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Timeline of OpenAI

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** Sort the full timeline by "Event type" and look for the group of rows with value "Partnership".
** You will read collaborations with organizations like {{w|DeepMind}} and {{w|Microsoft}}.
** What are some significant fundings granted to OpenAI by donors?
** Sort the full timeline by "Event type" and look for the group of rows with value "Donation".
** You will see names like the {{w|Open Philanthropy Project}}, and {{w|Nvidia}}, among others.
| 2016 || {{dts|May 31}} || Generative models || 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}} || {{w|Reinforcement learning}} || 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> OpenAI Gym is considered by some as "a huge opportunity for speeding up the progress in the creation of better reinforcement algorithms, since it provides an easy way of comparing them, on the same conditions, independently of where the algorithm is executed".<ref>{{cite web |title=OPENAI GYM |url=https://www.theconstructsim.com/tag/openai_gym/ |website=theconstructsim.com |accessdate=16 May 2020}}</ref>
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| 2016 || {{dts|June 10}} || Generative models || 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}} || Generative models || 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 15}} || Generative models || Publication || "Improving Variational Inference with Inverse Autoregressive Flow", a paper on generative models, is submitted to the {{w|ArXiv}}. We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to earlier published flows, scales well to high-dimensional latent spaces.<ref>{{cite web |last1=Kingma |first1=Diederik P. |last2=Salimans |first2=Tim |last3=Jozefowicz |first3=Rafal |last4=Chen |first4=Xi |last5=Sutskever |first5=Ilya |last6=Welling |first6=Max |title=Improving Variational Inference with Inverse Autoregressive Flow |url=https://arxiv.org/abs/1606.04934 |website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2016 || {{dts|June 16}} || Generative models || Publication || OpenAI publishes post describing four projects on generative models, a branch of {{w|unsupervised learning}} techniques in machine learning.<ref>{{cite web |title=Generative Models |url=https://openai.com/blog/generative-models/ |website=openai.com |accessdate=5 April 2020}}</ref> |-| 2016 || {{dts|June 21}} || || Publication || "Concrete Problems in AI Safety" by Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané is submitted to the {{w|arXiv}}. The paper explores practical problems in machine learning systems.<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> The paper would receive a shoutout from the Open Philanthropy Project.<ref>{{cite web|url = https://www.openphilanthropy.org/blog/concrete-problems-ai-safety|title = Concrete Problems in AI Safety|last = Karnofsky|first = Holden|date = June 23, 2016|accessdate = April 18, 2020}}</ref> It would become a landmark in AI safety literature, and many of its authors would continue to do AI safety work at OpenAI in the years to come.
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| 2016 || {{Dts|July}} || || Team || Dario Amodei joins OpenAI<ref>{{cite web |url=https://www.crunchbase.com/person/dario-amodei |title=Dario Amodei - Research Scientist @ OpenAI |publisher=Crunchbase |accessdate=May 6, 2018}}</ref>, working on the Team Lead for AI Safety.<ref name="Dario Amodeiy"/><ref name="orgwatch.issarice.com"/>
| 2016 || {{dts|August 29}} || Infrastructure || Publication || "Infrastructure for Deep Learning" is published. The post shows how deep learning research usually proceeds. It also describes the infrastructure choices OpenAI made to support it, and open-source kubernetes-ec2-autoscaler, a batch-optimized scaling manager for {{w|Kubernetes}}.<ref>{{cite web |title=Infrastructure for Deep Learning |url=https://openai.com/blog/infrastructure-for-deep-learning/ |website=openai.com |accessdate=28 March 2020}}</ref>
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| 2016 || {{dts|October 11}} || {{w|Robotics}} || Publication || "Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model", a paper on {{w|robotics}}, is submitted to the {{w|ArXiv}}. It investigates settings where the sequence of states traversed in simulation remains reasonable for the real world.<ref>{{cite web |last1=Christiano |first1=Paul |last2=Shah |first2=Zain |last3=Mordatch |first3=Igor |last4=Schneider |first4=Jonas |last5=Blackwell |first5=Trevor |last6=Tobin |first6=Joshua |last7=Abbeel |first7=Pieter |last8=Zaremba |first8=Wojciech |title=Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model |url=https://arxiv.org/abs/1610.03518 |website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2016 || {{dts|October 18}} || Safety || Publication || "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data", a paper on safety, is submitted to the {{w|ArXiv}}. It shows an approach to providing strong privacy guarantees for training data: Private Aggregation of Teacher Ensembles (PATE).<ref>{{cite web |last1=Papernot |first1=Nicolas |last2=Abadi |first2=Martín |last3=Erlingsson |first3=Úlfar |last4=Goodfellow |first4=Ian |last5=Talwar |first5=Kunal |title=Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data |url=https://arxiv.org/abs/1610.05755 |website=arxiv.org |accessdate=28 March 2020}}</ref>
