Ryan's work is legitimately interesting and novel "LLM reasoning" research! The core idea:

> get GPT-4o to generate around 8,000 python programs which attempt to implement the transformation, select a program which is right on all the examples (usually there are 3 examples), and then submit the output this function produces when applied to the additional test input(s)

Roughly, he's implemented an outer loop and using 4o to sample reasoning traces/programs from training data and test. Hybrid DL + program synthesis approaches are solutions we'd love to see more of.

A couple important notes:

1. this result is on the public eval set vs private set (ARC Prize $).

2. the current private set SOTA ~35% solution also performed ~50% on the public set. so this new result might be SOTA but hasn't been validated or scrutinized yet.

All said, I do expect verified public set results to flow down to the private set over time. We'll be publishing all the SOTA scores and open source reproductions here once available: https://arcprize.org/leaderboard

EDIT: also, congrats and kudos to Ryan for achieving this and putting the effort in to document and share his approach. we hope to inspire more frontier AI research sharing like this

> In addition to iterating on the training set, I also did a small amount of iteration on a 100 problem subset of the public test set

and

> it's unfortunate that these sets aren’t IID: it makes iteration harder and more confusing

It’s not unfortunate: generalizing beyond the training distribution is a crucial part of intelligence that ARC is trying to measure! Among other reasons, developing with test-set data is a bad practice in ML because it hides the difficulty this challenge. Even worse, writing about a bunch of tricks that help results on this subset is extending the test-set leakage the blog post's readers. This is why I'm glad the ARC Prize has a truly hidden test set

Because the thing we have now is data-hungry. Your brain is pre-trained on other similar challenges as well. What's the point of requiring it to "generalize beyond the training distribution" with so few samples?

Really, I thought LLMs ended this "can we pretrain on in-house prepared private data for ILSVRC" flame war already.

The concern with the data-hungry approach to machine learning, that at least some of us have, is that it has given up on the effort to figure out how to learn good background theories and turned instead to getting the best performance possible in the dumbest possible way, relying on the largest available amount of examples and compute. That's a trend against everything else in computer science (and even animal intelligence) where the effort is to make everything smaller, cheaper, faster, smarter: it's putting all the eggs in the basket of making it big, slow and dumb, and hoping that this will somehow solve... intelligence. A very obvious contradiction.

Suppose we lived in a world that didn't have a theory of computational complexity and didn't know that some programs are more expensive to run than others. Would it be the case in that world, that computer scientists competed in solving ever larger instances of the Traveling Salesperson Problem, using ever larger computers, without even trying to find good heuristics exploiting the structure of the problem and simply trying to out-brute-force each other? That world would look a lot like where we are now with statistical machine learning: a pell-mell approach to throwing all resources at a problem that we just don't know how to solve, and don't even know if we can solve.

Mostly tangential to the article but I never really like this argument. Like you're playing a game a specific way and somebody else comes in with a new approach and mops the floor with you and you're going to tell me "they played wrong"? Like no, you were playing wrong the whole time.

Those "early pioneers" were people like Alan Turing, Claude Shannon, Marvin Minsky, Donald Michie and John McCarthy, all of whom were chess players themselves and were prone to thinking of computer chess as a window into the inner workings of the human mind. Here's what McCarthy had to say when Deep Blue beat Kasparov:

In 1965 the Russian mathematician Alexander Kronrod said, "Chess is the Drosophila of artificial intelligence." However, computer chess has developed much as genetics might have if the geneticists had concentrated their efforts starting in 1910 on breeding racing Drosophila. We would have some science, but mainly we would have very fast fruit flies.

Three features of human chess play are required by computer programs when they face harder problems than chess. Two of them were used by early chess programs but were abandoned in substituting computer power for thought.

http://www-formal.stanford.edu/jmc/newborn/newborn.html

Then he goes on to discuss those three features of human chess play. It doesn't really matter which they are but it's clear that he is not complaining about anyone "playing wrong", he's complaining about computer chess taking a direction that fails to contribute to a scientific understanding of human, and I would also say machine, intelligence.

It goes beyond simple sunk cost and into the realm of reality slapping them with a harsh "humans aren't special, grow up", which I think is especially bitter for people who aren't already absurdists or nihilists.

I think probably the general idea of dynamic structures that are versatile in their ability to approximate functional models is at least a solid hypothesis for how some biological intelligence works at some level (I think maybe the "fluid/crystallized" intelligence distinction some psychology uses is informative here - a strong world model probably informs a lot of quick acquisition of relationships, but most intelligent systems clearly posess strong feedback mechanisms for capturing new models), though I definitely agree that a focus on how best to throw a ton of scale at these models doesn't seem like a fruitful path for actionably learning how to build or analyze intelligent systems in the way we usually think about, nor is it, well, sustainable. Moore's law appeals to business people because buying more computronium feels more like a predictable input-output relationship to put capital into, but even if we're just talking about raw computation speed advances in algorithms tend to dwarf advances in computing power in the long run. I think the same will hold true in AGI

But even if this kind of thinking is totally organic, I think it could arise from the delayed nature of the results of data-driven methods. Often a major structural breakthrough for a data-driven approach drastically predates the most obviously impactful results from that breakthrough, because the result impressive enough to draw people's attention comes from throwing lots of data and compute at the breakthrough. The people who got the impressive result might not even be the same team as the one that invented the structure they're relying on, and it's really easy to get the impression that what changed the game was the scale alone, I imagine even if you're on one of those research teams. I've been really impressed by some of the lines of research that show that you can often distill some of these results to not rely so heavily on massive datasets and enormous parallel training runs, and think we should properly view results that come from these to be demonstrations of the power of the underlying structural insights rather than new results. But I think this clashes with the organizational priorities of large tech firms, which often view scale as a moat, and thus are motivated to emphasize the need for it

Like you say, large tech corps clearly see big data approaches as a moat, as a game that they can play better than anyone else: they got the data, they got the compute, and they got the millions to hoover up all the "talent". Obviously, when it's corporations driving research they are not going to drive it towards a deepening of understanding and an enriching of knowledge, the only thing they care about is selling stuff to make money, and to hell with whether that stuff works or not and why. I'm worried even that this is going to have a degrading effect on the output of science and technology in general, not just AI and CS. It's like a substantial minority of many fields of science have given up on basic research and are instead feeding data to big neural nets and poking LLMs to see what will fall out. This is a very bad situation. Not a winter but an Eternal Summer.

