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| 2016 || {{dts|January 9}} || Education || The OpenAI research team does an AMA ("ask me anything") on r/MachineLearning, the subreddit dedicated to machine learning.<ref>{{cite web |url=https://www.reddit.com/r/MachineLearning/comments/404r9m/ama_the_OpenAI_research_team/ |publisher=reddit |title=AMA: the OpenAI Research Team • r/MachineLearning |accessdate=May 5, 2018}}</ref>
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| 2016 || {{dts|February 25}} || Publication || "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks", a paper on optimization, is first submitted to the {{w|ArXiv}}. The paper presents weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction.<ref>{{cite web |last1=Salimans |first1=Tim |last2=Kingma |first2=Diederik P. |title=Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks |url=https://arxiv.org/abs/1602.07868 |website=arxiv.org |accessdate=27 March 2020}}</ref>
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| 2016 || {{dts|March 31}} || Staff || A blog post from this day announces that {{W|Ian Goodfellow}} has joined OpenAI.<ref>{{cite web |url=https://blog.OpenAI.com/team-plus-plus/ |publisher=OpenAI Blog |title=Team++ |date=March 22, 2017 |first=Greg |last=Brockman |accessdate=May 6, 2018}}</ref>
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| 2016 || {{dts|April 27}} || Software release || The public beta of OpenAI Gym, an open source toolkit that provides environments to test AI bots, is released.<ref>{{cite web |url=https://blog.OpenAI.com/OpenAI-gym-beta/ |publisher=OpenAI Blog |title=OpenAI Gym Beta |date=March 20, 2017 |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=https://www.wired.com/2016/04/OpenAI-elon-musk-sam-altman-plan-to-set-artificial-intelligence-free/ |title=Inside OpenAI, Elon Musk's Wild Plan to Set Artificial Intelligence Free |date=April 27, 2016 |publisher=[[wikipedia:WIRED|WIRED]] |accessdate=March 2, 2018 |quote=This morning, OpenAI will release its first batch of AI software, a toolkit for building artificially intelligent systems by way of a technology called "reinforcement learning"}}</ref><ref>{{cite web |url=http://www.businessinsider.com/OpenAI-has-launched-a-gym-where-developers-can-train-their-computers-2016-4?op=1 |first=Sam |last=Shead |date=April 28, 2016 |title=Elon Musk's $1 billion AI company launches a 'gym' where developers train their computers |publisher=Business Insider |accessdate=March 3, 2018}}</ref>
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| 2016 || {{dts|May 31}} || Publication || "VIME: Variational Information Maximizing Exploration", a paper on generative models, is submitted to the {{w|ArXiv}}. The paper introduces Variational Information Maximizing Exploration (VIME), an exploration strategy based on maximization of information gain about the agent's belief of environment dynamics.<ref>{{cite web |last1=Houthooft |first1=Rein |last2=Chen |first2=Xi |last3=Duan |first3=Yan |last4=Schulman |first4=John |last5=De Turck |first5=Filip |last6=Abbeel |first6=Pieter |title=VIME: Variational Information Maximizing Exploration |url=https://arxiv.org/abs/1605.09674 |website=arxiv.org |accessdate=27 March 2020}}</ref>
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| 2016 || {{dts|June 5}} || Publication || "OpenAI Gym", a paper on {{w|reinforcement learning}}, is submitted to the {{w|ArXiv}}. It presents OpenAI Gym as a toolkit for reinforcement learning research.<ref>{{cite web |last1=Brockman |first1=Greg |last2=Cheung |first2=Vicki |last3=Pettersson |first3=Ludwig |last4=Schneider |first4=Jonas |last5=Schulman |first5=John |last6=Tang |first6=Jie |last7=Zaremba |first7=Wojciech |title=OpenAI Gym |url=https://arxiv.org/abs/1606.01540 |website=arxiv.org |accessdate=27 March 2020}}</ref>
