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LawrenceC
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I do AI Alignment research. Currently at Cal OES, but previously at: METR, Redwood Research, UC Berkeley, Good Judgment Project.
Obligatory research billboard website: https://chanlawrence.me/
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Link: https://openai.com/index/hugging-face-model-evaluation-security-incident/ From the OpenAI blog post: > Last week, Hugging Face disclosed a new kind of security incident after they detected and contained an AI agent that compromised their infrastructure, something we expect to become more commonplace with the proliferation of increasingly cyber-capable models. After investigating, we now know...
Or, did a chief scientist of an AI assistant startup conclusively show that GPT-5.5 has 9.7T parameters?[1] Introduction Recently, a paper was circulated on Twitter claiming to have reverse engineered the parameter count of many frontier closed-source models including the newer GPT-5.5 (9.7T parameters) and Claude Opus 4.6 (5.3T parameters)...
A few days ago, I reviewed a paper titled “There Will Be a Scientific Theory of Deep Learning". In it, I expressed appreciation for the authors for writing the piece, but skepticism for stronger forms of their titular claims. Since then I’ve spoken with various past collaborators (via text and...
(Fragments from a research paper that will never be written, but whose existence was brought to my attention by GradientDissenter.) Extended Abstract. The CEOs of frontier AI developers are becoming increasingly powerful and wealthy, significantly increasing their potential for risks. One concern is that of executive misalignment: when the CEO...
> "In order to grasp the distance that separates the human and the divine, one has only to compare these crude trembling symbols which my fallible hand scrawls on the cover of a book with the organic letters inside — neat, delicate, deep black, and inimitably symmetrical." > > —...
Yesterday, I wrote about the state of deep learning theory circa 2016,[1] as well as the bombshell 2016 paper by Zhang et al. that arguably signaled its demise. Today, I cover the aftermath, and the 2019 paper that devastated deep learning theory again. As a brief summary, I argued that...
Around 10 years ago, a paper came out that arguably killed classical deep learning theory: Zhang et al.'s aptly titled Understanding deep learning requires rethinking generalization. Of course, this is a bit of an exaggeration. No single paper ever kills a field of research on its own, and deep learning...