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Hey! This is fantastic and actually ties in some very high disparate parts of math. Basically, reorient & reformulate all of math/epistomology around discrete s

by ikrima 1y ago

Hey! This is fantastic and actually ties in some very high disparate parts of math. Basically, reorient & reformulate all of math/epistomology around discrete sampling the continuum. Invert our notions of Aleph/Beth/Betti numbers as some sort of triadic Grothendieck topoi that encode our human brain's sensory instruments that nucleate discrete samples of continuum of reality (ontology) Then every modal logic becomes some mapping of 2^(N) to some set of statements. The only thing that matters is how predictive they are with some sort of objective function/metric/measure but you can always induce an "ultra metric" around notions of cognitive complexity classes i.e. your brain is finite and can compute finite thoughts/second. Thus for all cognition models that compute some meta-logic around some objective F, we can motivate that less complex models are "better". There comes the ultra measure to tie disparate logic systems. So I can take your Peano Axioms and induce a ternary logic (True, False, Maybe) or an indefinite-definite logic (True or something else entirely). I can even induce bayesian logics by doing power sets of T/F. So a 2x2 bayesian inference logic: (True Positive, True Negative, False Positive, False Negative) Fun stuff! Edit: The technical tldr that I left out is unification all math imho: algebraic topology + differential geometry + tropical geometry + algebraic analysis. D-modules and Microlocal Calculus from Kashiwara and the Yoneda lemma encode all of epistemology as relational: either between objects or the interaction between objects defined as collision less Planck hyper volumes. basically encodes the particle-wave duality as discrete-continuum and all of epistemology is Grothendieck topoi + derived categories + functorial spaces between isometry of those dual spaces whether algebras/coalgebra (discrete modality) or homologies/cohomologies (continuous actions) Edit 2: The thing that ties everything together is Noether's symmetry/conserved quantities which (my own wild ass hunch) are best encoded as "modular forms", arithmetic's final mystery. The continuous symmetry I think makes it easy to think about diffeomorphisms from different topoi by extracting homeomorphisms from gauge invariant symmetries (in the discrete case it's a lattice, but in the continuous we'd have to formalize some notion of liquid or fluid bases? I think Kashiwara's crystal bases has some utility there but this is so beyond my understanding )

ricardobeat 1y ago
> Invert our notions of Aleph/Beth/Betti numbers as some sort of triadic Grothendieck topoi that encode our human brain's sensory instruments that nucleate discrete samples of continuum of reality (ontology) There’s probably ten+ years of math education encoded in this single sentence?
- ikrima 1y ago
  well, you're in luck because I'm about to make a fool of myself in trying to tease Terence Tao over at https://mathstodon.xyz/@mathemagical https://mathstodon.xyz/@mathemagical Wish me luck!
- ikrima 1y ago
  you know what, I nerd sniped myself, here's a more fleshed out sketch of the [Discrete Continuum Bridge https://github.com/ikrima/topos.noether/blob/master/discrete-continuum-homoiconic-bridge.md#discrete-continuum-bridge https://github.com/ikrima/topos.noether/blob/master/discrete...
  - bobsh 1y ago
    There are others out here thinking along similar lines (in my case, with massive help from LLMs). Proof: https://claude.ai/share/a8128fde-ea47-4dd8-a284-16a1fd76240c https://claude.ai/share/a8128fde-ea47-4dd8-a284-16a1fd76240c . Also, I have a GitHub too: https://github.com/bobshafer/PITkit/blob/main/Links.md https://github.com/bobshafer/PITkit/blob/main/Links.md
  - gjm11 1y ago
    It seems to be entirely written by an LLM. [EDITED to add:] This is worth noting because today's LLMs really don't seem to understand mathematics very well. (This may be becoming less so with e.g. o3-pro and o4, but I'm pretty sure that document was not written by either of those.) They're not bad at pushing mathematical words around in plausible-looking ways; they can often solve fairly routine mathematical problems, even ones that aren't easy for humans who unlike the LLMs haven't read every bit of mathematical writing produced to date by the human race; but they don't really understand what they're doing, and the nature of the mistakes they make shows that. (For the avoidance of doubt, I am not making the tired argument that of course LLMs don't understand anything, they're just pattern-matching, something something stochastic parrots something. So far as I can tell it's perfectly possible that better LLMs, or other near-future AI systems that have a lot in common with LLMs or are mostly built out of LLMs, will be as good at mathematics as the best humans are. I'm just pretty sure that they're still some way off.) (In particular, if you want to say "humans also don't really understand mathematics, they just push words and symbols around, and some have got very good at it", I don't think that's 100% wrong. Cf. the quotation attributed to John von Neumann: "Young man, in mathematics you don't understand things, you just get used to them." I don't think it's 100% right either, and some of the ways in which some humans are good at mathematics -- e.g., geometric intuition, visualization -- match up with things LLMs aren't currently good at. Anyway, I know of no reason why AI systems couldn't be much better at mathematics than the likes of Terry Tao, never mind e.g. me, but they aren't close enough to that yet for "hey, ChatGPT, please evaluate my speculation that we should be unifying continuous and discrete mathematics via topoi in a way that links aleph, beth and Betti numbers and shows how our brains nucleate discrete samples of continuum reality" to produce output that has value for anything other than inspiration.)
ikrima 1y ago
You know what, since you put in all that work, here's my version using p-adic geometry to generalize the concept of time as a local relativistic "motive" (from category theory) notion of ordering (i.e. analogous to Grothendieck's generalization of functions as being point samples along curves of a basis of distributions to generalize notions of derivatives): https://github.com/ikrima/topos.noether/blob/aeb55d403213089fb8820abffbe3fb6737ea8369/algebraic-effects-time-paper.md https://github.com/ikrima/topos.noether/blob/aeb55d403213089...

