> It's not "just" sequence prediction, because sequence prediction is the very essence of what the human brain does.
Agree wrt the brain.
Sure, LLMs are also sequence predictors, and this is a large part of why they appear intelligent (intelligence = learning + prediction). The other part is that they are trained to mimic their training data, which came from a system of greater intelligence than their own, so by mimicking a more intelligent system they appear to be punching above their weight.
I'm not sure that "JUST sequence predictors" is so inappropriate though - sure sequence prediction is a powerful and critical capability (the core of intelligence), but that is ALL that LLMs can do, so "just" is appropriate.
Of course additionally not all sequence predictors are of equal capability, so we can't even say, "well, at least as far as being sequence predictors goes, they are equal to humans", but that's a difficult comparison to make.
> Your points on learning and memory are similarly weak word play. Memory means holding some quantity constant over time in the internal state of a model. Learning means being able to update those quantities. LLMs obviously do both.
Well, no...
1) LLMs do NOT "hold some quantity constant over time in the internal state of the model". It is a pass-thru architecture with zero internal storage. When each token is generated it is appended to the input, and the updated input sequence is fed into the model and everything is calculated from scratch (other than the KV cache optimization). The model appears to be have internal memory due to the coherence of the sequence of tokens it is outputting, but in reality everything is recalculated from scratch, and the coherence is due to the fact that adding one token to the end of a sequence doesn't change the meaning of the sequence by much, and most of what is recalculated will therefore be the same as before.
2) If the model has learnt something, then it should have remembered it from one use to another, but LLMs don't do this. Once the context is gone and the user starts a new conversation/session, then all memory of the prior session is gone - the model has NOT updated itself to remember anything about what happened previously. If this was an employee (an AI coder, perhaps) then it would be perpetual groundhog day. Every day it came to work it'd be repeating the same mistakes it made the day before, and would have forgotten everything you might have taught it. This is not my definition of learning, and more to the point the lack of such incremental permanent learning is what'll make LLMs useless for very many jobs. It's not an easy fix, which is why we're stuck with massively expensive infrequent retrainings from scratch rather than incremental learning.
Agree wrt the brain.
Sure, LLMs are also sequence predictors, and this is a large part of why they appear intelligent (intelligence = learning + prediction). The other part is that they are trained to mimic their training data, which came from a system of greater intelligence than their own, so by mimicking a more intelligent system they appear to be punching above their weight.
I'm not sure that "JUST sequence predictors" is so inappropriate though - sure sequence prediction is a powerful and critical capability (the core of intelligence), but that is ALL that LLMs can do, so "just" is appropriate.
Of course additionally not all sequence predictors are of equal capability, so we can't even say, "well, at least as far as being sequence predictors goes, they are equal to humans", but that's a difficult comparison to make.
> Your points on learning and memory are similarly weak word play. Memory means holding some quantity constant over time in the internal state of a model. Learning means being able to update those quantities. LLMs obviously do both.
Well, no...
1) LLMs do NOT "hold some quantity constant over time in the internal state of the model". It is a pass-thru architecture with zero internal storage. When each token is generated it is appended to the input, and the updated input sequence is fed into the model and everything is calculated from scratch (other than the KV cache optimization). The model appears to be have internal memory due to the coherence of the sequence of tokens it is outputting, but in reality everything is recalculated from scratch, and the coherence is due to the fact that adding one token to the end of a sequence doesn't change the meaning of the sequence by much, and most of what is recalculated will therefore be the same as before.
2) If the model has learnt something, then it should have remembered it from one use to another, but LLMs don't do this. Once the context is gone and the user starts a new conversation/session, then all memory of the prior session is gone - the model has NOT updated itself to remember anything about what happened previously. If this was an employee (an AI coder, perhaps) then it would be perpetual groundhog day. Every day it came to work it'd be repeating the same mistakes it made the day before, and would have forgotten everything you might have taught it. This is not my definition of learning, and more to the point the lack of such incremental permanent learning is what'll make LLMs useless for very many jobs. It's not an easy fix, which is why we're stuck with massively expensive infrequent retrainings from scratch rather than incremental learning.