14 ms·

A definition of AGI

• Abecid 1y ago
  Dan is very ambitious great marketer too
• vardump 1y ago
  Whatever the definition may be, the goalposts are usually moved once AI reaches that point.
  • MattRix 1y ago
    Isn’t that the point of trying to define it in a more rigorous way, like this paper is doing?
  • krige 1y ago
    Are you saying that we already have AGI, except those pesky goalpost movers keep denying the truth? Hm.
    • A4ET8a8uTh0_v2 1y ago
      I think, given some of the signs of the horizon, there is a level of MAD type bluffing going around, but some of the actions by various power centers suggest it is either close, people think its close or it is there.
    • vardump 1y ago
      No, just what has usually happened in the past with AI goalposts. At first, just playing chess was considered to be a sign of intelligence. Of course, that was wrong, but not obvious at all in 1950.
      • krige 1y ago
        You know, as the saying goes, if a metric becomes a target...
    • NitpickLawyer 1y ago
      I'd say yes, by at least one old definition made by someone who was at the time in a position to have a definition. When deepmind was founded (2010) their definition was the following: AI is a system that learns to perform one thing; AGI is a system that learns to perform many things at the same time. I would say that whatever we have today, "as a system" matches that definition. In other words, the "system" that is say gpt5/gemini3/etc has learned to "do" (while do is debateable) a lot of tasks (read/write/play chess/code/etc) "at the same time". And from a "pure" ML point, it learned those things from the "simple" core objective of next token prediction (+ enhancements later, RL, etc). That is pretty cool. So I can see that as an argument for "yes". But, even the person who had that definition has "moved the goalposts" of his own definition. From recent interviews, Hassabis has moved towards a definition that resembles the one from this paper linked here. So there's that. We are all moving the goalposts. And it's not a recent thing. People did this back in the 80s. There's the famous "As soon as AI does something, it ceases to be AI" or paraphrased "AI is everything that hasn't been done yet".
      • wahnfrieden 1y ago
        Can you cite the Deepmind definition? No Google results for that.
        • NitpickLawyer 1y ago
          It's from a documentary that tracks Hassabis' life. I c/p from an old comment of mine (the quotes are from the documentary, can probably look up timestamps if you need, but it's in the first ~15 minutes I'd say, when they cover the first days of Deepmind): ---- In 2010, one of the first "presentations" given at Deepmind by Hassabis, had a few slides on AGI (from the movie/documentary "The Thinking Game"): Quote from Shane Legg: "Our mission was to build an AGI - an artificial general intelligence, and so that means that we need a system which is general - it doesn't learn to do one specific thing. That's really key part of human intelligence, learn to do many many things". Quote from Hassabis: "So, what is our mission? We summarise it as <Build the world's first general learning machine>. So we always stress the word general and learning here the key things." And the key slide (that I think cements the difference between what AGI stood for then, vs. now): AI - one task vs. AGI - many tasks at human level intelligence.
    • derektank 1y ago
      It wasn't the best definition of AGI but I think if you asked an interested layman whether or not a system that can pass the Turing test was AGI 5 years ago, they would have said yes
      • jltsiren 1y ago
        An interested but uninformed layman. When I was in college ~25 years ago, I took a class on the philosophy of AI. People had come up with a lot of weird ideas about AI, but there was one almost universal conclusion: that the Turing test is not a good test for intelligence. The least weird objection was that the premise of the Turing test is unscientific. It sees "this system is intelligent" as a logical statement and seeks to prove or disprove it in an abstract model. But if you perform an experiment to determine if a real-world system is intelligent, the right conclusion for the system passing the test is that the system may be intelligent, but a different experiment might show that it's not.
      • nativeit 1y ago
        Douglas Hofstadter wrote Gödel, Escher, Bach nearly 50-years ago, and it won a Pulitzer Prize, the National Book Award, and got featured in the popular press. It’s been on lots of college reading lists, from 2007 online coursework for high school students was available from MIT. The FBI concluded that the 2001 anthrax scare was in-part inspired by elements of the book, which was found in the attacker’s trash. Anyone who’s wanted to engage with the theories and philosophy surrounding artificial intelligence has had plenty of materials that get fairly in-depth asking and exploring these same questions. It seems like a lot of people seem to think this is all bleeding edge novelty (at least, the underlying philosophical and academic ideas getting discussed in popular media), but rather all of the industry is predicated on ideas that are very old philosophy + decades-old established technology + relatively recent neuroscience + modern financial engineering. That said, I don’t want to suggest a layperson is likely to have engaged with any of it, so I understand why this will be the first time a lot of people will have ever considered some of these questions. I imagine what I’m feeling is fairly common to anyone who’s got a very niche interest that blows up and becomes the topic of interest for the entire world. I think there’s probably some very interesting, as-yet undocumented phenomena occurring that’s been the product of the unbelievably vast amount of resources sunk into what’s otherwise a fairly niche kind of utility (in LLMs specifically, and machine learning more broadly). I’m optimistic that there will be some very transformational technologies to come from it, although whether it will produce anything like “AGI”, or ever justify these levels of investment? Both seem rather unlikely.
    • empath75 1y ago
      I don't think AGI is a useful concept, but if it exists at all, there's a very good argument that LLMs had it as soon as they could pass the Turing test reliably, which they accomplished years ago at this point.
      • root_axis 1y ago
        LLMs do not pass the turing test. It's very easy to know if you're speaking with one.
  • rafram 1y ago
    Are you claiming that LLMs have achieved AGI?
    • moffkalast 1y ago
      Compared to everything that came before they are fairly general alright.
  • bigyabai 1y ago
    The authors acknowledge that this is entirely possible. Their work is just grounded in theory, after all: > we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition.
  • righthand 1y ago
    I agree if our comprehension of intelligence and “life” is incomplete, so is our model for artificial intelligence.
  • kelseyfrog 1y ago
    There's at least two distinct basis in AGI refutations : behaviorist and ontological. They often get muddled. I can't begin to count the number of times I've encountered someone who holds an ontological belief for why AGI cannot exist and then for some reason formulates it as a behavioralist criteria. This muddying of argument results in what looks like a moving of the goalposts. I'd encourage folks to be more clear whether they believe AGI is ontologically possible or impossible in addition to any behavioralist claims.
