Software compresses our models of the physical universes via successive, tight functional descriptions of physics, mechanics, electrics, logic, automata, language, behavior. Amazing that this all works!
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Replying to @Plinz @Saigarich
All human knowledge compressed into ~16,700 wikipedia articles! Reckon I've captured about 95% of concept usage in what I'm calling: 'Wikipedia-Prime': http://www.zarzuelazen.com/CoreKnowledgeDomains2.html …
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Replying to @zarzuelazen @Saigarich
There is probably no sequence in which a human being can read all the Wikipedia articles about math or physics and end up with a working understanding of each field. Do you think that if a machine could parse all articles simultaneously it might converge on an understanding?
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Replying to @Plinz @Saigarich
I think the approx. 16,700 articles I've listed in my wiki-books are in fact enough to bring a human up to a 'fair' level of technical understanding in all fields! I know, because I spent 2 years compiling and reading them all! I think my 'wikipedia-prime' is a good data-set
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Replying to @zarzuelazen @Saigarich
Does this mean that you can now implement a computational model of a black hole in Perl? At which level of depth can you explain the Riemann hypothesis and M theory? In how many ways does the article on Direct Realism fly in the face of everything that makes sense in Wikipedia?
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Replying to @Plinz @Saigarich
I reckon I could do the model of a black hole, no problem with this information from my wikipedia-prime: Programming: https://en.wikipedia.org/wiki/User:Zarzuelazen/Books/Reality_Theory:_Programming%26Web_Apps … Cosmology&Astrophysics: https://en.wikipedia.org/wiki/User:Zarzuelazen/Books/Reality_Theory:_Cosmology%26Astrophysics … Geometry&Analysis: https://en.wikipedia.org/wiki/User:Zarzuelazen/Books/Reality_Theory:_Geometry%26Analysis …
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M-Theory and RH are very specialized topics; reading wikipedia articles won't make you an expert, but even there I got surprisingly far; Quantum Mechanics: https://en.wikipedia.org/wiki/User:Zarzuelazen/Books/Reality_Theory:_Quantum_Mechanics … Algebra&NumberTheory: https://en.wikipedia.org/wiki/User:Zarzuelazen/Books/Reality_Theory:_Algebra%26Number_Theory … Geometry&Analysis (again): https://en.wikipedia.org/wiki/User:Zarzuelazen/Books/Reality_Theory:_Geometry%26Analysis …
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Normally knowledge is presented in a very compartmentalized and abstract form. But magic starts to happen when two things occur: (1) When you see how all the concepts are connected, (2) When you see the motivations behind the concepts - how they're applied to problem solving
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It's these 2 things that the wikipedia data-set can give you. Not *depth* of knowledge, but unprecedented *breadth* of knowledge - how concepts *connect together* into a big picture, and how they're *applied* to solving practical problems
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As to the relevance of naive realism to what I'm talking about; It's important to remember that concepts are in the *mind*. We don't need to talk about reality for understanding concepts, only, how we *model* reality. That's what multi-level modeling is: Machine Psychology!
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(I was just upset that the naive realism article reflected extremely poor thinking on behalf of its authors, and as a result deep incompatibility with articles about physics, programming or perception.)
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