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Well, humans are just trained on language tokens too (and of course, supplementary images etc). All the people stating that "real understanding" is significant

Well, humans are just trained on language tokens too (and of course, supplementary images etc). All the people stating that "real understanding" is significantly different than learning through inference of language are likely going to be proven wrong in the end. There's nothing special about humans that makes our thinking any more sophisticated. With enough examples and the right learning model, systems should be able to be implicitly inferred from language, just as humans infer systems from language. If we can do it, why can't machines?

Timwi 4y ago
It’s not a question of whether machines can do it at all. The question is whether our current approach of training LLMs can do it. We don’t know how the human brain works, so we have no idea if there’s something in the brain that is fundamentally different from training an LLM. Obviously machines can theoretically do what a brain can do because a machine can theoretically simulate a brain. But then it’s not an LLM anymore.
- adam_arthur 4y ago
  It's a neural network at the end of the day... it can compute any result or "understand" any system if properly weighted and structured. It may be that LLM style training techniques are not sufficient to "understand" systems, or it may be that at a certain scale of input data, and some fine tuning, it is sufficient to be indistinguishable from other training methods. Many people's sense of what qualifies as "intelligence" are too grandiose/misplaced. The main thing differentiating us from a neural network is that we have wants and desires, and the ability to prompt and conduct our own training as a result of those.
  - Retric 4y ago
    A LLM isn’t going to learn how to drive a car because of how they are trained even if a neutral network could. It isn’t that people’s views on intelligence are grandiose, it’s that the specific approach used has massive inherent limitations. ChatGPT 4 is still relatively bad at chess, 1 win, 1 draw, 1 loss vs a 1400 isn’t impressive objectively and looks much worse when considering the amount of processing power they are using. The only impressive thing about this is how general their approach is, but in a wider context it’s still quite limited. IMO the next jump of being able to toss 100x as much pressing power at the problem will see LLM’s tossed aside for even more general approaches like say using YouTube videos.
  - tsimionescu 4y ago
    > It's a neural network at the end of the day... it can compute any result or "understand" any system if properly weighted and structured. That's not even remotely close to being demonstrated. For one thing, neural networks can only approximate continuous functions. For another, the fact that in principle there exists a neural network that can approximate any continuous function to arbitrary precision doesn't in any way tell us that there is a way to "train" that network by any known algorithm. There isn't even reason to believe that such an algorithm exists for the general case, at least not one with a finite number of examples.
    - adam_arthur 4y ago
      Approximating continuous functions is likely quite the same as what people do too. You think there isn’t some mathematical model under the hood of how the brain works too? That it doesn’t break down into functions with interpretable results? Is it spiritual or mystical in your mind? These takes are so bad and pervasive on here, honestly. This is what I mean by grandiose thinking. A machine that approximates functions, that otherwise is indistinguishable from human, is effectively intelligent like a human. Incentives, wants, desires, and the ability to conduct our own training is the only difference at that point.
      - YeGoblynQueenne 4y ago
        >> Is it spiritual or mystical in your mind? No, they're just saying there are continuous functions, and then there are discrete functions, and neural nets can't approximate discrete functions, while humans certainly can (e.g. integer addition). And that even when it comes to approximating any continuous function, neural nets can do that in principle, but we don't know how to do it in practice, just like we know time travel, stable wormholes and the Alcubierre drive are feasible in principle, but we can't realise them in practice. So please don't say it's "spiritual and mystical" in the other person's mind just because it's not very clear in yours. Also, what the OP didn't say is that a Transformer architecture is not the kind of architecture used to show the universality of neural nets. That was shown for a multi-layer perceptron (MLP) with one hidden layer, not a deep neural net like a Tansformer, and certainly not a network with attention heads. If you wanted to be all theoretical about it and claim that because there's that old proof, someone will eventually find out how to do it in practice, then the Transformer architecture has already taken a wrong turn and is moving away from the target. There aren't no universality results for Transformers. I mean, that would be the day! The reason that that proof was derived for a MLP with one hidden layer is that this makes the proof much, much easier, than if you wanted to show the same for another architecture.
anonzzzies 4y ago
The current models are still too simple and missing things we did not figure out; when a human (or other animal) learns, it only needs a tiny (compared to the corpus of text etc gpt gets trained on) corpus to become a smart human. So the model needs something we have built in that makes learning vastly more efficient. Then there will be another big jump. That’ll come, that or a new AI winter.
Lewton 4y ago
> Well, humans are just trained on language tokens too This is not true at all You can teach someone chess without language
- gambiting 4y ago
  And how would you do that? Show certain moves and point your thumb up for ok, down for not ok? Then sorry, you're still using a language, just without using words.
  - deleted 4y ago
    [deleted]
  - wizofaus 4y ago
    It doesn't seem so far-fetched to believe somebody could learn chess just by watching others play it, no language needed at all (except perhaps reading the body language of being glad to win). But I imagine LLMs will soon have the ability to turn image sequences into information that can be interpreted and ingested much the same way as they can text, and thus "learn" how to play chess just from analyzing videos of actual games being played.
    - johnaspden 4y ago
      In legend, Paul Morphy learned in this way.
IIAOPSW 4y ago
The fact that humans pick up language so soon after birth is the motivating question behind the biggest theory in all of linguistics, namely Chomsky's Universal Grammar. The simple fact is, teacher never stands in front of the class and says "here is how you don't talk. Purple if alligator burp arming has why't." Yet, despite the paucity of negative examples, everyone figures it out. You can't explain that in the current paradigm. There's a lot you can do unreasonably well despite virtually no prior experience. You probably did not need to crash a car for 10k generations before finally making it down the street, nor simulate it in your head. We are missing something fundamental and algorithmic and can only patch over our lack of understanding with volumes of training data for so long. The idea of "reinforcement learning" is just a rehash of "Hebbian learning". It works for some things, but you can't explain language acquisition is pure reward function and stats.
- radarsat1 4y ago
  > Yet, despite the paucity of negative examples, everyone figures it out. After spending more than a year babbling nonsense and discovering a tiny bit more every time about the meaning of certain combinations of phonemes based on the positive or negative response you get. > You probably did not need to crash a car for 10k generations before finally making it down the street, nor simulate it in your head. Are you sure we don't simulate in our head what would happen if we drove the car into the lamp post / brick wall / other car / person, etc.? I find it highly unlikely that this kind of learning does not involve a large amount of simulation. > There's a lot you can do unreasonably well despite virtually no prior experience. That's true, but there's a lot we can't do well without repetitive practice, and most things that we can do well in a one-shot fashion depend on having prior practice or familiarity with similar things.
  - IIAOPSW 4y ago
    You're digging your heals in on a rehash of a model from the 40s, glibly dismissing the problems it doesn't account for bought up by linguists in the 50s and 60s as if they are unaware that babies go through a period of babbling. The amount of time spent acquiring language is already priced in and not enough to account for as pure reward and training. >Are you sure we don't simulate in our head what would happen if we drove the car into the lamp post / brick wall / other car / person, etc.? You left out the 10k times part. You're ignoring the huge training data sizes these models need even for basic inferences. No, I don't think it takes all that much full scale simulation to distill car speed as a function of pedal parameters, and estimate the control problem needed. In many instances, humans can seemingly extrapolate from far less data. The algorithms to do this are missing. Training with loads of more data isn't a viable long term substitution.

