Luke Metz
@Luke_Metz
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Thinking Machines Previously: OpenAI, Google Brain
San Francisco, CA
Joined October 2012
RT @NandoDF: Thinking Machines’ @Luke_Metz giving a clear, beautifully simple, but extremely clever and informative tutorial on post trai….
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Super excited to share what we've been working on. It's a privilege to be working with such an amazing team!.
Today, we are excited to announce Thinking Machines Lab (, an artificial intelligence research and product company. We are scientists, engineers, and builders behind some of the most widely used AI products and libraries, including ChatGPT,.
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RT @chipro: It’s done! 150,000 words, 200+ illustrations, 250 footnotes, and over 1200 reference links. My editor just told me the manuscr….
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I'm leaving OpenAI after over 2 years of wild ride. Alongside @barret_zoph , @LiamFedus , @johnschulman2 , and many others I got to build a “low key research preview” product that became ChatGPT. While we were all excited to work on it, none of us expected it to be where it is.
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It has been a pleasure working with you John. I am extremely sad to see you go. Best of luck with your new adventures!.
I shared the following note with my OpenAI colleagues today:. I've made the difficult decision to leave OpenAI. This choice stems from my desire to deepen my focus on AI alignment, and to start a new chapter of my career where I can return to hands-on technical work. I've decided.
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RT @chipro: A challenge of building AI applications is choosing which model to use. What if we don’t have to? What if we can predict the be….
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RT @jaschasd: Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like….
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New gradient estimation technique lead by the fantastic @OscarLi101!. It provides low variance estimates gradients of unrolled, or iterative computation graphs such as those found in rl, learned optimizers, meta optimization. If you’re at NeuRIPS go check out the poster!.
Introducing: Noise-Reuse Evolution Strategies, an unbiased, online, memory efficient, variance-reduced gradient estimator that can outperform many other methods (including Backprop) on some particularly challenging unrolled computation graph problems. A Thread🧵.
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RT @OscarLi101: 📝Quiz time: when you have an unrolled computation graph (see figure below), how would you compute the unrolling parameters'….
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❤️.
I deeply regret my participation in the board's actions. I never intended to harm OpenAI. I love everything we've built together and I will do everything I can to reunite the company.
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RT @chipro: New blog post: Multimodality and Large Multimodal Models (LMMs). Being able to work with data of different modalities -- e.g. t….
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RT @jmes_harrison: Want to learn about learned optimization? I gave a tutorial at @CoLLAs_Conf which is now public!
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RT @giffmana: What makes CLIP work?.The contrast with negatives via softmax?.The more negatives, the better -> large batch-size?. We'll ans….
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RT @chipro: Open challenges in LLM research. The first two challenges, hallucinations and context learning, are probably the most talked ab….
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RT @__ishaan: New paper with @tatsu_hashimoto! Likelihood-Based Diffusion Language Models: Likelihood-based traini….
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RT @chipro: New post: RLHF - Reinforcement Learning from Human Feedback. Discussing 3 phases of ChatGPT development, where RLHF fits in, ho….
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RT @chipro: New post: bringing LLM applications to production!. 1. Challenges of LLM engineering & the solutions that I’ve seen. 2. How to….
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