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Whether or not the current technology is useless is an empirical question. How many people are using ChatGPT, Stable Diffusion, etc. for economically or persona

Whether or not the current technology is useless is an empirical question. How many people are using ChatGPT, Stable Diffusion, etc. for economically or personally valuable activities? We actually don't know. Even if we assume the technology is useless in its current state, it is still incremental progress. Could we have predicted 10 years ago what neural networks would be capable of today? Now, tell me what neural networks will be doing in 10 years. If you think you know the answer with any degree of certainty, you're probably deluded.

thomastjeffery 4y ago
My point is that ML-based NLP (like chatGPT) has a clear ceiling, and we seem to have reached that. We can get coherent (understandable) output all day long, but we can never introduce logic. ML-based NLP is a semantic word-guessing machine. It's based on entirely on how often words show up near each other in the training datasets. There is no room to add logic. The entire exercise is like a magic trick: impressive sure, but at the end of the day, a fool's errand.
- fasterik 4y ago
  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.

thomastjeffery 4y ago

My point is that ML-based NLP (like chatGPT) has a clear ceiling, and we seem to have reached that. We can get coherent (understandable) output all day long, but we can never introduce logic. ML-based NLP is a semantic word-guessing machine. It's based on entirely on how often words show up near each other in the training datasets. There is no room to add logic. The entire exercise is like a magic trick: impressive sure, but at the end of the day, a fool's errand.

fasterik 4y ago
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.