Timwi 4y ago

It’s not a question of whether machines can do it at all. The question is whether our current approach of training LLMs can do it. We don’t know how the human brain works, so we have no idea if there’s something in the brain that is fundamentally different from training an LLM. Obviously machines can theoretically do what a brain can do because a machine can theoretically simulate a brain. But then it’s not an LLM anymore.

adam_arthur 4y ago
It's a neural network at the end of the day... it can compute any result or "understand" any system if properly weighted and structured. It may be that LLM style training techniques are not sufficient to "understand" systems, or it may be that at a certain scale of input data, and some fine tuning, it is sufficient to be indistinguishable from other training methods. Many people's sense of what qualifies as "intelligence" are too grandiose/misplaced. The main thing differentiating us from a neural network is that we have wants and desires, and the ability to prompt and conduct our own training as a result of those.
- Retric 4y ago
  A LLM isn’t going to learn how to drive a car because of how they are trained even if a neutral network could. It isn’t that people’s views on intelligence are grandiose, it’s that the specific approach used has massive inherent limitations. ChatGPT 4 is still relatively bad at chess, 1 win, 1 draw, 1 loss vs a 1400 isn’t impressive objectively and looks much worse when considering the amount of processing power they are using. The only impressive thing about this is how general their approach is, but in a wider context it’s still quite limited. IMO the next jump of being able to toss 100x as much pressing power at the problem will see LLM’s tossed aside for even more general approaches like say using YouTube videos.
- tsimionescu 4y ago
  > It's a neural network at the end of the day... it can compute any result or "understand" any system if properly weighted and structured. That's not even remotely close to being demonstrated. For one thing, neural networks can only approximate continuous functions. For another, the fact that in principle there exists a neural network that can approximate any continuous function to arbitrary precision doesn't in any way tell us that there is a way to "train" that network by any known algorithm. There isn't even reason to believe that such an algorithm exists for the general case, at least not one with a finite number of examples.
  - adam_arthur 4y ago
    Approximating continuous functions is likely quite the same as what people do too. You think there isn’t some mathematical model under the hood of how the brain works too? That it doesn’t break down into functions with interpretable results? Is it spiritual or mystical in your mind? These takes are so bad and pervasive on here, honestly. This is what I mean by grandiose thinking. A machine that approximates functions, that otherwise is indistinguishable from human, is effectively intelligent like a human. Incentives, wants, desires, and the ability to conduct our own training is the only difference at that point.
    - YeGoblynQueenne 4y ago
      >> Is it spiritual or mystical in your mind? No, they're just saying there are continuous functions, and then there are discrete functions, and neural nets can't approximate discrete functions, while humans certainly can (e.g. integer addition). And that even when it comes to approximating any continuous function, neural nets can do that in principle, but we don't know how to do it in practice, just like we know time travel, stable wormholes and the Alcubierre drive are feasible in principle, but we can't realise them in practice. So please don't say it's "spiritual and mystical" in the other person's mind just because it's not very clear in yours. Also, what the OP didn't say is that a Transformer architecture is not the kind of architecture used to show the universality of neural nets. That was shown for a multi-layer perceptron (MLP) with one hidden layer, not a deep neural net like a Tansformer, and certainly not a network with attention heads. If you wanted to be all theoretical about it and claim that because there's that old proof, someone will eventually find out how to do it in practice, then the Transformer architecture has already taken a wrong turn and is moving away from the target. There aren't no universality results for Transformers. I mean, that would be the day! The reason that that proof was derived for a MLP with one hidden layer is that this makes the proof much, much easier, than if you wanted to show the same for another architecture.