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| 2016 || {{dts|June 10}} || Publication || "Improved Techniques for Training GANs", a paper on generative models, is submitted to the {{w|ArXiv}}. It presents a variety of new architectural features and training procedures that OpenAI applies to the generative adversarial networks (GANs) framework.<ref>{{cite web |last1=Salimans |first1=Tim |last2=Goodfellow |first2=Ian |last3=Zaremba |first3=Wojciech |last4=Cheung |first4=Vicki |last5=Radford |first5=Alec |last6=Chen |first6=Xi |title=Improved Techniques for Training GANs |url=https://arxiv.org/abs/1606.03498 |website=arxiv.org |accessdate=27 March 2020}}</ref>
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| 2016 || {{dts|June 12}} || Publication || "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets", a paper on generative models, is submitted to {{w|ArXiv}}. It describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.<ref>{{cite web |title=InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets |url=https://arxiv.org/abs/1606.03657 |website=arxiv.org |accessdate=27 March 2020}}</ref>
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| 2016 || {{dts|June 21}} || Publication || "Concrete Problems in AI Safety" is submitted to the {{w|arXiv}}.<ref>{{cite web |url=https://arxiv.org/abs/1606.06565 |title=[1606.06565] Concrete Problems in AI Safety |date=June 21, 2016 |accessdate=July 25, 2017}}</ref>
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| 2017 || {{dts|March}} || Donation || The Open Philanthropy Project awards a grant of $30 million to {{w|OpenAI}} for general support.<ref name="donations-portal-open-phil-ai-risk">{{cite web |url=https://donations.vipulnaik.com/donor.php?donor=Open+Philanthropy+Project&cause_area_filter=AI+safety |title=Open Philanthropy Project donations made (filtered to cause areas matching AI safety) |accessdate=July 27, 2017}}</ref> The grant initiates a partnership between Open Philanthropy Project and OpenAI, in which {{W|Holden Karnofsky}} (executive director of Open Philanthropy Project) joins OpenAI's board of directors to oversee OpenAI's safety and governance work.<ref>{{cite web |url=https://www.openphilanthropy.org/focus/global-catastrophic-risks/potential-risks-advanced-artificial-intelligence/OpenAI-general-support |publisher=Open Philanthropy Project |title=OpenAI — General Support |date=December 15, 2017 |accessdate=May 6, 2018}}</ref> The grant is criticized by {{W|Maciej Cegłowski}}<ref>{{cite web |url=https://twitter.com/Pinboard/status/848009582492360704 |title=Pinboard on Twitter |publisher=Twitter |accessdate=May 8, 2018 |quote=What the actual fuck… “Open Philanthropy” dude gives a $30M grant to his roommate / future brother-in-law. Trumpy!}}</ref> and Benjamin Hoffman (who would write the blog post "OpenAI makes humanity less safe")<ref>{{cite web |url=http://benjaminrosshoffman.com/OpenAI-makes-humanity-less-safe/ |title=OpenAI makes humanity less safe |date=April 13, 2017 |publisher=Compass Rose |accessdate=May 6, 2018}}</ref><ref>{{cite web |url=https://www.lesswrong.com/posts/Nqn2tkAHbejXTDKuW/OpenAI-makes-humanity-less-safe |title=OpenAI makes humanity less safe |accessdate=May 6, 2018 |publisher=[[wikipedia:LessWrong|LessWrong]]}}</ref><ref>{{cite web |url=https://donations.vipulnaik.com/donee.php?donee=OpenAI |title=OpenAI donations received |accessdate=May 6, 2018}}</ref> among others.<ref>{{cite web |url=https://www.facebook.com/vipulnaik.r/posts/10211478311489366 |title=I'm having a hard time understanding the rationale... |accessdate=May 8, 2018 |first=Vipul |last=Naik}}</ref>
