Apple Machine Learning Journal VS Distill

For your reference, Apple's pages for Machine Learning for Developers and for their research. The Apple Neural Engine was custom designed to work better with their proprietary machine learning programs -- and they've been opening up access to developers by extending support / compatibility for TensorFlow and PyTorch. They've also got CoreML, CreateML, and various APIs they are making to allow more use of their... Source: about 1 year ago

We even host annual poster sessions of those PhD intern’s work while at our company, and it’ll give you an idea of the caliber of work. It may not be as great as Nvidia, Stryker, Waymo, or Tesla (which are not part of MAANG but I believe are far more ahead in CV), but it’s worth of considering. Source: about 1 year ago

They have something for ML: https://machinelearning.apple.com. - Source: Hacker News / about 2 years ago

They're more subtle about it, I think. https://machinelearning.apple.com/ Some of the papers are pretty good. I don't disagree with your sentiment in aggregate, though. Source: about 2 years ago

Siri is not where it needs to be because Apple refuses to mine user data to enrich it. They also are very hesitant to allow researchers to publish their breakthroughs which makes recruitment very hard. Although this is changing https://machinelearning.apple.com/. - Source: Hacker News / about 2 years ago

Distill was a new take at publishing research/ideas in deep learning in a visual way: https://distill.pub/ I love their articles and while it was hard to sustain, the quality of the ones in their are pretty good. They provide some tips and templates on how to develop such visual storytelling articles. - Source: Hacker News / 8 months ago

Explainable AI is far from early stages. Read into anthropic ai’s work in mechanistic interpretability like toy models of superposition along with the rest of the transformer-circuits papers. Read chris olah’s distill papers. Read neel nanda’s recent work on reverse engineering how language models grok modular addition. Read kevin meng’s work on locating and editing facts inside of gpt. Read openai’s paper on... Source: 11 months ago

I also wasn't aware of either The Pudding or distill.pub. So thanks for just mentioning those. Source: about 1 year ago

Anything from Setosa [0] is really good. It contains interactive, animated illustrations of several Machine Learning ideas. I _loved_ reading papers from Distill Pub [1] as they contained interactive diagrams. My most favorite one so far is the thread on Differentiable Self-organizing Systems [2]. I liked the lizard example very much as it is interactive, and lizards grow lost organs back. I think this is funny.... - Source: Hacker News / over 1 year ago

If you include deep learning in CS then https://distill.pub/ has a lot to offer in this category. - Source: Hacker News / over 1 year ago