mlcommons.org
MLCommons is an AI engineering consortium, built on a philosophy of open collaboration to improve AI systems. Through our collective engineering efforts, we continually measure and improve AI technologies' accuracy, safety, speed, and efficiency.
👤 Joined on 21 November 2024
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@mlcommons.bsky.social@mlcommons.bsky.social → @mlcommons.org@mlcommons.org maintains a few repositories of people’s speech for AI training (which was done consensually and is made open). I’m sure there’s places that would love to maintain this archive if provided.
Show PostMLPerf Inference 6.0 results are out! Lots of new goodness here, including a bunch of new benchmark tests (and models) and some very large clusters reaching new peak performance numbers.
Show PostSo proud to co‑chair this work. It has been almost two years in the making and I'm grateful to the many brilliant colleagues who contributed their ideas, feedback, and energy.
Show PostAnnouncing our latest paper: CommonLID In collaboration with @commoncrawl.bsky.social @mlcommons.org @jhu.edu we built a LID benchmark on actual Common Crawl text covering 109 languages. Existing evaluations overestimate how well LangID works on web data. arxiv.org/abs/2601.18026
Show PostWe’re doing testing on jailbreaking most of the modern Agentic interfaces today at @mlcommons.org. You can take a look at some of our artifacts here: mlcommons.org/ailuminate/j... Suffice it to say, these types of exploits are real (as you’ve noted), very dangerous (again, noted) & persistent
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