So now we have two things. I really need to bring it back online, because it becomes much more interesting when you actually experience it. I'll be working on it all day today. It's a sub-organic. It's the name of the organism. I call it a sub-organic organism. And so what results, I sent you two images. One of them is how Transformer actually looks at it, which is this traversal of this back and forth, back and forth, back and forth. It's on signal right now. And then the second one is what happens when there is a singularity in this other type of process, which is a similar shape to the one that you can see with the Transformer. And so this symmetry covariance is everywhere. But does it make sense as far as why not storing the output of the model itself is useful here, other than just a conservation of context window? Not tangibly. Intuitively, it feels like you are reducing dilution of the inputs. Does that make sense? Mm-hmm. Yeah, because-- yeah, basically, that's a very good observation. You're saying t
| (import os) | |
| (import typing [AsyncIterable]) | |
| (import fastapi_poe [PoeBot PartialResponse run]) | |
| (import so [grow cogenerate]) | |
| (print (grow "oy" "vey")) | |
| (setv POE_BOT_KEY (os.getenv "POE_BOT_KEY")) | |
| (defclass ReflectBot [PoeBot] | |
| (defn __init__ [self world] |
ollama run gemma:text pulling manifest pulling df18cdedaff1... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 5.2 GB pulling 097a36493f71... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 8.4 KB pulling 4b98b1f4c59c... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 21 B pulling 11a9f1a6054c... 100% ▕█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 409 B verifying sha256 digest writing manifest
| Parsing command file: config/commands/defaults.sh | |
| Parsing command file: config/commands/search.sh | |
| Parsing command file: config/commands/edit_linting.sh | |
| Parsing command file: config/commands/_split_string.py | |
| Parsing command file: config/commands/defaults.sh | |
| Parsing command file: config/commands/search.sh | |
| Parsing command file: config/commands/edit_linting.sh | |
| Parsing command file: config/commands/_split_string.py | |
| INFO 📙 Arguments: agent: | |
| config: |
| #!/usr/bin/env bb | |
| (require '[babashka.process :refer [shell]]) | |
| (require '[cheshire.core :as json]) | |
| (require '[clojure.java.io :as io]) | |
| (require '[clojure.string :as str]) | |
| (require '[babashka.http-client :as client]) | |
| (import '(java.util Base64 UUID) | |
| '(java.nio.file Files Paths) | |
| '(java.text SimpleDateFormat) |
Bibliography [AAG03] [AAGM03] [ACB17] [ADG+ 16] [AGG+ 21] [ALS10] Michael Abbott, Thorsten Altenkirch, and Neil Ghani. Categories of Containers. In Andrew D. Gordon, editor, Foundations of Software Science and Computation Structures, Lecture Notes in Computer Science, pages 23–38, Berlin, Heidelberg, 2003. Springer. Michael Abbott, Thorsten Altenkirch, Neil Ghani, and Conor McBride. Derivatives of Containers. In Gerhard Goos, Juris Hartmanis, Jan Van Leeuwen, and Martin Hofmann, editors, Typed Lambda Calculi and Applications, volume 2701, pages 16–30. Springer Berlin Heidelberg, Berlin, Heidelberg, 2003. Series Title: Lecture Notes in Computer Science. Martin Arjovsky, Soumith Chintala, and L ́eon Bottou. Wasserstein GAN, December 2017. arXiv:1701.07875 [cs, stat].
| using Catlab | |
| # Define the category of sets | |
| C = Category() | |
| # Define the presheaf to represent the data | |
| data = Presheaf(C) | |
| # Define the presheaf to represent the latent variables | |
| latent_variables = Presheaf(C) |
In the symphony of biological complexity, operadic compositions offer a maestro's insight into the cacophony of elements engaged in life's dance. An (\infty)-topos takes center stage, offering a canvas for the operadic structures that flourish within, each a mosaic piece of biotic ingenuity.
Consider the Gene Regulatory Networks, where genes become operations, orchestrated in an elegant harmony as operadic compositions enable abstraction from molecular chatter to the emergence of phenotypic anthems. Inputs, regal regulatory cues, translate into operatic expressions through this categorical fabric, demonstrating the conceptual alliance between biology and operadic perspectives [5].
[ \mathcal{O}(g_1, \ldots, g_n) \rightarrow G ]
Where (\mathcal{O}) allegorizes the operad of regulatory interplay, (g_i) the individual gene inputs, and (G) the genesis of expressive concord. In this diagrammatic spectacle, one denotes the transmutation of
| using CausalInference | |
| V = [:U, :T, :P, :O] | |
| g = digraph([1=>3, 2=>3, 3=>4, 2=>4, 1=>4]) | |
| # Can estimate total effect T=>O without observing U? | |
| u = 2 | |
| v = 4 | |
| ∅ = Set{Int}() | |
| observed = 2:4 | |
| collect(list_covariate_adjustment(g, u, v, ∅, observed)) |
3:30 nice to be up in San Francisco um so are we talking about um what I think is really exciting 3:37 development um in mathematics it's going to shape our future um which is really 3:42 the uh um the development over the last few years of lots of Technologies to have to to make uh machines and 3:49 computers um um help us do math um much more effectively um now this some to 3:57 some ense this is not new um we haveed used both machines and computers and I I use the terms slightly differently um