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Timeline of machine learning

251 bytes added, 12:08, 25 February 2020
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| 2007 || || || {{w|scikit-learn}} is released in June.
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| 2009 (April 7) || || Software release || {{w|Apache Mahout}} is first released.
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| 2010 || || Kaggle Competition || [[wikipedia:Kaggle|Kaggle]], a website that serves as a platform for machine learning competitions, is launched.<ref>{{cite web|title=About|url=https://www.kaggle.com/about|website=Kaggle|publisher=Kaggle Inc|accessdate=16 June 2016}}</ref>
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| 2014 || || Leap in Face Recognition || [[wikipedia:Facebook|Facebook]] researchers publish their work on [[wikipedia:DeepFace|DeepFace]], a system that uses neural networks that identifies faces with 97.35% accuracy. The results are an improvement of more than 27% over previous systems and rivals human performance.<ref>{{cite journal|last1=Taigman|first1=Yaniv|last2=Yang|first2=Ming|last3=Ranzato|first3=Marc’Aurelio|last4=Wolf|first4=Lior|title=DeepFace: Closing the Gap to Human-Level Performance in Face Verification|journal=Conference on Computer Vision and Pattern Recognition|date=24 June 2014|url=https://research.facebook.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/|accessdate=8 June 2016}}</ref> "Facebook develops DeepFace, a software algorithm that is able to recognize or verify individuals on photos to the same level as humans can."<ref name="forbes.com"/> "DeepFace was a deep neural network created by Facebook, and they claimed that it could recognize a person with the same precision as a human can do."<ref name="javatpoint.comu"/>
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| 2014 (May 26) || || Software release || {{w|Apache Spark}} is first released.
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| 2014 || || Sibyl || Researchers from [[wikipedia:Google|Google]] detail their work on Sibyl,<ref>{{cite web|last1=Canini|first1=Kevin|last2=Chandra|first2=Tushar|last3=Ie|first3=Eugene|last4=McFadden|first4=Jim|last5=Goldman|first5=Ken|last6=Gunter|first6=Mike|last7=Harmsen|first7=Jeremiah|last8=LeFevre|first8=Kristen|last9=Lepikhin|first9=Dmitry|last10=Llinares|first10=Tomas Lloret|last11=Mukherjee|first11=Indraneel|last12=Pereira|first12=Fernando|last13=Redstone|first13=Josh|last14=Shaked|first14=Tal|last15=Singer|first15=Yoram|title=Sibyl: A system for large scale supervised machine learning|url=https://users.soe.ucsc.edu/~niejiazhong/slides/chandra.pdf|website=Jack Baskin School Of Engineering|publisher=UC Santa Cruz|accessdate=8 June 2016}}</ref> a proprietary platform for massively parallel machine learning used internally by Google to make predictions about user behavior and provide recommendations.<ref>{{cite news|last1=Woodie|first1=Alex|title=Inside Sibyl, Google’s Massively Parallel Machine Learning Platform|url=http://www.datanami.com/2014/07/17/inside-sibyl-googles-massively-parallel-machine-learning-platform/|accessdate=8 June 2016|work=Datanami|publisher=Tabor Communications|date=17 July 2014}}</ref>
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| 2016 || Software || FBLearner Flow || Facebook details FBLearner Flow, an internal software platform that allows Facebook software engineers to easily share, train and use machine learning algorithms.<ref>{{cite web|last1=Dunn|first1=Jeffrey|title=Introducing FBLearner Flow: Facebook's AI backbone|url=https://code.facebook.com/posts/1072626246134461/introducing-fblearner-flow-facebook-s-ai-backbone/|website=Facebook Code|publisher=Facebook|accessdate=8 June 2016|date=10 May 2016}}</ref> FBLearner Flow is used by more than 25% of Facebook's engineers, more than a million models have been trained using the service and the service makes more than 6 million predictions per second.<ref>{{cite news|last1=Shead|first1=Sam|title=There's an 'AI backbone' that over 25% of Facebook's engineers are using to develop new products|url=http://www.businessinsider.com.au/over-a-quarter-of-facebooks-employees-are-using-fblearner-flow-2016-5?r=UK&IR=T|accessdate=8 June 2016|work=Business Insider|publisher=Allure Media|date=10 May 2016}}</ref>
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| 2017 (April 25) || || Software release || {{w|Shogun (toolbox)}} is released.
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| 2017 || || || "In 2017, the Alphabet's Jigsaw team built an intelligent system that was able to learn the online trolling. It used to read millions of comments of different websites to learn to stop online trolling."<ref name="javatpoint.comu"/> "As part of its anti-harassment efforts, Alphabet’s Jigsaw team built a system that learned to identify trolling by reading millions of website comments. The underlying algorithms could be a huge help for sites with limited resources for moderation"<ref name="cloud.withgoogle.com"/>
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