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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Stanford AI Lab Blog

DECEMBER 6, 2021

The thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021 is being hosted virtually from Dec 6th - 14th. Some of the members in our SAIL community also serve as co-organizers of several exciting workshops that will take place on Dec 13-14, so we hope you will check them out! Smith, Scott W.

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Gyrus

MARCH 15, 2024

But to do this effectively, businesses need to leverage the power of Learning Management Systems (LMS) that have the capacity to manage content and instructional design – and help in carving out personalized learning paths for the learners. Learn how to leverage virtual reality to engage millennia’s.

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Gyrus

MARCH 15, 2024

But to do this effectively, businesses need to leverage the power of Learning Management Systems (LMS) that have the capacity to manage content and instructional design – and help in carving out personalized learning paths for the learners. Learn how to leverage virtual reality to engage millennia’s.

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Stanford AI Lab Blog

APRIL 25, 2022

Efficiently Assessing Machine Learning API Shifts Authors : Lingjiao Chen, Matei Zaharia, James Zou Contact : lingjiao@stanford.edu Links: Paper | Website Keywords : mlaas, performance shifts, ml systems We look forward to seeing you at ICLR 2022!

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The AI Alignment Forum

FEBRUARY 28, 2025

Moreover, this dynamic is often at the core of many stories of how we could get catastrophic risks from AI systems. The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret In this paper , we look at what happens when a learnt reward function is optimised.

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The AI Alignment Forum

FEBRUARY 7, 2025

Adversarial machine learning This cluster of research areas uses simulated red-team/blue-team exercises to expose the vulnerabilities of an LLM (or a system that incorporates LLMs). We think this adversarial style of evaluation and iteration is necessary to ensure an AI system has a low probability of catastrophic failure.

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Google Research AI blog

NOVEMBER 28, 2022

Posted by Cat Armato, Program Manager, Google This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems ( NeurIPS 2022 ), the biggest machine learning conference of the year. Thorben Frank, Oliver T.

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The AI Alignment Forum

MARCH 28, 2025

And a technical note: it needs to be in some first-order system or alternatively, you need to measure proof checking time as opposed to proof length. Daniel Filan (00:16:47): Well, it sounds like you could say that about any software system. And then you prove this, and the measure of compression is how long your proof is.

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