A Non-Technical Guide to Interpreting SHAP Analyses(aidancooper.co.uk)

11 points by reqo 1 year ago | 0 comments

Don't mock machine learning models in unit tests(eugeneyan.com)

55 points by 7d7n 2 years ago | 50 comments

Notable AI Models(epochai.org)

7 points by cheptsov 2 years ago | 0 comments

Attacks on machine learning models(rnikhil.com)

84 points by whoami_nr 2 years ago | 26 comments

The Training Example Lie Bracket(pbement.com)

25 points by pb1729 6 months ago | 12 comments

Experimentation Matters: Why Nuenki isn't using pairwise evaluations(nuenki.app)

4 points by Alex-Programs 1 year ago | 0 comments

GraphCast: AI model for weather forecasting(deepmind.google)

571 points by bretthoerner 2 years ago | 186 comments

Google DeepMind research blog post from November 2023 introducing GraphCast, an AI model for faster and more accurate global weather forecasting.

Gemini: Google's most capable AI model yet(blog.google)

711 points by tosh 2 years ago | 415 comments

Google blog post by Sundar Pichai and Demis Hassabis announcing Gemini, Google's largest and most capable AI model, dated December 6, 2023.

Gemma: New Open Models(blog.google)

1075 points by meetpateltech 2 years ago | 476 comments

Google blog post announcing Gemma, a family of open AI models built from the same research and technology as Gemini.

Gemma 3 QAT Models: Bringing AI to Consumer GPUs(developers.googleblog.com)

571 points by emrah 1 year ago | 259 comments

Google Developers Blog post announcing Gemma 3 models trained with quantization-aware training so they can run on consumer GPUs.

OpenAI, Google and Anthropic are struggling to build more advanced AI(bloomberg.com)

553 points by lukebennett 1 year ago | 534 comments

Bloomberg article reporting that OpenAI, Google and Anthropic are seeing diminishing returns in building more advanced AI models.

Pen and Paper Exercises in Machine Learning (2022)(arxiv.org)

389 points by ibobev 1 year ago | 53 comments

arXiv paper by Michael U. Gutmann collecting pen-and-paper exercises on machine learning topics like graphical models, HMM inference, sampling, and variational inference.