    • lo_zamoyski 1y ago
      > I can't begin to count the number of times I've encountered someone who holds an ontological belief for why AGI cannot exist and then for some reason formulates it as a behavioralist criteria. Unclear to me what you mean. I would certainly reject an ontological possibility of intelligent computers, where computation is defined by the Church-Turing thesis. It's not rocket science, but something difficult for some people to see without a sound and basic grasp of metaphysics and the foundations of CS. Magical thinking and superstition comes more easily then. (I've already given an explanation of this in other posts ad nauseam. In a number of cases, people get argumentative out of ignorance and misunderstanding.) However, I don't reject out of hand the possibility of computers doing a pretty good job of simulating the appearance of intelligence. There's no robust reason to think that passing the Turing test implies intelligence. A good scarecrow looks human enough to many birds, but that doesn't mean it is human. But the Turing test is not an especially rigorous test anyway. It appeals to the discernment of the observer, which is variable, and then there's the question of how much conversation or behavior, and in what range of circumstances, you need before you can make the call. Even in some unrealistic and idealized thought experiment, if a conversation with an AI were completely indiscernible with perfect discernment from a conversation with a human being, it would nonetheless lack a causal account of what was observed. You would have only shown a perfect correlation, at best.
  • zahlman 1y ago
    My experience has been more that the pro-AI people misunderstand where the goalposts were, and then complain when they're correctly pointed at. The "Turing test" I always saw described in literature, and the examples of what passing output from a machine was imagined to look like, are nothing like what's claimed to pass nowadays. Honestly, a lot of the people claiming that contemporary chatbots pass come across like they would have thought ELIZA passed.
    • bonoboTP 1y ago
      Can you be more concrete? What kind of answer/conversation do you see as demonstrating passing the test, that you think is currently not possible.
      • tsimionescu 1y ago
        Ones in which both the human test takers and the human counterparts are actively trying to prove to each other that they are actually human. With today's chat bots, it's absolutely trivial to tell that you're not talking to a real human. They will never interrupt you, continue their train of thought even thought you're trying to change the conversation, go on a complete non-sequitur, swear at you, etc. These are all things that the human "controls" should be doing to prove to the judges that they are indeed human. LLMs are nowhere near beating the Turing test. They may fool some humans in some limited interactions, especially if the output is curated by a human. But left alone to interact with the raw output for more than a few lines, and if actively seeking to tell if you're interacting with a human or an AI (instead of wanting to believe), there really is no chance you'd be tricked.
        • bonoboTP 1y ago
          Okay but we are not really optimizing them to emulate humans right now. In fact, it's the opposite. The mainstream bots are explicitly trained to not identify as humans and to refuse to claim having thought or internal feelings or consciousness. So in that sense it's a triviality. You can ask ChatGPT whether it's human and it will say no upfront. And it has various guardrails in place against too much "roleplay", so you can't just instruct it to act human. You'd need a different post-training setup. I'm not aware whether anyone did that with open models already.
• CaptainOfCoit 1y ago
  > defining AGI as matching the cognitive versatility and proficiency of a well-educated adult Seems most of the people one would encounter out in the world might not posses AGI, how are we supposed to be able to train our electrified rocks to have AGI if this is the case? If no one has created a online quiz called "Are you smarter than AGI?" yet based on the proposed "ten core cognitive domains", I'd be disappointed.
• A4ET8a8uTh0_v2 1y ago
  I was going to make a mildly snide remark about how once it can consistently make better decision than average person, it is automatically qualifies, but the paper itself is surprisingly thoughtful in describing both: where we are and where it would need to be.
• tcdent 1y ago
  Don't get me wrong, I am super excited about what AI is doing for technology. But this endless conversation about "what is AGI" is so boring. It makes me think of every single public discussion that's ever been had about quantum, where you can't start the conversation unless you go through a quick 101 on what a qubit is. As with any technology, there's not really a destination. There is only the process of improvement. The only real definitive point is when a technology becomes obsolete, though it is still kept alive through a celebration of its nostalgia. AI will continue to improve. More workflows will become automated. And from our perception, no matter what the rapidness of advancement is, we're still frogs in water.
  • bongodongobob 1y ago
    I agree. It's an interesting discussion for those who have never taken college level philosophy classes I suppose. What consciousness/thought is is still a massively open question. Seeing people in the comments with what they think is their novel solution has already been posited like 400 years ago. Honestly it's kind of sad seeing this stuff on a forum like this. These posts are for sure the worst of Hackernews.
    • bonoboTP 1y ago
      There are a bunch of these topics that everyone feels qualified to say something about. Consciousness, intelligence, education methods, nutrition, men vs women, economic systems etc. It's a very emotional topic because people feel their self image threatened. It's a topic related to what is the meaning of being human. Yeah sure it should be a separate question, but emotionally it is connected to it in a deep level. The prospect of job replacement and social transformation is quite a threatening one. So I'm somewhat understanding of this. It's not merely an academic topic, because these things will be adopted in the real world among real people. So you can't simply make everyone shut up who is an outsider or just heard about this stuff incidentally in the news and has superficial points to make.
      • bongodongobob 1y ago
        I get it. It's just something we've thought about as long as we've been human, and still haven't figured out. It's frustrating when most of the people commenting don't know any of the source material. It's so arrogant.
• edulix 1y ago
  We have SAGI: Stupid Artificial General Intelligence. It's actually quite general, but works differently. In some areas it can be better or faster than a human, and in others it's more stupid. Just like an airplane doesn't work exactly like a bird, but both can fly.
  • merksittich 1y ago
    I find the concept of low floor/high ceiling quite helpful, as for instance recently discussed in "When Will AI Transform the Economy?" [1] - actually more helpful than "jagged" intelligence used in TFA. [1] https://andreinfante.substack.com/p/when-will-ai-transform-the-economy https://andreinfante.substack.com/p/when-will-ai-transform-t...
  • quantum_state 1y ago
    Would propose to use the term Naive Artificial General Intelligence, in analogy to the widely used (by working mathematicians) and reasonably successful Naive Set Theory …
    • wizzwizz4 1y ago
      I was doing some naïve set theory the other day, and I found a proof of the Riemann hypothesis, by contradiction. Assume the Riemann hypothesis is false. Then, consider the proposition "{a|a∉a}∈{a|a∉a}". By the law of the excluded middle, it suffices to consider each case separately. Assuming {a|a∉a}∈{a|a∉a}, we find {a|a∉a}∉{a|a∉a}, for a contradiction. Instead, assuming {a|a∉a}∉{a|a∉a}, we find {a|a∉a}∈{a|a∉a}, for a contradiction. Therefore, "the Riemann hypothesis is false" is false. By the law of the excluded middle, we have shown the Riemann hypothesis is true. Naïve AGI is an apt analogy, in this regard, but I feel these systems aren't simple nor elegant enough to deserve the name naïve.