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| 2017 || {{dts|March 15}} || Publication || "Emergence of Grounded Compositional Language in Multi-Agent Populations" is first submitted to {{w|ArXiv}}. The paper proposes a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language.<ref>{{cite web |last1=Mordatch |first1=Igor |last2=Abbeel |first2=Pieter |title=Emergence of Grounded Compositional Language in Multi-Agent Populations |url=https://arxiv.org/abs/1703.04908 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2017 || {{dts|March}} || Reorganization || Greg Brockman and a few other core members of OpenAI begin drafting an internal document to lay out a path to {{w|artificial general intelligence}}. As the team studies trends within the field, they realize staying a nonprofit is financially untenable.<ref name="technologyreview.comñ">{{cite web |title=The messy, secretive reality behind OpenAI’s bid to save the world |url=https://www.technologyreview.com/s/615181/ai-OpenAI-moonshot-elon-musk-sam-altman-greg-brockman-messy-secretive-reality/ |website=technologyreview.com |accessdate=28 February 2020}}</ref>
| 2017 || {{dts|August 13}} || Coverage || ''{{W|The New York Times}}'' publishes a story covering the AI safety work (by Dario Amodei, Geoffrey Irving, and Paul Christiano) at OpenAI.<ref>{{cite web |url=https://www.nytimes.com/2017/08/13/technology/artificial-intelligence-safety-training.html |date=August 13, 2017 |publisher=[[wikipedia:The New York Times|The New York Times]] |title=Teaching A.I. Systems to Behave Themselves |author=Cade Metz |accessdate=May 5, 2018}}</ref>
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| 2017 || {{Dts|September 13}} || Publication || "Learning with Opponent-Learning Awareness" is first uploaded to the {{w|arXiv}}. The paper presents Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in an environment.<ref>{{cite web |url=https://arxiv.org/abs/1709.04326 |title=[1709.04326] Learning with Opponent-Learning Awareness |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=https://www.gwern.net/newsletter/2017/09 |author=gwern |date=August 16, 2017 |title=September 2017 news - Gwern.net |accessdate=March 2, 2018}}</ref>
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| 2017 || September || Staff || OpenAI Research Scientist Bowen Baker joins the organization.<ref>{{cite web |title=Bowen Baker |url=https://www.linkedin.com/in/bowen-baker-59b48a65/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
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| 2017 || {{dts|October 11}} || Software release || RoboSumo, a game that simulates {{W|sumo wrestling}} for AI to learn to play, is released.<ref>{{cite web |url=https://www.wired.com/story/ai-sumo-wrestlers-could-make-future-robots-more-nimble/ |title=AI Sumo Wrestlers Could Make Future Robots More Nimble |publisher=[[wikipedia:WIRED|WIRED]] |accessdate=March 3, 2018}}</ref><ref>{{cite web |url=http://www.businessinsider.com/elon-musk-OpenAI-virtual-robots-learn-sumo-wrestle-soccer-sports-ai-tech-science-2017-10 |first1=Alexandra |last1=Appolonia |first2=Justin |last2=Gmoser |date=October 20, 2017 |title=Elon Musk's artificial intelligence company created virtual robots that can sumo wrestle and play soccer |publisher=Business Insider |accessdate=March 3, 2018}}</ref>
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| 2017 || {{dts|October 17}} || Publication || "Domain Randomization and Generative Models for Robotic Grasping", a paper on {{w|robotics}}, is first submitted to the {{w|ArXiv}}. It explores a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis.<ref>{{cite web |title=Domain Randomization and Generative Models for Robotic Grasping |url=https://arxiv.org/abs/1710.06425 |website=arxiv.org |accessdate=27 March 2020}}</ref>
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| 2017 || {{dts|October 18}} || Publication || "Sim-to-Real Transfer of Robotic Control with Dynamics Randomization", a paper on {{w|robotics}}, is first submitted to {{w|ArXiv}}. It describes a solution for strategies that are successful in simulation but may not transfer to their real world counterparts due to modeling error.<ref>{{cite web |last1=Bin Peng |first1=Xue |last2=Andrychowicz |first2=Marcin |last3=Zaremba |first3=Wojciech |last4=Abbeel |first4=Pieter |title=Sim-to-Real Transfer of Robotic Control with Dynamics Randomization |url=https://arxiv.org/abs/1710.06537 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2017 || {{dts|October 26}} || Publication || "Meta Learning Shared Hierarchies", a paper on {{w|reinforcement learning}}, is submitted to the {{w|ArXiv}}. The paper describes the development of a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives.