6 ms·
Let me go against some skeptics and explain why I think full o3 is pretty much AGI or at least embodies most essential aspects of AGI. What has been lacking so
by nopinsight 2y ago
Let me go against some skeptics and explain why I think full o3 is pretty much AGI or at least embodies most essential aspects of AGI.
What has been lacking so far in frontier LLMs is the ability to reliably deal with the right level of abstraction for a given problem. Reasoning is useful but often comes out lacking if one cannot reason at the right level of abstraction. (Note that many humans can't either when they deal with unfamiliar domains, although that is not the case with these models.)
ARC has been challenging precisely because solving its problems often requires:
1) using multiple different *kinds* of core knowledge [1], such as symmetry, counting, color, AND
2) using the right level(s) of abstraction
Achieving human-level performance in the ARC benchmark, as well as top human performance in GPQA, Codeforces, AIME, and Frontier Math suggests the model can potentially solve any problem at the human level if it possesses essential knowledge about it. Yes, this includes out-of-distribution problems that most humans can solve.
It might not yet be able to generate highly novel theories, frameworks, or artifacts to the degree that Einstein, Grothendieck, or van Gogh could. But not many humans can either.
[1] https://www.harvardlds.org/wp-content/uploads/2017/01/SpelkeKinzler07-1.pdf https://www.harvardlds.org/wp-content/uploads/2017/01/Spelke...
ADDED:
Thanks to the link to Chollet's posts by lswainemoore below. I've analyzed some easy problems that o3 failed at. They involve spatial intelligence, including connection and movement. This skill is very hard to learn from textual and still image data.
I believe this sort of core knowledge is learnable through movement and interaction data in a simulated world and it will not present a very difficult barrier to cross. (OpenAI purchased a company behind a Minecraft clone a while ago. I've wondered if this is the purpose.)
- xvector 2y agoAgree. AGI is here. I feel such a sense of pride in our species.
- deleted 2y ago[deleted]
- timabdulla 2y agoWhat's your explanation for why it can only get ~70% on SWE-bench Verified? I believe about 90% of the tasks were estimated by humans to take less than one hour to solve, so we aren't talking about very complex problems, and to boot, the contamination factor is huge: o3 (or any big model) will have in-depth knowledge of the internals of these projects, and often even know about the individual issues themselves (e.g. you can say what was Github issue #4145 in project foo, and there's a decent chance it can tell you exactly what the issue was about!)
- slewis 2y agoI've spent tons of time evaluating o1-preview on SWEBench-Verified. For one, I speculate OpenAI is using a very basic agent harness to get the results they've published on SWEBench. I believe there is a fair amount of headroom to improve results above what they published, using the same models. For two, some of the instances, even in SWEBench-Verified, require a bit of "going above and beyond" to get right. One example is an instance where the user states that a TypeError isn't properly handled. The developer who fixed it handled the TypeError but also handled a ValueError, and the golden test checks for both. I don't know how many instances fall in this category, but I suspect its more than on a simpler benchmark like MATH.
- timabdulla 2y agoSo what percentage would you say falls to simple inability versus the other two factors you've mentioned?
- nopinsight 2y agoOne possibility is that it may not yet have sufficient experience and real-world feedback for resolving coding issues in professional repos, as this involves multiple steps and very diverse actions (or branching factor, in AI terms). They have committed to not training on API usage, which limits their ability to directly acquire training data from it. However, their upcoming agentic efforts may address this gap in training data.
- 2y ago
- Imnimo 2y ago>Achieving human-level performance in the ARC benchmark, as well as top human performance in GPQA, Codeforce, AIME, and Frontier Math strongly suggests the model can potentially solve any problem at the human level if it possesses essential knowledge about it. The article notes, "o3 still fails on some very easy tasks". What explains these failures if o3 can solve "any problem" at the human level? Do these failed cases require some essential knowledge that has eluded the massive OpenAI training set?
- nopinsight 2y agoGreat point. I'd love to see what these easy tasks are and would be happy to revise my hypothesis accordingly. o3's intelligence is unlikely to be a strict superset of human intelligence. It is certainly superior to humans in some respects and probably inferior in others. Whether it's sufficiently generally intelligent would be both a matter of definition and empirical fact.
- Imnimo 2y agoChollet has a few examples here: https://x.com/fchollet/status/1870172872641261979 https://x.com/fchollet/status/1870172872641261979 https://x.com/fchollet/status/1870173137234727219 https://x.com/fchollet/status/1870173137234727219 I would definitely consider them legitimately easy for humans.
