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Timeline of OpenAI

58 bytes added, 08:22, 24 May 2020
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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.
| 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 = Decemmber 23, 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 23, 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>
===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.
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