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Recent developments in artificial intelligence and machine learning have spurred interest in the growing field of AI safety, which studies how to prevent human-harming accidents when deploying AI systems.
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Genetic programming: on the programming of computers by means of natural selection , volume 1
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Marc Kirschner and John Gerhart · 1998
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Evolving artificial neural networks
Xin Yao · 1999
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Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines
Stefano Nolfi, Dario Floreano, and Director Dario Floreano · 2000
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Evolutionary techniques in physical robotics
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Interactive evolutionary computation: Fusion of the capabilities of ec optimization and human evaluation
Hideyuki Takagi · 2001
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Embodied evolution: Distributing an evolutionary algorithm in a population of robots
Richard A Watson, Sevan G Ficici, and Jordan B Pollack · 2002
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Open-ended artificial evolution
Russell K Standish · 2003
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Of rats, rice, and race: The great hanoi rat massacre, an episode in french colonial history
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Designing evolutionary algorithms for dynamic optimization problems
Jürgen Branke and Hartmut Schmeck · 2003
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Novelty detection: a review—part 1: statistical approaches
Markos Markou and Sameer Singh · 2003
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Coherent extrapolated volition
Eliezer Yudkowsky · 2004
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Building credit scoring models using genetic programming
Chorng-Shyong Ong, Jih-Jeng Huang, and Gwo-Hshiung Tzeng · 2005
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Evolution of neural networks for classification and regression
Miguel Rocha, Paulo Cortez, and José Neves · 2007
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Varying environments can speed up evolution
Nadav Kashtan, Elad Noor, and Uri Alon · 2007
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Picbreeder: evolving pictures collaboratively online
Jimmy Secretan, Nicholas Beato, David B D Ambrosio, Adelein Rodriguez, Adam Campbell, and Kenneth O Stanley · 2008
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Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios
Andrea Soltoggio, John A Bullinaria, Claudio Mattiussi, Peter Dürr, and Dario Floreano · 2008
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Jan Peters, and Juergen Schmidhuber · 2008
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A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
Procedural content generation in games
Noor Shaker, Julian Togelius, and Mark J Nelson · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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Low impact artificial intelligences
Stuart Armstrong and Benjamin Levinstein · 2017
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Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, and Shane Legg · 2017
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Detecting change in dynamic fitness landscapes
Hendrik Richter · 2009
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Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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Automatically discovering properties that specify the latent behavior of uml models
Heather J Goldsby and Betty HC Cheng · 2010
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Surrogate-assisted evolutionary computation: Recent advances and future challenges
Yaochu Jin · 2011
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Human-assisted neuroevolution through shaping, advice and examples
Igor V Karpov, Vinod K Valsalam, and Risto Miikkulainen · 2011
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Improving evolvability through novelty search and self-adaptation
Joel Lehman and Kenneth O Stanley · 2011
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Inverse reward design
Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J Russell, and Anca Dragan · 2017
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Uncertainty-aware reinforcement learning for collision avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine · 2017
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Leave no trace: Learning to reset for safe and autonomous reinforcement learning
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, and Sergey Levine · 2017
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Robust physical-world attacks on deep learning models
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2017
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Open-endedness: The last grand challenge you’ve never heard of
Kenneth O Stanley, Joel Lehman, and Lisa Soros · 2017
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Tom Everitt, Gary Lea, and Marcus Hutter · 2018
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Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg · 2018
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Supervising strong learners by amplifying weak experts
Paul Christiano, Buck Shlegeris, and Dario Amodei · 2018
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Geoffrey Irving, Paul Christiano, and Dario Amodei · 2018
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AI now report 2018
Meredith Whittaker, Kate Crawford, Roel Dobbe, Genevieve Fried, Elizabeth Kaziunas, Varoon Mathur, Sarah Mysers West, Rashida Richardson, Jason Schultz, and Oscar Schwartz · 2018
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Incomplete contracting and ai alignment
Dylan Hadfield-Menell and Gillian K Hadfield · 2018
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Data-efficient design exploration through surrogate-assisted illumination
Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret · 2018
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Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks
Andrea Soltoggio, Kenneth O Stanley, and Sebastian Risi · 2018
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Trial without error: Towards safe reinforcement learning via human intervention
William Saunders, Girish Sastry, Andreas Stuhlmueller, and Owain Evans · 2018
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A study on overfitting in deep reinforcement learning
Chiyuan Zhang, Oriol Vinyals, Remi Munos, and Samy Bengio · 2018
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Procedural level generation improves generality of deep reinforcement learning
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, and Sebastian Risi · 2018
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2018
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https://www.usatoday.com/story/tech/2015/07/01/google-apologizes-after-photos-identify-black-people-as-gorillas/29567465/
Google photos labeled black people ’gorillas’ · 2019
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Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O Stanley · 2019
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