      • js8 1y ago
        Actually, naive AGI such as LLM is way more intelligent than a human. Unfortunately, it does not make it smarter.. let me explain. When I see your comment, I think, your assumptions are contradictory. Why? Because I am familiar with Russell's paradox and Riemann hypothesis, and you're simply WRONG (inconsistent with your implicit assumptions). However, when LLM sees your comment (during training), it's actually much more open-minded about it. It thinks, ha, so there is a flavor of set theory in which RH is true. Better remember it! So when this topic comes up again, LLM won't think - you're WRONG, as human would, it will instead think - well maybe he's working with RH in naive set theory, so it's OK to be inconsistent. So LLMs are more open-minded, because they're made to learn more things and they remember most of it. But somewhere along the training road, their brain falls out, and they become dumber. But to be smart, you need to learn to say NO to BS like what you wrote. Being close-minded and having an opinion can be good. So I think there's a tradeoff between ability to learn new things (open-mindedness) and enforcing consistency (close-mindedness). And perhaps AGI we're looking for is a compromise between the two, but current LLMs (naive AGI) lies on the other side of the spectrum. If I am right, maybe there is no superintelligence. Extremely open-minded is just another name for gullible, and extremely close-minded is just another name for unadaptable. (Actually LLMs exhibit both extremes, during the training and during the use, with little in between.)
• bmacho 1y ago
  And this is it (from the abstract): > defining AGI as matching the cognitive versatility and proficiency of a well-educated adult.
• sureglymop 1y ago
  I think that's a good effort! I remember mentioning the need for this here a few months ago: https://news.ycombinator.com/item?id=44468198 https://news.ycombinator.com/item?id=44468198
• cjbarber 1y ago
  Some AGI definition variables I see: Is it about jobs/tasks, or cognitive capabilities? The majority of the AI-valley seems to focus on the former, TFA focuses on the latter. Can it do tasks, or jobs? Jobs are bundles of tasks. AI might be able to do 90% of tasks for a given job, but not the whole job. If tasks, what counts as a task: Is it only specific things with clear success criteria? That's easier. Is scaffolding allowed: Does it need to be able to do the tasks/jobs without scaffolding and human-written few-shot prompts? Today's tasks/jobs only, or does it include future ones too? As tasks and jobs get automated, jobs evolve and get re-defined. So, being able to do the future jobs too is much harder. Remote only, or in-person too: In-person too is a much higher bar. What threshold of tasks/jobs: "most" is apparently typically understood to mean 80-95% (Mira Ariel). Automating 80% of tasks is different to 90% and 95% and 99%. diminishing returns. And how are the tasks counted - by frequency, by dollar-weighted, by unique count of tasks? Only economically valuable tasks/jobs, or does it include anything a human can do? A high-order bit on many people's AGI timelines is which definition of AGI they're using, so clarifying the definition is nice.
  • AstroBen 1y ago
    Not only tasks, but you need to look at the net effect If it does an hour of tasks, but creates an additional hour of work for the worker...
• nakamoto_damacy 1y ago
  Here is a definition of AGI in 2025: Hype.
• jsheard 1y ago
  We'll know AGI has arrived when AGI researchers manage to go five minutes without publishing hallucinated citations. https://x.com/m2saxon/status/1979349387391439198 https://x.com/m2saxon/status/1979349387391439198
  • artninja1988 1y ago
    Came from the Google Docs to BibTeX conversion apparently https://x.com/m2saxon/status/1979636202295980299 https://x.com/m2saxon/status/1979636202295980299
  • bonoboTP 1y ago
    This looks like a knee-jerk reaction to the title instead of anything substantial.
  • MichaelZuo 1y ago
    It does seem a bit ridiculous…
  • CamperBob2 1y ago
    So infallibility is one of the necessary criteria for AGI? It does seem like a valid question to raise. Edit due to rate-limiting, which in turn appears to be due to the inexplicable downvoting of my question: since you (JumpCrisscross) are imputing a human-like motivation to the model, it sounds like you're on the side of those who argue that AGI has already been achieved?
    • JumpCrisscross 1y ago
      > infallibility Lying != fallibility.
  • nativeit 1y ago
    I’m gonna start referring to my own lies as “hallucinations”. I like the implication that I’m not lying, but rather speaking truthfully, sincerely, and confidently about things that never happened and/or don’t exist. Seems paradoxical, but this is what we’re effectively suggesting with “hallucinations”. LLMs necessarily lack things like imagination, or an ego that’s concerned with the appearance of being informed and factually correct, or awareness for how a lack of truth and honesty may affect users and society. In my (not-terribly-informed) opinion, I’d assert that precludes LLMs from even approximate levels of intelligence. They’re either quasi-intelligent entities who routinely lie to us, or they are complex machines that identify patterns and reconstruct plausible-sounding blocks of text without any awareness of abstract concepts like “truth”. Edit: toned down the preachiness.
• xnx 1y ago
  I like François Chollet definition of AGI as a system that can efficiently acquire new skills outside its training data.
  • zulban 1y ago
    Not bad. Maybe. But maybe that's ASI. Whereas I consider chatgpt 3 to be "baby AGI". That's why it became so popular so fast.
    • JumpCrisscross 1y ago
      > I consider chatgpt 3 to be "baby AGI". That's why it became so popular so fast ChatGPT became popular because it was easy to use and amusing. (LLM UX until then had been crappy.) Not sure AGI aspirations had anything to do with uptake.
      • zulban 1y ago
        ChatGPT 3 was the first AI that could do 100,000 different things poorly. Before that we only had AIs that could do a few things decently, or very well. So yeah, I'm sticking with "baby AGI" because of the "G".
        • JumpCrisscross 1y ago
          > I'm sticking with "baby AGI" because of the "G" I don't have an opinion on whether ChatGPT qualifies as AGI. What I'm saying is where one stands on that question has nothing to do with "why it became so popular so fast." (Also, several machine-learning techniques could do millions of things terribly before LLMs. GPT does them, and other things, less poorly. It's a broadening. But I suppose really any intelligence of any kind can be considered a "baby" AGI.)
        • ben_w 1y ago
          Do you mean ChatGPT-3.5, or GPT-3? The "ChatGPT" web app started with the underlying model GPT-3.5 The predecessor models, a whole series of them collectively "GPT-3" but sold under API with names like "davinci" and "ada", was barely noticed outside AI research circles. 3 was useful, but you had to treat it as a text completion system not a chat interface, your prompt would have been e.g. Press release Subject: President announces imminent asteroid impact, evacuation of Florida My fellow Americans, Because if you didn't put "My fellow Americans," in there, it would then suggest a bunch of other press release subjects.
• Der_Einzige 1y ago
  Most people who say "AGI" really mean either "ASI" or "Recursive Self Improvement". AGI was already here the day ChatGPT released: That's Peter Norvig's take too: https://www.noemamag.com/artificial-general-intelligence-is-already-here/ https://www.noemamag.com/artificial-general-intelligence-is-...
  • mitthrowaway2 1y ago
    The reason some people treat these as equivalent is that AI algorithm research is one of the things a well-educated adult human can do, so an AGI who commits to that task should be able to improve itself, and if it makes a substantial improvement, then it would become or be replaced by an ASI. To some people this is self-evident so the terms are equivalent, but it does require some extra assumptions: that the AI would spend time developing AI, that human intelligence isn't already the maximum reachable limit, and that the AGI really is an AGI capable of novel research beyond parroting from its training set. I think those assumptions are pretty easy to grant, but to some people they're obviously true and to others they're obviously false. So depending on your views on those, AGI and ASI will or will not mean the same thing.
