62,666
edits
Changes
| 2016 || {{dts|April}} || Staff || Shivon Zilis joins OpenAI as Advisor.<ref>{{cite web |title=Shivon Zilis |url=https://www.linkedin.com/in/shivonzilis/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
|-
| 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>
|-
| 2016 || {{dts|May 25}} || Publication || "Adversarial Training Methods for Semi-Supervised Text Classification" is submitted to the {{w|ArXiv}}. The paper proposes a method that achieves better results on multiple benchmark semi-supervised and purely supervised tasks.<ref>{{cite web |last1=Miyato |first1=Takeru |last2=Dai |first2=Andrew M. |last3=Goodfellow |first3=Ian |title=Adversarial Training Methods for Semi-Supervised Text Classification |url=https://arxiv.org/abs/1605.07725 |website=arxiv.org |accessdate=28 March 2020}}</ref>
| 2016 || {{dts|November 15}} || Publication || "#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning", a paper on {{w|reinforcement learning}}, is first submitted to the {{w|ArXiv}}.<ref>{{cite web |title=#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning |url=https://arxiv.org/abs/1611.04717 |website=arxiv.org |accessdate=28 March 2020}}</ref>
|-
| 2016 || {{dts|December 5}} || Software release || OpenAI's Universe, "a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications", is released.<ref>{{cite web |url=https://github.com/OpenAI/universe |accessdate=March 1, 2018 |publisher=GitHub |title=universe}}</ref><ref>{{cite web |url=https://techcrunch.com/2016/12/05/OpenAIs-universe-is-the-fun-parent-every-artificial-intelligence-deserves/ |date=December 5, 2016 |publisher=TechCrunch |title=OpenAI's Universe is the fun parent every artificial intelligence deserves |author=John Mannes |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=https://www.wired.com/2016/12/OpenAIs-universe-computers-learn-use-apps-like-humans/ |title=Elon Musk's Lab Wants to Teach Computers to Use Apps Just Like Humans Do |publisher=[[wikipedia:WIRED|WIRED]] |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=https://news.ycombinator.com/item?id=13103742 |title=OpenAI Universe |website=Hacker News |accessdate=May 5, 2018}}</ref>
|-
| 2016 || December 21 || Publication || "Faulty Reward Functions in the Wild" is published. The post explores a failed {{w|reinforcement learning}} algorithm, which leads to misspecifying the reward function.<ref>{{cite web |title=Faulty Reward Functions in the Wild |url=https://openai.com/blog/faulty-reward-functions/ |website=openai.com |accessdate=5 April 2020}}</ref>
| 2017 || {{dts|March 21}} || Publication || "One-Shot Imitation Learning", a paper on {{w|robotics}}, is first submitted to the {{w|ArXiv}}. The paper proposes a meta-learning framework for optimizing imitation learning.<ref>{{cite web |title=One-Shot Imitation Learning |url=https://arxiv.org/abs/1703.07326 |website=arxiv.org |accessdate=28 March 2020}}</ref>
|-
| 2017 || {{dts|March 24}} || Software release || OpenAI announces having discovered that [[w:Evolution strategy|evolution strategies]] rival the performance of standard {{w|reinforcement learning}} techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL’s inconveniences.<ref>{{cite web |title=Evolution Strategies as a Scalable Alternative to Reinforcement Learning |url=https://openai.com/blog/evolution-strategies/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 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|April}} || Coverage || An article entitled "The People Behind OpenAI" is published on {{W|Red Hat}}'s ''Open Source Stories'' website, covering work at OpenAI.<ref>{{cite web |url=https://www.redhat.com/en/open-source-stories/ai-revolutionaries/people-behind-OpenAI |title=Open Source Stories: The People Behind OpenAI |accessdate=May 5, 2018 |first1=Brent |last1=Simoneaux |first2=Casey |last2=Stegman}} In the HTML source, last-publish-date is shown as Tue, 25 Apr 2017 04:00:00 GMT as of 2018-05-05.</ref><ref>{{cite web |url=https://www.reddit.com/r/OpenAI/comments/63xr4p/profile_of_the_people_behind_OpenAI/ |publisher=reddit |title=Profile of the people behind OpenAI • r/OpenAI |date=April 7, 2017 |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://news.ycombinator.com/item?id=14832524 |title=The People Behind OpenAI |website=Hacker News |accessdate=May 5, 2018 |date=July 23, 2017}}</ref>