<ref>{{cite web |last1=Frans |first1=Kevin |last2=Ho |first2=Jonathan |last3=Chen |first3=Xi ChenXi |last4=Abbeel |first4=Pieter |last5=Schulman |first5=John |title=Meta Learning Shared Hierarchies |url=https://arxiv.org/abs/1710.09767 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2017 || {{dts|October 31}} || Publication || "Backpropagation through the Void: Optimizing control variates for black-box gradient estimation", a paper on {{w|reinforcement learning}}, is first submitted to the {{w|ArXiv}}. It introduces a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables.<ref>{{cite web |last1=Grathwohl |first1=Will |last2=Choi |first2=Dami |last3=Wu |first3=Yuhuai |last4=Roeder |first4=Geoffrey |last5=Duvenaud |first5=David |title=Backpropagation through the Void: Optimizing control variates for black-box gradient estimation |url=https://arxiv.org/abs/1711.00123 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2017 || {{dts|October}} || Staff || Jonathan Raiman joins OpenAI as Research Scientist.<ref>{{cite web |title=Jonathan Raiman |url=https://www.linkedin.com/in/jonathan-raiman-36694123/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
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| 2017 || {{dts|November 2}} || Publication || "Interpretable and Pedagogical Examples", a paper on language, is first submitted to the {{w|ArXiv}}. It shows that training the student and teacher iteratively, rather than jointly, can produce interpretable teaching strategies.<ref>{{cite web |last1=Milli |first1=Smitha |last2=Abbeel |first2=Pieter |last3=Mordatch |first3=Igor |title=Interpretable and Pedagogical Examples |url=https://arxiv.org/abs/1711.00694 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2017 || {{Dts|November 6}} || Staff || ''{{W|The New York Times}}'' reports that Pieter Abbeel (a researcher at OpenAI) and three other researchers from Berkeley and OpenAI have left to start their own company called Embodied Intelligence.<ref>{{cite web |url=https://www.nytimes.com/2017/11/06/technology/artificial-intelligence-start-up.html |date=November 6, 2017 |publisher=[[wikipedia:The New York Times|The New York Times]] |title=A.I. Researchers Leave Elon Musk Lab to Begin Robotics Start-Up |author=Cade Metz |accessdate=May 5, 2018}}</ref>
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| 2017 || {{dts|December 4}} || Publication || "Learning Sparse Neural Networks through ''L<sub>0</sub>'' Regularization", a paper on {{w|reinforcement learning}}, is submitted to the {{w|ArXiv}}. It describes a method which allows for straightforward and efficient learning of model structures with stochastic gradient descent.<ref>{{cite web |last1=Louizos |first1=Christos |last2=Welling |first2=Max |last3=Kingma |first3=Diederik P. |title=Learning Sparse Neural Networks through L0 Regularization |url=https://arxiv.org/abs/1712.01312 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2017 || {{dts|December}} || Publication || The 2017 AI Index is published. OpenAI contributed to the report.<ref>{{cite web |url=https://www.theverge.com/2017/12/1/16723238/ai-artificial-intelligence-progress-index |date=December 1, 2017 |publisher=The Verge |title=Artificial intelligence isn't as clever as we think, but that doesn't stop it being a threat |first=James |last=Vincent |accessdate=March 2, 2018}}</ref>
| 2018 || {{dts|January}} || Staff || Mathew Shrwed joins OpenAI as Software Engineer.<ref>{{cite web |title=Mathew Shrwed |url=https://www.linkedin.com/in/mshrwed/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