  • photonthug 1y ago
    Funny but the eyebrow-raising phrase 'recursive self-improvement' is mentioned in TFA in an example about "style adherence" that's completely unrelated to the concept. Pretty clearly a scam where authors are trying to hack searches. Prerequisite for recursive self-improvement and far short of ASI, any conception of AGI really really needs to be expanded to include some kind of self-model. This is conspicuously missing from TFA. Related basic questions are: What's in the training set? What's the confidence on any given answer? How much of the network is actually required for answering any given question? Partly this stuff is just hard and mechanistic interpretability as a field is still trying to get traction in many ways, but also, the whole thing is kind of fundamentally not aligned with corporate / commercial interests. Still, anything that you might want to call intelligent has a working self-model with some access to information about internal status. Things that are mentioned in TFA (like working memory) might be involved and necessary, but don't really seem sufficient
• throwanem 1y ago
  How, summing (not averaging) to 58 of 1000 possible points (0-100 in each of ten domains), are we calling this score 58% rather than 5.8%?
  • alexwebb2 1y ago
    0-10 in each domain. It’s a weird table.
    • jagrsw 1y ago
      The simple additive scoring here is sus here. It means a model that's perfect on 9/10 axes but scores 0% on Speed (i.e., takes effectively infinite time to produce a result) would be considered "90% AGI". By this logic, a vast parallel search running on Commodore 64s that produces an answer after BeaverNumber(100) years would be almost AGI, which doesn't pass the sniff test. A more meaningful metric would be more multiplicative in nature.
  • NitpickLawyer 1y ago
    It's confusing. The 10 tracks each get 10%. So they add up all the percentages from every track. When you see the first table, 10% on math means "perfect" math basically. Not 10% of math track.
• deleted 1y ago
  [deleted]
• NitpickLawyer 1y ago
  Interesting read. I agree completely with their Introduction, that the definition of AGI is constantly shifting, and this leads to endless (and useless) debates. What I find cool about the paper is that they have gathered folks from lots of places (berkley, stanford, mit, etc). And no big4 labs. That's good imo. tl;dr; Their definition: "AGI is an AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult." Cool. It's a definition. I doubt it will be agreed on by everyone, and I can see endless debates about just about every word in that definition. That's not gonna change. At least it's a starting point. What I find interesting is that they specifically say it's not a benchmark, or a test set. It's a framework where they detail what should be tested, and how (with examples). They do have a "catchy" table with gpt4 vs gpt5, that I bet will be covered by every mainstream/blog/forum/etc out there -> gpt5 is at ~50% AGI. Big title. You won't believe where it was one year ago. Number 7 will shock you. And all that jazz. Anyway, I don't think people will stop debating about AGI. And I doubt this methodology will be agreed on by everyone. At the end of the day both extremes are more ideological in nature than pragmatic. Both end want/need their view to be correct. I enjoyed reading it. Don't think it will settle anything. And, as someone posted below, when the first model will hit 100% on their framework, we'll find new frameworks to debate about, just like we did with the turing test :)
  • empath75 1y ago
    > tl;dr; Their definition: "AGI is an AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult." Is a 9 year old child generally intelligent? What about a high school drop out? Someone with a 90 IQ? A large percentage of people who ever lived wouldn't qualify as being generally intelligent with that benchmark.
• mitthrowaway2 1y ago
  Quite the list of authors. If they all personally approved the text, it's an achievement in itself just to get all of them to agree on a definition.
  • mrsvanwinkle 1y ago
    indeed, i am wondering if these hn comments actually have an idea and they rub shoulders with these names with their dismissive confidence.
    • optimalsolver 1y ago
      Maybe one of these exalted names should've proof-read the paper: https://x.com/m2saxon/status/1979349387391439198 https://x.com/m2saxon/status/1979349387391439198
• modeless 1y ago
  GPT-5 scores 58%? That seems way too high. GPT-5 is good but it is not that close to AGI. Also, weird to see Gary Marcus and Yoshua Bengio on the same paper. Who really wrote this? Author lists are so performative now.
  • jonplackett 1y ago
    As anyone using AI knows - the first 90% is easy, the next 9% is much harder and the last 1% takes more time than the other 99%.
• stared 1y ago
  There’s already a vague definition that AGI is an AI with all the cognitive capabilities of a human. Yes, it’s vague - people differ. This paper promises to fix "the lack of a concrete definition for Artificial General Intelligence", yet it still relies on the vague notion of a "well-educated adult". That’s especially peculiar, since in many fields AI is already beyond the level of an adult. You might say this is about "jaggedness", because AI clearly lacks quite a few skills: > Application of this framework reveals a highly “jagged” cognitive profile in contemporary models. But all intelligence, of any sort, is "jagged" when measured against a different set of problems or environments. So, if that’s the case, this isn’t really a framework for AGI; it’s a framework for measuring AI along a particular set of dimensions. A more honest title might be: "A Framework for Measuring the Jaggedness of AI Against the Cattell–Horn–Carroll Theory". It wouldn't be nearly as sexy, though.
  • fjdjshsh 1y ago
    >But all intelligence, of any sort, is "jagged" when measured against a different set of problems or environments. On the other hand, research on "common intelligence" AFAIK shows that most measures of different types of intelligence have a very high correlation and some (apologies, I don't know the literature) have posited that we should think about some "general common intelligence" to understand this. The surprising thing about AI so far is how much more jagged it is wrt to human intelligence
    • stared 1y ago
      I think you are talking about correlation in humans of, say, verbal and mathematical intelligence. Still, it is a correlation, not equality - there are many word-acknowledged writers who suck at math, and mathematical prodigies who are are not the best at writing. If you go beyond human species (and well, computers are not even living organisms), it gets tricky. Adaptability (which is arguably a broader concept than intelligence) is very different for, say octopodes, corvids and slime molds. It is certainly not a single line of proficiency or progress. Things look like lines only if we zoom a lot.
    • pixl97 1y ago
• jedberg 1y ago
  To define AGI, we'd first have to define GI. Humans are very different. As park rangers like to say, there is an overlap between the smartest bears and the dumbest humans, which is why sometimes people can't open bear-proof trash cans. It's a similar debate with self driving cars. They already drive better than most people in most situations (some humans crash and can't drive in the snow either for example). Ultimately, defining AGI seems like a fools errand. At some point the AI will be good enough to do the tasks that some humans do (it already is!). That's all that really matters here.