|-
| 2017 || {{dts|April 6}} || Software release || OpenAI unveils an unsupervised system which is able to perform a excellent {{w|sentiment analysis}}, despite being trained only to predict the next character in the text of Amazon reviews.<ref>{{cite web |title=Unsupervised Sentiment Neuron |url=https://openai.com/blog/unsupervised-sentiment-neuron/ |website=openai.com |accessdate=5 April 2020}}</ref><ref>{{cite web |url=https://techcrunch.com/2017/04/07/OpenAI-sets-benchmark-for-sentiment-analysis-using-an-efficient-mlstm/ |date=April 7, 2017 |publisher=TechCrunch |title=OpenAI sets benchmark for sentiment analysis using an efficient mLSTM |author=John Mannes |accessdate=March 2, 2018}}</ref>
|-
| 2017 || {{dts|April 6}} || Software release || OpenAI unveils reuse of an old field called “neuroevolution”, and a subset of algorithms from it called “evolution strategies,” which are aimed at solving optimization problems. In one hour training on an Atari challenge, an algorithm is found to reach a level of mastery that took a reinforcement-learning system published by DeepMind in 2016 a whole day to learn. On the walking problem the system took 10 minutes, compared to 10 hours for DeepMind's approach.<ref>{{cite web |title=OpenAI Just Beat Google DeepMind at Atari With an Algorithm From the 80s |url=https://singularityhub.com/2017/04/06/OpenAI-just-beat-the-hell-out-of-deepmind-with-an-algorithm-from-the-80s/ |website=singularityhub.com |accessdate=29 June 2019}}</ref>
|-
| 2017 || {{dts|April}} || Staff || Matthias Plappert joins OpenAI as Researcher.<ref>{{cite web |title=Matthias Plappert |url=https://www.linkedin.com/in/matthiasplappert/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
|-
| 2017 || {{dts|May 15}} || Software release || OpenAI releases Roboschool, an open-source software for robot simulation, integrated with OpenAI Gym.<ref>{{cite web |title=Roboschool |url=https://openai.com/blog/roboschool/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2017 || {{dts|May 16}} || Software release || OpenAI introduces a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it done once.<ref>{{cite web |title=Robots that Learn |url=https://openai.com/blog/robots-that-learn/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2017 || {{dts|May 24}} || Software release || OpenAI releases Baselines, a set of implementations of reinforcement learning algorithms.<ref>{{cite web |url=https://blog.OpenAI.com/OpenAI-baselines-dqn/ |publisher=OpenAI Blog |title=OpenAI Baselines: DQN |date=November 28, 2017 |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://github.com/OpenAI/baselines |publisher=GitHub |title=OpenAI/baselines |accessdate=May 5, 2018}}</ref>
|-
| 2017 || {{dts|May}} || Staff || {{w|Kevin Frans}} joins OpenAI as Research Intern.<ref>{{cite web |title=Kevin Frans |url=https://www.linkedin.com/in/kevinfrans/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
| 2017 || {{dts|July}} || Staff || OpenAI Research Scientist Joshua Achiam joins the organization.<ref>{{cite web |title=Joshua Achiam |url=https://www.linkedin.com/in/joshua-achiam-13887199/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
|-
