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>People's comments, including in this very thread, seem to suggest otherwise (c.f. comments about "goal post moving"). But you don't see this kind of discussio

>People's comments, including in this very thread, seem to suggest otherwise (c.f. comments about "goal post moving"). But you don't see this kind of discussion on the narrow models/techniques that made strides on this benchmark, do you ? >People have a colloquial understanding of AGI whose consequence is a significant change to daily life, not the tortured technical definition that you are using And ChatGPT has represented a significant change to the daily lives of many. It's the fastest adopted software product in history. In just 2 years, it's one of the top ten most visited sites on the planet worldwide. A lot of people have had the work they do significant change since its release. This is why I ask, what is world altering ? >How about you? I get the impression that you think AGI was achieved some time ago. Sure >It's a bit difficult to simultaneously argue both that we achieved AGI in GPT-N and also that GPT-(N+X) is now the real breakthrough AGI I have never claimed GPT-N+X is the "new breakthrough AGI". As far as I'm concerned, we hit AGI sometime ago and are making strides in competence and/or enabling even more capabilities. You can recognize ENIAC as a general purpose computer and also recognize the breakthroughs in computing since then. They're not mutually exclusive. And personally, I'm more impressed with o3's Frontier Math score than ARC. >I think everyone's definition of AGI includes, as a component, significant changes to the world Sure >which probably would be something like rapid GDP growth or unemployment What people imagine as "significant change" is definitely not in any broad agreement. Even in science fiction, the existence of general intelligences more competent than today's LLMs does not necessarily precursor massive unemployment or GDP growth. And for a lot of people, the clincher stopping them from calling a machine AGI is not even any of these things. For some, that it is "sentient" or "cannot lie" is far more important than any spike of unemployment.