Take it away, Tom.

https://youtu.be/0qjCrX9KS88?si=CEXr0cv-lUeG4UNO

So maybe we can cure LLMs of the hallucinatory leprosy just by bathing them about 333 times in the mundane Jordan river of incremental bolt ons and modifications to formulas.

You should be able to think of the LLM as a random hallucination generator then ask yourself "how do I wire ten thousand random hallucination generators together into a brain?" It's almost certain that there's an answer... And it's almost certain that the answer is even going to be very simple in hindsight. Why? Because llms are already more versatile than the most basic components of the brain and we have not yet integrated them in the scale that components are integrated in the brain.

It's very likely that this is what our brains do at the component level - we run a bunch of feedback coupled hallucination generators that, when we're healthy, generates a balanced and generalizing consciousness - a persistent, reality coupled hallucinatory experience that we sense and interpret and work within as the world model. That just emerges from a network of self correcting natural hallucinators. For evidence, consider work in Cortical Columns and the Thousand brains theory. This suggests our brains have about a million Cortical Columns. Each loads up random inaccurate models of the world... And when we do integration and error correction over that, we get a high level conscious overlay. Sounds like what the author of the currently discussed SOTA did, but with far more sophistication. If the simplest most obvious approach to jamming 5,000 llms together into a brain gives us some mileage, then it's likely that more reasoned and intelligent approach could get these things doing feats like the fundamentally error prone components of our own brains can do when working together.

So I see absolutely no reason we couldn't build an analogy of that with llms as the base hallucinator. They are versatile and accurate enough. We could also use online training llms and working memory buffers as the base components of a Jepa model.

It's pretty easy to imagine that a society of 5000 gpt4 hallucinators could, with the right self administered balances and utilities, find the right answers. That's what the author did to win the 50%.

Therefore I propose that for the current generation it's okay to just mash a bunch of hallucinators together and whip them into the truth. We should be able to do it because our brains have to be able to do it. And if you're really smart, you will find a very efficient mathematical decomposition... Or a totally new model. But for every current LLM inability, it's likely to turn out that sequence of simple modifications can solve it. Will probably accrue a large number of such modifications before someone comes along and thinks of an all-new model then does way better, perhaps taking inspirations from the proposed solutions, or perhaps exploring the negative space around those solutions.

Think about it this way: ten years ago, would you think that hallucinations have anything to do with intelligence? If it were 2012, would you think that convolutions, or ReLus, are the basis of intelligence instead?

I'm saying there is a clear tendency within AI research, and without, to assume that whatever big new idea is currently trending is "it" and that's how we solve AI. Every generation of AI reseachers since the 1940's has fallen down that pit. In fact, no lesser men than Walter Pitts and Warren McCulloch, the inventors of the artificial neuron in 1943, firmly believed that the basis of intelligence is propositional logic. That's right. Propositional logic. That was the hot stuff at the time. Besides, the first artificial neuron was a propositional logic circuit that learned its own boolean function.

So keep an eye out for being carried away on the wings of the latest hype and thinking we got the solution to every problem just because we can do yet another thing, with computers, that we couldn't do before.

The mainstream attention LLMs have garnered has added a bunch of noise to the way we talk about machine learning systems, and unfortunately the companies releasing them are partially to blame for this. That doesn't mean we should change the definition of success for various benchmarks to better suit lay misunderstandings of how this all works

> if given the entire test set.

I don't want the entire test set. Or any single one in the test set.

The problem here is ARC challenge deliberately give a training set with different distribution than both the public and the private test set. It's like having only 1+1=2, 3+5=8, 9+9=18 in training set and then 1+9=10, 5*5=25, 16/2=8, (0!+0!+0!+0!)!=24 in test set.

I can see the argument of "giving the easy problems as demonstration of rules and then with 'intelligence' [1] you should be able to get harder ones (i.e. a different distribution)", but I don't believe it's a good way to benchmark current methods, mainly because there are shortcuts. Like I can teach my kids how factorial works and ! means factorial, instead of teaching them how addition works only and make them figure out how multiplication, division and factorial works and what's the notation.

[1] Whatever that means.

Like only having [1+1=2, 4+5=9, 2+10=12] in the training set and [2*5=10, 3/4=.75, 2^8=256] in the test set would be bad, but something like [1+1=2, 3+4*2=11, 5*3=15, 2*7=14, 1+3/5=1.8, 3^3=27] vs [2+4*3=14, 3+3^2+4=16, 2*3/4+2^3/2^4=2] might not be, depending on what they're trying to test

Compositionality of information, especially of abstractions (like rules or models of a phenomenon), is a key criterion in a lot of people's attempts to operationally define "intelligence" (which I agree is overall a nebulous and overloaded concept, but if we're going to make claims about it we need at least a working definition for any particular test we're doing) I could see that meaning that the test set problems need to be "harder" in the sense that presenting compositions of rules in training doesn't preclude memorizing the combinations. But this is just a guess, I'm not involved in ARC and don't know, obviously*

Yes. From https://arcprize.org/guide:

    Please note that the public training set consists of simpler tasks whereas the public evaluation set is roughly the same level of difficulty as the private test set.
    The public training set is significantly easier than the others (public evaluation and private evaluation set) since it contains many "curriculum" type tasks intended to demonstrate Core Knowledge systems. It's like a tutorial level.

Like with our toy "algebra" examples, sure there's a lot of emphasis on repetition and rote in primary education on these subjects, and that's one way to get people more consistent at getting the calculations right, but to be frank I don't think it's the best way, or as crucial as it's made out to be. What someone really needs to understand about algebra is how the notation works and what the symbols mean. Like I can't unsee the concept of "+" as a function that takes two operands and starts counting for as many steps as one would in the right operand, starting at the value of the left operand. When looking at algebra, the process I go through relies on a bunch of conceptual frameworks, like "Anything in the set of all arabic numerals can be considered a literal value". "Anything in the roman alphabet is likely a variable". "Any symbol is likely an infix operator, that is, a function whose operands are on either side of it". Some of the concepts I'm using are just notational convention. At some point I memorized the set of arabic numerals, what they look like, what each of them means, how they're generally written in relation to each other to express quantities combinatorically. Some of the concepts are logical relations about quantities, or definitions of functions. But crucially, the form of these distillations makes them composable. If I didn't really understand what "+" does, then maybe someone could give me some really bad homework that goes

1 + 30 = 31

20 + 7 = 27

3 + 10 = 13

And then present me the problem

20 + 10 + 3 = ?

