Search Results for author: Jack Clark

Found 19 papers, 6 papers with code

Regulatory Markets: The Future of AI Governance

no code implementations11 Apr 2023 Gillian K. Hadfield, Jack Clark

Appropriately regulating artificial intelligence is an increasingly urgent policy challenge.

In-context Learning and Induction Heads

no code implementations24 Sep 2022 Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, Chris Olah

In this work, we present preliminary and indirect evidence for a hypothesis that induction heads might constitute the mechanism for the majority of all "in-context learning" in large transformer models (i. e. decreasing loss at increasing token indices).

Why and How Governments Should Monitor AI Development

no code implementations28 Aug 2021 Jess Whittlestone, Jack Clark

In this paper we outline a proposal for improving the governance of artificial intelligence (AI) by investing in government capacity to systematically measure and monitor the capabilities and impacts of AI systems.

Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications

no code implementations5 Aug 2021 Sandhini Agarwal, Gretchen Krueger, Jack Clark, Alec Radford, Jong Wook Kim, Miles Brundage

Recently, there have been breakthroughs in computer vision ("CV") models that are more generalizable with the advent of models such as CLIP and ALIGN.

Image Classification

Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models

no code implementations4 Feb 2021 Alex Tamkin, Miles Brundage, Jack Clark, Deep Ganguli

On October 14th, 2020, researchers from OpenAI, the Stanford Institute for Human-Centered Artificial Intelligence, and other universities convened to discuss open research questions surrounding GPT-3, the largest publicly-disclosed dense language model at the time.

Language Modelling Philosophy

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