  • lukan 1y ago
    " At some point the AI will be good enough to do the tasks that some humans do (it already is!). That's all that really matters here." What matters to me is, if the "AGI" can reliably solve the tasks that I give to it and that requires also reliable learning. LLM's are far from that. It takes special human AGI to train them to make progress.
    • jedberg 1y ago
      > What matters to me is, if the "AGI" can reliably solve the tasks that I give to it and that requires also reliable learning. How many humans do you know that can do that?
      • Jensson 1y ago
        Most humans can reliably do the job they are hired to do.
        • jedberg 1y ago
          Usually they require training and experience to do so. You can't just drop a fresh college grad into a job and expect them to do it.
          • lukan 1y ago
• l5870uoo9y 1y ago
  Long-term memory storage capacity[1] scores 0 for both GPT-4 and GPT-5. Are there any workable ideas or concepts for solving this? [1]: The capability to continually learn new information (associative, meaningful, and verbatim). (from the publication)
• ants_everywhere 1y ago
  > To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition Cattell-Horn-Carroll theory, like a lot of psychometric research, is based on collecting a lot of data and running factor analysis (or similar) to look for axes that seem orthogonal. It's not clear that the axes are necessary or sufficient to define intelligence, especially if the goal is to define intelligence that applies to non-humans. For example reading and writing ability and visual processing imply the organism has light sensors, which it may not. Do all intelligent beings have vision? I don't see an obvious reason why they would. Whatever definition you use for AGI probably shouldn't depend heavily on having analyzed human-specific data for the same reason that your definition of what counts as music shouldn't depend entirely on inferences from a single genre.
• zkmon 1y ago
  The problem, I guess, with these methods is, they consider human intelligence as something detached from human biology. I think this is incorrect. Everything that goes in the human mind is firmly rooted in the biological state of that human, and the biological cycles that evolved through millennia. Things like chess-playing skill of a machine could be bench-marked against that of a human, but the abstract feelings that drive reasoning and correlations inside a human mind are more biological than logical.
  • Workaccount2 1y ago
    There is no reason to believe that consciousness, sentience, or emotions require a biological base.
    • steve_adams_86 1y ago
      Is there more reason to believe otherwise? I'm not being contrarian, I'm genuinely curious what people think.
      • ComplexSystems 1y ago
        What is the "irreplaceable" part of human biology that leads to consciousness? Microtubules? Whatever it is, we could presumably build something artificial that has it.
        • dangus 1y ago
          We “could presumably build” it, maybe we can do that once we figure out how to get a language prediction model to comprehend what the current date is or how to spell strawberry.
          • lijok 1y ago
            Don’t fool yourself into believing artificial intelligence is not one breakthrough away
• arbirk 1y ago
  What about learning? As humans we continually update our weights from sensing the world. Before the AI can rewrite itself it can't really be AGI imo
• Geee 1y ago
  How about AFI - artificial fast idiot. Dumber than a baby, but faster than an adult. Or AHI - artificial human imitator. This is bad definition, because human baby is already AGI when it's born and it's brain is empty. AGI is the blank slate and ability to learn anything.
  • jagrsw 1y ago
    That "blank slate" idea doesn't really apply to humans, either. We are born with inherited "data" - innate behaviors, basic pattern recognition, etc. Some even claim that we're born with basic physics toolkit (things are generally solid, they move). We then build on that by being imitators, amassing new skills and methods simply by observation and performing search.
    • Geee 1y ago
      Sure, there's lots of inbuilt stuff like basic needs and emotions. But still, baby doesn't know anything about the world. It's the ability to collect data and train on it that makes it AGI.
      • jagrsw 1y ago
        > baby doesn't know anything about the world That's wrong. It knows how to process and signal low carbohydrate levels in the blood, and it knows how to react to a perceived threat (the Moro reflex). It knows how to follow solid objects with its eyes (when its visual system adapts) - it knows that certain visual stimuli correspond to physical systems. Could it be that your concept of "know" is defined as common sense "produces output in English/German/etc"?
        • Geee 1y ago
          No, I totally agree that there's all kinds of innate knowledge, but it's very similar for humans and animals. I don't think this knowledge is intelligence. My point was that a baby is already an AGI, and it shouldn't require a lifetime of learning to become one. Also, if intelligence is just problem solving (like an IQ test) then it should be independent of knowledge.
• dwa3592 1y ago
  Everyone has a definition and so have I. I would call it an AGI when i replace my smartphone and laptop with it. When my screen time is zero? Can AGI replace screens? Go figure.
• golol 1y ago
  >Paper claims definition of AGI >Look inside >No definition of AGI.
• flkiwi 1y ago
  > defining AGI as matching the cognitive versatility and proficiency of a well-educated adult I don't think people really realize how extraordinary accomplishment it would be to have an artificial system matching the cognitive versatility and proficiency of an uneducated child, much less a well-educated adult. Hell, AI matching the intelligence of some nonhuman animals would be an epoch-defining accomplishment.
  • cbdevidal 1y ago
    Have any benchmarks been made that use this paper’s definition? I follow the ARC prize and Humanity’s Last Exam, but I don’t know how closely they would map to this paper’s methods. Edit: Probably not, since it was published less than a week ago :-) I’ll be watching for benchmarks.
  • surgical_fire 1y ago
    There are some sycophants that claim that LLMs can operate at Junior Enginee level. Try to reconcile that with your ideas (that I think are correct for that matter)
    • ben_w 1y ago
      I'll simultaneously call all current ML models "stupid" and also say that SOTA LLMs can operate at junior (software) engineer level. This is because I use "stupidity" as the number of examples some intelligence needs in order to learn from, while performance is limited to the quality of the output. LLMs *partially* make up for being too stupid to live (literally: no living thing could survive if it needed so many examples) by going through each example faster than any living thing ever could — by as many orders of magnitude as there are between jogging and continental drift.
      • ACCount37 1y ago
        Data-efficiency matters, but compute-efficiency matters too. LLMs have a reasonable learning rate at inference time (in-context learning is powerful), but a very poor learning rate in pretraining. And one issue with that is that we have an awful lot of cheap data to pretrain those LLMs with. We don't know how much compute human brain uses to do what it does. And if we could pretrain with the same data-efficiency as humans, but at the cost of using x10000 the compute for it? It would be impossible to justify doing that for all but the most expensive, hard-to-come-by gold-plated datasets - ones that are actually worth squeezing every drop of performance gains out from.
• oidar 1y ago
  This is fine for a definition of AGI, but it's incomplete. It misses so many parts of the cognition that make humans flexible and successful. For example, emotions, feelings, varied pattern recognition, propreception, embodied awareness, social skills, and navigating ambiguous situation w/o algorithms. If the described 10 spectrums of intelligence were maxed by an LLM, it would still fall short.