And I'd think the answer is

20 + 10 + 3 = 213

That demonstrates some model of how to do these calculations, but it doesn't really capture all the important relationships the symbols represent

We can have any number of objections to this training set. Like I wasn't presented with any examples of adding two-digit numbers together! OR even any examples where I needed to combine numbers in the same rank!

Definitely all true. Probably mistakes we could make in educating a kid on algebraic notation too. It's really hard to do these things in a way that's both accomplishing the goal and testable, quantifiable. But many humans demonstrate the ability to distill conceptual understanding of concepts without exhaustive examples of their properties, so that's one of the things ARC seems to want to test. It's hard to get this perfectly right, but it's a reasonable thing to want

- can I train on my own private training set (which is harder)?

- can I pretrain on The Pile or something similar, a dataset full of texts crawled from web?

- can I pretrain on elementary school textbooks?

It seems like the latter two is acceptable given the use of GPT-4o here. But then, are the latter two that different to the first one? GPT-4o have the public test set in its training data (GPT-4o is definitely trained on public GitHub repos).

What's the point of having a training set with different distribution in this case, other than making participating harder? Maybe it's to discourage data-hungry approaches, but if there are legit shortcuts, anyone who seriously want to win would take it.

It's the most generic thing we have right now, right?

> Never will be.

If there is no other breakthrough anytime soon we can engineer AGI-like things around LLMs. I mean LLM trained to use different attachments. Which can be other models and algorithms. Examples will be image recognition models and databases for algorithms. Even now ChatGPT can use Bing search and Python interpreter. First steps done, others will follow. The result will be not a true AGI, but still a very capable system. And there is another factor. Next models can be trained on high quality data generated by current models. Instead of internet random garbage. This should improve their spacial and logical abilities.

Do grade 1 kids have AGI? (Haha)

But seriously, all professions need to train in context to solve complex problems. You can train in adjacent realms and reason about problems but to truly perform, you need more training.

A general surgeon might be better than an electrician as a vet, but that I’d rather have a veterinary surgeon operate on my dog.

So some things are “AGI” able and other things need specific training.

We should also expect machine learning systems to have somewhat different properties from human minds. Like computers are more likely to accomplish perfect recall, and we can scale the size of their memory and their processing speed. All these confounding variables can make it hard to make binary tests of a capability, which is really what ARC seems like it's trying to do. One such capability that AI researchers will often talk about is conceptual compositionality. People care about compositionality because it's a good way to demonstrate that an abstract model is being used to reason about a situation, which can be used in unseen but perhaps conceptually similar situations. This "generalization" or "abstraction" capability is really the goal, but it's hard to reason about how to test it, and "composition" (That is, taking a situation that's novel, but a straightforward application of two or more different abstractions the agent should already "know") is one more testable way to try to tease it out.

As you point out, humans often fail this kind of test, and we can rightly claim that in those cases, they didn't correctly grasp the insight we were hoping they had. Testing distilled abstractions versus memorization or superficial pattern recognition isn't just important to AI research, it's also a key problem in lots of places in human education

One core idea we've been advocating with ARC is that pure LLM scaling (parameters...) is insufficient to achieve AGI. Something new is needed. And OPs approach using a novel outer loop is one cool demonstration of this.

I mean, generating tens of thousands of possible solutions, to find one that works does not, to me, signify AGI.

After all, the human solving these problem doesn't make 10k attempts before getting a solution, do they?

The approach here, due to brute force, can't really scale: if a random solution to a very simple problem has a 1/10k chance of being right, you can't scale this up to non-trivial problems without exponentially increasing the computational power used. Hence, I feel this is brute-force.

Please learn a bit of combinatorics.

> After all, the human solving these problem doesn't make 10k attempts before getting a solution, do they?

No. People have much better "early rejection", also human brain has massive parallel compute capacity.

It's ridiculous to demand GPT-4 performs as good as a human. Obviously its vision is much worse and it doesn't have 'video' and physics priors people have, so it has to guess more times.

Brute searching literally means generating solutions until one works. Which is exactly what is being done here.

> Please learn a bit of combinatorics.

Don't be condescending - I understand the problem space just fine. Fine enough to realise that the problem was constructed specifically to ensure that "solutions" such as this just won't work.

Which is why this "solution" is straight-up broken (doesn't meet the target, exceeds the computationally bounds, etc).

> It's ridiculous to demand GPT-4 performs as good as a human.

Wasn't the whole point of this prize to spur interest in a new approach to learning? What does GPT-[1234] have to do with the contest rules? Especially since this solution broke those rules anyway?

> Obviously its vision is much worse and it doesn't have 'video' and physics priors people have, so it has to guess more times.

That's precisely my point - it has to guess. Humans aren't guessing for those types of problems (not for the few that I saw anyway).

It's similar that a lot of wrong answers are being thrown up, but I think this is more like a probabilistic system which is being pruned than a walk of the solution space. It's much smarter, but not as smart as we would like.

Sure, but not an exhaustive one - you stop when you get a solution[1]. Brute force does not require an exhaustive search in order to be called brute-force.

GP was using the argument that because it is not exhaustive, it cannot be brute-force. That's the wrong argument. Brute-force doesn't have to be exhaustive to be brute-force.

[1] Or a good enough solution.

For a reference, check Cormen's "Introduction to Algorithms". Every mention of brute-force search is specifically to exhaustive search is which not feasible for bigger spaces.

> I mean, the approach under discussion is literally exactly this.

It's literally not. It DOES NOT REDUCE the candidate set. It generates most likely candidates, but it doesn't reduce anything.

You lack basic understanding. Solutions are pixels grids, not Python programs. There's no search over pixel grids in the article. Not every search is exhaustive search.

This is like saying theoretical physicists are "brute-forcing" physics by generating candidate theories and testing them. Ridiculous.

Also: lol at your "who hasn't" comment. Because you clearly haven't.

Reference? Link, even?

> don't care about whatever random wiki page you might find to "support your claims".

That isn't some "random wiki" page; that's the wikipedia page for this specific term.

I'm not claiming to have defined this term, I'm literally saying I only agree with the sources for this term.

> Also: lol at your "who hasn't" comment. Because you clearly haven't.

Talk about cringe-worthy.