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| 2018 || {{dts|February 3}} || Publication || "DeepType: Multilingual Entity Linking by Neural Type System Evolution" a paper on {{w|reinforcement learning}}, is submitted to the {{w|ArXiv}}.<ref>{{cite web |last1=Raiman |first1=Jonathan |last2=Raiman |first2=Olivier |title=DeepType: Multilingual Entity Linking by Neural Type System Evolution |url=https://arxiv.org/abs/1802.01021 |website=arxiv.org |accessdate=26 March 2020}}</ref>|-| 2018 || {{dts|February 13}} || Publication || "Evolved Policy Gradients", a {{w|reinforcement learning}} paper, is first submitted to the {{w|ArXiv}}. It proposes a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms.<ref>{{cite web |last1=Houthooft |first1=Rein |last2=Chen |first2=Richard Y. |last3=Isola |first3=Phillip |last4=Stadie |first4=Bradly C. |last5=Wolski |first5=Filip |last6=Ho |first6=Jonathan |last7=Abbeel |first7=Pieter |title=Evolved Policy Gradients |url=https://arxiv.org/abs/1802.04821 |website=arxiv.org |accessdate=26 March 2020}}</ref>|-| 2018 || {{dts|February 20}} || Publication || The report "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation" is publishedsubmitted to the {{w|ArXiv}}. The report It forecasts malicious use of artificial intelligence in the short term and makes recommendations on how to mitigate these risks from AI. The report is authored by individuals at Future of Humanity Institute, Centre for the Study of Existential Risk, OpenAI, Electronic Frontier Foundation, Center for a New American Security, and other institutions.<ref>{{cite web |url=https://arxiv.org/abs/1802.07228 |title=[1802.07228] The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation |accessdate=February 24, 2018}}</ref><ref>{{cite web |url=https://blog.OpenAI.com/preparing-for-malicious-uses-of-ai/ |publisher=OpenAI Blog |title=Preparing for Malicious Uses of AI |date=February 21, 2018 |accessdate=February 24, 2018}}</ref><ref>{{cite web |url=https://maliciousaireport.com/ |author=Malicious AI Report |publisher=Malicious AI Report |title=The Malicious Use of Artificial Intelligence |accessdate=February 24, 2018}}</ref><ref name="musk-leaves" /><ref>{{cite web |url=https://www.wired.com/story/why-artificial-intelligence-researchers-should-be-more-paranoid/ |title=Why Artificial Intelligence Researchers Should Be More Paranoid |first=Tom |last=Simonite |publisher=[[wikipedia:WIRED|WIRED]] |accessdate=March 2, 2018}}</ref>
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| 2018 || {{dts|February 20}} || Donation || OpenAI announces changes in donors and advisors. New donors are: {{W|Jed McCaleb}}, {{W|Gabe Newell}}, {{W|Michael Seibel}}, {{W|Jaan Tallinn}}, and {{W|Ashton Eaton}} and {{W|Brianne Theisen-Eaton}}. {{W|Reid Hoffman}} is "significantly increasing his contribution". Pieter Abbeel (previously at OpenAI), {{W|Julia Galef}}, and Maran Nelson become advisors. {{W|Elon Musk}} departs the board but remains as a donor and advisor.<ref>{{cite web |url=https://blog.OpenAI.com/OpenAI-supporters/ |publisher=OpenAI Blog |title=OpenAI Supporters |date=February 21, 2018 |accessdate=March 1, 2018}}</ref><ref name="musk-leaves">{{cite web |url=https://www.theverge.com/2018/2/21/17036214/elon-musk-OpenAI-ai-safety-leaves-board |date=February 21, 2018 |publisher=The Verge |title=Elon Musk leaves board of AI safety group to avoid conflict of interest with Tesla |accessdate=March 2, 2018}}</ref>
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| 2018 || {{dts|February 26}} || Publication || "Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research" is first submitted to the {{w|ArXiv}}. The paper introduces a suite of challenging continuous control tasks based on currently existing robotics hardware, and presents a set of concrete research ideas for improving {{w|reinforcement learning}} algorithms.<ref>{{cite web |title=Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research |url=https://arxiv.org/abs/1802.09464 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{dts|February}} || Staff || Lilian Weng joins OpenAI as Research Scientist.<ref>{{cite web |title=Lilian Weng |url=https://www.linkedin.com/in/lilianweng/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