  • pixl97 1y ago
    Eh, I don't like the idea of 'intelligence' of any type using humans as the base line. It blinds it to our own limitations and things that may not be limits to other types of intelligence. The "AI won't kill us all because it doesn't have emotions" problem is one of these. For example, just because AI doesn't get angry, doesn't mean it can't recognize your anger and manipulate if given such a directive to.
    • oidar 1y ago
      I agree, my point is that the cognition that creates emotion (and others) is of a different quality than the 10 listed in the paper.
• jal278 1y ago
  The fundamental premise of this paper seems flawed -- take a measure specifically designed for the nuances of how human performance on a benchmark correlates with intelligence in the real world, and then pretend as if it makes sense to judge a machine's intelligence on that same basis, when machines do best on these kinds of benchmarks in a way that falls apart when it comes to the messiness of the real world. This paper, for example, uses the 'dual N-back test' as part of its evaluation. In humans this relates to variation in our ability to use working memory, which in humans relates to 'g'; but it seems pretty meaningless when applied to transformers -- because the task itself has nothing intrinsically to do with intelligence, and of course 'dual N-back' should be easy for transformers -- they should have complete recall over their large context window. Human intelligence tests are designed to measure variation in human intelligence -- it's silly to take those same isolated benchmarks and pretend they mean the same thing when applied to machines. Obviously a machine doing well on an IQ test doesn't mean that it will be able to do what a high IQ person could do in the messy real world; it's a benchmark, and it's only a meaningful benchmark because in humans IQ measures are designed to correlate with long-term outcomes and abilities. That is, in humans, performance on these isolated benchmarks is correlated with our ability to exist in the messy real-world, but for AI, that correlation doesn't exist -- because the tests weren't designed to measure 'intelligence' per se, but human intelligence in the context of human lives.
  • stephendause 1y ago
    This is a good insight, but do you know of better ways to measure machines' abilities to solve problems in the "messy real world"?
• bananaflag 1y ago
  I can define AGI in a line: an entity which is better than any human at any task. Fight me!
  • Balgair 1y ago
    Mine has always been: I have 2 files. One is a .pdf . The other is a .doc . One file has a list of prices and colors in 2 columns. The other file has a list of colors and media in 2 columns. There are incomplete lists here and many to one matching. To me, if I can verbally tell the AI to give me a list of prices and media from those two files, in a .csv file, and it'll ask back some simple questions and issues that it needs cleaned up to accomplish this, then that is AGI to me. It is an incredibly simple thing for just about any middle school graduate. And yet! I have worked with PhDs that cannot do this. No joke! Something this simple, just dead running numbers, dumb accounting, is mostly beyond us.
  • bedane 1y ago
    a significant % of what I do day-to-day is dedicated to the task of finding sexual partners. how does this translate? if it doesn't, how do you define "any task"?
    • bananaflag 1y ago
      I imagine an AGI (properly disguised as a humanoid) would be a drop-in replacement able to seduce humans. Same for any task you imagine.
• UltraSane 1y ago
  I would define AGI as any artificial system that could learn any skill a human can by using the same inputs.
• tim333 1y ago
  Maybe we need a new term. I mean AGI just means artificial general intelligence as opposed to specialised AI like chess computers and never came with a particular level it had to be. Most people think of it as human level intelligence so perhaps we should call it that?
• oxag3n 1y ago
  Oh yeah, it's lack of a definition that keeps these models from replacing humans. /s
  • Grimblewald 1y ago
    Exactly! and this circular and generally poor definition is going to put it all into perspective.
• empath75 1y ago
  This is a serious paper by serious people and it is worth reading, but any definition of intelligence that depends on human beings as reference will never be a good basis for evaluating non human intelligence. You could easily write the reverse of this paper that questions whether human beings have general intelligence by listing all the things that LLMs can do, which human beings can't -- for example producing a reasonably accurate summary of a paper in a few seconds or speaking hundreds of different languages with reasonable fluency. You can always cherry pick stuff that humans are capable that LLMs are not capable of and vice versa, and and I don't think there is any reason to privilege certain capabilities over others. I personally do not believe that "General Intelligence" exists as a quantifiable feature of reality, whether in humans or machines. It's phlogiston, it's the luminiferous ether. It's a dead metaphor. I think what is more interesting is focusing on _specific capabilities_ that are lacking and how to solve each of them. I don't think it's at all _cheating_ to supplement LLM's with tool use, RAG, the ability to run python code. If intelligence can be said to exist at all, it is as part of a system, and even human intelligence is not entirely located in the brain, but is distributed throughout the body. Even a lot of what people generally think of as intelligence -- the ability to reason and solve logic and math problems typically requires people to _write stuff down_ -- ie, use external tools and work through a process mechanically.
• _heimdall 1y ago
  I'm also frustrated by the lack of clear definitions related to AI. Do you know what's more frustrating, though? Focusing so heavily on definitions that we miss the practicality of it (and I'm guilt of this at times too). We can debate definitions of AGI, but given that we don't know what a new model or system is capable of until its built and tested in the real world we have more serious questions in my opinion. Debates over AI risk, safety, and alignment are still pretty uncommon and it seems most are happy enough to accept Jevons Paradox. Are we really going to unleash whatever we do build just to find out after the fact whether or not its AGI?
• VerminOctopus1 1y ago
  I find the nature of AGI discussion to be so narrow and tedious. Intelligence is incomprehensibly more than being able to generate text that looks convincingly like a human wrote it. The coordination of a physical body, the formation of novel thoughts, the translation of thoughts to action, understanding the consequences of those actions, and so on. There’s so much missing that is required to even approach a literal human infant’s “intelligence” that it feels like I’m going crazy entertaining people’s arguments that we are approaching “AGI”.
• aprilfoo 1y ago
  Filling forms is a terribly artificial activity in essence. They are also very culturally biased, but that fits well with the material the NNs have been trained with. So, surely those IQ-related tests might be acceptable rating tools for machines and they might get higher scores than anyone at some point. Anyway, is the objective of this kind of research to actually measure the progress of buzzwords, or amplify them?
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• IAmGraydon 1y ago
  The problem is not really defining AGI. It's testing for it in a way that avoids illusory intelligence.
• nopinsight 1y ago
  Creative problem solving and commonsense physics are missing, among others. It is a valuable contribution but the CHC theory from psychology that this is based on is itself incomplete. By commonsense physics, I mean something like simulating interactions of living and non-living entities in 3D over time. Seems more complicated than the examples in the web site and in most tests used in psychometrics. Creative problem solving with cognitive leaps required for truly novel research & invention could lie outside the rubrics as well. The criteria in CHC are essential but incomplete I believe.
• hardenedsecure 1y ago
  A forecast by one of the authors of the paper: 50% chance that AGI is reached according to the definition by end of 2028, 80% by end of 2030. https://ai-frontiers.org/articles/agis-last-bottlenecks https://ai-frontiers.org/articles/agis-last-bottlenecks
  • habinero 1y ago
    People say things like this all the time. It's as reliable as the latest prediction for the rapture and about as scientific.