Sure, here's definition for "brute force" from university textbook material written by pllk, who has taught algorithms for 20 years and holds a 2400 rating on Codeforces:

https://tira.mooc.fi/kevat-2024/osa9/

"Yleispätevä tapa ratkaista hakuongelmia on toteuttaa raakaan voimaan (brute force) perustuva haku, joka käy läpi kaikki ratkaisut yksi kerrallaan."

edit:

Here's an English language book written by the same author, though the English source does not precisely define the term:

https://cses.fi/book/book.pdf

In chapter 5:

"Complete search is a general method that can be used to solve almost any algorithm problem. The idea is to generate all possible solutions to the problem using brute force ..."

And a bit further down chapter 5:

"We can often optimize backtracking by pruning the search tree. The idea is to add ”intelligence” to the algorithm so that it will notice as soon as possible if a partial solution cannot be extended to a complete solution. Such optimizations can have a tremendous effect on the efficiency of the search."

Your mistake is that you for some reason believe that any search over solution space is a brute force solution. But there are many ways to search over a solution space. A "dumb search" over solution space is generally considered to be brute force, whereas a "smart search" is generally not considered to be brute force.

Here's the Codeforces profile of the author: https://codeforces.com/profile/pllk

edit 2:

Ok now I think I understand what causes your confusion. When an author writes "One way to speed up a brute-force algorithm ..." you think that the algorithm can still be called "brute force" after whatever optimizations were applied. No. That's not what that text means. This is like saying "One way to make a gray car more colorful is by painting it red". Is it still a gray car after it has been painted red? No it is not.

Already the 2021 paper Drori (and many papers since) did similar things.

It's a common idea in this space...

How many thousands of Python programs does a human need to solve a single ARC task? That's what you get with reasoning: you don't need oodles of compute and boodles of sampling.

And I'm sorry to be so mean, but ARC is a farce. It's supposed to be a test for AGI but its only defense from a big data approach (what Francois calls "memorisation") is that there are few examples provided. That doesn't make the tasks hard to solve with memorisation it just makes it hard for a human researcher to find enough examples to solve with memorisation. Like almost every other AI-IQ test before it, ARC is testing for the wrong thing, with the wrong assumptions. See the Winograd Schema Challenge (but not yet the Bongard problems).

My guess is supported by the experience that, in AI research, every time someone came up with a plausible test for intelligence, an AI system eventually passed the test only to make it clear that the test was not really testing intelligence after all (edit: I don't just mean formal tests; e.g. see how chess used to "require intelligence" right up until Deep Blue vs Kasparov).

Some people see that as "moving the goalposts" and it's certainly frustrating but the point is that we don't know what intelligence is, exactly, so it's very hard to test for its existence or not, or to measure it.

My preference would be for everyone in AI research to either stop what they're doing and try to understand what the hell intelligence is in the first place, to create a theory of intelligence so that AI can be a scientific subject again, or to at least admit they're not interested in creating artificial intelligence. I, for example, am not, but all my background is in subjects that are traditionally labelled "AI" so I have to suck it up, I guess.

Quite the contrary, Chollet seems convinced that a test for artificial intelligence, like an IQ test for AI, can be created and he has not only created one but also organised a Kaggle competition on it, and now is offering a $1 million prize to solve it. So how is anything he says or does compatible with what I say above, that it's likely there can't be a test for artificial intelligence?

Why do you say it's sampling programs from "training data"? With that choice of words, you're rhetorically assuming the conclusion.

If he only sampled 20 programs, instead of 8000, will we still say the programs came from "training data", or will we say it's genuine OOD generalization? At what point do we attribute the intelligence to the LLM itself instead of the outer loop?

This isn't meant to be facetious. Because clearly, if the N programs sampled is very large, it's easy to get the right solution with little intelligence by relying on luck. But as N gets small the LLM has to be intelligent and capable of OOD generalization, assuming the benchmark is good.

Adding the close-to ad-hominem attack on Francois Chollet with the comics at the beginning (Francois never claimed to be a neuro-symbolic believer), this work does a significant disservice to the community.

His response:

"This has been the most promising branch of approaches so far -- leveraging a LLM to help with discrete program search, by using the LLM as a way to sample programs or branching decisions. This is exactly what neurosymbolic AI is, for the record..."

"Deep learning-guided discrete search over program space is the approach I've been advocating, yes... there are many different flavors it could take though. This is one of them (perhaps the simplest one)."

https://x.com/fchollet/status/1802773156341641480

Pitching him against LLMs in such a binary fashion is deceiving and unfair.

Nope. This is neurosymbolic AI:

Abductive Knowledge Induction From Raw Data

https://www.doc.ic.ac.uk/~shm/Papers/abdmetarawIJCAI.pdf

That's a symbolic learning engine trained in tandem with a neural net. The symbolic engine is learning to label examples for the neural net that learns to label examples for the symbolic engine. I call that cooking!

(Full disclosure: the authors of the paper are my thesis advisor and a dear colleague).

A lot of top researchers claim that obvious deficiencies in LLM training are fundamental flaws in transformer architecture, as they are interested in doing some new research.

This work show that temporary issues are temporary. E.g. LLM is not trained on grid inputs, but can figure things out after preprocessing.

If this article wanted to attack Chollet, it could have made more hay out of another thing that's "hidden in the middle of the article", the note that the solution actually gets 72% on the subset of problems on which humans get ~85%. The fact that the claimed human baseline for ARC-AGI as a whole is based on an easy subset is pretty suspect.

Intelligence is an ability that is naturally gradual and emerges over many domains. It is a collection of tools via which general abstractive principles can be applied, not a singular universally applicable ability to think in abstractions. GPT-4, compared to a human, is a very very small brain trained for the single purpose of textual thinking with some image capabilities. Claiming that ARC is the absolute market of general intelligence fails to account for the big picture of what intelligence is.

People in general are interested in capabilities or economic impact, and GPT-2 cleared no notable thresholds in those regards.

I prefer the exact opposite approach: let’s use a strict definition, and have levels to make it really explicit what we are talking about.

Here is a good one:

“Levels of AGI for Operationalizing Progress on the Path to AGI”

https://arxiv.org/abs/2311.02462

Then stop selling it as a tool to replace humans. A fast moving car breaking through a barrier and flying off the cliff could be called "an airborne means of transportation, just a very bad one" yet nobody is suggesting it should replace school busses if only we could add longer wings to it. What the LLM community refuses to see is that there is a limit to the patience and the financing the rest of the world will grant you before you're told, "it doesn't work mate."

