I suspect they will conquer multiple modalities, or at least make decent progress there. They have a good foundation model for audio now (Whisper), and had grea

I suspect they will conquer multiple modalities, or at least make decent progress there. They have a good foundation model for audio now (Whisper), and had great results with text/image on both CLIP (search) and DALLE (generation). There are also lots of modes throughout NLP that can be articulated (translation between languages, formats, "downstream tasks"). I bet GPT4 makes use of audio/image and text. But no, I don't think it will be "general AI". It's more like the semantic engine we develop as humans to be able to strategize with "seemingly unrelated" concepts. We attach some arbitrary meaning to it, then form associations to other things we have seen previously, if any exist. GPT3, DALLE, CLIP, etc. make sense to use a similar sort of "backend" of semantics. But, they crucially don't possess the ability to do short or long term "planning", and as a sibling comment mentions - they aren't "online" after tranining and can't do gradient descent "on-the-fly". Proposals for AGI tend to include a "module" for planning (although no one has articulated how to do that, or what it even means), as well as the ability to run end-to-end gradient descent on-the-fly (in a finite amount of time). The systems we have currently do seem to "generalize" to the distribution of data rather than the data itself. Indeed, this is part of what makes them useful - they can be used to create content not previously seen that is still accurate to some known modality. "an armchair in the form of a [blarn]". The human still does all the planning/prompt-engineering, however.