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Everyone who is serious about human-level AI is also interested in understanding human intelligence. And there is in fact a lot of inspiration that can be drawn from psychology and neuroscience (perhaps more from psychology than neuroscience).
But the state of our understanding across these fields is such that it seems more tractable to understand intelligence by attempting to build it from scratch than by studying brains.
Intelligence is nothing more than a model of the world that first encompasses predators, then expands to include conspecifics, then recurses to include “how conspecifics model ME”
There’s been no call for this sort of “mental models of others” in standard AI tasks, hence no progress along this dimension. IF we’re going to go in that direction (a VERY dubious idea IMHO) that’s what you need — AIs that evolve to model/compete against other AIs.
Building problem solving ignoring biological intelligence will not stumble upon principles of brain functioning. Ironically ML results are compared with human performance. We expect being so smart for figuring it out from scratch when not smart enough for decrypting the blueprint
that's precisely my approach and it is yielding great results - work out intelligence from the bottom up. go simple, remove noise and preexisting know-how and you boil down cognition to its bare elements. The complex stuff comes for free
Understood. I'm suggesting a middle ground solution. For example, I have an understanding of the underlying mechanism of the brain that I obtained 1990-1993 by careful observation of how I invented something for my PhD. I later discovered (in Aug 2008) that it appears capable =>
of everything else we associate with intelligence. If I'm right, and I may not be, then the AI work I've seen, including ML and hybrids, is not based the underlying mechanism of the brain. If that's the case then it would, I think, explain your original point re progress since =>
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