In the past I have worked at OpenAI and been a coach for the USA Computing Olympiad and an instructor at SPARC. I also consult part-time for Open Philanthropy. I particularly value creative, curious thinkers who are excited to revisit the conceptual foundations of the field. I seek students who are technically strong, broad-minded, and want to improve the world through their research.
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Meanwhile, measuring empirical test accuracy on a fixed distribution is insufficient to analyze phenomena such as robustness to distributional shift.
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Theories of statistical generalization do not account for the extreme types of generalization considered above, and decision theory does not account for cases where the reward function is only approximate. These challenges require rethinking both the theoretical and empirical paradigms of ML. How can we design ML systems that conform to interpretable abstractions? How do we enable meaningful human oversight at training and deployment time despite the large scale? How will these large-scale systems affect societal equilibria?
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