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.
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.
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]
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.
> 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.
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.
Much like other forms of psychometry, especially related to so called intelligence, it's mainly about stratification and discrimination for ideological purposes.
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).
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.
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.
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.
This is what is called "spikey" intelligence, where a model might be able to crack phd physics problems and solve byzantine pattern matching games at the 90th percentile, but also can't figure out how to look up a company and copy their address on the "customer" line of an invoice.
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.
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.
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?
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.
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.
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
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.).
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.
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.
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.
TIL there actually is something called "no free lunch in search and optimization"[1].
See however that the theorem is quite weak. Requires eg the assumption that the search space has no structure. They even have the example of quadratic problems. It's mostly a useless saying, it appears to me.
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.
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[ 0.21 ms ] story [ 65.3 ms ] threadA 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.
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]
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.
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.
Francois explicitly says that's not how ARC is supposed to be interpreted.
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.
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.
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.
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?
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
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.
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.
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.
See however that the theorem is quite weak. Requires eg the assumption that the search space has no structure. They even have the example of quadratic problems. It's mostly a useless saying, it appears to me.
[1] https://en.m.wikipedia.org/wiki/No_free_lunch_in_search_and_...
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.