> So at what point does a human go from not generally intelligent to generally intelligent?

Developmental psychology would be a good place to start looking for answers to this question. Also, forgetting scientific approach and going with common sense, we do not allow young humans to operate complex machinery, decide who is allowed to become a doctor, or go to jail. Intelligence is something that is not equally distributed across the human population and some of us never have much of it, yet we function and have a role in society. Our behaviour, choices, preferences, opinions are not just based on our intelligence, but often on our past experiences and circumstances. It is also not the sole quality we use to compare ourselves against each other. A not very intelligent person is capable of making the right choices (an otherwise obedient soldier refusing to press the button and blow up a building full of children); similarly, a highly intelligent person can become a hard-to-find serial criminal (a gynecologist impregnating his patients).

What intelligent and creative people hold against LLMs is not that they replace them, but that they replace them with a shit version of them relegating thousands of years of human progress and creativity to the dustbin of the models and layers of tweaks to the output that still produce unreliable crap. I think the person who wrote this sign summed it up best https://x.com/gvanrossum/status/1802378022361911711

The point about LLM's is they may have a lot of drawbacks right now but they're improving at a rapid pace. They already are very useful. There are hundreds of stories coming out of companies effectively leveraging them to replace workers in many natural-language related tasks. They're far more useful than a car that goes off a cliff.

Nobody more useful than an LLM is being effectively replaced by an LLM. Those few companies that jump the gun too early are suffering for it.

>That sign

We already have dishwashers and washing machines. Companies are working on making humanoid robots that can do those things, it's just that it's harder to develop a fully-fledged embodied humanoid than it is to create the diffusion models and LLM's being used today. It's not some conspiracy to let AI do all the fun stuff first.

Nobody is preventing anyone from making art or writing poetry. If someone finds value in AI art or writing, either you have to accept that they weren't the audience member you wanted, or you have to accept that your ability to be creative is a learnable algorithm same as anything else.

I don't understand why people assume that the purpose of any tool is to "replace humans". Automation doesn't replace humans and never has and never will. It simply does certain tasks that humans used to do, freeing people up to do different tasks. There is not a limited amount of work that can be done, there isn't a limited amount of _creative_ work that can be done. Even if AIs were good enough to do every creative task done by humans today (and they aren't and won't be any time soon), that doesn't mean that humans will have nothing of value to produce, or that humans will have been "replaced". There is always going to be work for humans to do, even in a universe where AI have super human capabilities at all tasks.

In particular, human beings strongly value the opinions and creative output of _human beings_ simply for the reason that they are human and similar to them. That will never change, no matter how intelligent that AIs get.

It's an existential complaint. "Why won't the nerds make something for meeeee." Do it yourself. Make that robot.

Sucks to think that you're not that special. Most art isn't. Most music isn't. Any honest artist will agree. Most professional artists are graphic designers, not brilliant once in a generation visionaries. It's the new excuse for starving artists. AI or no, they'd still be unsuccessful. That's the way it's always been.

That plan B is now going away, and a music career will be much more like a sports career: either you make it in football, or you need to find another career where your football skills won’t be very useful.

That is obviously scary for many.

Which I guess is appropriate, because he was literally crazy. He suffered from psychotic episodes and delusions and died from suicide, depressed and in poverty.

That’s the opposite of “making it”. It’s zero consolation that people like his work now, he never even knew.

So is building a tool that will only generate "approved" art. We need to be able to express our idea, feelings, our perception of the world in ways that do not fit corporate standards of text, audio, or visual communication. It's part of being human.

And so is eating ice cream with your forehead. Are we just doing non sequiturs now? I didn’t defend image generation tools in the slightest.

> We need to be able to express our idea, feelings, our perception of the world in ways that do not fit corporate standards of text, audio, or visual communication.

I agree. My point started and ended with “van Gogh in an awful example when talking about artist who ‘made it’”. That’s it. There is nothing in there to be extrapolated to AI or any other subject.

Your response is “learn to art” “The nerds dont owe you anything.” “Most of You would be unsuccessful anyway”

You brought in absolutely unrelated items.

1) Learn art - that is baked into what the Sign is saying. There is no Terminal Point for being an artist.

2) Nerds dont… - Where nerds come in as a class for this conversation?

2.1) if you can speak for all nerds, please note that I sure as heck dont want Warhammer 40k, I want Star Trek.

3) Most would be unsuccessful - so what?

Are they happy practicing their craft? Do they have the choice to spend their time on those pursuits and enrich their lives, and share their joys with others around them?

But people choose to be in denial.

As long as this level hasn't been achieved in all domains, it isn't AGI.

Furthermore, you're counting cases where humans do things the computer cannot but ignoring cases where the computer does things humans cannot. For instance, I doubt any human alive, let alone average humans can give reasonable explanations for short snippets of computer code in as many languages as GPT-4o, or formulate poetry in as many styles on arbitrary topics, or rattle off esoteric trivia and opinions about obscure historic topics, .... I think you get the point. It has already surpassed average human abilities in many categories of intellectually challenging tasks, but with your definition if it fails at even one task an average human can do, then it lacks "general intelligence."

I suggest that your definition is one for "AHI" (Average Human Intelligence), not one for "AGI" (Artificial General Intelligence.)

Not so with GPT. It will try, and fail, but that it tries at all was unimaginable five years ago.

I don't want to get into the weeds on what intelligence is or what "attempt" means or "try" means (you can probably guess I disagree with your position), but do you have a disagreement on pure input/output behavior? Do you disagree that if I put adequate words in, words will come out that will resemble an attempt to do the task, for nearly any task that exists?

Lol “very poor”. You’re attempting to argue that if there’s any output at all in response to an input prompt, then GPT is “trying” and showing signs of intelligence, no matter what the output is. By this logic, you contradicted yourself: the chess engine can play checkers, poorly. By this logic, asking the sky to play a game means the sky is trying because it changes, or asking a random number generator to play a game means it resembles an attempt to play because there is “very poor” output.

There are lots of games GPT can’t play, like hide-and-seek, tag, and tennis. Playing a game means playing by the rules of the game, giving coherent output, and trying to win. GPT can’t play games it hasn’t seen before, and no I don’t agree that “very poor” output counts. It doesn’t (currently) learn the rules from your prompts; you can’t teach it to play a new game by talking to it, and the “very poor” output from a game it wasn’t trained on will never improve. And, to my actual point, GPT will not play any games at all unless you ask it to.

