[Tv9m]

You might like some of the work being done under the label "Factored Cognition". It's an approach that treats LLMs as building blocks instead of being complete AIs. Instead of asking the LM to solve a problem directly in one pass, you ask it to divide the problem between several different virtual copies of itself, which then themselves subdivide further, and so on until each subtask is small enough that the LM can solve it directly. For this to work the original problem needs to be acyclic and fairly tree-like, i.e., not something that requires having a sudden "Eureka!" moment to solve.

But I've only seen this done with a single model. Sometimes it gets prompted to act like a different agent in different contexts, or given API access to external tools, but it's still just one set of weights.

[toss1]

Hmm, that sounds like a nod in the right direction, but a rapid initial skim maybe indicates that it's more parallelizing the problem than abstracting it. I've got to read more about it - thanks!

While Minsky & Papert's book on Perceptrons was enormously destructive, I think there is something to their general concept of Society Of Mind, that multiple sub-calculating 'agents' collude to actually produce real cognition.

We aren't doing conscious reasoning about the edges detected in the first couple layers of our visual cortex (which we can't really even access, 'tho I think Picasso maybe could). We're doing reasoning about the concepts of the people or objects or abstract concepts or whatever many layers up. The first layers are highly parallel - different parts of the retina connecting to different parts of the visual cortex, and then starting to abstract out edges, zones, motion, etc. and then synthesize objects, people, etc.

I think we need to take a GPT and a Stable Diffusion and some yet-to-be-built 3D spatial machine learning/reasoning engine, and start combining them, then adding more layer(s) synthesizing about that, and maybe that'll get closer to reasoning...