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Timeline of OpenAI

15 bytes added, 17:57, 15 May 2020
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| 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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