Comment by antonvs
2 months ago
> It's really disingenuous for the industry to call warming tokens for output, "reasoning," as if some autocomplete before more autocomplete is all we needed to solve the issue of consciousness.
There's no obvious connection between reasoning and consciousness. It seems perfectly possible to have a model that can reason without being conscious.
Also, dismissing what these models do as "autocomplete" is extremely disingenuous. At best it implies you're completely unfamiliar with the state of the art, at worst it implies an dishonest agenda.
In terms of functional ability to reason, these models can beat a majority of humans in many scenarios.
Understanding is always functional, we don't study medicine before going to the doctor, we trust the expert. Like that we do with almost every topic or system. How do you "understand" a company or a complex technological or biological system? Probably nobody does end to end. We can only approximate it with abstractions and reasoning. Not even a piece of code can be understood - without execution we can't tell if it will halt or not.
It would require you to change the definition of reasoning, or it would require you to believe computers can think.
A locally trained text-based foundation model is indistinguishable from autocompletion, and outputs very erratic text, and the further you train it's ability to diminish irrelevant tokens, or guide it to produce specifically formatted output, you've just moved its ability to curve fit specific requirements.
So it may be disingenuous to you, but it does behave very much like a curve fitting search algorithm.
> It would require you to change the definition of reasoning
What matters here is a functional definition of reasoning: something that can be measured. A computer can reason if it can pass the same tests that humans can pass of reasoning ability. LLMs blew past that milestone quite a while back.
If you believe that "thinking" and "reasoning" have some sort of mystical aspect that's not captured by such tests, it's up to you to define that. But you'll quickly run into the limits of such claims, because if you want to attribute some non-functional properties to reasoning or thinking, that can't be measured, then you also can't prove that they exist. You quickly get into an intractable area of philosophy, which isn't really relevant to the question of what AI models can actually do, which is what matters.
> it does behave very much like a curve fitting search algorithm.
This is just silly. I can have an hours-long coding session with an LLM in which it exhibits a strong functional understanding of the codebase its working on, a strong grasp of the programming language and tools its working with, and writes hundreds or thousands of lines of working code.
Please plot the curve that it's fitting in a case like this.
If you really want to stick to this claim, then you also have to acknowledge that what humans do is also "behave very much like a curve fitting search algorithm." If you disagree, please explain the functional difference.
> or it would require you to believe computers can think.
Unless you can show us that humans can calculate functions outside the Turing computable, it is logical to conclude that computers can be made to think due to Turing equivalence and the Church Turing thesis.
Given we have zero evidence to suggest we can exceed the Turing computable, to suggest we can is an extraordinary claim that requires extraordinary evidence.
A single example of a function that exceeds the Turing computable that humans can compute, will do.
Until you come up with that example, I'll asume computer can be made to think.