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Moving from Red AI to Green AI, Part 1: How to Save the Environment and Reduce Your Hardware Costs

DataRobot

They are used for different applications, but nonetheless they suggest that the development in infrastructure (access to GPUs and TPUs for computing) and the development in deep learning theory has led to very large models. To better quantify this, we have developed methods to measure efficiency.

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The Theoretical Reward Learning Research Agenda: Introduction and Motivation

The AI Alignment Forum

Concretely, this research agenda involves answering questions such as: What is the right method for expressing goals and instructions to AI systems? Similarly, a complete answer to (3) would be a (pseudo)metric d on the space of all reward functions which quantifies their similarity.

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Stanford AI Lab Papers and Talks at NeurIPS 2021

Stanford AI Lab Blog

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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Research directions Open Phil wants to fund in technical AI safety

The AI Alignment Forum

Were interested in more research on this, and other stress tests of todays state-of-the-art alignment methods. We want to fund research that identifies the conditions under which these failure modes occur, and makes progress toward robust methods of mitigating or avoiding them.

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Other Papers About the Theory of Reward Learning

The AI Alignment Forum

We also managed to leverage these results to produce a new method for conservative optimisation, that tells you how much (and in what way) you can optimise a proxy reward, based on the quality of that proxy (as measured by a STARC metric ), in order to be guaranteed that the true reward doesnt decrease (and thereby prevent the Goodhart drop).

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Google at NeurIPS 2022

Google Research AI blog

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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AXRP Episode 40 - Jason Gross on Compact Proofs and Interpretability

The AI Alignment Forum

And the way you said it just then, it sounded more like the first one: heres a new nice metric of how good your mechanistic explanation is. 00:26:47): And so what this gives us is an interaction metric where we can measure how bad this hypothesis is. I dont know if theres some area-under-the-curve metric or something.

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