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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>
| 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>
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| 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"/>
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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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| 2017 || {{dts|January}} || || Staff || Paul Christiano joins OpenAI to work on AI alignment.<ref>{{cite web |url=https://paulfchristiano.com/ai/ |title=AI Alignment |date=May 13, 2017 |publisher=Paul Christiano |accessdate=May 6, 2018}}</ref> He was previously an intern at OpenAI in 2016.<ref>{{cite web |url=https://blog.openai.com/team-update/ |publisher=OpenAI Blog |title=Team Update |date=March 22, 2017 |accessdate=May 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>
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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>
| 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 || {{dtsDts|May 162}} || safety || Publication || OpenAI releases an analysis showing that since 2012The paper "AI safety via debate" by Geoffrey Irving, Paul Christiano, and Dario Amodei is uploaded to the amount of compute used arXiv. The paper proposes training agents via self play on a zero sum debate game, in the largest AI training runs has been increasing exponentially with order to adress tasks that are too complicated for a 3.4-month doubling timehuman to directly judge.<ref>{{cite web |url=https://arxiv.org/abs/1805.00899 |title=[1805.00899] AI and Compute safety via debate |accessdate=May 5, 2018}}</ref><ref>{{cite web |url=https://openaiblog.OpenAI.com/blog/ai-and-computedebate/ |websitepublisher=OpenAI Blog |title=AI Safety via Debate |date=May 3, 2018 |first1=Geoffrey |last1=Irving |first2=Dario |last2=openai.com Amodei |accessdate=May 5 April 2020, 2018}}</ref>
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| 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>|-| 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|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>
| 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>
| 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>
| 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>
| 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>
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| 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 21}} || {{w|Reinforcement learning}} || 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>
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| 2019 || {{dts|December 3}} || {{w|Reinforcement learning}} || 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>
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| 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> The paper is summarized on the OpenAI blog.<ref>{{cite web|url = https://openai.com/blog/deep-double-descent/|title = Deep Double Descent|publisher = OpenAI|date = December 5, 2019|accessdate = 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|url = https://www.lesswrong.com/posts/FRv7ryoqtvSuqBxuT/understanding-deep-double-descent|title = Understanding “Deep Double Descent”|date = December 5, 2019|accessdate = 24 May 2020|publisher = LessWrong|last = Hubinger|first = Evan}}</ref> and follows up with a post on the AI safety implications.<ref>{{cite web|url = https://www.lesswrong.com/posts/nGqzNC6uNueum2w8T/inductive-biases-stick-around|title = Inductive biases stick around|date = December 18, 2019|accessdate = 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>
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| 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>|-| 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===
62,430
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