| 2017 || {{dts|July 27}} || Software release || OpenAI announces having found that adding adaptive noise to the parameters of {{w|reinforcement learning}} algorithms frequently boosts performance.<ref>{{cite web |title=Better Exploration with Parameter Noise |url=https://openai.com/blog/better-exploration-with-parameter-noise/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2017 || {{dts|August 12}} || Achievement || OpenAI's Dota 2 bot beats Danil "Dendi" Ishutin, a professional human player, (and possibly others?) in one-on-one battles.<ref>{{cite web |url=https://techcrunch.com/2017/08/12/OpenAI-bot-remains-undefeated-against-worlds-greatest-dota-2-players/ |date=August 12, 2017 |publisher=TechCrunch |title=OpenAI bot remains undefeated against world's greatest Dota 2 players |author=Jordan Crook |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=https://www.theverge.com/2017/8/14/16143392/dota-ai-OpenAI-bot-win-elon-musk |date=August 14, 2017 |publisher=The Verge |title=Did Elon Musk's AI champ destroy humans at video games? It's complicated |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=http://www.businessinsider.com/the-international-dota-2-OpenAI-bot-beats-dendi-2017-8 |date=August 11, 2017 |title=Elon Musk's $1 billion AI startup made a surprise appearance at a $24 million video game tournament — and crushed a pro gamer |publisher=Business Insider |accessdate=March 3, 2018}}</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>
|-
| 2017 || {{dts|August 18}} || Software release || OpenAI releases two implementations: ACKTR, a {{w|reinforcement learning}} algorithm, and A2C, a synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C).<ref>{{cite web |title=OpenAI Baselines: ACKTR & A2C |url=https://openai.com/blog/baselines-acktr-a2c/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 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>
| 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>
|-
| 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>
|-
| 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>
| 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>
|-
| 2017 || {{dts|December 6}} || Software release || OpenAI releases highly-optimized GPU kernels for networks with block-sparse weights, an underexplored class of neural network architectures. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE.<ref>{{cite web |title=Block-Sparse GPU Kernels |url=https://openai.com/blog/block-sparse-gpu-kernels/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 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|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>
|-
| 2018 || {{dts|February 26}} || Software release || OpenAI releases eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for OpenAI research over the previous year. These environments were to train models which work on physical robots.<ref>{{cite web |title=Ingredients for Robotics Research |url=https://openai.com/blog/ingredients-for-robotics-research/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 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>
| 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>
|-
| 2018 || {{dts|June 11}} || Software release || OpenAI announces having obtained significant results on a suite of diverse language tasks with a scalable, task-agnostic system, which uses a combination of transformers and unsupervised pre-training.<ref>{{cite web |title=Improving Language Understanding with Unsupervised Learning |url=https://openai.com/blog/language-unsupervised/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2018 || {{dts|June 17}} || Publication || OpenAI publishes paper on learning policy representations in multiagent systems. The paper proposes a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data.<ref>{{cite web |title=Learning Policy Representations in Multiagent Systems |url=https://arxiv.org/abs/1806.06464 |website=arxiv.org |accessdate=26 March 2020}}</ref>
|-
| 2018 || {{Dts|June 25}} || Software release || 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>
|-
| 2018 || {{Dts|June 26}} || Notable comment || {{w|Bill Gates}} comments on {{w|Twitter}}: {{Quote|AI bots just beat humans at the video game Dota 2. That’s a big deal, because their victory required teamwork and collaboration – a huge milestone in advancing artificial intelligence.}}<ref>{{cite web |last1=Papadopoulos |first1=Loukia |title=Bill Gates Praises Elon Musk-Founded OpenAI’s Latest Dota 2 Win as “Huge Milestone” in Field |url=https://interestingengineering.com/bill-gates-praises-elon-musk-founded-OpenAIs-latest-dota-2-win-as-huge-milestone-in-field |website=interestingengineering.com |accessdate=14 June 2019}}</ref>