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| 2016 || {{dts|November 15}} || || Partnership || A partnership between OpenAI and Microsoft's artificial intelligence division is announced. As part of the partnership, Microsoft provides a price reduction on computing resources to OpenAI through {{W|Microsoft Azure}}.<ref>{{cite web |url=https://www.theverge.com/2016/11/15/13639904/microsoft-OpenAI-ai-partnership-elon-musk-sam-altman |date=November 15, 2016 |publisher=The Verge |first=Nick |last=Statt |title=Microsoft is partnering with Elon Musk's OpenAI to protect humanity's best interests |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=https://www.wired.com/2016/11/next-battles-clouds-ai-chips/ |title=The Next Big Front in the Battle of the Clouds Is AI Chips. And Microsoft Just Scored a Win |publisher=[[wikipedia:WIRED|WIRED]] |first=Cade |last=Metz |accessdate=March 2, 2018 |quote=According to Altman and Harry Shum, head of Microsoft new AI and research group, OpenAI's use of Azure is part of a larger partnership between the two companies. In the future, Altman and Shum tell WIRED, the two companies may also collaborate on research. "We're exploring a couple of specific projects," Altman says. "I'm assuming something will happen there." That too will require some serious hardware.}}</ref>
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| 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>
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| 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>
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| 2016 2017 ||{{dts| December 21 January}} || || Publication Staff || "Faulty Reward Functions in the Wild" is publishedPaul Christiano joins OpenAI to work on AI alignment. The post explores a failed <ref>{{wcite web |url=https://paulfchristiano.com/ai/ |title=AI Alignment |date=May 13, 2017 |publisher=Paul Christiano |reinforcement learningaccessdate=May 6, 2018}} algorithm, which leads to misspecifying the reward function</ref> He was previously an intern at OpenAI in 2016.<ref>{{cite web |title=Faulty Reward Functions in the Wild |url=https://blog.openai.com/blog/faulty-rewardteam-functionsupdate/ |websitepublisher=OpenAI Blog |title=openai.com Team Update |date=March 22, 2017 |accessdate=5 April 2020May 6, 2018}}</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|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>
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| 2017 || {{dts|April 6}} || || Publication || "Learning to Generate Reviews and Discovering Sentiment" is published.<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>
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| 2017 || {{dts|April 6}} || Neuroevolution || Research progress || 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|May 15}} || Robotics || 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>
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| 2017 || {{dts|May 16}} || Robotics || 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>
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| 2017 || {{dts|May 24}} || Reinforcement learning || 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>
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| 2017 || {{dts|June 12}} || Safety || Publication || "Deep reinforcement learning from human preferences" is first uploaded to the arXiv. The paper is a collaboration between researchers at OpenAI and Google DeepMind.<ref>{{cite web |url=https://arxiv.org/abs/1706.03741 |title=[1706.03741] Deep reinforcement learning from human preferences |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=https://www.gwern.net/newsletter/2017/06 |author=gwern |date=June 3, 2017 |title=June 2017 news - Gwern.net |accessdate=March 2, 2018}}</ref><ref>{{cite web |url=https://www.wired.com/story/two-giants-of-ai-team-up-to-head-off-the-robot-apocalypse/ |title=Two Giants of AI Team Up to Head Off the Robot Apocalypse |publisher=[[wikipedia:WIRED|WIRED]] |accessdate=March 2, 2018 |quote=A new paper from the two organizations on a machine learning system that uses pointers from humans to learn a new task, rather than figuring out its own—potentially unpredictable—approach, follows through on that. Amodei says the project shows it's possible to do practical work right now on making machine learning systems less able to produce nasty surprises.}}</ref>
| 2017 || {{dts|June 28}} || Robotics || Open sourcing || OpenAI open sources a high-performance [[w:Python (programming language)|Python]] library for robotic simulation using the MuJoCo engine, developed over OpenAI research on robotics.<ref>{{cite web |title=Faster Physics in Python |url=https://openai.com/blog/faster-robot-simulation-in-python/ |website=openai.com |accessdate=5 April 2020}}</ref>
