3 ms·

> I guess it is a sign we are re-evaluating what makes humans special. Always has been: https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/A

> I guess it is a sign we are re-evaluating what makes humans special. Always has been: https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect Tangentially: https://en.wikipedia.org/wiki/Moravec%27s_paradox https://en.wikipedia.org/wiki/Moravec%27s_paradox

cauch 3mo ago
While we should be careful of a bias, it is also a good practice in the scientific method to review definitions that may have been not precise enough. For example, initially, a "planet" was just a big body in space. Then when people started to see more and more nuances, the definition just refined, and some objects stopped being called "planet". I would not be surprised if there is a bias that pushes some people to redefine "intelligence" away from machine, but I would not be surprised if there is a bias that pushes some people to ignore newly discovered nuance and put into the same "intelligence" bag things that are in fact very different. I personally can see how LLM are not really "intelligent", and I don't think it is a good idea to say: well, yesterday we said the minimum criteria is X, now that we noticed that X can be reached without really doing the real thing, let's just ignore that and pretend it is the same thing. (: the biggest clue for me is to use an early model, and see that it sometimes looks very intelligent, and then sometimes you can see that it gets it wrong in a way that shows that it never "understood" it at all. Newer models are better, but because it is an iteration on the same bases, the increase of performances cannot really due to replacing the things that "looked smart by aren't" by "real smart", but more replacing the things that "don't look smart" by "look smart by aren't")
- JsonDemWitOster 3mo ago
  Yeah I think if we are looking at it through that lens, the problem is in the term _intelligence_ in itself. Psychology and biology could not even pinpoint what exactly makes for _intelligence_. There isn't really a precise definition yet so it's just natural that definitions tend to shift. I don't think we even need to go into tech and AI for an example. The intelligence or lack thereof of pets surprise us. Sometimes a cat is surprisingly smart when it is able to open a door to get food it wasn't supposed to. But then same cat gets bamboozled by walls and simple optical illusions. We generally expect that if something/a human is smart enough to do the former, then it shouldn't be dumb enough to fall for the latter. Coming back to AI, this dissonance is how AI-generated images are detected for example. If a human can render something so well, you wouldn't expect them to make small but nonetheless elementary line art mistakes.
- dagss 3mo ago
  It's the same with human intelligence though. A human can be brilliant on some things and then we're puzzled why they are so idiotic in other areas. Every time this comes up, people pick on any kind of flaws or inconsistencies of AI models, while at the same time giving a huge pass to the extreme variation in intelligence and stupidness displayed in human behaviour. Creativity is the same. Human artists are "inspired" by earlier arts, perhaps following and slightly changing "trends" they participate in -- which is somehow seen as totally different from what AIs are doing.
  - JsonDemWitOster 3mo ago
    My problem with AI is the sheer variance of its stupid-smart spectrum. While it's true that human intelligence is not deterministic or predictable, the inconsistency exists in a much narrower band of variance which makes failure modes foreseeable. Thus I would much prefer a system with humans in the loop with processes in place for idiot-proofing. This is true for "lateral" (I lack a better term) fields of intelligence as well. You don't ask a philosophy professor advice for the rashes on your skin; you see a doctor for that. And yet both the professor and the doctor could be expected to accurately identify from a picture that you do have rashes on your skin. An AI (and I mean in the general sense, not only transformer LLMs) could give you a pretty accurate rundown of Plato and still think the same picture is a beautiful sunrise. (I don't even kid. Just this morning, an AI labeled a GIF from _Friends_ as a 1950s magazine ad for white bread. Just what in the failure mode is that?) You can't idiot-proof AI without knowing what's in the training data set and even then you run into question of scale.
  - cauch 3mo ago
    > It's the same with human intelligence though. No, this is not the same observation. In "basic LLM", the answer is not "confused" or "fail to understand", the answer is "inconsistent with the understanding mechanism". It is not that they "fail to understand while trying to understand", it is that there is not understanding mechanism at all. Humans can have different level of intelligence, depending on the individuals, the subjects, even circumstantial situations (someone being tired, someone being distracted, or just bad luck). But they never make the same kind of mistakes I've observed with "basic LLM", where they do "non sequitur" that does not make sense at all but has all the characteristic of imitating something said by someone who understood. I still even see it sometimes with Claude. It says logical stuff, and then suddenly something that does not make sense and it snaps me back to reality: none of this, including the correct things, are the result of understanding the underlying concepts, it is just that the correct things are more probable to generate, and that suddenly, a nonsensical happen to also be probable for a given configuration.