| 2018 || {{dts|July 26}} || Publication || OpenAI publishes paper on variational option discovery algorithms. The paper highlights a tight connection between variational option discovery methods and variational autoencoders, and introduces Variational Autoencoding Learning of Options by Reinforcement (VALOR), a new method derived from the connection.<ref>{{cite web |last1=Achiam |first1=Joshua |last2=Edwards |first2=Harrison |last3=Amodei |first3=Dario |last4=Abbeel |first4=Pieter |title=Variational Option Discovery Algorithms |website=arxiv.org |accessdate=26 March 2020}}</ref>
|-
| 2018 || {{Dts|July 30}} || Software release || OpenAI announces a robotics system that can manipulate objects with humanlike dexterity. The system is able to develop these behaviors all on its own. It uses a reinforcement model, where the AI learns through trial and error, to direct robot hands in grasping and manipulating objects with great precision.<ref>{{cite web |title=OpenAI’s ‘state-of-the-art’ system gives robots humanlike dexterity |url=https://venturebeat.com/2018/07/30/OpenAIs-state-of-the-art-system-gives-robots-humanlike-dexterity/ |website=venturebeat.com |accessdate=14 June 2019}}</ref><ref>{{cite web |last1=Coldewey |first1=Devin |title=OpenAI’s robotic hand doesn’t need humans to teach it human behaviors |url=https://techcrunch.com/2018/07/30/OpenAIs-robotic-hand-doesnt-need-humans-to-teach-it-human-behaviors/ |website=techcrunch.com |accessdate=14 June 2019}}</ref>
|-
| 2018 || {{dts|August 1}} || Publication || OpenAI publishes paper describing the use of {{w|reinforcement learning}} to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.<ref>{{cite web |title=Learning Dexterous In-Hand Manipulation |url=https://arxiv.org/abs/1808.00177 |website=arxiv.org |accessdate=26 March 2020}}</ref>
| 2018 || {{dts|October}} || Staff || Mark Chen joins OpenAI as Research Scientist.<ref>{{cite web |title=Mark Chen |url=https://www.linkedin.com/in/markchen90/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
|-
| 2018 || {{dts|October 31}} || Software release || OpenAI unveils its Random Network Distillation (RND), a prediction-based method for encouraging {{w|reinforcement learning}} agents to explore their environments through curiosity, which for the first time exceeds average human performance on videogame Montezuma’s Revenge.<ref>{{cite web |title=Reinforcement Learning with Prediction-Based Rewards |url=https://openai.com/blog/reinforcement-learning-with-prediction-based-rewards/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2018 || {{Dts|November 1}} || Publication || OpenAI publishes research paper detailing AI able to defeat humans at the retro platformer [[w:Montezuma's Revenge (video game)|Montezuma’s Revenge]]. The top-performing iteration found 22 of the 24 rooms in the first level, and occasionally discovered all 24.<ref>{{cite web |last1=Wiggers |first1=Kyle |title=OpenAI made a system that’s better at Montezuma’s Revenge than humans |url=https://venturebeat.com/2018/11/01/OpenAI-made-a-system-thats-better-at-montezumas-revenge-than-humans/ |website=venturebeat.com |accessdate=15 June 2019}}</ref><ref>{{cite web |last1=Vincent |first1=James |title=New research from OpenAI uses curious AI to beat video games |url=https://www.theverge.com/2018/11/1/18051196/ai-artificial-intelligence-curiosity-OpenAI-montezumas-revenge-noisy-tv-problem |website=theverge.com |accessdate=15 June 2019}}</ref>
| 2018 || {{dts|November}} || Staff || Amanda Askell joins OpenAI as Research Scientist (Policy).<ref>{{cite web |title=Amanda Askell |url=https://www.linkedin.com/in/amanda-askell-1ab457175/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
|-
