evaluated on ARC-AGI. I remember on Twitter Sam made some dumb post suggesting they had beaten the benchmark internally and Francois calling him out on his vagueposting. Soon as they publicly released the scores, it was like he was all-in on reasoning. Which I have to admit I was kind of disappointed by.
        • brcmthrowaway 1y ago
• roenxi 1y ago
  By both definitions of intelligence in the presentation we should be saying "how we got to AGI" in the past tense. We're already there. AI's can deal with situations they weren't prepared for in any sense that a human can. They might not do well, but they'll have a crack at it. We can trivially build systems that collect data and do a bit more offline training if that is what someone wants to see, but there doesn't really seem to be a commercial need for that right now. Similarly, AIs can whip most humans at most domains that require intelligence. I think the debate hqas been flat-footed by the speed all this happened. We're not talking AGI any more, we're talking about how to build superintelligences hitherto unseen in nature.
  • cubefox 1y ago
    Well, there is also robotics, active inference, online learning, etc. Things animals can do well.
    • AIPedant 1y ago
      Current robots perform very badly on my patented and highly scientific ROACH-AGI benchmark - "is this thing smarter at navigating unfamiliar 3D spaces than a cockroach?"
  • tmvphil 1y ago
    According to this presentation at least, ARC-AGI-2 shows that there is a big meaningful gap in fluid intelligence between normal non-genius humans and the best models currently, which seems to indicate we are not "already there".
    • saberience 1y ago
      There's already a big meaningful gap between the things AIs can do which humans can't, so why do you only count as "meaningful" the things humans can do which AIs can't? I enjoy seeing people repeatedly move the goalposts for "intelligence" as AIs simply get smarter and smarter every week. Soon AI will have to beat Einstein in Physics, Usain Bolt in running, and Steve Jobs in marketing to be considered AGI...
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• TheAceOfHearts 1y ago
  The first highlight from this video is getting to see a preview of the next ARC dataset. Otherwise it feels like most of what Chollet says here has already been repeated in his other podcast appearances and videos. It's a good video if you're not familiarized with his work, but if you've seen some of his recent interviews then you can probably skip the first 20 minutes. The second highlight from this video is the section from 29 minutes onward, where he talks about designing systems that can build up rich libraries of abstractions which can be applied to new problems. I wish he had lingered more on exploring and explaining this approach, but maybe they're trying to keep a bit of secret sauce because it's what his company is actively working on. One of the major points which seems to be emerging from recent AI discourse is that the ability to integrate continuous learning seems like it'll be a key element in building AGI. Context is fine for short tasks, but if lessons are never preserved you're severely capped with how far the system can go.
• vixen99 1y ago
  Is the text available for those who don't hear so well?
  • jasonlotito 1y ago
    At the very least, YouTube provides a transcript and a "Show Transcript" button in the video description, which you can click on to follow along.
    • heymijo 1y ago
      When I watched the video I had the subtitles on. The automatic transcript is pretty good. "Test-time" which is used frequently gets translated as "Tesla" so watch out for that.
• saberience 1y ago
  The Arc prize/benchmark is a terrible judge of whether we got to AGI. If we assume that humans have "general intelligence", we would assume all humans could ace Arc... but they can't. Try asking your average person, i.e. supermarket workers, gas station attendants etc to do the Arc puzzles, they will do poorly, especially on the newer ones, but AI has to do perfectly to prove they have general intelligence? (not trying to throw shade here but the reality is this test is more like an IQ test than an AGI test). Arc is a great example of AI researchers moving the goal posts for what we consider intelligent. Let's get real, Claude Opus is smarter than 99% of people right now, and I would trust its decision making over 99% of people I know in most situations, except perhaps emotion driven ones. Arc agi benchmark is just a gimmick. Also, since it's a visual test and the current models are text based it's actually a rigged (against the AI models) test anyway, since their datasets were completely text based. Basically, it's a test of some kind, but it doesn't mean quite as much as Chollet thinks it means.
  • leumon 1y ago
    He said in the video that they tested regular people (uber driver, etc.) on arc-agi2 and at least 2 people were able to solve each task (an average of 9-10 people saw each task). Also this quote from the paper: None of the self-reported demographic factors recorded for all participants—including occupation, industry, technical experience, programming proficiency, mathematical background, puzzle-solving aptitude, and var- ious other measured attributes—demonstrated clear, statistically significant relationships with performance outcomes. This finding suggests that ARC-AGI-2 tasks assess general problem-solving capabilities rather than domain-specific knowledge or specialized skills acquired through particular professional or educational experiences.
