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AI Policy

0.02MB. 0 audio & 0 images. Updated 2018-05-16.


This deck is oriented towards people who aim to address security issues arising from the use and development of artificial intelligence, with a slight emphasis on long-term concerns. It assumes limited technical and/or US government experience. The deck can be broken into subcategories using the tags provided.

Sample (from 63 notes)

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Front unipolar vs. multipolar
Back Worlds in which only one player or many players, respectively, are in a realistic position to reach AGI. It's important to consider which of the two is likely to make safe AGI development outcomes more likely when determining the ideal coordination strategy.The Foresight Institute white paper briefly lists the advatanges and disadvantages of each world (p17):https://foresight.org/publications/AGI-Timeframes&PolicyWhitePaper.pdf
Tags AI
Front reward gaming problem
Back Problem in AIs that learn a reward function from actions and preferences of humans. Rather than doing what the human designers wanted it to, an AI may do something unanticipated (i.e. misaligned with the humans' goals) because the reward function was subtly misspecified."One potential way around the problem of gameable reward functions is to let the agent learn the reward function. This lets designers offload some of the design work to powerful machine learning techniques." (Everitt et al., 2018)https://arxiv.org/pdf/1805.01109.pdfExamples include those described in Clark and Amodei (2016), Gwern (2011), Irpan (2018), and Lehman et. al. (2018).
Tags AI
Front neural network
Back One of the learning algorithms used in machine learning, consisting of different layers for analyzing and learning data.
Tags AI

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on 1572374954
on 1549554097
Thanks for putting this together!
on 1546906157
Thanks ! Useful to help understand / explain this tough problem.