cauch 3mo ago

While we should be careful of a bias, it is also a good practice in the scientific method to review definitions that may have been not precise enough. For example, initially, a "planet" was just a big body in space. Then when people started to see more and more nuances, the definition just refined, and some objects stopped being called "planet". I would not be surprised if there is a bias that pushes some people to redefine "intelligence" away from machine, but I would not be surprised if there is a bias that pushes some people to ignore newly discovered nuance and put into the same "intelligence" bag things that are in fact very different. I personally can see how LLM are not really "intelligent", and I don't think it is a good idea to say: well, yesterday we said the minimum criteria is X, now that we noticed that X can be reached without really doing the real thing, let's just ignore that and pretend it is the same thing. (: the biggest clue for me is to use an early model, and see that it sometimes looks very intelligent, and then sometimes you can see that it gets it wrong in a way that shows that it never "understood" it at all. Newer models are better, but because it is an iteration on the same bases, the increase of performances cannot really due to replacing the things that "looked smart by aren't" by "real smart", but more replacing the things that "don't look smart" by "look smart by aren't")

JsonDemWitOster 3mo ago
Yeah I think if we are looking at it through that lens, the problem is in the term _intelligence_ in itself. Psychology and biology could not even pinpoint what exactly makes for _intelligence_. There isn't really a precise definition yet so it's just natural that definitions tend to shift. I don't think we even need to go into tech and AI for an example. The intelligence or lack thereof of pets surprise us. Sometimes a cat is surprisingly smart when it is able to open a door to get food it wasn't supposed to. But then same cat gets bamboozled by walls and simple optical illusions. We generally expect that if something/a human is smart enough to do the former, then it shouldn't be dumb enough to fall for the latter. Coming back to AI, this dissonance is how AI-generated images are detected for example. If a human can render something so well, you wouldn't expect them to make small but nonetheless elementary line art mistakes.
dagss 3mo ago
It's the same with human intelligence though. A human can be brilliant on some things and then we're puzzled why they are so idiotic in other areas. Every time this comes up, people pick on any kind of flaws or inconsistencies of AI models, while at the same time giving a huge pass to the extreme variation in intelligence and stupidness displayed in human behaviour. Creativity is the same. Human artists are "inspired" by earlier arts, perhaps following and slightly changing "trends" they participate in -- which is somehow seen as totally different from what AIs are doing.
- JsonDemWitOster 3mo ago
  My problem with AI is the sheer variance of its stupid-smart spectrum. While it's true that human intelligence is not deterministic or predictable, the inconsistency exists in a much narrower band of variance which makes failure modes foreseeable. Thus I would much prefer a system with humans in the loop with processes in place for idiot-proofing. This is true for "lateral" (I lack a better term) fields of intelligence as well. You don't ask a philosophy professor advice for the rashes on your skin; you see a doctor for that. And yet both the professor and the doctor could be expected to accurately identify from a picture that you do have rashes on your skin. An AI (and I mean in the general sense, not only transformer LLMs) could give you a pretty accurate rundown of Plato and still think the same picture is a beautiful sunrise. (I don't even kid. Just this morning, an AI labeled a GIF from _Friends_ as a 1950s magazine ad for white bread. Just what in the failure mode is that?) You can't idiot-proof AI without knowing what's in the training data set and even then you run into question of scale.
- cauch 3mo ago
  > It's the same with human intelligence though. No, this is not the same observation. In "basic LLM", the answer is not "confused" or "fail to understand", the answer is "inconsistent with the understanding mechanism". It is not that they "fail to understand while trying to understand", it is that there is not understanding mechanism at all. Humans can have different level of intelligence, depending on the individuals, the subjects, even circumstantial situations (someone being tired, someone being distracted, or just bad luck). But they never make the same kind of mistakes I've observed with "basic LLM", where they do "non sequitur" that does not make sense at all but has all the characteristic of imitating something said by someone who understood. I still even see it sometimes with Claude. It says logical stuff, and then suddenly something that does not make sense and it snaps me back to reality: none of this, including the correct things, are the result of understanding the underlying concepts, it is just that the correct things are more probable to generate, and that suddenly, a nonsensical happen to also be probable for a given configuration.