  • daveguy 1y ago
    It is not a judge of whether we got to AGI. And literally no one except straw-manning critics are trying to claim it is. The point is, an AGI should easily be able to pass it. But it can obviously be passed without getting to AGI (as . It's a necessary but not sufficient criteria. If something can't pass a test as simple as AGI (which no AI currently can) then it's definitely not AGI. Anyone claiming AGI should be able to point their AI at the problem and have an 80+% solution rate. Current attempts on the second ARC are less than 10% with zero shot attempts even worse. Even the better performing LLMs on the first ARC couldn't do well without significant pre-training. In short, the G in AGI stands for general.
• chromaton 1y ago
  Current AI systems don't have a great ability to take instructions or information about the state of the world and produce new output based upon that. Benchmarks that emphasize this ability help greatly in progress toward AGI.
• jacquesm 1y ago
  Let's not. Seriously. I absolutely love François and have used his work extensively. But looking around me at the social impact of AI I am really not convinced that this is what the world needs right now and that if we can stave off the turning point for another decade or two that humanity will likely benefit from that. The last thing we need is to inject yet another instability into a planet that is already fighting existential crisis on a number of fronts.
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  • thatguy0900 1y ago
    It doesn't matter what should or should not happen. Technology will continue to race forward at breakneck speed while everyone involved pats each other on the back for making a bunch of money before the consequences hit
    • nessbot 1y ago
      technology doesn't just advance itself
      • lo_zamoyski 1y ago
        This is true. We have a choice...in principle. But in practice, it's like stopping an arms race.
        • hollerith 1y ago
          Which arms race? The nuclear arms race of the Cold War? That one got stopped.
          • thatguy0900 1y ago
            It's hard to say the nuclear arms race is stopped when the US actively bombed a countries nuclear processing plants recently, and the US is also talking about building the Golden dome.
• modeless 1y ago
  ARC-AGI 3 remindes me of PuzzleScript games: https://www.puzzlescript.net/Gallery/index.html https://www.puzzlescript.net/Gallery/index.html There are dozens of ready-made, well-designed, and very creative games there. All are tile-based and solved with only arrow keys and a single action button. Maybe someone should make a PuzzleScript AGI benchmark?
  • mNovak 1y ago
    This game is great! https://nebu-soku.itch.io/golfshall-we-golf https://nebu-soku.itch.io/golfshall-we-golf Maybe someone can make an MCP connection for the AIs to practice. But I think the idea of the benchmark is to reserve some puzzles for private evaluation, so that they're not in the training data.
• visarga 1y ago
  I think intelligence is search. Search is exploration and learning. So intelligence is not in the model, or in the environment, but in their mutual dance. A river is not the banks, nor the water, but their relation.
• visarga 1y ago
  I think intelligence is search. Search is exploration + learning. So intelligence is not in the model or in the environment, but in their mutual dance. A river is not the banks, nor the water, but their relation. ARC is just a frozen snapshot of the banks, not the dynamic environment we have.
  • ipunchghosts 1y ago
    I agree strongly with this take but find it hard to convince others of it. Instead, people keep thinking there is a magic bullet to discover resulting in a lot of wasted resources and money.
• bogtog 1y ago
  I wonder how much slow progress on ARC can be explained by their visual properties making them easy for humans but hard for LLMs. My impression is that models are pretty bad at interpreting grids of characters. Yesterday, I was trying to get Claude to convert a message into a cipher where it converted a 98-character string into 7x14 grid where the sequential letters moved 2-right and 1-down (i.e., like a knight it chess). Claude seriously struggled. Yet, Francois always pumps up the "fluid intelligence" component of this test and emphasizes how easy these are for humans. Yet, humans would presumably be terrible at the tasks if they looked at it character-by-character This feels like a somewhat similar (intuition-lie?) case as the Apple paper showing how reasoning model's can't do tower of hanoi past 10+ disks. Readers will intuitively think about how they themselves could tediously do an infinitely long tower of hanoi, which is what the paper is trying to allude to. However, the more appropriate analogy would be writing out all >1000 moves on a piece of paper at once and being 100% correct, which is obviously much harder
  • krackers 1y ago
    I thought so too back when the test was first released, but now that we have multimodal models which can take images directly as input, shouldn't this point be moot?
    • bogtog 1y ago
      I think the top performer afaik (ChatGPT o3) is still treating ARC as a series of characters. I imagine complex reasoning in multimodal processing wouldn't be nearly as advanced so treating it as characters is still better
      • krackers 1y ago
        interesting, I thought one of the whole points of o3 was mixed multimodal reasoning (e.g. everyone doing those geoguesser challenges). But maybe that's just a parlor trick and it's not actually implemented that way. I wonder when they're going to extend chain-of-thought to work with image tokens, seems like that'd help for solving spatial challenges like this.
