#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.

https://screwlisp.small-web.org/conditions/symbolic-d-l/
#Symbolic #deepLearning #inferencing with #commonLisp #conditions

The #DL from before, but it works via a mixture of condition handlers and restarts.

This turned out to be condition example boilerplate, but it was interesting to me personally, at least!

Not sure about this construction I used (paraphrasing):

(prog ((c nil))
start
(restart-case
(if c
(signal c))
(resignal (condition) (setq c condition) (go start))))

#programming #ai

NEWAVE. Делаем интеллектуальный ретривал музыки

Двуэнкодерные нейросети, контрастивное обучение, десять датасетов и late fusion. Как мы строили ML-систему ретривала, понимающую человеческий язык вместо фильтров Ну и как же?

https://habr.com/ru/articles/989756/

#CLAP #biencoder #contrastive_learning #retrieval #feature_engineering #ML #DL #machine_learning #project #deep_learning

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

Инференс нейросетевых моделей для табличных данных с помощью ONNX Runtime на C++

ONNX Runtime (ORT) — это высокопроизводительный движок для выполнения моделей в формате Open Neural Network Exchange (ONNX). Он предлагает оптимизированные реализации для CPU и GPU, поддержку различных аппаратных ускорителей и, что ключевое, простой C++ API. В этой статье мы разберем, как выполнить инференс модели для табличных данных, используя ONNX Runtime в C++ проекте. Ссылка для скачивания: Библиотеку можно получить через официальный GitHub (сборка из исходников). Для простоты в проектах часто достаточно забрать предсобранные бинарники из релизов .

https://habr.com/ru/articles/991430/

#onnxruntime #onnx #ORT #DL #TabularDL #C++ #инференс_моделей

A better and #symbolic #deeplearning #algorithm in #commonLisp .

https://screwlisp.small-web.org/fundamental/a-better-deep-learning-algorithm/

I hope I come across as slightly tongue-in-cheek! Though all my points and notes are in fact genuine.

tl;dr I define completely explainable and interpretable deep learning / inferencing in terms of operations on sets of symbols.

I think it successfully underscores the wild misapprehensions about deep learning that abound in the wild. What do you think?

#ai #programming #DL

The HU - Black Thunder ft Serj Tankian & DL of Bad Wolves

https://www.youtube.com/watch?v=KYsmMLvmAQo

#TheHU #BlackThunder #SerjTankian #DL #BadWolves #Music

Red Hat Accelerates AI Trust and Security with Chatterbox Labs Acquisition https://www.redhat.com/en/about/press-releases/red-hat-accelerates-ai-trust-and-security-chatterbox-labs-acquisition #AI #ML #DL #NN #oss #opensource

#YGO-ZEXAL #DL
https://x.com/punsukabooks/status/2003370537360457749
謝謝梅拉古落地……梅拉古跟Ⅳ在動畫沒講過話的遺憾在DL實現了……