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| 2017 || {{dts|June}} || {{w|Reinforcement learning}} || Partnership || OpenAI partners with {{w|DeepMind}}’s safety team in the development of an algorithm which can infer what humans want by being told which of two proposed behaviors is better. The learning algorithm uses small amounts of human feedback to solve modern {{w|reinforcement learning}} environments.<ref>{{cite web |title=Learning from Human Preferences |url=https://OpenAI.com/blog/deep-reinforcement-learning-from-human-preferences/ |website=OpenAI.com |accessdate=29 June 2019}}</ref>
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| 2017 || {{dts|July 27}} || {{w|Reinforcement learning}} || Research progress || 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>
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| 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>
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| 2017 || {{dts|August 18}} || {{w|Reinforcement learning}} || 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>
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| 2017 || {{Dts|September 13}} || {{w|Reinforcement learning}} || 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 || {{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|November 6}} || || Team || ''{{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 6}} || {{w|Neural network}} || 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>
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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>
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| 2018 || {{dts|February 20}} || Safety || Publication || The report "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation" is submitted to the {{w|ArXiv}}. 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}} || Robotics || 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>
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| 2018 || {{dts|March 3}} || || Event hosting || 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>
| 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>
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| 2018 || {{Dts|May 2}} || safety || 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>|-| 2018 || {{dts|May 16}} || {{w|Computation}} || Publication || OpenAI releases an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time.<ref>{{cite web |title=AI and Compute |url=https://openai.com/blog/ai-and-compute/ |website=openai.com |accessdate=5 April 2020}}</ref>
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| 2018 || {{dts|June 11}} || {{w|Unsupervised learning}} || Research progress || 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>
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| 2018 || {{Dts|June 25}} || {{w|Neural network}} || 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>
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| 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 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>
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| 2018 || {{Dts|July 30}} || Robotics || 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>
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| 2018 || {{Dts|August 7}} || || Achievement || Algorithmic team OpenAI Five defeats a team of semi-professional {{w|Dota 2}} players ranked in the 99.95th percentile in the world, in their second public match in the traditional five-versus-five settings, hosted in {{w|San Francisco}}.<ref>{{cite web |last1=Whitwam |first1=Ryan |title=OpenAI Bots Crush the Best Human Dota 2 Players in the World |url=https://www.extremetech.com/gaming/274907-OpenAI-bots-crush-the-best-human-dota-2-players-in-the-world |website=extremetech.com |accessdate=15 June 2019}}</ref><ref>{{cite web |last1=Quach |first1=Katyanna |title=OpenAI bots thrash team of Dota 2 semi-pros, set eyes on mega-tourney |url=https://www.theregister.co.uk/2018/08/06/OpenAI_bots_dota_2_semipros/ |website=theregister.co.uk |accessdate=15 June 2019}}</ref><ref>{{cite web |last1=Savov |first1=Vlad |title=The OpenAI Dota 2 bots just defeated a team of former pros |url=https://www.theverge.com/2018/8/6/17655086/dota2-OpenAI-bots-professional-gaming-ai |website=theverge.com |accessdate=15 June 2019}}</ref><ref>{{cite web |last1=Rigg |first1=Jamie |title=‘Dota 2’ veterans steamrolled by AI team in exhibition match |url=https://www.engadget.com/2018/08/06/OpenAI-five-dumpsters-dota-2-veterans/ |website=engadget.com |accessdate=15 June 2019}}</ref>
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| 2018 || {{dts|August 16}} || {{w|Arboricity}} || Publication || OpenAI publishes paper on constant arboricity spectral sparsifiers. The paper shows that every graph is spectrally similar to the union of a constant number of forests.<ref>{{cite web |last1=Chu |first1=Timothy |last2=Cohen |first2=Michael B. |last3=Pachocki |first3=Jakub W. |last4=Peng |first4=Richard |title=Constant Arboricity Spectral Sparsifiers |url=https://arxiv.org/abs/1808.05662 |website=arxiv.org |accessdate=26 March 2020}}</ref>