• ltbarcly3 1y ago
  [flagged]
  • deleted 1y ago
    [deleted]
• ltbarcly3 1y ago
  There is some kind of massive brigading happening on this thread. Lots of thoughtful comments are downmodded or flagged (including mine, which I thought was pretty thoughtful. I even said poop instead of shit.). https://news.ycombinator.com/item?id=44492241 https://news.ycombinator.com/item?id=44492241 My comment was basically instantly flagged. I see at least 3 other flagged comments that I can't imagine deserve to be flagged.
  • layer8 1y ago
    You didn’t address anything from the actual talk.
    • ltbarcly3 1y ago
      I addressed the entire concept of the talk, and made other relevant points. The correct response to "let me tell you something I can't possibly know" isn't to argue the points within that frame. If you see a talk like: "How we will develop diplomacy with the rat-people of TRAPPIST-5." you don't have to make some argument about super-earths and gravity and the rocket equation. You can just point out it's absurd to pretend to know something like whether there are rat-people there. Either way, it isn't flag-able!
      • layer8 1y ago
        Did you actually watch the talk? The flagging is probably due to your aggressively indignant style.
        • ltbarcly3 1y ago
          I'm not indignant, I'm dismissive. And I explain very carefully why so that someone else can rip what I say to shreds. Or they can flag it, and mommy will make the mean man stop disagreeing during nap time.
  • starchild3001 1y ago
    I've noticed that Hacker News has become increasingly strict with tone lately; almost to the point of aggressive "tone policing." It feels like you’re expected to maintain a very diplomatic tone: praise the author, acknowledge the strengths of the article or video, and only then, very carefully, offer any criticism. You can't just bash it, and not get downvoted :)
• acegod 1y ago
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  • abeppu 1y ago
    I think you're basically saying that ARC-AGI doesn't achieve a goal that _it didn't set_. The point of ARC-AGI is not to benchmark LLMs specifically. The point is to measure fluid intelligence in a way which supports comparisons between models and between models and humans. It's not the obligation of the test to be tailored to the form of model that's most popular now.
    • acegod 1y ago
      Right, that's exactly what I'm saying. >The point is to measure fluid intelligence in a way which supports comparisons between models and between models and humans. It's not the obligation of the test to be tailored to the form of model that's most popular now. The problem is that the test may not be giving an accurate comparison because the test is problematic when used to assess LLMs, which are the kind of model that people are most interested in assessing for general capabilities.
• lawlessone 1y ago
  How do we define AGI? I would have thought/considered AGI to be something that is constantly aware, a biological brain is always on. An LLM is on briefly while it's inferring. A biological brain constantly updates itself adds memories of things. Those memories generally stick around.
• khalic 1y ago
  This quest for an ill defined AGI is going to create a million of Cpt Ahab
• gtech1 1y ago
  This may be a silly question, I'm no expert. But why not simply define as AGI any system that can answer a question that no human can. So for example, ask AGI to find out, from current knowledge, how to reconcile gravity and qed.
  • m11a 1y ago
    That would be ASI I think. But consider: technically AlphaTensor found new algorithms no human did before (https://en.wikipedia.org/wiki/Matrix_multiplication_algorithm#AlphaTensor https://en.wikipedia.org/wiki/Matrix_multiplication_algorith...). So isn't it AGI by your definition of answering a question no human could before: how to do 4x4 matrix multiplication in 47 steps?
  • imiric 1y ago
    "What is the meaning of life, the universe, and everything?"
    • ta8645 1y ago
      42
  • soVeryTired 1y ago
    Computers can already do a lot of things that no human can though. They can reliably find the best chess or go move better than a human. It's conceivable (though not likely) that given training enough training in symbolic mathematics and some experimental data, an LLM-style AI could figure out a neat reconciliation of the two theories. I wouldn't say that makes it AGI though. You could achieve that unification with an AI that was limted to mathematics rather than being something that can function in many domains like a human can.
    • goatlover 1y ago
      Wouldn't this unification need to be backed by empirical data? Let's say the AI discovers the two theories can be unified using let's say some configuration 8 spatial dimensions and 2 time dimensions. Neat trick, but how do we know the world actually has those dimensions?
• EigenLord 1y ago
  I've been thinking lately about how AGI runs up against the No Free Lunch Theorem. This is what irritates me: science is not determining the narrative. Money is. I highly recommend mathematician David Wolpert's work on the topic. I think he inadvertently proved that ASI is physically impossible. Certainly he proved that AOI (artificial omniscient intelligence) is impossible. One thing he showed is that you can't have a universe with two omniscient intelligences (as it would be intractable for them to predict the other's behavior.) It's also very questionable whether "humanlike" intelligence is truly general in the first place. I think cognitive neurobiologists would agree that we have a specific "cognitive niche", and while this symbolic niche seems sufficiently general for a lot of problems, there are animals that make us look stupid in other respects. This whole idea that there is some secret sauce special algorithm for universal intelligence is extremely suspect. We flatter ourselves and have committed to a fundamental anthropomorphic fallacy that seems almost cartoonishly elementary for all the money behind it.