| 2018 || {{dts|December 4}} || Software release || OpenAI announces having discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training on a wide range of tasks.<ref>{{cite web |title=How AI Training Scales |url=https://openai.com/blog/science-of-ai/ |website=openai.com |accessdate=4 April 2020}}</ref>
|-
| 2018 || {{Dts|December 6}} || Software release || OpenAI publishes CoinRun, a training environment designed to test the adaptability of reinforcement learning agents.<ref>{{cite web |title=OpenAI teaches AI teamwork by playing hide-and-seek |url=https://venturebeat.com/2019/09/17/OpenAI-and-deepmind-teach-ai-to-work-as-a-team-by-playing-hide-and-seek/ |website=venturebeat.com |accessdate=24 February 2020}}</ref><ref>{{cite web |title=OpenAI’s CoinRun tests the adaptability of reinforcement learning agents |url=https://venturebeat.com/2018/12/06/OpenAIs-coinrun-tests-the-adaptability-of-reinforcement-learning-agents/ |website=venturebeat.com |accessdate=24 February 2020}}</ref>
|-
| 2018 || {{dts|December 14}} || Publication || OpenAI publishes paper demonstrating that a simple and easy-to-measure statistic called the gradient noise scale predicts the largest useful batch size across many domains and applications, including a number of {{w|supervised learning}} datasets, {{w|reinforcement learning}} domains, and even generative model training.<ref>{{cite web |last1=McCandlish |first1=Sam |last2=Kaplan |first2=Jared |last3=Amodei |first3=Dario |last4=OpenAI Dota Team |title=An Empirical Model of Large-Batch Training |url=https://arxiv.org/abs/1812.06162 |website=arxiv.org |accessdate=25 March 2020}}</ref>
| 2019 || {{dts|February 4}} || Publication || OpenAI publishes paper showing computational limitations in robust classification and win-win results.<ref>{{cite web |last1=Degwekar |first1=Akshay |last2=Nakkiran |first2=Preetum |last3=Vaikuntanathan |first3=Vinod |title=Computational Limitations in Robust Classification and Win-Win Results |url=https://arxiv.org/abs/1902.01086 |website=arxiv.org |accessdate=25 March 2020}}</ref>
|-
| 2019 || {{Dts|February 14}} || Software release || OpenAI unveils its language-generating system called GPT-2, a system able to write the news, answer reading comprehension problems, and is beginning to show promise at tasks like translation.<ref>{{cite web |title=An AI helped us write this article |url=https://www.vox.com/future-perfect/2019/2/14/18222270/artificial-intelligence-open-ai-natural-language-processing |website=vox.com |accessdate=28 June 2019}}</ref> However, the data or the parameters of the model are not released, under expressed concerns about potential abuse.<ref>{{cite web |last1=Lowe |first1=Ryan |title=OpenAI’s GPT-2: the model, the hype, and the controversy |url=https://towardsdatascience.com/OpenAIs-gpt-2-the-model-the-hype-and-the-controversy-1109f4bfd5e8 |website=towardsdatascience.com |accessdate=10 July 2019}}</ref>
|-
| 2019 || {{dts|February 19}} || Publication || "AI Safety Needs Social Scientists" is published. The paper argues that long-term AI safety research needs social scientists to ensure AI alignment algorithms succeed when actual humans are involved.<ref>{{cite journal |last1=Irving |first1=Geoffrey |last2=Askell |first2=Amanda |title=AI Safety Needs Social Scientists |doi=10.23915/distill.00014 |url=https://distill.pub/2019/safety-needs-social-scientists/}}</ref><ref>{{cite web |title=AI Safety Needs Social Scientists |url=https://openai.com/blog/ai-safety-needs-social-scientists/ |website=openai.com |accessdate=5 April 2020}}</ref>
| 2019 || {{dts|March 2}} || Publication || OpenAi publishes paper presenting an artificial intelligence research environment that aims to simulate the {{w|natural environment}} setting in microcosm.<ref>{{cite web |last1=Suarez |first1=Joseph |last2=Du |first2=Yilun |last3=Isola |first3=Phillip |last4=Mordatch |first4=Igor |title=Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents |url=https://arxiv.org/abs/1903.00784 |website=arxiv.org |accessdate=25 March 2020}}</ref>
|-
