DataRobot

APRIL 21, 2022

This increase in accuracy is important to make AI applications good enough for production , but there has been an explosion in the size of these models. In the graph below, borrowed from the same article, you can see how some of the most cutting-edge algorithms in deep learning have increased in terms of model size over time.

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The AI Alignment Forum

FEBRUARY 28, 2025

The answer to this question should be something like a metric over some type of task specification (such as reward functions), according to which two task specifications have a small distance if and only if they are similar (in some relevant and informative sense).

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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 28, 2025

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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The AI Alignment Forum

FEBRUARY 7, 2025

Applications ( here ) start with a simple 300 word expression of interest and are open until April 15, 2025. We have plans to fund $40M in grants and have available funding for substantially more depending on application quality. Inter-model messages: Some applications of LLMs involve multiple models working together.

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Google Research AI blog

NOVEMBER 28, 2022

A Workshop for Algorithmic Efficiency in Practical Neural Network Training Workshop Organizers include: Zachary Nado , George Dahl , Naman Agarwal , Aakanksha Chowdhery Invited Speakers include: Aakanksha Chowdhery , Priya Goyal Human in the Loop Learning (HiLL) Workshop Organizers include: Fisher Yu, Vittorio Ferrari Invited Speakers include: Dorsa (..)

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The AI Alignment Forum

MARCH 28, 2025

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. But I dont know, it feels kind of surprising for that to be the explanation.

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