anonzzzies 4y ago

The current models are still too simple and missing things we did not figure out; when a human (or other animal) learns, it only needs a tiny (compared to the corpus of text etc gpt gets trained on) corpus to become a smart human. So the model needs something we have built in that makes learning vastly more efficient. Then there will be another big jump. That’ll come, that or a new AI winter.

Lewton 4y ago

> Well, humans are just trained on language tokens too This is not true at all You can teach someone chess without language

gambiting 4y ago
And how would you do that? Show certain moves and point your thumb up for ok, down for not ok? Then sorry, you're still using a language, just without using words.
- deleted 4y ago
  [deleted]
- wizofaus 4y ago
  It doesn't seem so far-fetched to believe somebody could learn chess just by watching others play it, no language needed at all (except perhaps reading the body language of being glad to win). But I imagine LLMs will soon have the ability to turn image sequences into information that can be interpreted and ingested much the same way as they can text, and thus "learn" how to play chess just from analyzing videos of actual games being played.
  - johnaspden 4y ago
    In legend, Paul Morphy learned in this way.

IIAOPSW 4y ago

The fact that humans pick up language so soon after birth is the motivating question behind the biggest theory in all of linguistics, namely Chomsky's Universal Grammar. The simple fact is, teacher never stands in front of the class and says "here is how you don't talk. Purple if alligator burp arming has why't." Yet, despite the paucity of negative examples, everyone figures it out. You can't explain that in the current paradigm. There's a lot you can do unreasonably well despite virtually no prior experience. You probably did not need to crash a car for 10k generations before finally making it down the street, nor simulate it in your head. We are missing something fundamental and algorithmic and can only patch over our lack of understanding with volumes of training data for so long. The idea of "reinforcement learning" is just a rehash of "Hebbian learning". It works for some things, but you can't explain language acquisition is pure reward function and stats.

radarsat1 4y ago
> Yet, despite the paucity of negative examples, everyone figures it out. After spending more than a year babbling nonsense and discovering a tiny bit more every time about the meaning of certain combinations of phonemes based on the positive or negative response you get. > You probably did not need to crash a car for 10k generations before finally making it down the street, nor simulate it in your head. Are you sure we don't simulate in our head what would happen if we drove the car into the lamp post / brick wall / other car / person, etc.? I find it highly unlikely that this kind of learning does not involve a large amount of simulation. > There's a lot you can do unreasonably well despite virtually no prior experience. That's true, but there's a lot we can't do well without repetitive practice, and most things that we can do well in a one-shot fashion depend on having prior practice or familiarity with similar things.
- IIAOPSW 4y ago
  You're digging your heals in on a rehash of a model from the 40s, glibly dismissing the problems it doesn't account for bought up by linguists in the 50s and 60s as if they are unaware that babies go through a period of babbling. The amount of time spent acquiring language is already priced in and not enough to account for as pure reward and training. >Are you sure we don't simulate in our head what would happen if we drove the car into the lamp post / brick wall / other car / person, etc.? You left out the 10k times part. You're ignoring the huge training data sizes these models need even for basic inferences. No, I don't think it takes all that much full scale simulation to distill car speed as a function of pedal parameters, and estimate the control problem needed. In many instances, humans can seemingly extrapolate from far less data. The algorithms to do this are missing. Training with loads of more data isn't a viable long term substitution.