  • fragmede 1y ago
    When hasn't money determined the narrative?
    • jxjnskkzxxhx 1y ago
      Ironically, you missed the point. The person you're responding to is saying the opposite: the narrative (that there is something unique about how our brain solves problems) determines the money (invested).
  • FilosofumRex 1y ago
    AGI can't be defined, because it's the means by which definitions are created. You can only measure it contemporaneously by some consensus method such as ARC. You can't define AGI, any more than you can define ASA (artificial sports ability). Intelligence, like athleticism changes both quantitively and qualitatively. The Greek Olympic champions of 2K yrs ago wouldn't qualify for high school championships today, however, they were once regarded as great athletes.
  • lern_too_spel 1y ago
• timonofathens 1y ago
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• stared 1y ago
  I dislike the term AGI, as intelligence (of any type) always involves tradeoffs. Being exceptional at solving 2D grid-based pattern tasks is just one skill. Humans have a strong visual bias, while some hypothetical superintelligent slime molds might value entirely different problems. I know smart people (PhDs in STEM fields at major universities) who struggle with geometric puzzles, yet excel at linguistic or algebraic ones. Getting a perfect ARC-AGI-n score isn't a smoking gun indicator of general intelligence. Rather, it simply means we're now able to solve a class of problems previously beyond AI capabilities (which is exciting in itself!). I view ARC-AGI primarily as a benchmark (similar in spirit to Raven's matrices) that makes memorization substantially harder. Compare this with vocabulary-focused IQ tests, where cognitive skills certainly matter, but results depend heavily on exposure to a particular language.
  • spectre9 1y ago
    Call me crazy but we should be optimizing for human visual intelligence rather than slime mold symbolic space
    • ACCount36 1y ago
      It's obvious why having human visual intelligence in a machine is desirable. But if slime mold symbolic space is better suited for something like understanding of biology or abstract math, that's a good damn reason to go for the slime mold route too.
• noiv 1y ago
  Did anybody already let o3 crack Bongard Problems? o1 did not convince: https://arxiv.org/abs/2410.19546 https://arxiv.org/abs/2410.19546
• kcaj 1y ago
  From the talk: “Intelligence is the conversion ratio between past experience and potential operating area [where the potential operating area of interest is the area of problems that system has no experience of].” So we might say, “General Intelligence is the ability to do the things we haven’t yet thought of.” “Like what?” “Well, as soon as I name something it stops counting.” Gödellian - I like it. Does that mean a constructive definition of General Intelligence is uncomputable?
• GarnetFloride 1y ago
  You would think we would have to take a statistical approach to AGI. Look how we learned physics. Aristotelian physics was "An object in motion tends to come to a stop." That looked right most of the time a bowling ball on sand, grass, or even dirt comes to a stop pretty fast. But once you have a nice smooth marble floor the ball goes a lot further. Newtonian physics solved that and several other issues and works fine, most of the time, but has corner cases when going very fast or getting near a high gravity location. Then relativity and the rest. We need to build a system that we can teach like we do children that lets them reason that something is true under certain circumstances but may not hold generally so have to update what true is. And that looks like statistics.
  • desertrider12 1y ago
    The Cyc project basically achieved what you're talking about, even without approaching AGI. They manually programmed concepts and relationships between things into a huge knowledge graph. Then they had heuristics for choosing the appropriate version of facts for a given context (e.g. level of rigor). It was arguably able to use a library of abstractions similarly to what Chollet is talking about, but couldn't learn new ones automatically through exploration or play.
• PostOnce 1y ago
  Today we asked Gemini CLI to check if any of the files in /home/user/dirname contained a word. It churned for >5 minutes and didn't solve the problem. grep, of course, solved the problem in under a second. AGI is going to be a while, and your jobs are safe.
  • arthurcolle 1y ago
    Try it with Claude Code on Opus Gemini CLI isn't very good Edit: Also, the more competent models (Opus/ Sonnet to a lesser degree) are good at very complex subtask delegation that it can blow through and attempt and then verify in seconds, so not sure hand crafted regex examples are the best counter examples here New code patch models that I didn't even take seriously are actually really impressive and pretty new
• esbranson 1y ago
  > As long as it's easy to come up with tasks that any one of you can do, that are easy for humans, but that AI cannot figure out … we don't have AGI yet. And you will know you are close to having AGI when it becomes increasingly difficult to come up with such tasks. Man I don't know. A random 10 year old has general intelligence and ain't gonna do too well on these tests. AGI is not consciousness, I feel like that also gets confused, and general intelligence is not superhuman intelligence.