- nopinsight 2y agoThanks! I added some comments on this at the bottom of the post above.
- phil917 2y agoQuote from the creators of the AGI-ARC benchmark: "Passing ARC-AGI does not equate achieving AGI, and, as a matter of fact, I don't think o3 is AGI yet. o3 still fails on some very easy tasks, indicating fundamental differences with human intelligence."
- 93po 2y agothey say it isn't AGI but i think the way o3 functions can be refined to AGI - it's learning to solve a new, novel problems. we just need to make it do that more consistently, which seems achievable
- CooCooCaCha 2y agoYeah the real goalpost is reliable intelligence. A supposed phd level AI failing simple problems is a red flag that we’re still missing something.
- gremlinsinc 2y agoYou've never met a Doctor who couldn't figure out how to work their email? Or use street smarts? You can have a PHD but be unable to reliably handle soft skills, or any number of things you might 'expect' someone to be able to do. Just playing devils' advocate or nitpicking the language a bit...
- CooCooCaCha 2y agoAn important distinction here is you’re comparing skill across very different tasks. I’m not even going that far, I’m talking about performance on similar tasks. Something many people have noticed about modern AI is it can go from genius to baby-level performance seemingly at random. Take self driving cars for example, a reasonably intelligent human of sound mind and body would never accidentally mistake a concrete pillar for a road. Yet that happens with self-driving cars, and seemingly here with ARC-AGI problems which all have a similar flavor.
- nuancebydefault 2y ago
- nyrikki 2y agoGPQA scores are mostly from pre-training, against content in the corpus. They have gone silent but look at the GPT4 technical report which calls this out. We are nowhere close to what Sam Altman calls AGI and transformers are still limited to what uniform-TC0 can do. As an example the Boolean Formula Value Problem is NC1-complete, thus beyond transformers but trivial to solve with a TM. As it is now proven that the frame problem is equivalent to the halting problem, even if we can move past uniform-TC0 limits, novelty is still a problem. I think the advancements are truly extraordinary, but unless you set the bar very low, we aren't close to AGI. Heck we aren't close to P with commercial models.
- sebzim4500 2y agoIsn't any physically realizable computer (including our brains) limited to what uniform-TC0 can do?
- drdeca 2y agoDo you just mean because any physically realizable computer is a finite state machine? Or...? I wouldn't describe a computer's usual behavior as having constant depth. It is fairly typical to talk about problems in P as being feasible (though when the constant factors are too big, this isn't strictly true of course). Just because for unreasonably large inputs, my computer can't run a particular program and produce the correct answer for that input, due to my computer running out of memory, we don't generally say that my computer is fundamentally incapable of executing that algorithm.
- nyrikki 2y agoNeither TC0 nor uniform-TC0 are physically realizable, they are tools not physical devices. The default nonuniform circuits classes are allowed to have a different circuit per input size, the uniform types have unbounded fan-in Similar to how a k-tape TM doesn't get 'charged' for the input size. With Nick Class (NC) the number of components is similar to traditional compute time while depth relates to the ability to parallelize operations. These are different than biological neurons, not better or worse but just different. Human neurons can use dendritic compartmentalization, use spike timing, can retime spikes etc... While the perceptron model we use in ML is useful, it is not able to do xor in one layer, while biological neurons do that without anything even reaching the soma, purely in the dendrites. Statistical learning models still comes down to a choice function, no matter if you call that set shattering or... With physical computers the time hierarchy does apply and if TIME(g(n)) is given more time than TIME(f(n)), g(n) can solve more problems. So you can simulate a NTM with exhaustive search with a physical computer. Physical computers also tend to have NAND and XOR gates, and can have different circuit depths. When you are in TC0, you only have AND, OR and Threshold (or majority) gates. Think of instruction level parallelism in a typical CPU, it can return early, vs Itanium EPIC, which had to wait for the longest operation. Predicated execution is also how GPUs work. They can send a mask and save on load store ops as an example, but the cost of that parallelism is the consent depth. It is the parallelism tradeoff that both makes transformers practical as well as limit what they can do. The IID assumption and autograd requiring smooth manifolds plays a role too. The frame problem, which causes hard problems to become unsolvable for computers and people alike does also. But the fact that we have polynomial time solutions for the Boolean Formula Value Problem, as mentioned in my post above is probably a simpler way of realizing physical computers aren't limited to uniform-TC0.
