Great! New thread then: What are the ways in which people in the neural net community think that today's ANN's are NOT perfect models of the brain? Where do you want to dig deeper, and why?https://twitter.com/timkietzmann/status/1200159447651438592 …
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Not a neuroscientist, but my own opinion is that weighted gating, compositionality, and learning are the foundations of bio neural nets, and all other phenomena that we care about are built on these blocks. Traditional computers only do the first, but DL can do all three.
say what? deep learning is very weak at compositionality; classical AI did they well.
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Classical AI was bad at compositionality *unless* the domain was perfectly understood and describable. How would you embed compositionality in a classical image analysis system? You'd need to define every intermediate feature, which is impossible.
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directory structures in modern computers are all compositional and work (essentially) perfectly reliably; lots of semantic parsers still use compositionality, reasonably effectively. compositionality has not worked as well for vision, but even Hinton sees we need progress there
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