ARC-AGI is (to our knowledge) the only eval which measures AGI: a system that can efficiently acquire new skill and solve novel, open-ended problems. Most AI evals measure skill directly vs the acquisition of new skill.
Francois created the eval in 2019, SOTA was 20% at inception, SOTA today is only 34%. Humans score 85-100%. 300 teams attempted ARC-AGI last year and several bigger labs have attempted it.
While most other skill-based evals have rapidly saturated to human-level, ARC-AGI was designed to resist “memorization” techniques (eg. LLMs)
Solving ARC-AGI tasks is quite easy for humans (even children) but impossible for modern AI. You can try ARC-AGI tasks yourself here: https://arcprize.org/play
ARC-AGI consists of 400 public training tasks, 400 public test tasks, and 100 secret test tasks. Every task is novel. SOTA is measured against the secret test set which adds to the robustness of the eval.
Solving ARC-AGI tasks requires no world knowledge, no understanding of language. Instead each puzzle requires a small set of “core knowledge priors” (goal directedness, objectness, symmetry, rotation, etc.)
At minimum, a solution to ARC-AGI opens up a completely new programming paradigm where programs can perfectly and reliably generalize from an arbitrary set of priors. At maximum, unlocks the tech tree towards AGI.
Our goal with this competition is:
1. Increase the number of researchers working on frontier AGI research (vs tinkering with LLMs). We need new ideas and the solution is likely to come from an outsider! 2. Establish a popular, objective measure of AGI progress that the public can use to understand how close we are to AGI (or not). Every new SOTA score will be published here: https://x.com/arcprize 3. Beat ARC-AGI and learn something new about the nature of intelligence.
Happy to answer questions!
I'm collecting data for how humans are solving ARC tasks, and so far collected 4100 interaction histories (https://github.com/neoneye/ARC-Interactive-History-Dataset). Besides ARC-AGI, there are other ARC like datasets, these can be tried in my editor (https://neoneye.github.io/arc/).
I have made some videos about ARC:
Replaying the interaction histories, and you can see people have different approaches. It's 100ms per interaction. IRL people doesn't solve task that fast. https://www.youtube.com/watch?v=vQt7UZsYooQ
When I'm manually solving an ARC task, it looks like this, and you can see I'm rather slow. https://www.youtube.com/watch?v=PRdFLRpC6dk
What is weird. The way that I implement a solver for a specific ARC task is much different than the way that I would manually solve the puzzle. Having to deal with all kinds of edge cases.
Huge thanks to the team behind the ARC Prize. Well done.
The short story. I needed something that could render thumbnails of tasks, so I could visual debug what was going on in my solver. However I have never gotten around to make the visual inspection tool. After having the thumbnail renderer, mid january 2024, then it eventually turned into what it is now.
But the people involved in this haven't signaled that they are in that path, either in the message about the challenge (precisely the opposite) or seemingly in their careers so far
So I guess I don't share the concern but a better way to phrase your comment could be -
"how can we be sure the human-provided solutions won't turn out to be just fodder for training a RL model or something that will later be monetized, closed and proprietary? Do the challenge organizers provide any guarantees on that?"
If I can make one criticism/observation of the tests, it seems that most of them reason about perfect information in a game-theoretic sense. However, many if not most of the more challenging problems we encounter involve hidden information. Poker and negotiations are examples of problem solving in imperfect information scenarios. Smoothly navigating social situations also requires a related problem of working with hidden information.
One of the really interesting things we humans are able to do is to take the rules of a game and generate strategies. While we do have some algorithms which can "teach themselves" e.g. to play go or chess, those same self-play algorithms don't work on hidden information games. One of the really interesting capabilities of any generally-intelligent system would be synthesizing a general problem solver for those kinds of situations as well.
I swear, not enough people have kids.
Now, is it 10k examples? No, but I think it was on the order of hundreds, if not thousands.
One thing kids do is they'll ask for confirmation of their guess. You'll be reading a book you've read 50 times before and the kid will stop you, point at a dog in the book, and ask "dog?"
And there is a development phase where this happens a lot.
Also kids can get mad if they are told an object doesn't match up to the expected label, e.g. my son gets really mad if someone calls something by the wrong color.
Another thing toddlers like to do is play silly labeling games, which is different than calling something the wrong name on accident, instead this is done on purpose for fun. e.g. you point to a fish and say "isn't that a lovely llama!" at which point the kid will fall down giggling at how silly you are being.