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| 2018 || {{dts|March 3}} || Publication || "Some Considerations on Learning to Explore via Meta-Reinforcement Learning", a paper on {{w|reinforcement learning}}, is first submitted to {{w|ArXiv}}. It considers the problem of exploration in meta reinforcement learning.<ref>{{cite web |last1=Stadie |first1=Bradly C. |last2=Yang |first2=Ge |last3=Houthooft |first3=Rein |last4=Chen |first4=Xi |last5=Duan |first5=Yan |last6=Wu |first6=Yuhuai |last7=Abbeel |first7=Pieter |last8=Sutskever |first8=Ilya |title=Some Considerations on Learning to Explore via Meta-Reinforcement Learning |url=https://arxiv.org/abs/1803.01118 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{dts|March 3}} || Event host || OpenAI hosts its first hackathon. Applicants include high schoolers, industry practitioners, engineers, researchers at universities, and others, with interests spanning healthcare to {{w|AGI}}.<ref>{{cite web |url=https://blog.OpenAI.com/hackathon/ |publisher=OpenAI Blog |title=OpenAI Hackathon |date=February 24, 2018 |accessdate=March 1, 2018}}</ref><ref>{{cite web |url=https://blog.OpenAI.com/hackathon-follow-up/ |publisher=OpenAI Blog |title=Report from the OpenAI Hackathon |date=March 15, 2018 |accessdate=May 5, 2018}}</ref>
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| 2018 || {{dts|March 8}} || Publication || "On First-Order Meta-Learning Algorithms", a paper on {{w|reinforcement learning}}, is submitted to {{w|ArXiv}}. It analyzes meta-learning problems, where there is a distribution of tasks.<ref>{{cite web |last1=Nichol |first1=Alex |last2=Achiam |first2=Joshua |last3=Schulman |first3=John |title=On First-Order Meta-Learning Algorithms |url=https://arxiv.org/abs/1803.02999 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{dts|March 15}} || Publication || "Improving GANs Using Optimal Transport", a paper on generative models, is first submitted to the {{w|ArXiv}}. It presents Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution.<ref>{{cite web |last1=Salimans |first1=Tim |last2=Zhang |first2=Han |last3=Radford |first3=Alec |last4=Metaxas |first4=Dimitris |title=Improving GANs Using Optimal Transport |url=https://arxiv.org/abs/1803.05573 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{dts|March 20}} || Publication || "Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines", a paper on {{w|reinforcement learning}}, is submitted to the {{w|ArXiv}}. The paper shows that the general idea of including additional information in baselines for improved variance reduction can be extended to partially observed and multi-agent tasks.<ref>{{cite web |last1=Wu |first1=Cathy |last2=Rajeswaran |first2=Aravind |last3=Duan |first3=Yan |last4=KumarVikash Kumar |first4=Vikash |last5=Bayen |first5=Alexandre M |last6=Kakade |first6=Sham |last7=Mordatch |first7=Igor |last8=Abbeel |first8=Pieter |title=Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines |url=https://arxiv.org/abs/1803.07246 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{dts|March}} || Staff || Diane Yoon joins OpenAI as Operations Manager.<ref>{{cite web |title=Diane Yoon |url=https://www.linkedin.com/in/diane-yoon-a0a8911b/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
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| 2018 || {{dts|April 9}} || Commitment || OpenAI releases a charter. The charter says in part that OpenAI commits to stop competing with a value-aligned and safety-conscious project that comes close to building artificial general intelligence, and also that OpenAI expects to reduce its traditional publishing in the future due to safety concerns.