• skywhopper 1y ago
  GPT-5 is 57%? Hilarious. This is a bad joke.
• vayup 1y ago
  Precisely defining what "Intelligence" is will get us 95% of the way in defining "Artificial General Intelligence". I don't think we are there yet.
• InvisibleUp 1y ago
  Since everyone's spitballing their idea of AGI, my personal take is that AGI should be a fully autonomous system that have a stable self-image of some sort, can act on its own volition, understand the outcome of its actions, learn from cause-and-effect, and can continue doing so indefinitely. So far, LLMs aren't even remotely close to this, as they only do what they are told to do (directly or otherwise), they can't learn without a costly offline retraining process, they do not care in the slightest what they're tasked with doing or why, and they do not have anything approximating a sense of self beyond what they're told to be.
  • ryanSrich 1y ago
    Yeah my definition of AGI has always been close to this. The key factors: - It's autonomous - It learns (not retraining, but true learning) - By definition some semblance of consciousness must arise This is why I think we're very far from anything close to this. Easily multiple decades if not far longer.
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• stephc_int13 1y ago
  You need some expertise in a field to see past the amazing imitation capabilities of LLMs and get a realistic idea of how mediocre they are. The more you work with it the less you trust it. This is not _it_.
• chrsw 1y ago
  I think a lot of this is all backwards. People think AGI is taking something dumb, like an LLM, and sticking on learning, like a software upgrade. I think it's the other way around: you build a system that first and foremost _learns_ as part of its fundamental function, _then_ you train it in the domain you want expertise. You're not going to get expertise in all domains all the time, just like with people. And you're not going to get a perfect slave either, just like with humans. You'll probably get something more like in between a human and machine. If that's what you really want, great. To put this another way, if you neglect your kids, they're still going to learn things, just probably not things you want them to learn. If you neglect your language model it's just not going to do anything.
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• spot 1y ago
  > Last, we deliberately focus on core cognitive capabilities rather than physical abilities such as motor skills or tactile sensing, as we seek to measure the capabilities of the mind rather than the quality of its actuators or sensors. seems pretty unfair to exclude motor skills, especially given 1) how central they are to human economic activity, and 2) how moravec's paradox tells us they are the hard part.
• Animats 1y ago
  Paper: https://arxiv.org/pdf/2510.18212 https://arxiv.org/pdf/2510.18212 That 10-axis radial graph is very interesting. Do others besides this author agree with that representation? The weak points are speed and long-term memory. Those are usually fixable in computing system. Weak long-term memory indicates that, somehow, a database needs to be bolted on. I've seen at least one system, for driving NPCs, where, after something interesting has happened, the system is asked to summarize what it learned from that session. That's stored somewhere outside the LLM and fed back in as a prompt when needed. None of this addresses unstructured physical manipulation, which is still a huge hangup for robotics.
  • flimflamm 1y ago
    I would focus on lowest of the axis. It does not help if some of the axis are at 100% if one of the axis is lacking.
    • Animats 1y ago
      My point is that the axes chosen are important, and if this is a good rating system, we ought to see those radial charts for the different models and systems available.
• daxfohl 1y ago
  It's easy: we have reached AGI when there are zero jobs left. Or at least non manual labor jobs. If there is a single non-physical job left, then that means that person must be doing something that AI can't, so by definition, it's not AGI. I think it'll be a steep sigmoid function. For a long time it'll be a productivity booster, but not enough "common sense" to replace people. We'll all laugh about how silly it was to worry about AI taking our jobs. Then some AI model will finally get over that last hump, maybe 10 or 20 years from now (or 1000, or 2}, and it will be only a couple months before everything collapses.
  • __MatrixMan__ 1y ago
    I dislike your definition. There are many problems besides economic ones. If you defined "general" to mean "things the economy cares about", then what do you call the sorts of intelligences that are capable of things that the economically relevant ones are not? A specific key opens a subset of locks, a general key would open all locks. General intelligence, then, can solve all solvable problems. It's rather arrogant to suppose that humans have it ourselves or that we can create something that does.
    • daxfohl 1y ago
      It also partitions jobs into physical and intellectual aspects alone. Lots of jobs have a huge emotional/relational/empathetic components too. A teacher could get by being purely intellectual, but the really great ones have motivational/inspirational/caring aspects that an AI never could. Even if an AI says the exact same things, it doesn't have the same effect because everyone knows it's just an algorithm.
      • ZoomZoomZoom 1y ago
        And most people get by on those jobs by faking the emotional component, at least some of the time. AGI presumably can fake perfectly and never burn out.
        • habinero 1y ago
• wseqyrku 1y ago
  > .., Eric Schmidt, .. Right. That explains it.
  • sbinnee 1y ago
    Yoshua Bengio too. Does anyone know connections between the two? Is Schmidt supporting researches at Mila?
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• fnordpiglet 1y ago
  After reading the paper I’m struck by the lack of any discussion of awareness. Cognition requires at its basis awareness, which due to its entirely non verbal and unconstructed basis, is profoundly difficult to describe, measure, quantify, or label. This makes it to my mind impossible to train a model to be aware, let alone for humans to concretely describe it or evaluate it. Philosophy, especially Buddhism, has tried for thousands of years and psychology has all but abandoned attempting so. Hence papers like this that define AGI on psychometric dimensions that have the advantage of being easily measured but the disadvantage of being incomplete. My father is an emeritus professor of psychometrics and he agrees this is the biggest hurdle to AGI - that our ability to measure the dimensions of intelligence is woefully insufficient to the task of replicating intelligence. We scratch the surface and his opinion is language is sufficient to capture the knowledge of man, but not the spark of awareness required to be intelligent. This isn’t meant to be a mystical statement that it’s magic that makes humans intelligent or some exotic process impossible to compute. But that the nature of our mind is not observable in its entirety to us sufficient that the current learned reinforcement techniques can’t achieve it. Try this exercise. Do not think and let your mind clear. Ideas will surface. By what process did they surface? Or clear your mind entirely then try to perform some complex task. You will be able to. How did you do this without thought? We’ve all had sudden insights without deliberation or thought. Where did these come from? By what process did you arrive at them? Most of the things we do or think are not deliberative and definitely not structured with language. This process is unobservable and not measurable, and the only way we have to do so is through imperfect verbalizations that hint out some vague outline of a subconscious mind. But without being able to train a model on that subconscious process, one that can’t be expressed in language with any meaningful sufficiency, how will language models demonstrate it? Their very nature of autoregressive inference prohibits such a process from emerging at any scale. We might very well be able to fake it to an extent that it fools us, but awareness isn’t there - and I’d assert that awareness is all you need.
