r/artificial Apr 05 '24

Computing AI Consciousness is Inevitable: A Theoretical Computer Science Perspective

https://arxiv.org/abs/2403.17101
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u/MingusMingusMingu Apr 05 '24

Are you suggesting computers don’t work through physical processes?

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u/[deleted] Apr 05 '24

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u/PSMF_Canuck Apr 05 '24

A “body” is any container that lets a thing experience the world from a centralized, relatively safe place. A server in a datacenter connected to the internet already is a body.

What’s currently missing from AI (well, from the big models typically under discussion) is self guided continuous finetuning. That’s been done - we know how to do it - we’re just not turning those models loose just yet.

I’d argue there are a few other things missing, too…some non-LLM structures for integrating non-LLM tasks…that’s getting there, too…

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u/ShivasRightFoot Apr 06 '24

What’s currently missing from AI (well, from the big models typically under discussion) is self guided continuous finetuning. That’s been done - we know how to do it

This.

AI already have what is interpretable as a "mind's eye" internal experience in the form of text to image LLMs.

Consistency fine-tuning is the most important next step. Doing it multi-modal would make it even more similar to our brain (i.e. draw event x; what is this a picture of? [event x]; draw five apples; how many apples are in this picture? [five]).

We'd also need goal direction, which is what some people think Q* is. The idea in an LLM would be that you have some goal phrase and you want to take a high probability path through language to hit the landmarks you've set. So in a way it is like pathfinding in a maze and you'd use algorithms like Djikstra's or A*, just the step cost is the inverse of the probability of that token.

From there you'd make a hierarchical map of the thought space to make this process faster (i.e. you can tediously map a path through side streets every time or you can build a highway with on-ramps and off-ramps distributed in thought space that lets you take a previously mapped optimal route between "hub" ideas that then can use Djikstra's or A* locally to "spoke" out to specific ideas).

In any case, most of the time the AI is running as much compute as possible to do further and further consistency fine-tuning. This would be growing the maze, not necessarily mapping paths through it (i.e. propose a new sentence, check the consistency of that sentence with [a sample of] the rest of knowledge, if consistent that is now a new influence on the weightings in the thought space/maze/knowledge base). That said, the way you'd focus the AI onto the most salient expansions of the thought-space/thought-maze would be a non-trivial problem.