<ref>{{cite web |url=https://blog.OpenAI.com/OpenAI-charter/ |publisher=OpenAI Blog |title=OpenAI Charter |date=April 9, 2018 |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://www.lesswrong.com/posts/e5mFQGMc7JpechJak/OpenAI-charter |title=OpenAI charter |accessdate=May 5, 2018 |date=April 9, 2018 |author=wunan |publisher=[[wikipedia:LessWrong|LessWrong]]}}</ref><ref>{{cite web |url=https://www.reddit.com/r/MachineLearning/comments/8azk2n/d_OpenAI_charter/ |publisher=reddit |title=[D] OpenAI Charter • r/MachineLearning |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://news.ycombinator.com/item?id=16794194 |title=OpenAI Charter |website=Hacker News |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://thenextweb.com/artificial-intelligence/2018/04/10/the-ai-company-elon-musk-co-founded-is-trying-to-create-sentient-machines/ |title=The AI company Elon Musk co-founded intends to create machines with real intelligence |publisher=The Next Web |date=April 10, 2018 |author=Tristan Greene |accessdate=May 5, 2018}}</ref>
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| 2018 || {{dts|April 10}} || Publication || "Gotta Learn Fast: A New Benchmark for Generalization in RL", a paper on {{w|reinforcement learning}}, is first submitted to the {{w|ArXiv}}. The report presents a new {{w|reinforcement learning}} benchmark intended to measure the performance of transfer learning and few-shot learning algorithms in the reinforcement learning domain.<ref>{{cite web |last1=Nichol |first1=Alex |last2=Pfau |first2=Vicki |last3=Hesse |first3=Christopher |last4=Klimov |first4=Oleg |last5=Schulman |first5=John |title=Gotta Learn Fast: A New Benchmark for Generalization in RL |url=https://arxiv.org/abs/1804.03720 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{Dts|April 19}} || Financial || ''{{W|The New York Times}}'' publishes a story detailing the salaries of researchers at OpenAI, using information from OpenAI's 2016 {{W|Form 990}}. The salaries include $1.9 million paid to {{W|Ilya Sutskever}} and $800,000 paid to {{W|Ian Goodfellow}} (hired in March of that year).<ref>{{cite web |url=https://www.nytimes.com/2018/04/19/technology/artificial-intelligence-salaries-OpenAI.html |date=April 19, 2018 |publisher=[[wikipedia:The New York Times|The New York Times]] |title=A.I. Researchers Are Making More Than $1 Million, Even at a Nonprofit |author=Cade Metz |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://www.reddit.com/r/reinforcementlearning/comments/8di9yt/ai_researchers_are_making_more_than_1_million/dxnc76j/ |publisher=reddit |title="A.I. Researchers Are Making More Than $1 Million, Even at a Nonprofit [OpenAI]" • r/reinforcementlearning |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://news.ycombinator.com/item?id=16880447 |title=gwern comments on A.I. Researchers Are Making More Than $1M, Even at a Nonprofit |website=Hacker News |accessdate=May 5, 2018}}</ref>
| 2018 || {{dts|April}} || Staff || Peter Zhokhov joins OpenAI as Member of the Technical Staff.<ref>{{cite web |title=Peter Zhokhov |url=https://www.linkedin.com/in/peter-zhokhov-b68525b3/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
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| 2018 || {{Dts|May 2}} || Publication || The paper "AI safety via debate" by Geoffrey Irving, Paul Christiano, and Dario Amodei is uploaded to the arXiv. The paper proposes training agents via self play on a zero sum debate game, in order to adress tasks that are too complicated for a human to directly judge.<ref>{{cite web |url=https://arxiv.org/abs/1805.00899 |title=[1805.00899] AI safety via debate |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://blog.OpenAI.com/debate/ |publisher=OpenAI Blog |title=AI Safety via Debate |date=May 3, 2018 |first1=Geoffrey |last1=Irving |first2=Dario |last2=Amodei |accessdate=May 5, 2018}}</ref>
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| 2018 || {{dts|May}} || Staff || Susan Zhang joins OpenAI as Research Engineer.<ref>{{cite web |title=Susan Zhang |url=https://www.linkedin.com/in/suchenzang/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