The human brain develops really slowly[1], and a sense of linear time encoding doesn't really exist for quite awhile. (Even at 3, everything is either yesterday, today, or tomorrow) so who the hell knows how things are being processed, but what we do know is that kids gather information through a bunch of senses, that are operating at an absurd data collection rate 12-14 hours a day, with another 10-12 hours of downtime to process the information.
[1] Watch a baby discover they have a right foot. Then a few days later figure out they also have a left foot. Watch kids who are learning to stand develop a sense of "up above me" after they bonk their heads a few time on a table bottom. Kids only learn "fast" in the sense that they have nothing else to do for years on end.
I have kids so I'm presuming I'm allowed to have an opinion here.
This is ignoring the fact that babies are not just learning labels, they're learning the whole of language, motion planning, sensory processing, etc.
Once they have the basics down concept acquisition time shrinks rapidly and kids can easily learn their new favorite animal in as little as a single example.
Compare this to LLMs which can one-shot certain tasks, but only if they have essentially already memorized enough information to know about that task. It gives the illusion that these models are learning like children do, when in reality they are not even entirely capable of learning novel concepts.
Beyond just learning a new animal, humans are able to learn entirely new systems of reasoning in surprisingly few examples (though it does take quite a bit of time to process them). How many homework questions did your entire calc 1 class have? I'm guessing less than 100 and (hopefully) you successfully learned differential calculus.
Second that. I think I've learned as much as my children have.
> Watch a baby discover they have a right foot. Then a few days later figure out they also have a left foot.
Watching a baby's awareness grow from pretty much nothing to a fully developed ability to understand the world around is one of the most fascinating parts of being a parent.
This reminds of the story of Adam learning names, or how some languages can express a lot more in fewer words. And it makes sense that LLMs look intelligent to us.
My kid loves repeating the names of things he learned recently. For past few weeks, after learning 'spider' and 'snake' and 'dangerous' he keeps finding spiders around, no snakes so makes up snakes from curly drawn lines and tells us they are dangerous.
I think we learn fast because of stereo (3d) vision. I have no idea how these models learn and don't know if 3d vision will make multi model LLMs better and require exponentially less examples.
Of course for a human this can either mean "I have an idea about what a dog is, but I'm not sure whether this is one" or it can mean "Hey this is a... one of those, what's the word for it again?"
Babies need few examples for complex tasks because they get constant infinitely complex examples on tasks which are used for transfer learning.
Current models take a nuclear reactors worth of power to run back prop on top of a small countries GDP worth of hardware.
They are _not_ going to generalize to AGI because we can't afford to run them.
My friends toddler, who grew up with a cat in the house, would initially call all dogs "cat". :-D
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If I was presented with 10 pictures of 2 species I'm unfamiliar with, about as different as cats and dogs, I expect I would be able to classify further images as either, reasonably accurately.
She also saw an eagle this spring out the car window and said “an eagle! …no, it’s a bird,” so I guess she’s still working on those image classifications ;)
My child experiences the world in a really pure way. They don’t care much about labels or colours or any other human inventions like that. He picks up his carrot, he doesn’t care about the name or the color . He just enjoys it through purely experiencing eating it. He can also find incredible flow state like joy from playing with river stones or looking at the moon.
I personally feel bad I have to each them to label things and but things in boxes. I think your child is frustrated at times because it’s a punish of a game. The departure from “the oceanic feeling.
Your comment would make sense to me if the end game of our brains and human experience is labelling things. It’s not. It’s useful but it’s not what living is about.
The optimization process that trained the human brain is called evolution, and it took a lot more than 10,000 examples to produce a system that can differentiate cats vs dogs.
Put differently, an LLM is pre-trained with very light priors, starting almost from scratch, whereas a human brain is pre-loaded with extremely strong priors.
Asserted without evidence. We have essentially no idea at what point living systems were capable of differentiating cats from dogs (we don't even know for sure which living systems can do this).
A human brain that doesn't get visual stimulus at the critical age between 0 and 3 years old will never be able to tell the difference between a cat and a dog because it will be forevermore blind.
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A human that has never seen a dog or a cat could probably determine which is which based on looking at the two animals and their adaptations. This would be an interesting test for AIs, but I'm not quite sure how one would formulate a eval for this.
https://en.m.wikipedia.org/wiki/Bouba/kiki_effect
well, maybe. We view things in three dimensions at high fidelity: viewing a single dog or cat actually ends up being thousands of training samples, no?
