Another failure mode for deep learning, precisely anticipated by my 2001 book, The Algebraic Mind.https://www.newscientist.com/article/2198761-deepmind-created-a-maths-ai-that-can-add-up-to-6-but-gets-7-wrong/ …
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I suspect you need to be more specific to what capabilities are missing. This will align with what many researchers are seeking. Example: is analogy making sufficient or is something else needed? I've at least have a model that captures this.
sorry, but it’s really not fair to accuse me of being vague if you haven’t read the detailed and specific explication of variables and operations over variables etc in the book or engaged it any way.
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Is it sufficiently specified that it can be implementable or is it more of an analogue of existing symbolic computation (i.e. variables)?
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Physical world objects interact with each other by defining a symbol over other objects(abstract away rest of the world by mean field ) and computes(interacts).Cognition (and physics)is computation over symbols so DNN must implement this behaviour
End of conversation
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