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| 2018 || {{dts|September}} || || Team || Dario Amodei becomes OpenAI's Research Director.<ref name="Dario Amodeiy"/>
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| 2018 || {{dts|October 31}} || {{w|Reinforcement learning}} || 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>
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| 2018 || {{Dts|November 8}} || {{w|Reinforcement learning}} || Education || OpenAI launches Spinning Up, an educational resource designed to teach anyone deep reinforcement learning. The program consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials.<ref>{{cite web |title=Spinning Up in Deep RL |url=https://OpenAI.com/blog/spinning-up-in-deep-rl/ |website=OpenAI.com |accessdate=15 June 2019}}</ref><ref>{{cite web |last1=Ramesh |first1=Prasad |title=OpenAI launches Spinning Up, a learning resource for potential deep learning practitioners |url=https://hub.packtpub.com/OpenAI-launches-spinning-up-a-learning-resource-for-potential-deep-learning-practitioners/ |website=hub.packtpub.com |accessdate=15 June 2019}}</ref><ref>{{cite web |last1=Johnson |first1=Khari |title=OpenAI launches reinforcement learning training to prepare for artificial general intelligence |url=https://flipboard.com/@venturebeat/OpenAI-launches-reinforcement-learning-training-to-prepare-for-artificial-genera/a-TxuPmdApTGSzPr0ny7qXsw%3Aa%3A2919225365-bafeac8636%2Fventurebeat.com |website=flipboard.com |accessdate=15 June 2019}}</ref>
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| 2018 || {{Dts|November 9}} || || Notable comment || {{w|Ilya Sutskever}} gives speech at the AI Frontiers Conference in {{w|San Jose}}, and declares: {{Quote|We (OpenAI) have reviewed progress in the field over the past six years. Our conclusion is near term AGI should be taken as a serious possibility.}}<ref>{{cite web |title=OpenAI Founder: Short-Term AGI Is a Serious Possibility |url=https://syncedreview.com/2018/11/13/OpenAI-founder-short-term-agi-is-a-serious-possibility/ |website=syncedreview.com |accessdate=15 June 2019}}</ref>
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| 2018 || {{Dts|November 19}} || {{w|Reinforcement learning}} || Partnership || OpenAI partners with {{w|DeepMind}} in a new paper that proposes a new method to train {{w|reinforcement learning}} agents in ways that enables them to surpass human performance. The paper, titled ''Reward learning from human preferences and demonstrations in Atari'', introduces a training model that combines human feedback and reward optimization to maximize the knowledge of RL agents.<ref>{{cite web |last1=Rodriguez |first1=Jesus |title=What’s New in Deep Learning Research: OpenAI and DeepMind Join Forces to Achieve Superhuman Performance in Reinforcement Learning |url=https://towardsdatascience.com/whats-new-in-deep-learning-research-OpenAI-and-deepmind-join-forces-to-achieve-superhuman-48e7d1accf85 |website=towardsdatascience.com |accessdate=29 June 2019}}</ref>
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| 2018 || {{dts|December 4}} || {{w|Reinforcement learning}} || Researh progress || 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>
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| 2018 || {{Dts|December 6}} || {{w|Reinforcement learning}} || Software release || OpenAI releases 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>
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| 2019 || {{Dts|February 14}} || {{w|Natural-language generation}} || Software release || OpenAI unveils its language-generating system called GPT-2, a system able to write news, answer reading comprehension problems, and shows 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> OpenAI initially tries to communicate the risk posed by this technology.<ref name="ssfr"/>
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| 2019 || {{dts|February 19}} || Safety || 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>
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| 2019 || {{dts|March 4}} || {{w|Reinforcement learning}} || 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>
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| 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}} || || Team || {{w|Sam Altman}} leaves his role as the president of {{w|Y Combinator}} to become the {{w|Chief executive officer}} of OpenAI.<ref>{{cite web |title=Sam Altman’s leap of faith |url=https://techcrunch.com/2019/05/18/sam-altmans-leap-of-faith/ |website=techcrunch.com |accessdate=24 February 2020}}</ref><ref>{{cite web |title=Y Combinator president Sam Altman is stepping down amid a series of changes at the accelerator |url=https://techcrunch.com/2019/03/08/y-combinator-president-sam-altman-is-stepping-down-amid-a-series-of-changes-at-the-accelerator/ |website=techcrunch.com |accessdate=24 February 2020}}</ref><ref name="orgwatch.issarice.com"/>