- norir 2y agoPersonally I find "human-level" to be a borderline meaningless and limiting term. Are we now super human as a species relative to ourselves just five years ago because of our advances in developing computer programs that better imitate what many (but far from all) of us were already capable of doing? Have we reached a limit to human potential that can only be surpassed by digital machines? Who decides what human level is and when we have surpassed it? I have seen some ridiculous claims about ai in art that don't stand up to even the slightest scrutiny by domain experts but that easily fool the masses.
- razodactyl 2y agoNo I think we're just tired and depressed as a species... Existing systems work to a degree but aren't living up to their potential of increasing happiness according to technological capabilities.
- PaulDavisThe1st 2y ago> It might not yet be able to generate highly novel theories, frameworks, or artifacts to the degree that Einstein, Grothendieck, or van Gogh could. Every human does this dozens, hundreds or thousands of times ... during childhood.
- ec109685 2y agoThe problem with ARC is that there are a finite number of heuristics that could be enumerated and trained for, which would give model a substantial leg up on this evaluation, but not be generalized to other domains. For example, if they produce millions of examples of the type of problems o3 still struggles on, it would probably do better at similar questions. Perhaps the private data set is different enough that this isn’t a problem, but the ideal situation would be unveiling a truly novel dataset, which it seems like arc aims to do.
- golol 2y agoIn order to replace actual humans doing their job I think LLMs are lacking in judgement, sense of time and agenticism.
- Kostchei 2y agoI mean fkcu me when they have those things, however, maybe they are just lazy and their judgement is fine, for a lazy intelligence. Inner-self thinks "why are these bastards asking me to do this? ". I doubt that is actually happening, but now, .. prove it isn't.
- deleted 2y ago[deleted]
- puttycat 2y agoGreat comment. See this as well for another potential reason for failure: https://arxiv.org/abs/2402.10013 https://arxiv.org/abs/2402.10013
- dimitri-vs 2y agoHave we really watered down the definition of AGI that much? LLMs aren't really capable of "learning" anything outside their training data. Which I feel is a very basic and fundamental capability of humans. Every new request thread is a blank slate utilizing whatever context you provide for the specific task and after the tread is done (or context limit runs out) it's like it never happened. Sure you can use databases, do web queries, etc. but these are inflexible bandaid solutions, far from what's needed for AGI.
- theptip 2y ago> LLMs aren't really capable of "learning" anything outside their training data. ChatGPT has had for some time the feature of storing memories about its conversations with users. And you can use function calling to make this more generic. I think drawing the boundary at “model + scaffolding” is more interesting.
- dimitri-vs 2y agoCalling the sentence or two it arbitrarily saves when you statd your preferences and profile info "memories" is a stretch. True equivalent to human memories would require something like a multimodal trillion token context window. RAG is just not going to cut it, and if anything will exacerbated problems with hallucinations.
- theptip 2y agoWell, now you’ve moved the goalposts from “learn anything” to “learn at human level”. Sure, they don’t have that yet.
- deleted 2y ago[deleted]
- TrapLord_Rhodo 2y agoThats the whole point of llama index? I can connect my LLM to any node or context i want. Syncing it to a real time data flow like an API and it can learn...? How is that different than a human? Once optimus is up an working by the 100k+, the spatial problems will be solved. We just don't have enough spatial awareness data, or for a way for the LLM to learn about the physical world.
- uncomplexity_ 2y agoon the spatial data i see it as a highly intelligent head of a machine that just needs better limbs and better senses. i think that's where most hardware startups will specialize with in the coming decades, different industries with different needs.
- deleted 2y ago[deleted]
- hollowturtle 2y agoPlease stop it calling AGI, we don’t even know or agree universally what that should actually mean. How far did we get with hype calling a lossy probabilistic compressor firing slowly at us words AGI? That’s a real bummer to me
- razodactyl 2y agoIs this comment voted down because of sentiment / polarity? Regardless the critical aspect is valid, AGI would be something like Cortana from Halo.
- ryoshu 2y agoAsk o3 is P=NP?
- amelius 2y agoIt will just answer with the current consensus on the matter.
- deleted 2y ago[deleted]
- deleted 2y ago[deleted]
- zwnow 2y agoThis is not AGI lmao.
- tangjurine 2y agohow about if it can work at a job? people can do that, can o3 do it?