4 ms·

The article seems to define "smart" as being good at spatial awareness and navigating a body through 3D space and such. Thus, a mice is smarter than an LLM. Th

by dagss 3mo ago

The article seems to define "smart" as being good at spatial awareness and navigating a body through 3D space and such. Thus, a mice is smarter than an LLM. That's the first time in my life I hear this definition. Until now, the word "smart" has meant doing exactly the things LLMs do, and mice don't. I guess it is a sign we are re-evaluating what makes humans special.

JsonDemWitOster 3mo ago
> 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.
yread 3mo ago
I still remember when "smart" meant knowing the number of Rs in strawberry
- krackers 3mo ago
  Was that ever solved? It seems that entire retort faded overnight, yet to my knowledge there was never any systematic analysis on cause or tokenizer change that fixed it. Maybe we just decided that this failure mode doesn't have any practical bearing given the existence of tool-use?
  - Wowfunhappy 3mo ago
    ...I think it really is irrelevant, isn't it? The LLM gets words as tokens, not strings of letters. If you asked me how many of the letter s is in Mississippi, but said I'm not allowed to spell out the word in my head and count the letters, I don't think I could do it. This isn't a great analogy, because part of the challenge would be preventing myself from picturing the spelling in my head. But my point is, the AI is not getting the words as letters. The correct solution is tool use.
    - krackers 3mo ago
      LLMs can learn to do arithmetic (without tool use), and they can learn a mapping from tokens to the letter counts contained therein (you could imagine trivially training on synthetic data). So there doesn't seem to be any fundamental barrier.

by dagss 3mo ago

JsonDemWitOster 3mo ago

> 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.

yread 3mo ago

I still remember when "smart" meant knowing the number of Rs in strawberry

krackers 3mo ago
Was that ever solved? It seems that entire retort faded overnight, yet to my knowledge there was never any systematic analysis on cause or tokenizer change that fixed it. Maybe we just decided that this failure mode doesn't have any practical bearing given the existence of tool-use?
- Wowfunhappy 3mo ago
  ...I think it really is irrelevant, isn't it? The LLM gets words as tokens, not strings of letters. If you asked me how many of the letter s is in Mississippi, but said I'm not allowed to spell out the word in my head and count the letters, I don't think I could do it. This isn't a great analogy, because part of the challenge would be preventing myself from picturing the spelling in my head. But my point is, the AI is not getting the words as letters. The correct solution is tool use.
  - krackers 3mo ago
    LLMs can learn to do arithmetic (without tool use), and they can learn a mapping from tokens to the letter counts contained therein (you could imagine trivially training on synthetic data). So there doesn't seem to be any fundamental barrier.