2016
Selekoa’s 25 records dated 2016, in date order, grouped by topic.
Selective: what the records hold, not a history of the year.
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People 2 records
April 2016
· snapshot · dated to the day
OpenAI announced on 26 April 2016 that Pieter Abbeel was taking leave from UC Berkeley to work with OpenAI full-time. Evidence ↗· snapshot · dated to the day
OpenAI announced on 26 April 2016 that Shivon Zilis was becoming an official advisor to OpenAI. Evidence ↗
Ideas & lineages 23 records
Dated to 2016 only
· publication · dated to the year
Alignment for Advanced Machine Learning Systems credits Eliezer Yudkowsky as a co-author. Evidence ↗
March 2016
· publication · dated to the day
“Optimization daemons” (2016-03-22) credits Eliezer Yudkowsky as its author. Evidence ↗· publication · dated to the day
The Arbital page Optimization daemons says that in human evolution such rapid capability gain occurred that a context change took place. Evidence ↗· publication · dated to the day
The Arbital page Optimization daemons uses evolution as an analogy: brains pursued goals that tracked reproductive fitness until rapid capability gain changed the context. Evidence ↗
April 2016
May 2016
· publication · dated to the month
“The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment” (2016-05) credits Daron Acemoglu as one of its 2 authors. Evidence ↗· publication · dated to the month
“The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment” (2016-05) credits Pascual Restrepo as one of its 2 authors. Evidence ↗· publication · dated to the month
Acemoglu and Restrepo, “The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment” (2016-05) bears on M01. Positions the task model against the "more commonly-used" factor-augmenting models, which the authors say do not produce these effects. Evidence ↗Acemoglu and Restrepo, The Race Between Machine and Man (2016) →Claim page →
· publication · dated to the month
Acemoglu and Restrepo, “The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment” (2016-05) articulates M02. Introduces a task framework in which automation moves labour-performed tasks to capital while new tasks in which labour has comparative advantage are created; motivated by concerns about robotics and AI. Evidence ↗Acemoglu and Restrepo, The Race Between Machine and Man (2016) →Claim page →
· publication · dated to the month
Acemoglu and Restrepo, “The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment” (2016-05) articulates M05. States the full-automation case of the model, in which automation keeps pace with task creation so that labour becomes redundant; the authors immediately contrast it with the stable balanced-growth case on which they focus. Evidence ↗Acemoglu and Restrepo, The Race Between Machine and Man (2016) →Claim page →
· publication · dated to the month
Acemoglu and Restrepo, “The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment” (2016-05) bears on M05. Replies to Leontief's horse analogy (quoted earlier in the paragraph and attributed to him) by positing labour's comparative advantage in new tasks. Evidence ↗Acemoglu and Restrepo, The Race Between Machine and Man (2016) →Claim page →
· publication · dated to the month
Acemoglu and Restrepo, “The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment” (2016-05) articulates M06a. Model result that automation lowers employment and the labour share and may lower wages. Evidence ↗Acemoglu and Restrepo, The Race Between Machine and Man (2016) →Claim page →
· publication · dated to the month
Acemoglu and Restrepo, “The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment” (2016-05) bears on M09. After citing concerns that robotics and AI will leave workers unable to compete, the authors say no comprehensive framework for these effects exists. Evidence ↗Acemoglu and Restrepo, The Race Between Machine and Man (2016) →Claim page →
June 2016
· event · dated to the day
DeepMind published its research account of combining neural networks with reinforcement learning. Evidence ↗· event · dated to the day
DeepMind’s research account discusses reward-based learning and experience replay in DQN. Evidence ↗Deep Reinforcement Learning — DeepMind research account (2016) →Claim page →
· event · dated to the day
David Silver is credited as author of DeepMind’s Deep Reinforcement Learning account. Evidence ↗· publication · dated to the day
Concrete Problems in AI Safety discusses scalable oversight when detailed evaluation is costly. Evidence ↗
July 2016
· publication · dated to the day
MIRI announces publication of Alignment for Advanced Machine Learning Systems. Evidence ↗
November 2016
· publication · dated to the day
“The Simple Economics of Machine Intelligence” (2016-11-17) credits Ajay Agrawal as one of its 3 authors. Evidence ↗· publication · dated to the day
“The Simple Economics of Machine Intelligence” (2016-11-17) credits Avi Goldfarb as one of its 3 authors. Evidence ↗· publication · dated to the day
“The Simple Economics of Machine Intelligence” (2016-11-17) credits Joshua Gans as one of its 3 authors. Evidence ↗· publication · dated to the day
Agrawal, Gans, Goldfarb, “The Simple Economics of Machine Intelligence” (2016-11-17) articulates M04. Writing as economists, the authors treat machine intelligence as a prediction technology whose economic effect centres on a fall in the cost of prediction, and conclude that human judgment, a complement, becomes more valuable. Evidence ↗Agrawal, Gans, Goldfarb, The Simple Economics of Machine Intelligence (2016) →Claim page →