| 2019 || {{dts|March 4}} || Software release || OpenAI releases a Neural MMO (massively multiplayer online), a multiagent game environment for {{w|reinforcement learning}} agents. The platform supports a large, variable number of agents within a persistent and open-ended task.<ref>{{cite web |title=Neural MMO: A Massively Multiagent Game Environment |url=https://openai.com/blog/neural-mmo/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2019 || {{dts|March 6}} || Software release || OpenAI introduces activation atlases, created in collaboration with {{w|Google}} researchers. Activation atlases comprise a new technique for visualizing what interactions between neurons can represent.<ref>{{cite web |title=Introducing Activation Atlases |url=https://openai.com/blog/introducing-activation-atlases/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2019 || {{Dts|March 11}} || Reorganization || OpenAI announces the creation of OpenAI LP, a new “capped-profit” company owned and controlled by the OpenAI nonprofit organization’s board of directors. The new company is purposed to allow OpenAI to rapidly increase their investments in compute and talent while including checks and balances to actualize their mission.<ref>{{cite web |last1=Johnson |first1=Khari |title=OpenAI launches new company for funding safe artificial general intelligence |url=https://venturebeat.com/2019/03/11/OpenAI-launches-new-company-for-funding-safe-artificial-general-intelligence/ |website=venturebeat.com |accessdate=15 June 2019}}</ref><ref>{{cite web |last1=Trazzi |first1=Michaël |title=Considerateness in OpenAI LP Debate |url=https://medium.com/@MichaelTrazzi/considerateness-in-OpenAI-lp-debate-6eb3bf4c5341 |website=medium.com |accessdate=15 June 2019}}</ref>
| 2019 || {{dts|March 20}} || Publication || OpenAI publishes paper presenting techniques to scale MCMC based energy base models training on continuous neural networks.<ref>{{cite web |last1=Du |first1=Yilun |last2=Mordatch |first2=Igor |title=Implicit Generation and Generalization in Energy-Based Models |url=https://arxiv.org/abs/1903.08689 |website=arxiv.org |accessdate=25 March 2020}}</ref>
|-
| 2019 || {{dts|March 21}} || Software release || OpenAI announces progress towards stable and scalable training of energy-based models (EBMs) resulting in better sample quality and generalization ability than existing models.<ref>{{cite web |title=Implicit Generation and Generalization Methods for Energy-Based Models |url=https://openai.com/blog/energy-based-models/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2019 || {{dts|March}} || Staff || Ilge Akkaya joins OpenAI as Member Of Technical Staff.<ref>{{cite web |title=Ilge Akkaya |url=https://www.linkedin.com/in/ilge-akkaya-311b4631/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
| 2019 || {{Dts|April 23}} || Publication || OpenAI publishes paper announcing Sparse Transformers, a deep neural network for learning sequences of data, including text, sound, and images. It utilizes an improved algorithm based on the attention mechanism, being able to extract patterns from sequences 30 times longer than possible previously.<ref>{{cite web |last1=Alford |first1=Anthony |title=OpenAI Introduces Sparse Transformers for Deep Learning of Longer Sequences |url=https://www.infoq.com/news/2019/05/OpenAI-sparse-transformers/ |website=infoq.com |accessdate=15 June 2019}}</ref><ref>{{cite web |title=OpenAI Sparse Transformer Improves Predictable Sequence Length by 30x |url=https://medium.com/syncedreview/OpenAI-sparse-transformer-improves-predictable-sequence-length-by-30x-5a65ef2592b9 |website=medium.com |accessdate=15 June 2019}}</ref><ref>{{cite web |title=Generative Modeling with Sparse Transformers |url=https://OpenAI.com/blog/sparse-transformer/ |website=OpenAI.com |accessdate=15 June 2019}}</ref>
|-
