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You make very strong claims about things we know very little about. It's far from clear that we have reached a ceiling. Who can predict how systems with 10x the

You make very strong claims about things we know very little about. It's far from clear that we have reached a ceiling. Who can predict how systems with 10x the parameters and as-yet-undiscovered deep learning models will behave? We don't understand how humans do logic. It's entirely possible that whatever structure in the human brain is responsible for handling logic can emerge in a neural network. If we're talking about what it takes to get to true AGI in the near future, then I agree that a pure neural network approach might not cross the finish line first. I think Stuart Russell made this point in an interview, basically saying that a neural network is a very inefficient computational model and that we could do the same thing much more efficiently if we had the right "good old fashioned AI" algorithm. But fundamentally a neural network is just computing a function so there's nothing in principle preventing a neural network from doing whatever a symbolic system does. It's mostly a matter of efficiency and hardware availability.

thomastjeffery 4y ago
But we do know plenty about it. It's right there in front of us. Pretending there is some understanding just out of reach is called mysticism. What you are telling me is that I should place my expectations for the future, not on the reality in front of me, but on the hopes and dreams you have for the future. That's circular reasoning. The very reason that I don't place credibility in your assertions is the lack of reason itself: in your assertions, and in what a neural network is. Neural networks are like dreams. Wonderful only when your intention is to get lost in a swirl of memories. Useless if you want to actually accomplish something. Knowing the difference is crucial, because that difference can never be taught to a neural network without completely redefining what a neural network is in the first place. Knowing the difference is literally the thing neural networks are incapable of doing. They don't know anything. They just guess. That's literally the function. In the code. Guess what comes next. There is no sense pretending sense itself will magically appear out of a guessing machine. Neural networks are nonsense generators, and that is what they are forever doomed to be.
- fasterik 4y ago
  You made two claims: (1) current language models are useless and (2) current language models have reached a ceiling. I said: How many people are using ChatGPT, Stable Diffusion, etc. for economically or personally valuable activities? If (1) is true, then the answer to that question is "zero" or at least "close to zero". Do you really believe that? If (2) is true, then it is also true to say that transformer models will never exceed today's capabilities by a significant amount at any time in the future. Do you really believe that?
  - thomastjeffery 4y ago
    Yes. The ceiling is the floor. The limitation is inherent in the core design. There is no overcoming. This is not a hurdle or a wall. It's a design flaw. Is it totally useless to everyone? No. Not completely. It's like a coherent search engine: a way to find data that is close to other data. But "close to" in this case is only "semantically", and never "logically", so that's that. Is it going to get any less useless than it is? Only slightly. "It" will never get better. The only better version of "it" is a completely new ground-up redesign that doesn't resemble "it" at all.
    - fasterik 4y ago
      Modern neural network architectures are Turing complete [1]. So I don't see any argument for a limit in principle unless you are arguing that a Turing machine can't achieve language understanding. If that's what you're saying, then I wonder who is espousing mysticism here. [1] https://arxiv.org/abs/1901.03429 https://arxiv.org/abs/1901.03429
      - thomastjeffery 4y ago
        Are you forgetting the distinction between a program and a computer? Language understanding doesn't magically spawn itself as a process on your computer! Someone has to write that program first. And that's my point. ChatGPT transforms language, but it does not understand it. For that, we will need a different kind of program.

thomastjeffery 4y ago

But we do know plenty about it. It's right there in front of us. Pretending there is some understanding just out of reach is called mysticism. What you are telling me is that I should place my expectations for the future, not on the reality in front of me, but on the hopes and dreams you have for the future. That's circular reasoning. The very reason that I don't place credibility in your assertions is the lack of reason itself: in your assertions, and in what a neural network is. Neural networks are like dreams. Wonderful only when your intention is to get lost in a swirl of memories. Useless if you want to actually accomplish something. Knowing the difference is crucial, because that difference can never be taught to a neural network without completely redefining what a neural network is in the first place. Knowing the difference is literally the thing neural networks are incapable of doing. They don't know anything. They just guess. That's literally the function. In the code. Guess what comes next. There is no sense pretending sense itself will magically appear out of a guessing machine. Neural networks are nonsense generators, and that is what they are forever doomed to be.

fasterik 4y ago
You made two claims: (1) current language models are useless and (2) current language models have reached a ceiling. I said: How many people are using ChatGPT, Stable Diffusion, etc. for economically or personally valuable activities? If (1) is true, then the answer to that question is "zero" or at least "close to zero". Do you really believe that? If (2) is true, then it is also true to say that transformer models will never exceed today's capabilities by a significant amount at any time in the future. Do you really believe that?
- thomastjeffery 4y ago
  Yes. The ceiling is the floor. The limitation is inherent in the core design. There is no overcoming. This is not a hurdle or a wall. It's a design flaw. Is it totally useless to everyone? No. Not completely. It's like a coherent search engine: a way to find data that is close to other data. But "close to" in this case is only "semantically", and never "logically", so that's that. Is it going to get any less useless than it is? Only slightly. "It" will never get better. The only better version of "it" is a completely new ground-up redesign that doesn't resemble "it" at all.
  - fasterik 4y ago
    Modern neural network architectures are Turing complete [1]. So I don't see any argument for a limit in principle unless you are arguing that a Turing machine can't achieve language understanding. If that's what you're saying, then I wonder who is espousing mysticism here. [1] https://arxiv.org/abs/1901.03429 https://arxiv.org/abs/1901.03429
    - thomastjeffery 4y ago
      Are you forgetting the distinction between a program and a computer? Language understanding doesn't magically spawn itself as a process on your computer! Someone has to write that program first. And that's my point. ChatGPT transforms language, but it does not understand it. For that, we will need a different kind of program.