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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Replying to @zarzuelazen @Plinz
Considering providing links to sources is one of the cornerstones of wiki, when I look for in depth knowledge I treat it more as a navigation tool, rather than knowledge repository. Though summaries on the topic are extremely useful and often sufficient.
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YMMV, but in my experience, Wikipedia is often not helpful for learning complex formal knowledge, because each field uses their own formalisms, which are presupposed and not referenced in the article. Wikipedia is often written by and for people that don't remember not knowing.
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Replying to @Plinz @Saigarich
Yes, the pure math articles can be particularly hard to understand if read in isolation. That's why it's so important to connect together multiple concepts - reading about many related ideas to put things into context.
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One problem I found (which is really the main reason that making this compilation took me 2 years) is that unless you're an expert in a given field yourself, you really don't know what the central ideas and methods in that field are. Wikipedia didn't really help convey that.
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