Sorry – I’ve not been much involved: other projects. But I just saw this – haven’t looked in any detail yet.
DeepAlgebra - an outline of a program Authors: Przemyslaw Chojecki Categories: cs.AI math.AG Comments: 6 pages, https://przchojecki.github.io/deepalgebra/ \\ We outline a program in the area of formalization of mathematics to automate theorem proving in algebra and algebraic geometry. We propose a construction of a dictionary between automated theorem provers and (La)TeX exploiting syntactic parsers. We describe its application to a repository of human-written facts and definitions in algebraic geometry (The Stacks Project). We use deep learning techniques. \\ ( https://arxiv.org/abs/1610.01044 From: Tim Daly [mailto:axiom...@gmail.com] Sent: 01 September 2016 13:25 To: Richard Fateman Cc: Tim Daly; axiom-dev; Ralf Hemmecke; James Davenport; Mike Dewar; vdhoe...@lix.polytechnique.fr; D Zwillinger; albert_r...@msn.com Subject: Re: Design of Semantic Latex The weaver program can now process a latex document. The end result is a tree structure of the same document. There is still more to do, of course. Much more. It is clear that the semantics of the markup tags are all in the weaver program. This is obvious in hindsight since the markup needs to be transparent to the print representation. The parser needs to know the 'arity' of tags since \tag{a}{b} would parse one way, \tag{a}, for a 1-arity tag and another way \tag{a}{b} for a 2-arity tag. The code needs to be generalized to parse given the arity. The weaver program is structured so that the tree-parse output is independent of Axiom. The Axiom rewrite will take the tree as input and produce valid Axiom inputforms. This should make it possible to target any CAS. Onward and upward, as they say.... Tim On Sat, Aug 27, 2016 at 1:28 PM, Tim Daly <axiom...@gmail.com<mailto:axiom...@gmail.com>> wrote: On Sat, Aug 27, 2016 at 12:14 PM, Richard Fateman <fate...@berkeley.edu<mailto:fate...@berkeley.edu>> wrote: Take up a book on complex analysis and see what problems you have as you try to encode the statements, or especially the homework problems. I tried this decades ago with the text I used, https://www.amazon.com/Functions-Complex-Variable-Technique-Mathematics/dp/0898715954 but probably any other text would do. My last project at CMU (Tires) involved work on machine learning using natural language (and Good-Old-Fashioned-AI (GOFAI)). I'm not smart enough to make progress in natural language. I think the emphasis on handbook or reference book representation is natural, and I have certainly pursued this direction myself. However what you/we want to be able to encode is mathematical discourse. This goes beyond "has the algorithm reproduced the reference value for an integration." Can you encode in semantic latex a description of the geometry of the (perhaps infinitely layered) contour of a complex function? You might wonder if this is important, but then note that questions of this sort appear in the problem section for chapter 1. Like any research project, there has to be bounds on the ambition. At this point, the goal is to modify the markup to disambiguate a latex formula so the machine can import it. Axiom needs to import it to create a test suite measuring progress against existing knowledge. What you're describing seems to be a way to encode topological issues dealing with the structure of the space underlying the formulas. I have no idea how to encode the Bloch sphere or a torus or any other space except by referencing an Axiom domain, which implicitly encodes it. If the formula deals with quantum mechanics then the algorithms have an implicit, mechanistic way of dealing with the Bloch sphere. So markup that uses these function calls use this implicit grounding. Simllarly, markup that uses a branch cut implicitly uses the implementation semantics. Axiom and Mathematics have one set of branch cuts, Maple and Maxima have another (at far as I can tell). So the markup decisions have to be carefully chosen. Here's the challenge then. Take a mathematics book and "encode" it so that a program (hypothetically) could answer the problems at the end of each chapter. That's a much deeper can of worms than it appears. I spent a lot of time in the question-answering literature. I have no idea how to make progress in that area. The Tires project involved self-modifying lisp based on natural language interaction with a human in the limited domain of changing a car tire. See http://daly.axiom-developer.org/cmu/book.pdf (The grant ended before the projected ended. Sigh) Tim P.S. Tires is self-modifying lisp code. It "learns" by changing itself. The initial code (the seed code) becomes "something else". One interesting insight is that two versions of the seed code will diverge based on "experience". That implies that you can't "teach by copy", that is, you can't teach one system and then "just copy" it to another existing system since their experiences (and the code structure) will differ. Any system that "learns" will fail "teach by copy", I believe. That means that AI will not have the exponential growth that everyone seems to believe.
_______________________________________________ Axiom-developer mailing list Axiom-developer@nongnu.org https://lists.nongnu.org/mailman/listinfo/axiom-developer