foobarqux 2y ago
> But you don't see this kind of discussion on the narrow models/techniques that made strides on this benchmark, do you ? I don't understand what you are getting at. Ultimately there is no axiomatic definition of the term AGI. I don't think the colloquial understanding of the word is what you think it is (i.e. if you had described to people, pre-chatgpt, today's chatgpt behavior, including all the limitations and failings and the fact that there was no change in GDP, unemployment, etc), and asked if that was AGI I seriously doubt they would say yes.) More importantly I don't think anyone would say their life was much different from a few years ago and separately would say under AGI it would be. But the point that started all this discussion is the fact that these "evals" are not good proxies for AGI and no one is moving goal-posts even if they realize this fact only after the tests have been beaten. You can foolishly define AGI as beating ARC but the moment ARC is beaten you realize that you don't care about that definition at all. That doesn't change if you make a 10 or 100 benchmark suite.
- famouswaffles 2y ago
  >I don't understand what you are getting at. If such discussions only made when LLMs make strides in the benchmark then it's not just about beating the benchmark but also what kind of system is beating it. >You can foolishly define AGI as beating ARC but the moment ARC is beaten you realize that you don't care about that definition at all. If you change your definition of AGI the moment a test is beaten then yes, you are simply post moving. If you care about other impacts like "Unemployment" and "GDP rising" but don't give any time or opportunity to see if the model is capable of such then you don't really care about that and are just mindlessly shifting posts. How do such a person know o3 won't cause mass unemployment? The model hasn't even been released yet.
  - foobarqux 2y ago
    > If such discussions only made when LLMs make strides in the benchmark then it's not just about beating the benchmark but also what kind of system is beating it. I still don't understand the point you are making. Nobody is arguing that discrete program search is AGI (and the same counter-arguments would apply if they did). > If you change your definition of AGI the moment a test is beaten then yes, you are simply post moving. I don't think anyone changes their definition, they just erroneously assume that any system that succeeds on the test must do so only because it has general intelligence (that was the argument for chess playing for example). When it turns out that you can pass the test with much narrower capabilities they recognize that it was a bad test (unfortunately they often replace the bad test with another bad test and repeat the error). > If you care about other impacts like "Unemployment" and "GDP rising" but don't give any time or opportunity to see if the model is capable of such then you don't really care about that and are just mindlessly shifting posts. We are talking about what models are doing now (is AGI here now) not what some imaginary research breakthroughs might accomplish. O3 is not going to materially change GDP or unemployment. (If you are confident otherwise please say how much you are willing to wager on it).
    - famouswaffles 2y ago
      I'm not talking about any imaginary research breakthroughs. I'm talking about today, right now. We have a model unveiled today that seems a large improvement across several benchmarks but hasn't been released yet. You can be confident all you want but until the model has been given the chance to not have the effect you think it won't then it's just an assertion that may or may not be entirely wrong. If you say "this model passed this benchmark I thought would indicate AGI but didn't do this or that so I won't acknowledge it" then I can understand that. I may not agree on what the holdups are but I understand that. If however you're "this model passed this benchmark I thought would indicate AGI but I don't think it's going to be able to do this or that so it's not AGI" then I'm sorry but that's just nonsense. My thoughts or bets are irrelevant here. A few days ago I saw someone seriously comparing a site with nearly 4B visits a month in under 2 years to Bitcoin and VR. People are so up in their bubbles and so assured in their way of thinking they can't see what's right in front of them, nevermind predict future usefulness. I'm just not interested in engaging "I think It won't" arguments when I can just wait and see. I'm not saying you are one of such people. I just have no interest in such arguments. My bet ? There's no way i would make a bet like that without playing with the model first. Why would I ? Why Would you ?
Jensson 2y ago
> But you don't see this kind of discussion on the narrow models/techniques that made strides on this benchmark, do you ? This model was trained to pass this test, it was trained heavily on the example questions, so it was a narrow technique. We even have proof that it isn't AGI, since it scores horribly on ARC-AGI 2. It overfitted for this test.
- famouswaffles 2y ago
  >This model was trained to pass this test, it was trained heavily on the example questions, so it was a narrow technique. You are allowed to train on the train set. That's the entire point of the test. >We even have proof that it isn't AGI, since it scores horribly on ARC-AGI 2. It overfitted for this test. Arc 2 does not even exist yet. All we have are "early signs", not that that would be proof of anything. Whether I believe the models are generally intelligent or not doesn't depend on ARC
  - Jensson 2y ago
    > You are allowed to train on the train test. That's the entire point of the test. Right, but by training on those test cases you are creating a narrow model. The whole point of training questions is to create narrow models, like all the models we did before.
    - famouswaffles 2y ago
      That doesn't make any sense. Training on the train set does not make the models capabilities narrow. Models are narrow when you can't train them to do anything else even if you wanted to. You are not narrow for undergoing training and it's honestly kind of ridiculous to think so. Not even the ARC maintainers believe so.
      - Jensson 2y ago
        > Training on the train set does not make the models capabilities narrow Humans didn't need to see the training set to pass this, the AI needing it means it is narrower than the humans, at least on these kind of tasks. The system might be more general than previous models, but still not as general as humans, and the G in AGI typically means being as general as humans. We are moving towards more general models, but still not at the level where we call them AGI.

foobarqux 2y ago

> But you don't see this kind of discussion on the narrow models/techniques that made strides on this benchmark, do you ? I don't understand what you are getting at. Ultimately there is no axiomatic definition of the term AGI. I don't think the colloquial understanding of the word is what you think it is (i.e. if you had described to people, pre-chatgpt, today's chatgpt behavior, including all the limitations and failings and the fact that there was no change in GDP, unemployment, etc), and asked if that was AGI I seriously doubt they would say yes.) More importantly I don't think anyone would say their life was much different from a few years ago and separately would say under AGI it would be. But the point that started all this discussion is the fact that these "evals" are not good proxies for AGI and no one is moving goal-posts even if they realize this fact only after the tests have been beaten. You can foolishly define AGI as beating ARC but the moment ARC is beaten you realize that you don't care about that definition at all. That doesn't change if you make a 10 or 100 benchmark suite.

