I agree, I suspect AGI is possible right now with a similar system only slightly more sophisticated than this one. The right "glue" for existing models, and plugins to existing data sources all coordinated in a system. GTP-4 would do the managing, and handling, and some simple template API and handler script would allow it to call instances of itself or other models, track recursion depth, and automatically remind GTP-4 to stay on track and always use the correct 'templates' for requests. It could also remind it to create a condensed summary of it's goals and state that gets repeated back automatically to act as long term memory, and increase the effective context window size (edit: this is single variable external memory).

I am afraid to explain this, because I have tried a preliminary version of it that I supervised step by step, and it seems to work. I think it is obvious enough that I won't have been the only one to think of it, so it would be safer to put the information out there so people can prepare.

I see a big disconnect on here between people saying GPT-4 can't do things like this and is just a "stochastic parrot" or "glorified autocomplete," and people posting logs and summaries of it solving unexpectedly hard problems outside of any conceivable training set. My theory is that this disconnect is due to three major factors: * People confusing GPT-4 and GPT-3, as both are called "chatGTP" and most people haven't actually used GPT-4 because it requires a paid subscription, and don't realize how much better it is * Most popular conceptual explanations about how these models work imply that these actually observed capabilities should be fundamentally impossible * Expectations from movies, etc. about what AGI will be like, e.g. that it will never get confused or make mistakes, or that it won't have major shortcomings in specific areas. In practice this doesn't seem to limit it because it recognizes and fixes its mistakes automatically when it sees feedback (e.g. in programming)