by ikrima 1y ago

ricardobeat 1y ago

> Invert our notions of Aleph/Beth/Betti numbers as some sort of triadic Grothendieck topoi that encode our human brain's sensory instruments that nucleate discrete samples of continuum of reality (ontology) There’s probably ten+ years of math education encoded in this single sentence?

ikrima 1y ago
well, you're in luck because I'm about to make a fool of myself in trying to tease Terence Tao over at https://mathstodon.xyz/@mathemagical https://mathstodon.xyz/@mathemagical Wish me luck!
ikrima 1y ago
you know what, I nerd sniped myself, here's a more fleshed out sketch of the [Discrete Continuum Bridge https://github.com/ikrima/topos.noether/blob/master/discrete-continuum-homoiconic-bridge.md#discrete-continuum-bridge https://github.com/ikrima/topos.noether/blob/master/discrete...
- bobsh 1y ago
  There are others out here thinking along similar lines (in my case, with massive help from LLMs). Proof: https://claude.ai/share/a8128fde-ea47-4dd8-a284-16a1fd76240c https://claude.ai/share/a8128fde-ea47-4dd8-a284-16a1fd76240c . Also, I have a GitHub too: https://github.com/bobshafer/PITkit/blob/main/Links.md https://github.com/bobshafer/PITkit/blob/main/Links.md
- gjm11 1y ago
  It seems to be entirely written by an LLM. [EDITED to add:] This is worth noting because today's LLMs really don't seem to understand mathematics very well. (This may be becoming less so with e.g. o3-pro and o4, but I'm pretty sure that document was not written by either of those.) They're not bad at pushing mathematical words around in plausible-looking ways; they can often solve fairly routine mathematical problems, even ones that aren't easy for humans who unlike the LLMs haven't read every bit of mathematical writing produced to date by the human race; but they don't really understand what they're doing, and the nature of the mistakes they make shows that. (For the avoidance of doubt, I am not making the tired argument that of course LLMs don't understand anything, they're just pattern-matching, something something stochastic parrots something. So far as I can tell it's perfectly possible that better LLMs, or other near-future AI systems that have a lot in common with LLMs or are mostly built out of LLMs, will be as good at mathematics as the best humans are. I'm just pretty sure that they're still some way off.) (In particular, if you want to say "humans also don't really understand mathematics, they just push words and symbols around, and some have got very good at it", I don't think that's 100% wrong. Cf. the quotation attributed to John von Neumann: "Young man, in mathematics you don't understand things, you just get used to them." I don't think it's 100% right either, and some of the ways in which some humans are good at mathematics -- e.g., geometric intuition, visualization -- match up with things LLMs aren't currently good at. Anyway, I know of no reason why AI systems couldn't be much better at mathematics than the likes of Terry Tao, never mind e.g. me, but they aren't close enough to that yet for "hey, ChatGPT, please evaluate my speculation that we should be unifying continuous and discrete mathematics via topoi in a way that links aleph, beth and Betti numbers and shows how our brains nucleate discrete samples of continuum reality" to produce output that has value for anything other than inspiration.)

ikrima 1y ago

You know what, since you put in all that work, here's my version using p-adic geometry to generalize the concept of time as a local relativistic "motive" (from category theory) notion of ordering (i.e. analogous to Grothendieck's generalization of functions as being point samples along curves of a basis of distributions to generalize notions of derivatives): https://github.com/ikrima/topos.noether/blob/aeb55d403213089fb8820abffbe3fb6737ea8369/algebraic-effects-time-paper.md https://github.com/ikrima/topos.noether/blob/aeb55d403213089...