Oh, not much. Training a network to do a prompted task is a lot easier than training a network to do unguided action selection because we don't really have a good textual dataset for that.

> the chess engine can play checkers, poorly.

Disagree: you're not gonna get any valid checkers moves out of the engine. It's a 0-to-1 thing. GPT-3 gets you to 1. There's a lot of Youtube videos of GPT-3 doing chess games that basically get through the opening and then fall over with invalid moves. This is the level of performance I refer to.

> There are lots of games GPT can’t play, like hide-and-seek, tag, and tennis.

Disagree: you can probably get GPT to play these. Show it a picture of the area and have it select a hiding spot or give you a search order. For tennis... something like have it write Python code for a tennis bot using image recognition? It'll probably fail, but it'll fail in the course of a recognizable attempt.

> It doesn’t (currently) learn the rules from your prompts; you can’t teach it to play a new game by talking to it

There's papers saying you're wrong, have you actually tried this? The whole novelty of 3+ was in-context learning.

Understand what the human in the loop doing the prompting is asking for, for one thing.

The magical aspects of LLMs are on the input side, not the output.

We don’t have any strong evidence that GPT “understands” its input in general. We absolutely have examples of GPT failing to understand some inputs (and not knowing it, and insisting on bogus output). And we know for a fact that it was designed and built to produce statistically plausible output. GPT is a mechanical device designed by humans to pass the Turing test. We’ve designed and built something that is exceptionally good at making humans believe it is smarter than it is.

Yep, and even ELIZA could do that, to some extent. But at some point you'll need to define what "understanding" means, and explain why an LLM isn't doing it.

At the time I noticed that many of the ARC problems rely on visual-spatial priors that are "obvious" when viewing the grids, but become less so when transmuted to some other representation. Many of them rely on some kind of symmetry, counting, or the very human bias to assume a velocity or continued movement when seeing particular patterns.

I had always thought maybe multimodality was key: the model needs to have similar priors around grounded physical spaces and movement to be able to do well. I'm not sure the OP really fleshes this line of thinking out, brute forcing python solutions is a very "non human" approach.

[0] https://x.com/eatpraydiehard/status/1632671307254099968

[1] https://x.com/eatpraydiehard/status/1632683214329479169

It still is. It misses the solution so comprehensively that it needs an outer loop to figure out which one is the solution out of 8k programs GPT-4o generates.

I don't think the location of the outer-loop or the design of it really makes much difference. There is no flock of birds without the individuals, the flock itself doesn't really exist as a tangible thing, but what arises out of the collective adjustments between all these individuals gives rise to a flock. Similarly, we may find groups of LLMs and various outer control loops give rise to an emergent phenomena much greater than the sum of their parts.

ARC-AGI has odd features that leave me flummoxed by the naming and the attendant prize money and hype.

It is one singular task and frankly I strongly suspect someone could beat it within 30 days[1], in an unsatisfying way, as you note.

There's so much alpha that can be pieced together from here, ex. the last couple Google papers use the 1M context to do *500-shot*, i.e. 500 question answer examples. IIRC most recent showed raising travelling-salesman problem solve rate from 3 to 35%.

[1] I pre-registered this via a Twitter post, about 48 hours ago, i.e. before this result was announced.

Program synthesis has been mentioned as a promising approach by François Chollet, and that's exactly what this is.

The place I find slightly unsatisfying is this:

> Sample vast, vast numbers of completions (~5,000 per problem) from GPT-4o.

> Take the most promising 12 completions for each problem, and then try to fix each by showing GPT-4o what this program actually outputs on the examples, and then asking GPT-4o to revise the code to make it correct. We sample ~3,000 completions that attempt to fix per problem in total across these 12 starting implementations.

I'd been tossing around a MCTS idea similar to AlphaGo, based on the idea that the end transformation is a series of sub-transformations. I feel like this could work well alongside the GPT-4o completion catalog. (This isn't an original observation or anything)

To be precise, "this" -a bog-standard generate-and-test approach- is the dumbest possible way to do program synthesis. It's like sorting lists with bogosort and a very big computer.

It's exactly like bogosort: generate permutations and test. Except of course the system that generates permutations costs a few millions(?).

A heuristic btw, is something completely different than fine tuning, or filtering. Heuristic search is the closest thing we have to an approximation of the kind of goal-driven behaviour we see in animal intelligence.

I think you could argue that gradient optimisation or any kind of optimisation of some kind of objective function is the same (Rich Sutton has a paper titled "Reward is all you need"). I'm not sure where I stand with that.

The heuristic isn't the fine-tuning, it's the actual LLM, which is clearly pruning the set of possibilities massively. That's a reasonably common usage of the word. I agree combining it with some kind of search would be interesting, but still I think you're being overly negative about the results here.

I'm actually busy training an alphazero for the arc problems, which I plan to try and hook up to a language model for reward generation, so we'll see how that fares!

I've read that paper, but thanks for the reference, this comment section is a goldmine.

I can't see where that is. All I can see the author saying they did is prompting and filtering of returned answers, none of which is going anywhere near the weights of the language model (that's where I'm claiming the "generator" is residing).

>> I'm actually busy training an alphazero for the arc problems, which I plan to try and hook up to a language model for reward generation, so we'll see how that fares!

That sounds exciting. Good luck with your effort!

In your opinion, what has changed that would accelerate a solution to the next 30 days?

This sort of idea would then be shared openly on new sites, creating more attempts. Fallout I did not anticipate was getting widespread attentional on general tech news sites, and then getting public comment from a prize co-founder confirming it was acceptable.

There is a plan for a “public” leaderboard, but it currently has no entries, so we don’t actually know what the SOTA for the unrestrained version is. [1]

The general idea - test time augmentation - is what the current private set SOTA uses. [2] Generating more examples via transforming the samples is not a new idea.

Really, it seems like all the publicity has just gotten a bunch of armchair software architects coming up with 1-4 year-old ideas thinking they are geniuses.

[1] https://arcprize.org/leaderboard

[2] https://lab42.global/community-interview-jack-cole/

I don't think you "don't understand" anything :) I'd ask you, politely, to consider that when you're replying to other people in the future.

Better to bring to interactions the prior that your interlocutor is a presumably intelligent individual who can have a different interpretation of the same facts, than decide they just don't get it. The second is a quite lonely path.

> Entries don’t have access to the internet.

