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i co-lead a large team of product managers and research engineers working on ML recommenders and ranking models at Meta Core ML; we serve 3.6 billion people. i’m also a university lecturer at UCLA, teaching a class on advanced product design.
i spend my time thinking about real-world uses of ML, crypto, internet protocols, APIs, incentive mechanisms, contemporary art, and design.
i write the Substack letter Hash Collision, where i report on developments in AI/ML, science, tech, culture, economics, and ethics. i co-wrote “A History of Silicon Valley, 2nd Ed.“
in the past, i led an applications and platform machine learning team at Amazon Music in San Francisco. prior to that, i was a co-founder of Starbutter AI (Skydeck F17) and a quant debt and derivatives trader at PIMCO. i’m still a licensed attorney in the Wyoming State Bar (the Equality state, where women first got the right to vote, and LLCs and DAOs to exist) and was formerly a CFA charterholder.
i’m a grad of UCLA, Penn/Wharton, the University of Arizona College of Law, and the Indiana Academy.
i like books, running, yoga, math, grammar and style, dead ancient languages, modern computer languages, black hole physics, bears, cacti, and pad see-ew.
PINNED: Machine Learning (including Deep Learning and Reinforcement Learning) for Engineers — A Technical Primer (Part 2)
PINNED: Primer on AI and Machine Learning (Part 1, Non-Technical)
Leviathan: State, Corporation, Network (and Network State)
Harms of AI
Focus and Distraction with Technology
In Defense of Personalized Ads (and the Free Internet)
Self-Supervised Learning (SSL) – A Gentle Introduction
Notes on Education
Best Books of 2021 (Berkelians)
What the heck is Web3? A primer.
8 Surprising Things You Didn’t Know about the Metaverse, and How it Will Change Your Life More than Your iPhone
Why Crypto Matters (It’s a Lot More than Bitcoin)
Capitalism, Socialism, and Inequality
Best Books of 2020
Why the new AI/ML language model GPT-3 is a big deal
Musings on Datasets
A List of the World’s Best Artificial Intelligence (AI) Institutes and Think Tanks
Google: The Unreasonable Effectiveness of Data – 2009
Google: The Unreasonable Effectiveness of Data – Revisited 2017
Open AI: GPT-3: Language Models are Few-Shot Learners
MIT: The Data Nutrition Project
Open AI/Jack Clark: Import AI on new Machine Learning advances
UC Irvine: Machine Learning Repository of Datasets