It depends on how well you understand how the fancy autocomplete is working under the hood.
You could compare GPT-o1 chain of thought to something like IBM's DeepBlue chess-playing computer, which used MTCS (tree search, same as more modern game engines such as AlphaGo)... at the end of the day it's just using built-in knowledge (pre-training) to predict what move would most likely be made by a winning player. It's not unreasonable to characterize this as "fancy autocomplete".
In the case of an LLM, given that the model was trained with the singular goal of autocomplete (i.e. mimicking the training data), it seems highly appropriate to call that autocomplete, even though that obviously includes mimicking training data that came from a far more general intelligence than the LLM itself.
All GPT-o1 is adding beyond the base LLM fancy autocomplete is an MTCS-like exploration of possible continuations. GPT-o1's ability to solve complex math problems is not much different from DeepBlue's ability to beat Garry Kasparov. Call it intelligent if you want, but better to do so with an understanding of what's really under the hood, and therefore what it can't do as well as what it can.
Saying "it's just autocomplete" is not really saying anything meaningful since it doesn't specify the complexity of completion. When completion is a correct answer to the question that requires logical reasoning, for example, "just autocomplete" needs to be able to do exactly that if it is to complete anything outside of its training set.
It's just a shorthand way of referring to how transformer-based LLMs work. It should go without saying that there are hundreds of layers of hierarchical representation, induction heads at work, etc, under the hood. However, with all that understood (and hopefully not needed to be explicitly stated every time anyone wants to talk about LLMs in a technical forum), at the end of the day they are just doing autocomplete - trying to mimic the training sources.
The only caveat to "just autocomplete" (which again hopefully does not need to be repeated every time we discuss them), is that they are very powerful pattern matchers, so all that transformer machinery under the hood is being used to determine what (deep, abstract) training data patterns the input pattern best matches for predictive purposes - exactly what pattern(s) it is that should be completed/predicted.
This is the tough part to tell - are there any such questions that exist that have not already been asked?
The reason Chat-GPT works is its scale. to me, that makes me question how "smart" it is. Even the most idiotic idiot could be pretty decent if he had access to the entire works of mankind and infinite memory. Doesn't matter if his IQ is 50, because you ask him something and he's probably seen it before.
How confident are we this is not just the case with LLMs?
I'm highly confident that we haven't learnt every thing that can be learnt about the world, and that human intelligence, curiosity and creativity are still being used to make new scientific discoveries, create things that have never been seen before, and master new skills.
I'm highly confident that the "adjacent possible" of what is achievable/discoverable today, leveraging what we already know, is constantly changing.
I'm highly confident that AGI will never reach superhuman levels of creativity and discovery if we model it only on artifacts representing what humans have done in the past, rather than modelling it on human brains and what we'll be capable of achieving in the future.
Of course there are such questions. When it comes to even simple puzzles, there are infinitely many permutations possible wrt how the pieces are arranged, for example - hell, you could generate such puzzles with a script. No amount of precanned training data can possibly cover all such combinations, meaning that the model has to learn how to apply the concepts that make solution possible (which includes things such as causality or spatial reasoning).
Right, but typically LLMs are really poor at this. I can come up with some arbitrary systems of equations for it to solve and odds are it will be wrong. Maybe even very wrong.
That is more indicative of the quality of their reasoning than their ability to reason in principle, though. And maybe even quality of their reasoning specifically in this domain - e.g. it's not a secret that most major models are notoriously bad at tasks involving things like counting letters, but we also know that if you specifically train a model to do that, it does in fact drastically improve its performance.
On the whole I think it shouldn't be surprising that even top-of-the-line LLMs today can't reason as well as a human - they aren't anywhere near as complex as our brains. But if it is a question of quality rather than a fundamental disability, then larger models and better NN designs should be able to gradually push the envelope.
Well, tons of ways. I can't imagine what an "autocomplete only" human would look like, but it'd be pretty dire - maybe like an idiot savant with a brain injury who could recite whole books given the opening sentence, but never learn anything new.
You could compare GPT-o1 chain of thought to something like IBM's DeepBlue chess-playing computer, which used MTCS (tree search, same as more modern game engines such as AlphaGo)... at the end of the day it's just using built-in knowledge (pre-training) to predict what move would most likely be made by a winning player. It's not unreasonable to characterize this as "fancy autocomplete".
In the case of an LLM, given that the model was trained with the singular goal of autocomplete (i.e. mimicking the training data), it seems highly appropriate to call that autocomplete, even though that obviously includes mimicking training data that came from a far more general intelligence than the LLM itself.
All GPT-o1 is adding beyond the base LLM fancy autocomplete is an MTCS-like exploration of possible continuations. GPT-o1's ability to solve complex math problems is not much different from DeepBlue's ability to beat Garry Kasparov. Call it intelligent if you want, but better to do so with an understanding of what's really under the hood, and therefore what it can't do as well as what it can.