[kaba0]

> Do we even have a model or even an idea about what “thinking” is

At the least, it is a computable function (as we don’t have any physical system that would be more general than that, though some religions might disagree). Which already puts human brains ahead of LLM systems, as we are Turing-complete, while LLMs are not, at least in their naive application (their output can be feeded to subsequent invocations and that way it can be).

[swid]

I googled whether or not universal function approximators, which neural nets are considered, are also considered Turing complete. It seems the general consensus is kind of not, since they are continuous and can’t do discreet operations in the same way.

But also, that isn’t quite the whole story, since they can be arbitrarily precise in their approximation. Here[0] is a white paper addressing this issue which concludes attention networks are Turing complete.

0: https://jmlr.org/papers/volume22/20-302/20-302.pdf

[kaba0]

If I’m not mistaken that’s only for arbitrary precision, which is not realistic.

[ben_w]

Is it provably not turning complete? That property pops up everywhere even when not intended, like Magic: The Gathering card interactions.

Technically you may not want to call it Turing complete given the limited context window, but I'd say that's like insisting a Commodore 64 isn't Turing complete for the same reason.

Likewise the default settings may be a bit too random to be a Turing machine, but that criticism would also apply to a human.

[kaba0]

It is basically a single huge matrix multiplication — you need some form of loop/repetition/recursion to be Turing complete.

Sure it is not a hard property, excel, css with mouse movements, game of life are all that, but they need a “possibly forever running” part.

[ben_w]

ChatGPT does have a loop, that's why it produces more than one token.

In this context, that the possibility of running "forever" would also exclude the humans (to which it is being compared) is relevant — even if we spend all day thinking in words at the rate of 160wpm and .75 words per token, we fall asleep around every 200k tokens, and some models (not from OpenAI) exceed that in their input windows.

[kaba0]

Yet I can solve many sudoku problems in a single wake cycle.

Also, its output is language and it can’t change a former part of speech, can only append to it. When “thinking” about what to say next, it can’t “loop” over that, only whether to append some more text to it. Its looping is strictly within a “static context”.