famouswaffles 2y ago
>I don't understand what you are getting at. If such discussions only made when LLMs make strides in the benchmark then it's not just about beating the benchmark but also what kind of system is beating it. >You can foolishly define AGI as beating ARC but the moment ARC is beaten you realize that you don't care about that definition at all. If you change your definition of AGI the moment a test is beaten then yes, you are simply post moving. If you care about other impacts like "Unemployment" and "GDP rising" but don't give any time or opportunity to see if the model is capable of such then you don't really care about that and are just mindlessly shifting posts. How do such a person know o3 won't cause mass unemployment? The model hasn't even been released yet.
- foobarqux 2y ago
  > If such discussions only made when LLMs make strides in the benchmark then it's not just about beating the benchmark but also what kind of system is beating it. I still don't understand the point you are making. Nobody is arguing that discrete program search is AGI (and the same counter-arguments would apply if they did). > If you change your definition of AGI the moment a test is beaten then yes, you are simply post moving. I don't think anyone changes their definition, they just erroneously assume that any system that succeeds on the test must do so only because it has general intelligence (that was the argument for chess playing for example). When it turns out that you can pass the test with much narrower capabilities they recognize that it was a bad test (unfortunately they often replace the bad test with another bad test and repeat the error). > If you care about other impacts like "Unemployment" and "GDP rising" but don't give any time or opportunity to see if the model is capable of such then you don't really care about that and are just mindlessly shifting posts. We are talking about what models are doing now (is AGI here now) not what some imaginary research breakthroughs might accomplish. O3 is not going to materially change GDP or unemployment. (If you are confident otherwise please say how much you are willing to wager on it).
  - famouswaffles 2y ago
    I'm not talking about any imaginary research breakthroughs. I'm talking about today, right now. We have a model unveiled today that seems a large improvement across several benchmarks but hasn't been released yet. You can be confident all you want but until the model has been given the chance to not have the effect you think it won't then it's just an assertion that may or may not be entirely wrong. If you say "this model passed this benchmark I thought would indicate AGI but didn't do this or that so I won't acknowledge it" then I can understand that. I may not agree on what the holdups are but I understand that. If however you're "this model passed this benchmark I thought would indicate AGI but I don't think it's going to be able to do this or that so it's not AGI" then I'm sorry but that's just nonsense. My thoughts or bets are irrelevant here. A few days ago I saw someone seriously comparing a site with nearly 4B visits a month in under 2 years to Bitcoin and VR. People are so up in their bubbles and so assured in their way of thinking they can't see what's right in front of them, nevermind predict future usefulness. I'm just not interested in engaging "I think It won't" arguments when I can just wait and see. I'm not saying you are one of such people. I just have no interest in such arguments. My bet ? There's no way i would make a bet like that without playing with the model first. Why would I ? Why Would you ?

Jensson 2y ago

> But you don't see this kind of discussion on the narrow models/techniques that made strides on this benchmark, do you ? This model was trained to pass this test, it was trained heavily on the example questions, so it was a narrow technique. We even have proof that it isn't AGI, since it scores horribly on ARC-AGI 2. It overfitted for this test.

famouswaffles 2y ago
>This model was trained to pass this test, it was trained heavily on the example questions, so it was a narrow technique. You are allowed to train on the train set. That's the entire point of the test. >We even have proof that it isn't AGI, since it scores horribly on ARC-AGI 2. It overfitted for this test. Arc 2 does not even exist yet. All we have are "early signs", not that that would be proof of anything. Whether I believe the models are generally intelligent or not doesn't depend on ARC
- Jensson 2y ago
  > You are allowed to train on the train test. That's the entire point of the test. Right, but by training on those test cases you are creating a narrow model. The whole point of training questions is to create narrow models, like all the models we did before.
  - famouswaffles 2y ago
    That doesn't make any sense. Training on the train set does not make the models capabilities narrow. Models are narrow when you can't train them to do anything else even if you wanted to. You are not narrow for undergoing training and it's honestly kind of ridiculous to think so. Not even the ARC maintainers believe so.
    - Jensson 2y ago
      > Training on the train set does not make the models capabilities narrow Humans didn't need to see the training set to pass this, the AI needing it means it is narrower than the humans, at least on these kind of tasks. The system might be more general than previous models, but still not as general as humans, and the G in AGI typically means being as general as humans. We are moving towards more general models, but still not at the level where we call them AGI.