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Not OP, and not a computer scientist. But amateur neuroscientist. From what I understand, current models seem to have issue with planning and reasoning because

Not OP, and not a computer scientist. But amateur neuroscientist. From what I understand, current models seem to have issue with planning and reasoning because they “blurt” everything out zero shot. They can’t recurrently process information. Part of it is how we train these models, which is rapidly being experimented with and being improved. Another part is that our brain can both consciously and unconsciously process information recurrently until we solve a problem. Especially helpful when we haven’t solved a problem before. With LLMs, we can do that rudimentary with recurrent prompting. In a way, it can see the steps it has tried, re-evaluate and come up with a new try. But it’s not innate yet, where models can “think” about a complex problem until it solved it. Also- According to some theories around consciousness, this is when consciousness will really emerge (integrated information theory). I’m pretty sure we’ll be able to solve this recurrent processing issue in the next few years.

TeMPOraL 2y ago
> From what I understand, current models seem to have issue with planning and reasoning because they “blurt” everything out zero shot. They can’t recurrently process information. IMO, this is not necessarily a model issue, but an interface issue - recurrently processing information is enabled by just feeding the model's output back to it, perhaps many times, before surfacing it to the user. Right now, with the interfaces we're exposed to - direct evaluation, perhaps wrapped in a chatbot UI - the user must supply the recursion themselves. > But it’s not innate yet, where models can “think” about a complex problem until it solved it. That's indeed the part I see us struggling with - we can make the model recursive, but we don't know how to do it unsupervised, so we can box it behind an abstraction layer, letting it work stuff out on its own and break recursion at the right moment. I think the mental model I developed a good year ago still stands: LLM isn't best compared to a human mind, but to a human's inner voice. The bit that "blurts everything out zero-shot" into your consciousness. It has the same issues of overconfidence, hallucinations, and needing to be recursively fed back to itself.

TeMPOraL 2y ago

> From what I understand, current models seem to have issue with planning and reasoning because they “blurt” everything out zero shot. They can’t recurrently process information. IMO, this is not necessarily a model issue, but an interface issue - recurrently processing information is enabled by just feeding the model's output back to it, perhaps many times, before surfacing it to the user. Right now, with the interfaces we're exposed to - direct evaluation, perhaps wrapped in a chatbot UI - the user must supply the recursion themselves. > But it’s not innate yet, where models can “think” about a complex problem until it solved it. That's indeed the part I see us struggling with - we can make the model recursive, but we don't know how to do it unsupervised, so we can box it behind an abstraction layer, letting it work stuff out on its own and break recursion at the right moment. I think the mental model I developed a good year ago still stands: LLM isn't best compared to a human mind, but to a human's inner voice. The bit that "blurts everything out zero-shot" into your consciousness. It has the same issues of overconfidence, hallucinations, and needing to be recursively fed back to itself.