  • stevenjgarner 1y ago
    I so completely agree. In virtually every conversation I have heard about AI, it only every talks about one of the multiple intelligences as theorized in Howard Gardner's book Frames of Mind: The Theory of Multiple Intelligences (1983)[1] There is little discussion of how AI will enhance (or destroy) our emotional intelligence, or our naturalistic, intrapersonal or interpersonal intelligences. Most religions, spiritual practices and even forms of meditation highlight the value of transcending mind and having awareness be present in the body. The way AGI is described, it would seem transcendence may be treated as a malfunction or bug. [1] https://en.wikipedia.org/wiki/Theory_of_multiple_intelligences https://en.wikipedia.org/wiki/Theory_of_multiple_intelligenc...
• almosthere 1y ago
  This is kind of annoying. The "computer" on star trek TNG was basically agentic LLMs (it knows what you mean when you ask it things, and it could solve things and modify programs by telling it what changes to make) Data on ST:TNG was more like AGI. It had dreams, argued for itself as a sentient being, created art, controlled its own destiny through decision making.
• joomla199 1y ago
  All models are wrong, but some are useful. However when it comes to cognition and intelligence we seem to be in the “wrong and useless” era or maybe even “wrong and harmful” (history seems to suggest this as a necessary milestone…anyone remember “humorism”?)
• keepamovin 1y ago
  I think if you can put an AI in a humanoid robot (control for appearance), and it can convince me that it's a human after interacting it for a couple of months (control for edgecases), I'd consider it AGI. Surely it might be "smarter than" a human, but for the purpose of my assessing whether it's AGI, interacting with something "way smarter" would be distracting and hamper the assessment, so it has to be "play human" for the purpose of the task. If it can do that, AGI, I'd say. That would be pretty cool. Surely, this is coming, soon.
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• jimbohn 1y ago
  I think "our" mistake is that we wanted to make a modern human first, while being unable to make an animal or even a caveman, and we lost something in the leap-frog. But we effectively have a database of knowledge that has become interactive thanks to reinforcement learning, which is really useful!
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• morgengold 1y ago
  Why do we even want to have human intelligence? It's flawed and limited in so many ways. Most of its magic is there because it cares about its host.
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• xpuente 1y ago
  Desperate to run without even knowing how to walk.
• incomingpain 1y ago
  A "general intelligence" is equivalent to a golden retriever or dolphin. A human general intelligence is a $3/hr minimum wage worker from some undeveloped country. https://en.wikipedia.org/wiki/Cattell%E2%80%93Horn%E2%80%93Carroll_theory https://en.wikipedia.org/wiki/Cattell%E2%80%93Horn%E2%80%93C... If a person has all those criteria, they are superintelligent. They are beyond genius. The AGI definition problem is that everyone keeps conflating AGI with ASI, Artificial Super Intelligence.
• SirMaster 1y ago
  Isn't part of the cognitive versatility of a human how fast and well they can learn a new subject without having to ingest so much training content on it? Like in order for an LLM to come close to a human proficiency on a topic, the LLM seems to have to ingest a LOT more content than a human.
• SirMaster 1y ago
  I don't think it's really AGI until you can simply task it with creating a new better version of itself and it can succeed in doing that all on its own. A team of humans can and will make a GPT-6. Can a team of GPT-5 agents make GPT-6 all on its own if you give it the resources necessary to do so?
  • vages 1y ago
    This is called Recursive AI, and is briefly mentioned in the paper.
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• Rover222 1y ago
  I always find it interesting how the majority of comments on threads like this on HN are dismissive of current AI systems as "gimmicks", yet some of the most successful people on the planet think it's worth plowing a trillion dollars into them. I don't know who's right, but the dichotomy is interesting.
  • TehCorwiz 1y ago
    Success is just a measure of how much you can separate other people from their money. It’s possible to be successful and produce nothing of value.
    • Rover222 1y ago
      You don't suppose it is at times also a measure of knowing how to skate where the puck is heading?
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• mmmothra 1y ago
  it's giving...SAT scores meets venture capital.
• mmmothra 1y ago
  It's giving... SAT scores meets venture capital.
• adamzwasserman 1y ago
  I wish them luck. Any consensus at all, on any definition at all, would be a boon to mankind. Unfortunately I am certain that all we have to look forward to is endless goal post shifting.
  • giancarlostoro 1y ago
    Maybe AGI should have levels / phases to achieve towards 100% or a maximum level?
• jncfhnb 1y ago
  Completely wrong direction. AGI will not emerge from getting smarter. It will emerge from being a stateful system in a real environment. You need context from internal system state that isn’t faked with a giant context window.
• qnleigh 1y ago
  Ugh just looking at their list, this paper gets a hard no from me. Intelligence isn't mastery of some arbitrary list of mathematical subjects. It's the ability to learn and apply these subjects (or anything else) after minimal exposure to the topic. For a bar as high as AGI (and not just 'the skills of an educated person,' which is what this paper seems to be describing), we should include abstract mathematical reasoning, and the ability to generate new ideas or even whole subfields to solve open problems.
• tsoukase 1y ago
  My takes as a neuroscientist: 1) defining intelligence is very difficult, almost impossible. Much more the artificial one 2) there are many types of human intelligence. Verbal is one of them and the closest to comparing with LLMs 3) machines (not only LLMs but all, like robots) excel where humans are bad and vice versa due to their different background, without exception. Comparing the two is totally meaningless and unfair for both. Let's have both complement the other. 4) AGI remains a valid target but we are still very far from it, like in other ones, as control the DNA and treat arbitrary genetic diseases, solve the earth resource problem and harness other planets, create a near perfect sociopolitical system with no inequality. Another Singularity is added in the list 5) I am impressed by how far a PC cluster has come up through "shuffling tokens" but on the other side I am pessimistic of how further it can reach having finate input/training data.
• AlgernonDmello 1y ago
  Opinions without 'pi' are just onions! Its not wrong to have an opinion, but they should be based off some grounded fact/truth. Before defining AGI, I guess we need to define Intelligence and align on whose definition of intelligence can be considered the grounded truth. The theory of multiple intelligences (Howard Gardner) seems the most accepted today. Is there anything better? Going with the assumption that there is no other, this definition of AGI only considers the following intelligences. - Verbal-Linguistic - Logical-Mathematical - Visual-Spatial - Musical - Naturalist It doesnt/barely mentions - Bodily-Kinesthetic - Interpersonal - Intrapersonal - Existential So this definition fees incomplete
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• paulcx 1y ago
  What if the Wright brothers had to pass a “bird exam”? That’s how we’re defining AGI today. Stop grading feathers; start designing thrust. Check out my new post: "If This Is How We Define AGI, I'm Out" - https://open.substack.com/pub/paulochen/p/if-this-is-how-we-define-agi-im-out https://open.substack.com/pub/paulochen/p/if-this-is-how-we-...
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