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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 learningtheory has led to very large models. To better quantify this, we have developed methods to measure efficiency.
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.
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.
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.
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).
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.
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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