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| ▲ | cauch 5 hours ago | parent [-] | | 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 5 hours ago | parent | next [-] | | 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 4 hours ago | parent | prev [-] | | 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. | | |
| ▲ | cauch 2 hours ago | parent | next [-] | | > 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. | | |
| ▲ | dagss an hour ago | parent [-] | | Humans don't make the exact same errors of LLMs of course. Humans are very different beings. So you recognize that Claude is not a human. Humans make mistakes as well "inconsistent with the understanding mechanism", but they have a very different form, and you are so used to the particular failure mode of humans, that you don't think about it. But aliens visiting earth likely would find some aspects of human mind very peculiar! Examples: Humans learning algebra (or really anything like playing music, paddling a canoe, etc.) have to go through lots and lots of trivial basic mistakes, and only learn to avoid them through repetition and pattern matching on earlier experience, rather than relying on "reasoning". A "pure reasonable being" would simply be learned the rules for algebra then go ahead and make perfect deductions applying the rules -- but humans are very clearly not such beings. Humans can know the rules for algebra perfectly well, then still go ahead and make mistakes until enough training has been done until we say you have "learned" it (be able to pattern match on previous experience). Imagine humans being employed by aliens to do algebra, then aliens seeing humans basically do "2 + 2 = 5" (just on a higher complexity level). Like very human in first year in university WILL do with their formulas. What would you conclude about humans and their relation to "real understanding"? Or another example: Humans engage a lot in post-rationalization, having first made up ones mind, then finding the reasons for the choice afterwards. (Most striking example of post-rationalization is the experiments on patients with severed brain half connections where one brain half invents a reason it can believe in for a choice made by the other brain half; https://en.wikipedia.org/wiki/Split-brain -- but if you look at pretty much any political issue for instance it is clear that people are driven at least as much by being herd animals as by doing any reasoning -- the majority of humans decide what people they belong with first, then figure out why afterwards). |
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| ▲ | JsonDemWitOster 3 hours ago | parent | prev [-] | | 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. |
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