> The current LLMs are basically unfiltered raw thoughts that must be continuously refined. A similar thing happens in our brains and only a little bit of that is accessible to our consciousness

Exactly. But, AFAIK, it's also the part that does the bulk of actual thinking and decision-making for us. In that sense, LLMs may be closer to AGI than people expect, because they seem to be capturing the actual core of intelligence and reasoning - and the missing bits (like long-term memory and higher-level thought stream filter/censor) may be much easier to bolt on to them.

This is why we typically see better performance out of GPT when plugins are bolted in an chain|tree of though with reflection.

The output of LLMs is kind of like our stream of consciousness, there's a lot of things I think, then discount after internally reflecting on the thought which the often leads to a more correct solution. Having an LLM 'think' like this natively would massively increase the necessary the amount of compute needed, hence the expense, so at least in any public products it's not being done at this time.

Yup. I totally expect you'll be able to eke out significant performance boost if you chain up the LLMs, so that e.g. you feed the initial query to a first-stage GPT-4 several times (likely in parallel), feed those to some kind of filter models that pass or reject the output, looping until you have, say, 3 passing outputs, then feed that to a summarizer, etc. Maybe play with generating system prompts so that you have multiple entirely different takes on the same query, or stage it. Or, you know, have GPT-4 look at the query and propose a graph of subsequent invocations for you.

I wish I had time to play with it some more right now. The pace of progress in the field is giving me a serious case of FOMO.