Tho I only ever did undergrad stats, maybe ML isn’t even technically a linear regression at this point. Still, hopefully my gist is clear
https://youtu.be/UakqL6Pj9xo?si=iDH6iSNyz1Net8j7
ML models are starting from absolute zero, single celled organism level.
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Neither do machines. Lookup few-shot learning with things like CLIP.
Humans learn through a lifetime.
Or are we talking about newborn infants?
Would an intelligent but blind human be able to solve these problems?
I'm worried that we will need more than 800 examples to solve these problems, not because the abstract reasoning is so difficult, but because the problems require spatial knowledge that we intelligent humans learn with far more than 800 training examples.
Yann LeCun argues that humans are not general intelligence and that such a thing doesn't really exist. Intelligence can only be measured in specific domains. To the extent that this test represents a domain where humans greatly outperform AI, it's a useful test. We need more tests like that, because AIs are acing all of our regular tests despite being obviously less capable than humans in many domains.
> the problems require spatial knowledge that we intelligent humans learn with far more than 800 training examples.
Pretraining on unlimited amounts of data is fair game. Generalizing from readily available data to the test tasks is exactly what humans are doing.
> Would an intelligent but blind human be able to solve these problems?
I'm confident that they would, given a translation of the colors to tactile sensation. Blind humans still understand spatial relationships.
I don't think there's any rules about what knowledge/experience you build into your solution.
To OP: I like your project goal. I think you should look at prior, reasoning engines that tried to build common sense. Cyc and OpenMind are examples. You also might find use for the list of AGI goals in Section 2 of this paper:
https://arxiv.org/pdf/2308.04445
When studying intros of brain function, I also noted many regions tie into the hippocampus which might do both sense-neutral storage of concepts and make inner models (or approximations) of external world. The former helps tie concepts together through various senses. The latter helps in planning when we are imagining possibilities to evaluate and iterate on them.
Seems like AGI should have these hippocampus-like traits and those in the Cyc paper. One could test if an architecture could do such things in theory or on a small scale. It shouldn’t tie into just one type of sensory input either. At least two with the ability to act on what only exists in one or what is in both.
Edit: Children also have an enormous amount of unsupervised training on visual and spatial data. They get reinforcement through play and supervised training by parents. A realistic benchmark might similarly require GB of prettaining.
A similar vintage GOFAI project that might do better on these, with a suitable visual front end, is SOAR - a general purpose problem solver.
There are two countries both which lay claim to the same territory. There is a set X that contains Y and there is a set Z that contains Y. In the case that the common overlap is 3D and one in on top of the other, we can extend this to there is a set X that contains -Y and a set Z that contains Y, and just as you can only see one on top and not both depending on where you stand, we can apply the same property here and say set X and Z cannot both exist, and therefore if set X is on then -Y and if set Z then Y.
If you pay attention to the language you use youll start to realize how much of it uses spatial relationships to describe completely abstract things. For example, one can speak of disintigrating hegonomic economies. i.e turning things built on top of eachother into nothing, to where it came
We are after all, reasoning about things which happen in time and space.
And spatial != visual. Even if you were blind youd have to reason spatially, because again any set of facts are facts in space-time. What does it take to understand history? People in space, living at various distances from each other, producing goods from various locations of the earth using physical processes, and physically exchanging them. To understand battles you have to understand how armies are arranged physically, how moving supplies works, weather conditions, how weapons and their physical forms affect what they can physically do, etc.
Hell LLMs, the largest advancement we had in artificial intelligence do what exactly? Encode tokens into multi dimensional space.
Is there a number of dimensions that captures all reasoning? I don't know..
This is the wrong way to think about it IMO. Spatial relationships are just another type of logical relationship and we should expect AGI to be able to analyze relationships and generate algorithms on the fly to solve problems.
Just because humans can be biased in various ways doesn’t mean these biases are inherent to all intelligences.
Not really. By that reasoning, 5-dimensional spatial reasoning is "just another type of logical relationship" and yet humans mostly can't do that at all.
It's clear that we have incredibly specialized capabilities for dealing with two- and three-dimensional spatiality that don't have much of anything to do with general logical intelligence at all.
It’s similar to how chess problems are technically reasoning problems but they are not representative of general reasoning.
Blind people can have spatial reasoning just fine. Visual =/= spatial [0]. Now, one would have to adapt the colour-based tasks to something that would be more meaningful for a blind person, I guess.