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| 2019 || {{Dts|April 23}} || {{w|Deep learning}} || 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>
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| 2019 || {{Dts|April 25}} || {{w|Neural network}} || 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>
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| 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>
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| 2019 || {{Dts|May}} || {{w|Natural-language generation}} || 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>
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| 2019 || {{dts|June 13}} || {{w|Natural-language generation}} || Coverage || Connor Leahy publishes article entitled ''The Hacker Learns to Trust'' which discusses the work of OpenAI, and particularly the potential danger of its language-generating system GPT-2. Leahy highlights: "Because this isn’t just about GPT2. What matters is that at some point in the future, someone will create something truly dangerous and there need to be commonly accepted safety norms before that happens."<ref name="ssfr">{{cite web |title=The Hacker Learns to Trust |url=https://medium.com/@NPCollapse/the-hacker-learns-to-trust-62f3c1490f51 |website=medium.com |accessdate=5 May 2020}}</ref>
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| 2019 || {{dts|July 22}} || || Partnership || OpenAI announces an exclusive partnership with {{w|Microsoft}}. As part of the partnership, Microsoft invests $1 billion in OpenAI, and OpenAI switches to exclusively using {{w|Microsoft Azure}} (Microsoft's cloud solution) as the platform on which it will develop its AI tools. Microsoft will also be OpenAI's "preferred partner for commercializing new AI technologies."<ref>{{cite web|url = https://OpenAI.com/blog/microsoft/|title = Microsoft Invests In and Partners with OpenAI to Support Us Building Beneficial AGI|date = July 22, 2019|accessdate = July 26, 2019|publisher = OpenAI}}</ref><ref>{{cite web|url = https://news.microsoft.com/2019/07/22/OpenAI-forms-exclusive-computing-partnership-with-microsoft-to-build-new-azure-ai-supercomputing-technologies/|title = OpenAI forms exclusive computing partnership with Microsoft to build new Azure AI supercomputing technologies|date = July 22, 2019|accessdate = July 26, 2019|publisher = Microsoft}}</ref><ref>{{cite web|url = https://www.businessinsider.com/microsoft-OpenAI-artificial-general-intelligence-investment-2019-7|title = Microsoft is investing $1 billion in OpenAI, the Elon Musk-founded company that's trying to build human-like artificial intelligence|last = Chan|first= Rosalie|date = July 22, 2019|accessdate = July 26, 2019|publisher = Business Insider}}</ref><ref>{{cite web|url = https://www.forbes.com/sites/mohanbirsawhney/2019/07/24/the-real-reasons-microsoft-invested-in-OpenAI/|title = The Real Reasons Microsoft Invested In OpenAI|last = Sawhney|first = Mohanbir|date = July 24, 2019|accessdate = July 26, 2019|publisher = Forbes}}</ref>
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| 2019 || {{dts|August 20}} || {{w|Natural-language generation}} || 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>
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| 2019 || {{dts|September 17}} || || Research progress || 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>
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| 2019 || {{dts|October 16}} || {{w|Neural network}}s || Research progress || 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}} || {{w|Natural-language generation}} || 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>
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| 2019 || {{dts|November 521}} || {{w|Reinforcement learning}} || Software release || OpenAI releases the largest version (1.5B parameters) Safety Gym, a suite of its language-generating system GPT-2 along with code environments and model weights to facilitate detection of outputs of GPT-2 modelstools for measuring progress towards {{w|reinforcement learning}} agents that respect safety constraints while training.<ref>{{cite web |title=GPT-2: 1.5B Release Safety Gym |url=https://openai.com/blog/gptsafety-2-1-5b-releasegym/ |website=openai.com |accessdate=5 April 2020}}</ref>
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| 2019 || {{dts|November 21December 3}} || {{w|Reinforcement learning}} || Software release || OpenAI releases Safety GymProcgen Benchmark, a suite 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 tools for measuring progress towards BossFight) which provide a direct measure of how quickly a {{w|reinforcement learning}} agents that respect safety constraints while trainingagent learns generalizable skills. Procgen Benchmark prevents AI model overfitting.<ref>{{cite web |title=Safety Gym Procgen Benchmark |url=https://openai.com/blog/safetyprocgen-gymbenchmark/ |website=openai.com |accessdate=5 April 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>
