GELU, MMLU, & X-Risk Defense in Depth, with the Great Dan Hendrycks
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Why were benchmarks like MMLU and Machiavelli created and what do they assess?
The idea was that a lot of the linguistic understanding benchmarks were not being sufficiently difficult. Elon's description of it is it's basically like an undergraduate level knowledge and skill test. There's a benchmark called Machiavelli, which is largely assessing the propensities of LLM agents. See what sort of decisions do they make along the way. They screw people over, or are they generally nice? Do they lie a lot? There is something going on in making AI systems a lot more reliable to jailbreaking due to specific algorithmic advances that do not necessarily follow from just scaling the model. If they get expert level virologists, I don't know if I want that being released. Sorry to say, because you'll run some substantial risks of bioweapons in
Hello and welcome to the Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan LeBenz, joined by my co-host Eric Tornberg. Hello, and welcome back to the Cognitive Revolution. Today I am thrilled to be joined by Dan Hendricks, Executive Director of the Center for AI Safety, advisor to Elon Musk's XAI, and one of the most prolific and influential researchers in AI safety and alignment, full stop. While Dan has recently become famous in AI circles for his work developing and advocating for SB ten forty seven, I wanted to use this conversation to highlight what an all-purpose AI powerhouse Dan truly is, and to get his perspective on a number of questions that I've personally been thinking a lot about.
And so this is a long episode in which we cover a tremendous amount of ground, including his early work on activation functions, such as the widely used GELU function, which he developed as an undergrad. And his work on benchmarks, including the insights that allowed him to create, in MMLU and math, some of the longest lived and most often cited benchmarks in existence today, as well as what he's up to next with a project called Humanities Last Exam. We also cover his work on AI robustness and alignment, including early work on robustness in image classifiers, which showed how difficult robustness can be to achieve. His twenty twenty three paper introducing representation engineering, a top down alternative to mechanistic interpretability, which can be used to identify directions in light and space for both monitoring and steering purposes.
His twenty twenty four paper with past guests Andy Zhao and Zico Coulter on circuit breakers, which build refusal behaviors more deeply into LLM weights through a specialized fine tuning process. And most recently, his work on tamper resistant training, which aims to make it difficult for users of open source models to remove refusal behaviors via fine-tuning, and which suggests that it might become possible for companies like Meta to open source models with durable guardrails built in. We also touch on a number of big picture issues around AI governance and the geopolitics of AI development, philosophical questions about the nature of intelligence and consciousness, and sociological. questions about how AI might help us better forecast future events and generally improve our collective epistemics.
We even get a fascinating behind the scenes look at how he orchestrated the twenty twenty three AI X risk statement, which was signed by a remarkable set of industry leaders, including the CEOs of DeepMind, Anthropic, and yes, Sam Altman of OpenAI, and which said in full that quote, mitigating the risk of extinction from AI should be a global priority alongside other societal scale risks such as pandemics and nuclear war. I think what struck me most about this conversation was that, despite all his experience and success, Dan's approach remains fundamentally experimental and his outlook very empirical. He places far more weight on data and compute than on algorithmic insights as drivers of capabilities advances, and he remains very open minded, both about how far the current AI paradigm will go and about whether any of the safety approaches we're developing will really work when the chips are down.
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Chapters
7 chapters
1
Why were benchmarks like MMLU and Machiavelli created and what do they assess?
0:00–4:33
2
How does the GELU activation function work and why is it important?
4:33–22:20
3
Are we becoming scaling maximalists and what limits might data scaling face?
22:20–1:16:38
4
Why do Dan Hendrycks argue that functional explanations matter more than mechanistic ones in AI safety?
1:16:38–1:23:15
5
How do sparse autoencoders compare to representation‑engineering approaches for finding internal model concepts?
1:23:15–1:31:14
6
What are circuit breakers and tamper‑resistant training, and how can they protect AI systems from harmful behavior?
1:31:14–1:41:49
7
Why does Dan advocate a defense‑in‑depth strategy and discuss national‑competitiveness issues like chip bans in AI risk management?
1:41:49–2:31:11
Speakers
2 identifiedMore from "The Cognitive Revolution"
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