dl

https://toobnix.org/w/9oA5WZEkbjKkfbsSxEoJ8v #archive

Okay I guess on the hour it's the Sunday-morning-in-Europe #lispyGopherClimate .

a. I will lightly go over my recent #commonLisp #symbolic #DL ( #ai!) algo / article.

b. Excitingly that gave me occasion to use

(this-function foo &rest keys &key &allow-other-keys)

which I used to pass data-like parameters along without cluttering up arguements (waters' functions of seven arguments). People had tried to explain it to me previously but now I underst

#peertube

ALT

#lispyGopherClimate #lisp #technology #podcast #archive, #climate #haiku by @kentpitman
https://communitymedia.video/w/c3GdAXe7BQTbK3VrcXCm7E
& @ramin_hal9001
On the #climate I would like to talk about the company that found #curl and #openssl's #deeplearning many (10ish) 0-day vulns "using #ai ". (#llm s were involved).

This obviously relates to my #lisp #symbolic #DL https://screwlisp.small-web.org/conditions/symbolic-d-l/ (ffnn equiv). Thanks to everyone involved with that so far.

I implemented that using #commonLisp #condition handling viz KMP.

ALT

As someone who now has a feedforward neural network of a single hidden layer implementation (in #commonLisp) I learned a lot about what has a spot to be stuck into the algorithm.

https://screwlisp.small-web.org/fundamental/sharpsign-sharpsign-input-referential-ffnn-dl-data/

Something that turns out to be easy to stick in is making training data which copies data out of the current context or even to simply point the training data definition into the #deepLearning inference input context though I did not prove convergence for the latter case.

#ai #programming #dl