Luis Villa
@luisvilla
I do a lot of opens, at Wikipedia, Mozilla, Creative Commons, and elsewhere.
lu.isLuis Villa
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
SSRNpapers.ssrn.com
“At Google Research and Brain and DeepMind, we've invented the majority of machine learning techniques we're all using today, over the last 10 years of pioneering work. So that’s always been in our DNA, and we have quite a lot of senior research scientists that maybe other orgs don't have.”
AI and Trust - Schneier on Security
schneier.com
Data Futures Lab - Mozilla Foundation
foundation.mozilla.org
Apple announces LLM in a flash: Efficient Large Language Model Inference with Limited Memory
paper page: https://huggingface.co/papers/2312.11514... See more
Building Open Models Responsibly in the Gemini Era
opensource.googleblog.com
I appreciate the attention to detail here, and it’s generally a really careful, thoughtful post, but you then throw away pretty much all the good effort by settling on a term that the vast majority of people are not going to be able to tell apart from open source. “publicly available”, “free to use”… lots of other, non-confusing options right there.
The rise of statistical NLP in the early 2000s was another such cycle. But major methodological shifts come with major cultural shifts as well. Statistical NLP introduced a culture laser-focused on benchmarks and saw the end of a small, “high trust” research community.
Part of a long thread on the culture of ML, which I think is critical to understand whether/how Open ML might succeed/fail.
When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale
arxiv.org
Community Power as a pillar of AI governance — Promising Trouble
Rachel Coldicuttpromisingtrouble.net
One of the things I referred to in my recent Linux Foundation talk. I wonder what this approach - taken thoughtfully and seriously - would look like applied to the Waymo/Cruise problem in SF.