[0] https://hal.science/hal-03373840/document
There may (almost certainly will be) additional knowledge encoded in the solver to cover the spacial concepts etc. The distinction with the AGI-ARC test is the disparity between human and AI performance, and that it focuses on puzzles that are easier for humans.
It would be interesting to see a finetuned LLM just try and express the rule for each puzzle as english. It could have full knowledge of what ARC-AGI is and how the tests operate, but the proof of the pudding is simply how it does on the test set.
In it they question the ease of Chollet's tests: "One limitation on ARC’s usefulness for AI research is that it might be too challenging. Many of the tasks in Chollet’s corpus are difficult even for humans, and the corpus as a whole might be sufficiently difficult for machines that it does not reveal real progress on machine acquisition of core knowledge."
ConceptARC is designed to be easier, but then also has to filter ~15% of its own test takers for "[failing] at solving two or more minimal tasks... or they provided empty or nonsensical explanations for their solutions"
After this filtering, ConceptARC finds another 10-15% failure rate amongst humans on the main corpus questions, so they're seeing maybe 25-30% unable to solve these simpler questions meant to test for "AGI".
ConceptARC's main results show CG4 scoring well below the filtered humans, which would agree with a [Mensa] test result that its IQ=85.
Chollet and Mitchell could instead stratify their human groups to estimate IQ then compare with the Mensa measures and see if e.g. Claude3@IQ=100 compares with their ARC scores for their average human
[ConceptArc]https://arxiv.org/pdf/2305.07141 [Mensa]https://www.maximumtruth.org/p/ais-ranked-by-iq-ai-passes-10...
> We found that humans were able to infer the underlying program and generate the correct test output for a novel test input example, with an average of 84% of tasks solved per participant
I guess there might be a disagreement of whether the problems in ARC are a representative sample of all of the possible abstract programs which could be synthesized, but then again most LLMs are also trained on human data.
Maybe if you run into some exceptionally difficult tasks it might not be 100%, but there's no way the challenge can be called unfair because it's too difficult for humans too.
Game on for the million, if so :). If not, apologies for distracting from the good fight for OSS/noncorp devs!
E: it occurred to me on the drive home how easily we (engineers) can fall into competitiveness, even when we’ve all read the thinkpieces about why an AI Race would/will be/is incredibly dangerous. Maybe not “game on”, perhaps… “god I hope it’s impossible but best of luck anyway to both of us”?
I'd also urge you to use a different platform for communicating with the public because x.com links are now inaccessible without creating an account.
"Endow circuitry with consciousness and win a gift certificate for Denny's (may not be used in conjunction with other specials)"
We are also trialing a secondary leaderboard called ARC-AGI-Pub that imposes no limits or constraints. Not part of the prize today but could be in the future: https://arcprize.org/leaderboard
AGI will take much more than that to build, and once you have it, if all you can monetize it for is a million dollars, you must be doing something extremely wrong.
However, I do disagree that this problem represents “AGI”. It’s just a different dataset than what we’ve seen with existing ML successes, but the approaches are generally similar to what’s come before. It could be that some truly novel breakthrough which is AGI solves the problem set, but I don’t think solving the problem set is a guaranteed indicator of AGI.
Imo there's no evidence whatsoever that nailing this task will be true AGI - (e.g. able to write novel math proofs, ask insightful questions that nobody has thought of before, self-direct its own learning, read its own source code)
That's a stretch. This is a problem at which LLMs are bad. That does not imply it's a good measure of artificial general intelligence.
After working a few of the problems, I was wondering how many different transformation rules the problem generator has. Not very many, it seems. So the problem breaks down into extracting the set of transformation rules from the data, then applying them to new problems. The first part of that is hard. It's a feature extraction problem. The transformations seem to be applied rigidly, so once you have the transformation rules, and have selected the ones that work for all the input cases, application should be straightforward.
This seems to need explicit feature extraction, rather than the combined feature extraction and exploitation LLMs use. Has anyone extracted the rule set from the test cases yet?
The issue with that path is that the problems aren’t using a programmatic generator. The rule sets are anything a person could come up with. It might be as simple as “biggest object turns blue” but they can be much more complicated.
Additionally, the test set is private so it can’t be trained on or extracted from. It has rules that aren’t in the public sets.
[1] https://www.kaggle.com/competitions/abstraction-and-reasonin...