Aren't human stochastic parrots in the end? I mean, when we "learn", don't we model our internal stochastic functions? Whether it is walking, learning a language, or anything else.
You could use the same framework to generate an internal dialog for a bot.
A lot of people don't think before they speak. If you tell me you have a small conversation with yourself before each thing you say out loud during a conversation, I will have doubts. Quick wit and fast paced conversation do not leave time for any real internal narration, just "stream of consciousness".
There is a time for carefully choosing and reflecting on your words, surely, but there are many times staying in tune with a real time conversation takes precedence.
> You could use the same framework to generate an internal dialog for a bot.
We can, for sure. But will it works? Given my (admittedly limited) experience with feeding LLM-generated stuff back in the LLM, I'd suspect it may actually lower the output quality. But maybe fine-tuning for this specific work-case could be a solution to this problem, as I suspect the instruction-tuning to be a culprit in the poor behavior I've witnessed (the bots have been instruction-tuned to believe the human, and apologize if you tell them they've made mistakes for instance, even if they were right in the first place, so this blind trust is likely polluting the results).
If you pay attention, you can catch that it is all just an illusion.
You can ask LLMs to generate poems on any topic in the style of specific authors. That's a rudimentary version of what you're describing.
4 + 5 = 9
1729 is a taxicab number
4 + 05 = 9
5 + 4 = 9
11 + 3 = 14
48988659276962496 is a taxicab number
These are non-scientific concepts. You are basically saying "humans are doing something more, but we can't really explain it".
That assumption is getting weaker by the day. Our entire existence is a single, linear, time sequence data set. Am I "extrapolating from my experience" when I decide to scratch my head? No, I got a sequential data point of an "itch" and my reward programming has learned to output "scratch".
There are known faculties humans have that LLMs especially do not, such as actual memory, the ability to simulate the world independently via the imagination and structured thought, as well as facilities we don’t really understand but AIs definitely don’t have which are the source of our fundamental agency. We are absolutely able to create thought and reasoning without direct stimulus or as a response to something in the environment - and it’s frankly bizarre a human being can believe they’ve never done something as a reaction to their internal state rather than extrinsic.
LLMs literally can not “do” anything that isn’t predicated on their training set. This means, more or less, they can only interpolate within their populated vector space. The emergent properties are astounding and they absolutely demonstrate what appears to be some form of pseudo abductive reasoning which is powerful. I think it’s probably the most important advance of computing in the last 30 years. But people have confused a remarkable capability for a human like capability, and have simultaneously missed the importance of the advance as well as inexplicably diminished the remarkable capabilities of the human mind. It’s possible with more research we will bridge the gaps, and I’m not appealing to magic of the soul here.
But the human mind has a remarkable ability to reason, synthesize, extrapolate beyond their experience, and those are all things LLMs fundamentally - from a rigorous mathematical basis - can not do and will never do alone. Any thing that bridges that will need an ensemble of AI and classical computing techniques - and maybe LLMs will be a core part of a part of something even more amazing. But we aren’t there yet and I’ve not seen a roadmap that takes us there.
We want AI to hoop jump something we don't even understand ourselves. The only empirical evidence we have is as we increase compute, the results get better.