Stanford AI Lab Blog

DECEMBER 6, 2021

Kochenderfer Contact : philhc@stanford.edu Links: Paper Keywords : deep learning or neural networks, sparsity and feature selection, variational inference, (application) natural language and text processing Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss Authors : Jeff Z.

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The AI Alignment Forum

FEBRUARY 7, 2025

We think this adversarial style of evaluation and iteration is necessary to ensure an AI system has a low probability of catastrophic failure. Wed like to support more such evaluations, especially on scalable oversight protocols like AI debate. and Which rules are LLM agents happy to break, and which are they more committed to? .

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Google Research AI blog

APRIL 30, 2023

If you’re registered for ICLR 2023, we hope you’ll visit the Google booth to learn more about the exciting work we’re doing across topics spanning representation and reinforcement learning, theory and optimization, social impact, safety and privacy, and applications from generative AI to speech and robotics.

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The AI Alignment Forum

FEBRUARY 28, 2025

Some relevant criteria for evaluating a specification language include: How expressive is the language? What about inverse reinforcement learning? If a given specification learning learning algorithm is guaranteed to converge to a good specification, can we say anything about its sample complexity?

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Google Research AI blog

NOVEMBER 28, 2022

Derrick Xin , Behrooz Ghorbani , Ankush Garg , Orhan Firat , Justin Gilmer Associating Objects and Their Effects in Video Through Coordination Games Erika Lu , Forrester Cole , Weidi Xie, Tali Dekel , William Freeman , Andrew Zisserman , Michael Rubinstein Increasing Confidence in Adversarial Robustness Evaluations Roland S.

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The AI Alignment Forum

MARCH 28, 2025

This is extremely expensive, but you could do a sampling-based probabilistic version to make it cheaper. Daniel Filan (00:28:50): If people remember my singular learning theory episodes , theyll get mad at you for saying that quadratics are all there is, but its a decent approximation. (00:28:56): Daniel Filan (00:19:52): Okay.

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