| 2019 || {{Dts|April 25}} || Software release || OpenAI announces MuseNet, a deep {{w|neural network}} able to generate 4-minute musical compositions with 10 different instruments, and can combine multiple styles from [[w:Country music|country]] to {{w|Mozart}} to {{w|The Beatles}}. The neural network uses general-purpose unsupervised technology.<ref>{{cite web |title=MuseNet |url=https://OpenAI.com/blog/musenet/ |website=OpenAI.com |accessdate=15 June 2019}}</ref>
|-
| 2019 || {{Dts|April 27}} || Event hosting || OpenAI hosts the OpenAI Robotics Symposium 2019.<ref>{{cite web |title=OpenAI Robotics Symposium 2019 |url=https://OpenAI.com/blog/symposium-2019/ |website=OpenAI.com |accessdate=14 June 2019}}</ref>
| 2019 || {{dts|May 3}} || Publication || OpenAI publishes study on the transfer of adversarial robustness of [[w:deep learning|deep neural networks]] between different perturbation types.<ref>{{cite web |last1=Kang |first1=Daniel |last2=Sun |first2=Yi |last3=Brown |first3=Tom |last4=Hendrycks |first4=Dan |last5=Steinhardt |first5=Jacob |title=Transfer of Adversarial Robustness Between Perturbation Types |url=https://arxiv.org/abs/1905.01034 |website=arxiv.org |accessdate=25 March 2020}}</ref>
|-
| 2019 || {{Dts|May}} || Software release || OpenAI releases a limited version of its language-generating system GPT-2. This version is more powerful (though still significantly limited compared to the whole thing) than the extremely restricted initial release of the system, citing concerns that it’d be abused.<ref>{{cite web |title=A poetry-writing AI has just been unveiled. It’s ... pretty good. |url=https://www.vox.com/2019/5/15/18623134/OpenAI-language-ai-gpt2-poetry-try-it |website=vox.com |accessdate=11 July 2019}}</ref> The potential of the new system is recognized by various experts.<ref>{{cite web |last1=Vincent |first1=James |title=AND OpenAI's new multitalented AI writes, translates, and slanders |url=https://www.theverge.com/2019/2/14/18224704/ai-machine-learning-language-models-read-write-OpenAI-gpt2 |website=theverge.com |accessdate=11 July 2019}}</ref>
|-
| 2019 || {{dts|May 28}} || Publication || OpenAI publishes study on the dynamics of Stochastic Gradient Descent (SGD) in learning [[w:Deep learning|deep neural networks]] for several real and synthetic classification tasks.<ref>{{cite web |last1=Nakkiran |first1=Preetum |last2=Kaplun |first2=Gal |last3=Kalimeris |first3=Dimitris |last4=Yang |first4=Tristan |last5=Edelman |first5=Benjamin L. |last6=Zhang |first6=Fred |last7=Barak |first7=Boaz |title=SGD on Neural Networks Learns Functions of Increasing Complexity |url=https://arxiv.org/abs/1905.11604 |website=arxiv.org |accessdate=25 March 2020}}</ref>
| 2019 || {{dts|July}} || Staff || Irene Solaiman joins OpenAI as Policy Researcher.<ref>{{cite web |title=Irene Solaiman |url=https://www.linkedin.com/in/irene-solaiman/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
|-
| 2019 || {{dts|August 20}} || Software release || OpenAI announces plan to release a version of its language-generating system GPT-2, which stirred controversy after it release in February.<ref>{{cite web |title=OpenAI releases curtailed version of GPT-2 language model |url=https://venturebeat.com/2019/08/20/OpenAI-releases-curtailed-version-of-gpt-2-language-model/ |website=venturebeat.com |accessdate=24 February 2020}}</ref><ref>{{cite web |title=OpenAI Just Released an Even Scarier Fake News-Writing Algorithm |url=https://interestingengineering.com/OpenAI-just-released-an-even-scarier-fake-news-writing-algorithm |website=interestingengineering.com |accessdate=24 February 2020}}</ref><ref>{{cite web |title=OPENAI JUST RELEASED A NEW VERSION OF ITS FAKE NEWS-WRITING AI |url=https://futurism.com/the-byte/OpenAI-new-version-writing-ai |website=futurism.com |accessdate=24 February 2020}}</ref>
|-
| 2019 || {{dts|August}} || Staff || Melanie Subbiah joins OpenAI as Member Of Technical Staff.<ref>{{cite web |title=Melanie Subbiah |url=https://www.linkedin.com/in/melanie-subbiah-7b702a8a/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
| 2019 || {{dts|August}} || Staff || Cullen O'Keefe joins OpenAI as Research Scientist (Policy).<ref>{{cite web |title=Cullen O'Keefe |url=https://www.linkedin.com/in/ccokeefe-law/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
