As a (maybe misguided) side comment, model sketches like yours make me intuitively update for shorter AI timelines, because they give me a sense of a maturing field of computational cognitive science. Would be really interested in what others think about that.
I think I’m in a distinct minority on this forum, maybe a minority of 1, in thinking that there’s more than 50% chance that studying and reverse-engineering neocortex algorithms will be the first way we get AGI. (Obviously I’m not the only one in the world with this opinion, just maybe the only one on this forum.)
I think there’s a good outside-view argument, namely this is an active field of research, and at the end of it, we’re all but guaranteed to have AGI-capable algorithms, unlike almost any other research program.
I think there’s an even stronger (to me) inside-view argument, in which cortical uniformity plays a big role, because (1) if one algorithm can learn languages and image-processing and calculus, that puts a ceiling on the level of complexity and detail within that algorithm, and (2) my reading of the literature makes me think that we already understand the algorithm at least vaguely, and the details are starting to crystallize into view on the horizon … although I freely acknowledge that this might just be the Dunning-Kruger talking. :-)
That’s really interesting, I haven’t thought about this much, but it seems very plausible and big if true (though I am likely biased as a Cognitive Science student). Do you think this might be turned into a concrete question to forecast for the Metaculus crowd, i.e. “Reverse-engineering neocortex algorithms will be the first way we get AGI”? The resolution might get messy if an org like DeepMind, with their fair share of computational neuroscientists, will be the ones who get there first, right?
Yeah I think it would be hard to pin down. Obviously AGI will resemble neocortical algorithms in some respects, and obviously it will be different in some respects. For example, the neocortex uses distributed representations, deep neural nets use distributed representations, and the latter was historically inspired by the former, I think. And conversely, no way AGI will have synaptic vesicles! In my mind this probabilistic programming system with no neurons—https://youtu.be/yeDB2SQxCEs—is “more like the neocortex” than a ConvNet, but that’s obviously just a particular thing I have in mind, it’s not an objective assessment of how brain-like something is. Maybe a concrete question would be “Will AGI programmers look back on the 2010s work of people like Dileep George, Randall O’Reilly, etc. as being an important part of their intellectual heritage, or just 2 more of the countless thousands of CS researchers?” But I dunno, and I’m not sure if that’s a good fit for Metaculus anyway.