Correct. Per TFA, cofounder, Chollet, then me: this is an offline solution: the solution is the Python program found by an LLM.

> The HN comment from the prize co-founder specifically says the OP’s claims haven’t been scrutinized.

Objection: relevancy? Is your claim here that it might be false so we shouldn't be discussing it at all?

> (implicit: they won’t be for the prize set unless the OP submits with an open LLM implementation)

I don't know what this means, "open LLM implementation" is either a term of art I don't recognize, or a misunderstanding of the situation.

I do assume you read the article, so I'm not trying to talk down to you, but to clarify:

The solution is the Python program, not the LLM prompts that iterated on a Python program. A common thread that would describe the confusing experience of reading your comment phrased aggressively and disputing everything up until you agree with me: your observations assume I assume the solution requires a cloud-based LLM to run. As noted above, it doesn't, which is also the thrust of my comment: they found a way to skirt what I thought the rules are, and the co-founder and Chollett have embraced it, publicly.

> There is a plan for a “public” leaderboard, but it currently has no entries, so we don’t actually know what the SOTA for the unrestrained version is. [1]

This was false before you posted, when I checked this morning, and it was false as early as 4 days ago, June 14th, we can confirm via archive.is. (prefix the URL you provided with archive.is/ to check for yourself)

> The general idea - test time augmentation - is what the current private set SOTA uses. [2] Generating more examples via transforming the samples is not a new idea.

Did anyone claim it was?

> Really, it seems like all the publicity has just gotten a bunch of armchair software architects coming up with 1-4 year-old ideas thinking they are geniuses.

I don't know what this means other than you're upset, but yes, sounds like both you and I agree that having an LLM generate Python programs isn't quite what we'd thought would be an AGI solution in the eyes of Chollet.

Alas, here we are.

>> (implicit: they won’t be for the prize set unless the OP submits with an open LLM implementation)

> The solution is the Python program, not the LLM prompts that iterated on a Python program. A common thread that would describe the confusing experience of reading your comment phrased aggressively and disputing everything up until you agree with me: your observations assume I assume the solution requires a cloud-based LLM to run. As noted above, it doesn't, which is also the thrust of my comment: they found a way to skirt what I thought the rules are, and the co-founder and Chollett have embraced it, publicly.

I think the implication is that solutions that use an LLM via an API won't be eligible (the "no internet" rule).

This seems obvious to solve: can use GPT4 to generate catalogs in advance and a lesser, local LLM with good code abilities to select them.

I don't see why this skirts any rules you think were implied and I'm puzzled why you think it does.

> sounds like both you and I agree that having an LLM generate Python programs isn't quite what we'd thought would be an AGI solution in the eyes of Chollet.

> Alas, here we are.

Chollet noted that program synthesis was a promising approach, so it's not surprising to me that a program synthesis approach that also uses an LLM is effective.

>ARC-AGI-Pub is a secondary leaderboard (in beta) measuring the public evaluation set. … The public evaluation set imposes no limitations on internet access or compute. At this time, ARG-AGI-Pub is not part of ARC Prize 2024 (eg. no prizes are associated with this leaderboard).

And, all the entries at time of writing and in the archive link say “You?…”. “ARC-AGI 2024 HIGH SCORES” which does have entries is on the private test set.

>I don't think you "don't understand" anything :)

I genuinely don’t understand if we are viewing the same websites.

We are! I missed the nuance on you're looking for a public leaderboard on the private test set. I do see it now, but I'm still confused as to how that's relevant here.

Though on the other hand figuring out which manipulations are effective does teach us something. And I think most problems boil down to pattern matching, creating a true, easily testable AGI test may be tough.

This would sound more far-fetched if we knew exactly how they work, bit-by-bit. We've been training them statistically, via the data-for-code tradeoff. The question is not yet satisfactorily answered.

In this hypothetical, for every accusation that an LLM passes a test because it's been coached to do so, there's a counter that it was designed for "excessively human" AGI to begin with, maybe even that it was designed for the unconscious purpose of having humans pass it preferentially. The attorney for the hypothetical AGI in the LLM would argue that there are tons of "LLM AGI" problems it can solve that a human would struggle with.

Fundamentally, the tests are only useful insofar as they let us improve AI. The evaluation of novel approaches to pass them like this one should err in the approaches' favor, IMO. A 'gotcha' test is the least-useful kind.

The human brain is millions of years of brute force evolution in the making. Comparing it to a transformer or any other ANN really which essentially start from scratch relatively speaking doesn't mean much.

This isn't really true. If you give an LLM a large prompt detailing a new spoken language, programming language or logical framework with a couple examples, and ask it to do something with it, it'll probably do a lot better at it than if you just let an average human read the same prompt and do the same task.

Anyway, my point was that humans butter direct their energy than randomly spamming ideas, at least with the innovation of the scientific method. But an LLM struggles deeply to perform reasoning.

https://chatgpt.com/share/2fde1db5-00cf-404d-9ae5-192aa5ac90...

GPT-4 created a plan very similar to the article, i.e. it also suggested using Python to pre-process data. It also suggested using program synthesis. So I'd say it's already 90% there.

> "Execute the synthesized program on the test inputs."

> "Verify the outputs against the expected results. If the results are incorrect, iteratively refine the hypotheses and rules."

So people saying that it's ad-hoc are wrong. LLMs know how to solve these tasks, they are just not very good at coding, and iterative refinement tooling is in infancy.

Hard to find the right float but worth trying I think.

1) Most of the heavy lifting is being done by search. We're talking about having the LLM generate thousands of candidate solutions, and they're mostly bad enough that "just pick the ones that get kinda close on the examples" is a meaningful operation.

2) More samples improves performance despite the fact that GPT-4o's vision is not capable of parsing the inputs. I'm curious how much performance would degrade if you shuffled the images passed to the model (but used the correct images when evaluating which candidates to keep).

3) It's definitely true that the LLM has to be giving you something more than random programs. At the very least, the LLM knows how to craft parsimonious programs that are more likely to be the solution. It may be that it's providing more than that, but it's not clear to me exactly how much information on the correct search space is coming from the hand-crafted examples in the prompt.

Overall, the work to get this far is very impressive, but it doesn't really move the needle for me on whether GPT-4 can do ARC puzzles. It does, however, show me that search is surprisingly powerful on this task.

Maybe a AI version of Hanlons Razor. Never attribute to AGI what could be easily explained by being in the training set.

