I'm an electrical engineering graduate and i think it's very detrimental that my curriculum didn't require any evolutionary psychology classes. ai is not possible without them.
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And the math you need for this is so obviously *not* probability and statistics, but something different: computational logic. In my view, that's functional programming, functional models and non-classical logic. See my wiki-book 'Computational Logic': https://en.wikipedia.org/wiki/User:Zarzuelazen/Books/Reality_Theory:_Computational%26Non-Classical_Logic …
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Also, it's clear an entirely different paradigm is needed. Whereas statistics is based on the notion of symmetrical relations (a 'network' data structure), logic is based on asymmetric, hierarchical relations (that's a 'tree' data structure).
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So if one keeps a clear-head and thinks about this carefully, the general shape of the solution is visible in the mist! It's some kind of temporal or modal logic, in the form of some kind of advanced tree data structure, representing conceptual knowledge...
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I guess if you're trying to mimic human level intelligence, the blank slate is a poor choice of strategy.
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Innate categories? I seriously doubt that. Categories are obviously learned. What is assumed (innate) is that we live in a Yin-Yang or complementary universe and that the world is perfect and deterministic. The Bayesian stuff we hear so much about in AI circles about is absurd.
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Everyone in AGI research should read the Complementary Brain by Stephen Grossberg.
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Neurology has evolved under the constraints of a ratio between resource consumption and resource acquisition behavior. AI has not. AI lives in a Garden of Eden where humans provide it endless supplies of energy. AI evolves like a slime mold, not a neuron.
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And then the child grows up...and turns you into a paper clip.
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Q: relevant to ur (SP) description that there are actually many different kinds intelligence... is there good theory development that assumes General Intelligence as largely a measure of how well an entity can map each kind of intelligence to others that it possesses?
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Can't recommend it enough https://mitpress.mit.edu/books/made-minds …
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Build in constraints consistent w how real world behaves thereby removing many degrees of freedom in fitting (Eg backprop). Thereby hugely reduce overfitting - Ie reducing false predictions/categorizations.
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Most robust way for encoding this information = The schema system from Gary Drescher's "Made Up Minds"
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