Eric Feng @ericlfeng
ml @hazyresearch @stanford. i like math. our future is beautiful ❤️🚀 Joined November 2024-
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Eric Feng retweeted
Part 2 of our MLPs blog post is out! 👀 This time, we’re here to tell you the story 📖 of our quest for a construction that: ✅ Handles general embeddings 🌐 ✅ Asymptotically matches the information-theoretic limit 📊📈 ✅ Is usable within transformers 🤖✨
Happy 🦃 Thanksgiving weekend! 🍂 This year, we cooked up a new recipe for juicy fact-storing MLPs. Instead of picking apart trained models, we asked: Can we construct fact-storing MLPs from scratch? 🤔 Spoiler: we can & we figured out how to slot these hand-crafted MLPs into
Eric Feng retweeted
(6/6) Read more on our blog and paper: Intro: tinyurl.com/3y4d9jst Part 1: tinyurl.com/23afs6kj Part 2: tinyurl.com/bddyad6p Paper: tinyurl.com/45532xxz
Eric Feng retweeted
Data centers dominate AI, but they're hitting physical limits. What if the future of AI isn't just bigger data centers, but local intelligence in our hands? The viability of local AI depends on intelligence efficiency. To measure this, we propose intelligence per watt (IPW): intelligence delivered (capabilities) per unit of power consumed (efficiency). Today’s Local LMs already handle 88.7% of single-turn chat and reasoning queries, with local IPW improving 5.3× in 2 years—driven by better models (3.2×) and better accelerators (1.7×). As local IPW improves, a meaningful fraction of workloads can shift from centralized infrastructure to local compute, with IPW serving as the critical metric for tracking this transition. (1/N)
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