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treats the design of machine learning architectures as a machine learning problem

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  1. as of Apr 2018Capability
    Treats the design of machine learning architectures as a machine learning problem
    “This is basically treating machine learning—designing machine learning architectures—as its own machine learning problem.” 44:49
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45:50
A.I. Policy and Public Perception
Miles Brundage, Tim Hwang

Miles Brundage and Tim Hwang discuss how AI could be misused, how emerging capabilities may change the scale and reach of those risks, and why predictions about future applications require humility. They examine public perceptions of AI, interpretability, fairness, accountability, transparency, and robustness, including how these concerns arise in health care and across different regulatory settings. They describe the need for policy work to connect technical research with government, industry, and civil society, and discuss shared goals between near-term AI governance and long-term AI safety. The talk closes with debate about open publication and predictions for advances in game-playing systems and meta-learning.

Apr 25, 2018· 46 minDiscussed 44:49