I think we need those pieces, and also a piece for determining hypotheses in an efficient manner. Monte Carlo Tree Search could be that piece. Probabilistically choose a node to search, and then backpropagate the probabilities back to the root node.

Isn't the public test set public on github and therefore GPT-4o trained on it?

It will be good to see the private set results though.

So it's not at all surprising to me to see Arc already being mostly solved using existing models, just with different prompting techniques and some tool usage. At some point, the naysayers about LLMs are going to have to confront the problem that, if they are right about LLMs not really thinking/understanding/being sentient, then a very large percentage of people living today are also not thinking/understanding/sentient!

The LLM isn't doing the reasoning here, it's just pattern matching the before/after diff and generating thousands of Python programs. The actual reasoning is done by an agentic like loop wrapped around the LLM, as described in the linked blog.

I don't think it is like that but rather Chollet wants to see stronger neuroplasticity in these models. I think there is a divide between the effectiveness of existing AI models versus their ability to be autonomous, robust and consistently learn from unanticipated problems.

My guess is Chollet wants to see something more similar to biological organisms especially mammals or birds in their level of autonomous nature. I think people underestimate the degree of novel problems birds and mammals alone face in just simply navigating their environment and it is the comparison here that LLMs, for now at least, seem lacking.

So when he says LLMs are not sentient, he's asking to consider the novel problems animals let alone humans have to face in navigating their environment. This is especially apparent in young children but declines as we age and gain experience/lose a sense of novelty.

Chollet published his paper On the measure of intelligence in 2019. In Internet time that is a lifetime before the LLM hype started.

Is that true?

C.f. what we're discussing

He's actively encouraging using LLMs to solve his benchmark, called ARC AGI.

8 hours ago, from Chollet, re: TFA

"The best solution to fight combinatorial explosion is to leverage intuition over the structure of program space, provided by a deep learning model. For instance, you can use a LLM to sample a program..."

Source: https://x.com/fchollet/status/1802801425514410275

I don't mean to offend, but to be really straightforward: he's the one saying it's possible they might be AGI now. I'm as flummoxed as you, but I think its hiding the ball to file it under "he doesn't mean what he's saying, because he doesn't believe LLMs can ever be AGI." The only steelman for that is playing at: AGI-my-benchmark, which I say is for AGI, is not the AGI I mean

b) He is not being overly negative of LLMs. In fact he believes they will play a role in any AGI system.

c) OpenAI CTO has publicly said that ChatGPT 5 will not be significantly better than existing models. So the rate of improvements you believe in simply doesn't match reality.

This isn't that big a bullet to bite (https://www.lesswrong.com/posts/4AHXDwcGab5PhKhHT/humans-who... comes from well before ChatGPT's launch), and I myself am inclined to bite it. System 1 alone does not a general intelligence make, although the article is extremely interesting in asking the question "is System 1 plus Python enough for a general intelligence?". But it's not a very relevant philosophical point, because Chollet's position is consistent with humans being obsoleted and/or driven extinct whether or not the LLMs are "general intelligences".

His position is that training LLMs results in an ever-larger number of learned algorithms and no ability to construct new algorithms. This is consistent with the possibility that, after some threshold of size and training, the LLM has learned every algorithm it needs to supplant humans in (say) 99.9% of cases. (It would definitely be going out with a whimper rather than a bang, on that hypothesis, to be out-competed by something that _really is_ just a gigantic lookup table!)

Back in the days similar generalization was used for Deep Blue chess computer. Computer won in 1997, but the AGI abyss is still as big.

His definition of intelligence is interesting: something that can quickly achieve tasks with few priors or experience. I also think the idea of using human "Core Knowledge" priors is a clever way to make a test.

It literally just did this for me 15 minutes ago. You can't talk about AGI when it is this easy to push it over the edge into something it doesn't know.

Paper references have got better the last 12 months but just this week it made up both a book and paper for me that do not exist. The authors exist and they did not write what it said they did.

It is very interesting if you ask "do you understand your responses?" sometimes it will say yes and sometimes it will so no not like a human understands.

We should forget about AGI until it can at least say it doesn't know something. It is hardly a sign of intelligence in humans to make up answers to questions you don't know.

Unless you’re saying you have never held on to a wrong opinion that was at some point proven to be wrong?

The approach described in the article is exactly "brute-force search over some sort of DSL". The "DSL" is a model of Python syntax that GPT-4o has learned after training on the entire internet. This "DSL" is locked up in the black box of GPT-4o's weights, but just because no-one can see it, it doesn't mean it's not there; and we can see GPT-4o generating Python programs, so we know it is there, even if we don't know what it looks like.

That DSL may not be "domain specific" in the sense of being specifically tailored to solve ARC-AGI tasks, or any other particular task, but it is "domain specific" in the sense of generating Python programs for some subset of all possible Python programs that includes programs that can solve some ARC-AGI tasks. That's a very broad category, but that's why it over-generates so much: it needs to draw 8k samples total until one works for just 50% of the public eval set.

Seems like it relies on identification of objects and then mapping them somehow. Most of the cases so far that I've seen are based on some transformation or relation between the objects.

So far it seems like some search among common transformatiosn and relations could solve it. Plus some heuristics/computation for counting order, wholeness(boundary) or pattern.

IMO it can be solved by search of programs that combine these + some LLM to guide heuristics most likely.

The only hard one was applied noise or one testing understanding of "gravity".

Did anyone test human baseline for this?

https://www.youtube.com/watch?v=QWWgr2rN45o&t=46m20s

The truth is in the middle, I think. They learn in-context, but not as well as humans.

The approach in the article hides the unreliability of current LLMs by generating thousands of programs, and still the results aren't human-level. (This is impressive work though -- I'm not criticizing it.)

In general, there is too much fluff and confusion floating around about what these models are and are not capable of (regardless of the training mechanism.) I think more people need to read Song Mei's lovely slides[1] and related work by others. These slides are the best exposition I've found of neat ideas around ICL that researchers have been aware of for a while.

[1] https://www.stat.berkeley.edu/~songmei/Presentation/Algorith...

But you can have GPT write code to reliably convert the image grid into a textual representation, right? And code to convert back to image and auto-verify.

Program search mimics what humans do to a certain extent but not in entirety.

A more general world model and reference will be required for agi.

If you can't do ARC, you aren't general enough. But even if you can do ARC, you still might not be general enough.

General intelligence as we know it requires ability to receive education.