zlacker

This is an interesting question, but it seems at least possible that as long as the fundamental operation is simply "generate tokens", that it can't go beyond being just a form of next-token prediction. I don't think people were thinking of human thought as a stream of tokens until LLMs came along. This isn't a very well-formed idea, but we may require an AI for which "generating tokens" is just one subsystem of a larger system, rather than the only form of output and interaction.

>>Vegeno+(OP)
But that means any AI that just talks to you can't be AI by definition. No matter how decisively the AI passes the Turing test, it doesn't matter. It could converse with the top expert in any field as an equal, solve any problem you ask it to solve in math or physics, write stunningly original philosophy papers, or gather evidence from a variety of sources, evaluate them, and reach defensible conclusions. It's all just generating tokens.

Historically, a computer with these sorts of capabilities has always been considered true AI, going back to Alan Turing. Also of course including all sorts of science fiction, from recent movies like Her to older examples like Moon Is A Harsh Mistress.

>>Dennis+SK
It's just predicting tokens:

https://old.reddit.com/r/singularity/comments/1jl5qfs/its_ju...

>>Dennis+SK
I think one of the massive hurdles, maybe, to overcome when trying to achieve AGI, is how do you solve the issue of doing things without being prompted, you know curiosity and such.

Let's say we have a humanoid robot standing in a room that has a window open, at what point would the AI powering the robot decide that it's time to close the window?

That's probably one of the reasons why, I don't really see LLMs as much more than just algorithms that give us different responses just because we keep changing the seed...

>>Dennis+SK
I don't mean that the primary (or only) way that it interacts with a human can't be just text. Right now, the only way it interacts with anything is by generating a stream of tokens. To make any API calls, to use any tool, to make any query for knowledge, it is predicting tokens in the same way as it does when a human asks it a question. There may need to be other subsystems that the LLM subsystem interfaces with to make a more complete intelligence that can internally represent reality and fully utilize abstraction and relations.

>>Vegeno+JSb
I have not yet found any compelling evidence that suggests that there are limits to the maximum intelligence of a next token predictor.

Models can be trained to generate tokens with many different meanings, including visual, auditory, textual, and locomotive. Those alone seem sufficient to emulate a human to me.

It would certainly be cool to integrate some subsystems like a symbolic reasoner or calculator or something, but the bitter lesson tells us that we'd be better off just waiting for advancements in computing power.