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| 2019 || {{dts|December 34}} || || Software release Publication || OpenAI releases Procgen Benchmark, a set of 16 simple-"Deep Double Descent: Where Bigger Models and More Data Hurt" is submitted 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 the {{w|reinforcement learningArXiv}} agent learns generalizable skills. Procgen Benchmark prevents AI The paper shows that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as the model overfittingsize 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=Procgen Benchmark 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> The paper is summarized on the OpenAI blog.<ref>{{cite web|url=https://openai.com/blog/procgendeep-double-benchmarkdescent/ |websitetitle = Deep Double Descent|publisher = OpenAI|date =openai.com December 5, 2019|accessdate=2 March May 23, 2020}}</ref>MIRI researcher Evan Hubinger writes an explanatory post on the subject on LessWrong and the AI Alignment Forum,<ref>{{cite web |title=OpenAI’s Procgen Benchmark prevents AI model overfitting |url=https://venturebeatwww.lesswrong.com/2019posts/12FRv7ryoqtvSuqBxuT/03/openaisunderstanding-procgendeep-benchmarkdouble-overfitting/ descent|title = Understanding “Deep Double Descent”|websitedate =venturebeat.com December 5, 2019|accessdate=2 March 24 May 2020|publisher = LessWrong|last = Hubinger|first = Evan}}</ref>and follows up with a post on the AI safety implications.<ref>{{cite web |title=GENERALIZATION IN REINFORCEMENT LEARNING – EXPLORATION VS EXPLOITATION |url=https://analyticsindiamagwww.lesswrong.com/generalizationposts/nGqzNC6uNueum2w8T/inductive-inbiases-reinforcementstick-learning-exploration-vs-exploitation/ around|title = Inductive biases stick around|websitedate =analyticsindiamag.com December 18, 2019|accessdate=2 March 24 May 2020|last = Hubinger|first = Evan}}</ref>
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| 2019 || {{dts|December}} || || Team || Dario Amodei is promoted as OpenAI's Vice President of Research.<ref name="Dario Amodeiy">{{cite web |title=Dario Amodei |url=https://www.linkedin.com/in/dario-amodei-3934934/ |website=linkedin.com |accessdate=29 February 2020}}</ref>
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| 2020 || {{dts|January 30}} || {{w|Deep learning}} || Software adoption || OpenAI announces migration to the social network’s {{w|PyTorch}} {{w|machine learning}} framework in future projects, setting it as its new standard deep learning framework.<ref>{{cite web |title=OpenAI sets PyTorch as its new standard deep learning framework |url=https://jaxenter.com/OpenAI-pytorch-deep-learning-framework-167641.html |website=jaxenter.com |accessdate=23 February 2020}}</ref><ref>{{cite web |title=OpenAI goes all-in on Facebook’s Pytorch machine learning framework |url=https://venturebeat.com/2020/01/30/OpenAI-facebook-pytorch-google-tensorflow/ |website=venturebeat.com |accessdate=23 February 2020}}</ref>|-| 2020 || {{dts|February 5}} || Safety || Publication || Beth Barnes and Paul Christiano on <code>lesswrong.com</code> publish ''Writeup: Progress on AI Safety via Debate'', a writeup of the research done by the "Reflection-Humans" team at OpenAI in third and fourth quarter of 2019.<ref>{{cite web |title=Writeup: Progress on AI Safety via Debate |url=https://www.lesswrong.com/posts/Br4xDbYu4Frwrb64a/writeup-progress-on-ai-safety-via-debate-1#Things_we_did_in_Q3 |website=lesswrong.com |accessdate=16 May 2020}}</ref>
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| 2020 || {{dts|February 17}} || || Coverage || AI reporter Karen Hao at ''MIT Technology Review'' publishes review on OpenAI titled ''The messy, secretive reality behind OpenAI’s bid to save the world'', which suggests the company is surrendering its declaration to be transparent in order to outpace competitors. As a response, {{w|Elon Musk}} criticizes OpenAI, saying it lacks transparency.<ref name="Aaron">{{cite web |last1=Holmes |first1=Aaron |title=Elon Musk just criticized the artificial intelligence company he helped found — and said his confidence in the safety of its AI is 'not high' |url=https://www.businessinsider.com/elon-musk-criticizes-OpenAI-dario-amodei-artificial-intelligence-safety-2020-2 |website=businessinsider.com |accessdate=29 February 2020}}</ref> On his {{w|Twitter}} account, Musk writes "I have no control & only very limited insight into OpenAI. Confidence in Dario for safety is not high", alluding to OpenAI Vice President of Research Dario Amodei.<ref>{{cite web |title=Elon Musk |url=https://twitter.com/elonmusk/status/1229546206948462597 |website=twitter.com |accessdate=29 February 2020}}</ref>
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===How the timeline was built===
The initial version of the timeline was written by [[User:Issa|Issa Rice]]. It has been expanded considerably by [[User:Sebastian|Sebastian]].
{{funding info}} is available.
===What the timeline is still missing===
 
* [https://intelligence.org/blog/]
* [https://www.lesswrong.com/posts/Br4xDbYu4Frwrb64a/writeup-progress-on-ai-safety-via-debate-1]
===Timeline update strategy===
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