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| 2018 || {{dts|May}} || Staff || Daniel Ziegler joins OpenAI as Member Of Technical Staff.<ref>{{cite web |title=Daniel Ziegler |url=https://www.linkedin.com/in/daniel-ziegler-b4b61882/ |website=linkedin.com |accessdate=29 February 2020}}</ref>
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| 2018|| {{dts|June 2}} || Publication || OpenAI publishes "GamePad: A Learning Environment for Theorem Proving" in {{w|arXiv}}. The paper introduces a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.<ref>{{cite web |last1=Huang |first1=Daniel |last2=Dhariwal |first2=Prafulla |last3=Song |first3=Dawn |last4=Sutskever |first4=Ilya |title=GamePad: A Learning Environment for Theorem Proving |url=https://arxiv.org/abs/1806.00608 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{Dts|June 25}} || AI development || OpenAI announces set of AI algorithms able to hold their own as a team of five and defeat human amateur players at {{w|Dota 2}}, a multiplayer online battle arena video game popular in e-sports for its complexity and necessity for teamwork.<ref>{{cite web |last1=Gershgorn |first1=Dave |title=OpenAI built gaming bots that can work as a team with inhuman precision |url=https://qz.com/1311732/OpenAI-built-gaming-bots-that-can-work-as-a-team-with-inhuman-precision/ |website=qz.com |accessdate=14 June 2019}}</ref> In the algorithmic A team, called OpenAI Five, each algorithm uses a {{w|neural network}} to learn both how to play the game, and how to cooperate with its AI teammates.<ref>{{cite web |last1=Knight |first1=Will |title=A team of AI algorithms just crushed humans in a complex computer game |url=https://www.technologyreview.com/s/611536/a-team-of-ai-algorithms-just-crushed-expert-humans-in-a-complex-computer-game/ |website=technologyreview.com |accessdate=14 June 2019}}</ref><ref>{{cite web |title=OpenAI’s bot can now defeat skilled Dota 2 teams |url=https://venturebeat.com/2018/06/25/OpenAI-trains-ai-to-defeat-teams-of-skilled-dota-2-players/ |website=venturebeat.com |accessdate=14 June 2019}}</ref>
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| 2018 || {{dts|June}} || Staff || Karl Cobbe joins OpenAI as Machine Learning Fellow.<ref>{{cite web |title=Karl Cobbe |url=https://www.linkedin.com/in/kcobbe/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
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| 2018 || {{dts|July 9}} || Publication || "Glow: Generative Flow with Invertible 1x1 Convolutions" is first submitted to the {{w|ArXiv}}. The paper proposes a method for obtaining a significant improvement in log-likelihood on standard benchmarks.<ref>{{cite web |last1=Kingma |first1=Diederik P. |last2=Dhariwal |first2=Prafulla |title=Glow: Generative Flow with Invertible 1x1 Convolutions |url=https://arxiv.org/abs/1807.03039 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{Dts|July 18}} || Commitment || {{w|Elon Musk}}, along with other tech leaders, sign a pledge promising to not develop “lethal autonomous weapons.” They also call on governments to institute laws against such technology. The pledge is organized by the {{w|Future of Life Institute}}, an outreach group focused on tackling existential risks.<ref>{{cite web |last1=Vincent |first1=James |title=Elon Musk, DeepMind founders, and others sign pledge to not develop lethal AI weapon systems |url=https://www.theverge.com/2018/7/18/17582570/ai-weapons-pledge-elon-musk-deepmind-founders-future-of-life-institute |website=theverge.com |accessdate=1 June 2019}}</ref><ref>{{cite web |last1=Locklear |first1=Mallory |title=DeepMind, Elon Musk and others pledge not to make autonomous AI weapons |url=https://www.engadget.com/2018/07/18/deepmind-elon-musk-pledge-autonomous-ai-weapons/ |website=engadget.com |accessdate=1 June 2019}}</ref><ref>{{cite web |last1=Quach |first1=Katyanna |title=Elon Musk, his arch nemesis DeepMind swear off AI weapons |url=https://www.theregister.co.uk/2018/07/19/keep_ai_nonlethal/ |website=theregister.co.uk |accessdate=1 June 2019}}</ref>