|-
| 2019 || {{dts|September 17}} || Software release || OpenAI announces having observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training, the agents were able to build a series of six distinct strategies and counterstrategies, some of which were unknown to be supported by the environment.<ref>{{cite web |title=Emergent Tool Use from Multi-Agent Interaction |url=https://openai.com/blog/emergent-tool-use/ |website=openai.com |accessdate=4 April 2020}}</ref><ref>{{cite web |title=Emergent Tool Use From Multi-Agent Autocurricula |url=https://arxiv.org/abs/1909.07528 |website=arxiv.org |accessdate=4 April 2020}}</ref>
|-
| 2019 || {{dts|October 16}} || Software release || OpenAI announces having trained a pair of {{w|neural network}}s to solve the {{w|Rubik’s Cube}} with a human-like robot hand. The experiment demonstrates that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot.<ref>{{cite web |title=Solving Rubik's Cube with a Robot Hand |url=https://arxiv.org/abs/1910.07113 |website=arxiv.org |accessdate=4 April 2020}}</ref><ref>{{cite web |title=Solving Rubik’s Cube with a Robot Hand |url=https://openai.com/blog/solving-rubiks-cube/ |website=openai.com |accessdate=4 April 2020}}</ref>
|-
| 2019 || {{dts|November 5}} || Software release || OpenAI releases the largest version (1.5B parameters) of its language-generating system GPT-2 along with code and model weights to facilitate detection of outputs of GPT-2 models.<ref>{{cite web |title=GPT-2: 1.5B Release |url=https://openai.com/blog/gpt-2-1-5b-release/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2019 || {{dts|November}} || Staff || Ryan Lowe joins OpenAI as Member Of Technical Staff.<ref>{{cite web |title=Ryan Lowe |url=https://www.linkedin.com/in/ryan-lowe-ab67a267/ |website=linkedin.com |accessdate=28 February 2020}}</ref>
|-
| 2019 || {{dts|November 21}} || Software release || OpenAI releases Safety Gym, a suite of environments and tools for measuring progress towards {{w|reinforcement learning}} agents that respect safety constraints while training.<ref>{{cite web |title=Safety Gym |url=https://openai.com/blog/safety-gym/ |website=openai.com |accessdate=5 April 2020}}</ref>
|-
| 2019 || {{dts|December 3}} || Software release || OpenAI releases Procgen Benchmark, a set of 16 simple-to-use procedurally-generated environments (CoinRun, StarPilot, CaveFlyer, Dodgeball, FruitBot, Chaser, Miner, Jumper, Leaper, Maze, BigFish, Heist, Climber, Plunder, Ninja, and BossFight) which provide a direct measure of how quickly a {{w|reinforcement learning}} agent learns generalizable skills. Procgen Benchmark prevents AI model overfitting.<ref>{{cite web |title=Procgen Benchmark |url=https://openai.com/blog/procgen-benchmark/ |website=openai.com |accessdate=2 March 2020}}</ref><ref>{{cite web |title=OpenAI’s Procgen Benchmark prevents AI model overfitting |url=https://venturebeat.com/2019/12/03/openais-procgen-benchmark-overfitting/ |website=venturebeat.com |accessdate=2 March 2020}}</ref><ref>{{cite web |title=GENERALIZATION IN REINFORCEMENT LEARNING – EXPLORATION VS EXPLOITATION |url=https://analyticsindiamag.com/generalization-in-reinforcement-learning-exploration-vs-exploitation/ |website=analyticsindiamag.com |accessdate=2 March 2020}}</ref>
|-
| 2019 || {{dts|December 4}} || Publication || "Deep Double Descent: Where Bigger Models and More Data Hurt" is submitted to the {{w|ArXiv}}. The paper shows that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as the model size increases, performance first gets worse and then gets better.<ref>{{cite web |last1=Nakkiran |first1=Preetum |last2=Kaplun |first2=Gal |last3=Bansal |first3=Yamini |last4=Yang |first4=Tristan |last5=Barak |first5=Boaz |last6=Sutskever |first6=Ilya |title=Deep Double Descent: Where Bigger Models and More Data Hurt |website=arxiv.org |url=https://arxiv.org/abs/1912.02292|accessdate=5 April 2020}}</ref>
