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This paper claims that machine learning (ML) largely overlooks an important facet of general intelligence: robustness to a qualitatively unknown future in an open world.
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Predicting the impacts of an introduced species from its invasion history: an empirical approach applied to zebra mussel invasions
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Biological robustness
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Endless Forms Most Beautiful: The New Science of Evo Devo and the Making of the Animal Kingdom
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The plausibility of life: Resolving Darwin’s dilemma
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The logic of scientific discovery
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Artificial life
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The theory of facilitated variation
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Varying environments can speed up evolution
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Hippocampal contributions to control: the third way
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Evosphere: evolutionary dynamics in a population of fighting virtual creatures. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) . IEEE, 3066–3073
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Risk-sensitive loss functions for sparse multi-category classification problems
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Why scientific studies are so often wrong: The streetlight effect
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Double Q-learning
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Revising the evolutionary computation abstraction: minimal criteria novelty search. In Proceedings of the 12th annual conference on Genetic and evolutionary computation . 103–110
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Evolution: the extended synthesis
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The Black Swan: The Impact of the Highly Improbable" . Vol. 2
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Elements of financial risk management
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Abandoning objectives: Evolution through the search for novelty alone
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Improving evolvability through novelty search and self-adaptation. In 2011 IEEE congress of evolutionary computation (CEC) . IEEE, 2693–2700
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Imagenet classification with deep convolutional neural networks
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No entailing laws, but enablement in the evolution of the biosphere. In Proceedings of the 14th annual conference companion on Genetic and evolutionary computation . 1379–1392
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Evolution’s wedge: competition and the origins of diversity . Vol. 12
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Partially observable Markov decision processes
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The use of knowledge in society
Friedrich August Hayek. 2013 · 2013
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Evolvability is inevitable: Increasing evolvability without the pressure to adapt
Joel Lehman and Kenneth O Stanley. 2013 · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih. 2013 · 2013
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‘A device for being able to book P&L’: The Organizational Embedding of the Gaussian Copula
Donald MacKenzie and Taylor Spears. 2014 · 2014
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Superintelligence: Paths, dangers, strategies
Bostrom Nick. 2014 · 2014
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Identifying necessary conditions for open-ended evolution through the artificial life world of chromaria. In Artificial Life Conference Proceedings . MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info …, 793–800
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Antifragile: Things that gain from disorder . Vol. 3
Nassim Nicholas Taleb. 2014 · 2014
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Predicting failures of vision systems. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 3566–3573
Peng Zhang, Jiuling Wang, Ali Farhadi, Martial Hebert, and Devi Parikh. 2014 · 2014
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández. 2015 · 2015
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Bayesian reinforcement learning: A survey
Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar, et al · 2015
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Extinction events can accelerate evolution
Joel Lehman and Risto Miikkulainen. 2015 · 2015
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Investigating biological assumptions through radical reimplementation
Joel Lehman and Kenneth O Stanley. 2015 · 2015
Cited alongside, same era.
Why greatness cannot be planned: The myth of the objective
Kenneth O Stanley and Joel Lehman. 2015 · 2015
Cited alongside, same era.
Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. 2016 · 2016
Cited alongside, same era.
Towards open set deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition . 1563–1572
Abhijit Bendale and Terrance E Boult. 2016 · 2016
Cited alongside, same era.
Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z Leibo, Jack Rae, Daan Wierstra, and Demis Hassabis. 2016 · 2016
Cited alongside, same era.
Beyond IID: three levels of generalization for question answering on knowledge bases. In Proceedings of the Web Conference 2021 . 3477–3488
Yu Gu, Sue Kase, Michelle Vanni, Brian Sadler, Percy Liang, Xifeng Yan, and Yu Su. 2021 · 2021
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Unsolved problems in ml safety
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt. 2021 · 2021
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The hardware lottery
Sara Hooker. 2021 · 2021
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Mind the gap: Assessing temporal generalization in neural language models
Angeliki Lazaridou, Adhi Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomas Kocisky, Sebastian Ruder, et al · 2021
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The influence of search engine optimization on Google’s results: A multi-dimensional approach for detecting SEO. In Proceedings of the 13th ACM Web Science Conference 2021 . 12–20
Dirk Lewandowski, Sebastian Sünkler, and Nurce Yagci. 2021 · 2021
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Longbing Cao. 2016 · 2016
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RL 2 : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel. 2016 · 2016
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The age of Em: Work, love, and life when robots rule the earth
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Loizos Kounios, Jeff Clune, Kostas Kouvaris, Günter P Wagner, Mihaela Pavlicev, Daniel M Weinreich, and Richard A Watson. 2016 · 2016
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On the critical role of divergent selection in evolvability
Joel Lehman, Bryan Wilder, and Kenneth O Stanley. 2016 · 2016
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Ecological opportunity and adaptive radiation
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Why AI is harder than we think
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Reward is enough
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Invariant policy optimization: Towards stronger generalization in reinforcement learning. In Learning for Dynamics and Control . PMLR, 21–33
Anoopkumar Sonar, Vincent Pacelli, and Anirudha Majumdar. 2021 · 2021
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Open-ended learning leads to generally capable agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, et al · 2021
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Mastering atari games with limited data
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Distributionally adaptive meta reinforcement learning
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Constitutional ai: Harmlessness from ai feedback
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Why can gpt learn in-context? language models implicitly perform gradient descent as meta-optimizers
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Towards continual reinforcement learning: A review and perspectives
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Robust reinforcement learning: A review of foundations and recent advances
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Tesla drivers report a surge in ’phantom braking’
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Scalar reward is not enough: A response to silver, singh, precup and sutton (2021)
Peter Vamplew, Benjamin J Smith, Johan Källström, Gabriel Ramos, Roxana Rădulescu, Diederik M Roijers, Conor F Hayes, Fredrik Heintz, Patrick Mannion, Pieter JK Libin, et al · 2022
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Emergent abilities of large language models
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Distributional reinforcement learning
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The reversal curse: Llms trained on" a is b" fail to learn" b is a"
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Chain-of-verification reduces hallucination in large language models
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AI as agency without intelligence: on ChatGPT, large language models, and other generative models
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Benchmarking large language models as ai research agents. In NeurIPS 2023 Foundation Models for Decision Making Workshop
Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec. 2023a · 2023
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Simon McGregor. 2023 · 2023
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Modern language models refute Chomsky’s approach to language
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Knightian uncertainty
Cass R Sunstein. 2023 · 2023
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Human-timescale adaptation in an open-ended task space
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OpenAI o3 Breakthrough High Score on ARC-AGI-Pub
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