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To make AI systems broadly useful for challenging real-world tasks, we need them to learn complex human goals and preferences.
Computing π ( x ) \pi(x) : An analytic method
Jeffrey C Lagarias and Andrew M. Odlyzko · 1987
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Reasoning independently of prior belief and individual differences in actively open-minded thinking
Keith E Stanovich and Richard F West · 1997
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Individual differences and the belief bias effect: Mental models, logical necessity, and abstract reasoning
Donna Torrens · 1999
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The AI-box experiment
Eliezer Yudkowsky · 2002
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Rapid responding increases belief bias: Evidence for the dual-process theory of reasoning
Jonathan St. B. T. Evans and Jodie Curtis-Holmes · 2005
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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom · 2006
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Combinatorics of Go
John Tromp and Gunnar Farnebäck · 2006
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Reasoning under time pressure: A study of causal conditional inference
Jonathan St BT Evans, Simon J Handley, and Alison M Bacon · 2009
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Belief-based and analytic processing in transitive inference depends on premise integration difficulty
Glenda Andrews · 2010
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Negative emotions can attenuate the influence of beliefs on logical reasoning
Vinod Goel and Oshin Vartanian · 2011
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The superintelligent will: Motivation and instrumental rationality in advanced artificial agents
Nick Bostrom · 2012
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Introduction to the Theory of Computation
Michael Sipser · 2013
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Weekend update: You’d have to be science illiterate to think “belief in evolution” measures science literacy
Dan Kahan · 2014
Cited alongside, same era.
Research priorities for robust and beneficial artificial intelligence
Stuart J. Russell, Daniel Dewey, and Max Tegmark · 2016
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More on Dota 2
OpenAI · 2017
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
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Interpretable and pedagogical examples
Smitha Milli, Pieter Abbeel, and Igor Mordatch · 2017
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Emergent complexity via multi-agent competition
Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, and Igor Mordatch · 2017
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A unified game-theoretic approach to multiagent reinforcement learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Julien Perolat, David Silver, Thore Graepel, et al · 2017
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Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dandelion Mané · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Debatable
Radiolab · 2016
Cited alongside, same era.
Faulty reward functions in the wild
OpenAI · 2016
Cited alongside, same era.
Deep reinforcement learning from human preferences
Paul Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
Cited alongside, same era.
Mastering the game of Go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al
Cited in the paper.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al
Cited in the paper.
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Superintelligence
Nick Bostrom · 2017
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Mirror mirror: Reflections on quantitative fairness
Shira Mitchell and Jackie Shadlen · 2018
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Supervising strong learners by amplifying weak experts
Paul Christiano, Buck Shlegeris, and Dario Amodei · 2018
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