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Ziang Xie
Employee at Sapling.ai, based in Los Angeles
Ziang Xie is the CEO of Sapling Intelligence, a startup specializing in machine learning and natural language processing technologies, particularly focusing on large language models (LLMs) for enterprise applications. He has held this position since January 2019 and is based in Los Angeles, California.
Education and Early Career
Ziang Xie earned a Bachelor of Science in Electrical Engineering and Computer Science from the University of California, Berkeley, from 2009 to 2013. He then pursued a PhD in Computer Science at Stanford University from 2013 to 2018, where his research concentrated on deep learning and natural language processing.12 During his time at Stanford, he also interned at Google as part of the Brain Team in 2016 and worked as a software engineering intern at NASA's Jet Propulsion Laboratory in 2010.12
Contributions and Focus Areas
Under Xie's leadership, Sapling Intelligence has developed innovative AI solutions aimed at enhancing business intelligence through advanced language model applications. His expertise includes deep learning, generative AI, and speech recognition.13 Xie is also known for his academic contributions to the field of machine learning, with several publications that have garnered significant citations.4
Personal Interests
Outside of his professional endeavors, Ziang Xie enjoys activities such as running and visiting bookstores.3
Highlights
Recently, I've seen a number of peers in the tech / startup community ask about AI learning resources and reading lists. Here are 6 of the resources that I've found most useful post-Stanford AI PhD.
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r/LocalLLaMA: This Reddit community is the goto resource for getting candid reviews of models. Skews heavily towards qualitative impressions.
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Guide to AI, from Air Street Capital / Nathan Benaich: Monthly newsletter that covers AI policy, research, industry, and startups all in one. The Year in Review decks are especially good and reminiscent of Mary Meeker's Internet Trends reports.
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One Useful Thing, by Ethan Mollick: Thoughtful essays on AI innovation and its implications -- for those that have a thirst for longform.
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Ben's Bites: Good for overview of AI products, startups, and major research announcements.
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X / Twitter: Curate however you wish. A good resource for papers is @arankomatsuzaki. The redundant ones to avoid are the accounts that post "Company Y just dropped Model Z and the results are insane 🤯. Here are 5 screenshots...".
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Hacker News: Social tech news page that usually includes posts on the biggest AI news. The post comments are a good way of getting a sense of general impressions among developers not necessarily versed in AI.
My bias: As someone who lives in their inbox, newsletters are my go-to resource. X / Twitter, Reddit, and Hacker News can all be converted into daily or weekly emails with a tool such as Mailbrew.
"I'm afraid I can't do that."
The legendary 1968 phrase from "A Space Odyssey" now manifests in every commercial LLM (and many open-source LLMs) through safeguards from applying instruction fine-tuning ("be helpful and harmless").
🗞 🔮 Tuesday Paper Takeaways (2024-06-25) covers "Refusal in Language Models Is Mediated by a Single Direction" by Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Rimsky, Wes Gurnee, and Neel Nanda (code: https://lnkd.in/gyscn4sK, blog post: https://lnkd.in/gNTKNwew).
Across 13 popular open-source LLMs, the authors demonstrate that such refusals can be mediated by subtracting a single direction (n-dimensional vector) from the model's activations -- that is, the models can be made to comply and respond to prompts where they would previous refuse to respond.
How do they find this vector? Take (1) a set of harmless instructions and (2) a set of harmful instructions, then observe and take the mean difference between the model activations for each set of instructions.
Notes:
- There is no way to do this (as published) without access to the model weights and activations (hence the use of open-source model).
- The attack vector does not devastate model performance -- the model outputs maintain coherence.
- The same vector can be applied in the opposite direction to induce refusal for harmless prompts!
- The authors show that adversarial suffixes (a prompt-based attack) suppress the refusal mediating directions as well.
In high-dimensional spaces, many things are surprisingly linear.
