Deep Learning, the Engine of the AI Revolution

Deep Learning, the Engine of the AI Revolution

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  Deep Learning The engineof the AI revolution Rodrigo Agundez @rragundez September 2018
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  2016 2016 2015 2011 2010 2008 Data Maverick -GoDataDriven Data Scientist - Qualogy Ph.D. Theoretical Physics – TU Delft M.Sc. Nanoscience – TU Delft M.Sc. Nanotechnology – KU Leuven Physics - UABC
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  Deep Learning Initiatives
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  ? ~2007~1995 ? Computer InternetMobile ?
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  ~2007~1995 ~2018 Computer InternetMobile AI
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  AI
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  AI AlgorithmData Prediction The predictionresembles human intelligence The origin of the algorithm is not important Artificial Intelligence
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  ML
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  AI ML Learning Algorithm Data ModelNew Data Prediction ArtificialIntelligence Machine Learning
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  DL
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  AI Data ModelNew Data Prediction DLDeepLearning Artificial Intelligence MLMachine Learning MLMachine Learning Data
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  and not MLnor DL Artificial Intelligence AI
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  and not DL MLMachineLearning
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  Deep Learning DL
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  ML vs DL Traditional
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  Feature Extraction
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  Learning Algorithm Data ModelNew Data Prediction FeatureExtraction Feature Extraction ML Traditional
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  Data ModelNew Data Prediction DL FeatureExtraction
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  2006 2008 20102012 2014 2016 2018 2020 2022 ... Performance Unstructured data Clicks Images Text Logs Audio Video Reviews Calls Structured data Big DataRemember?
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  Amount of Data Performance TraditionalML Deep Learning ML vs DL Traditional
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  TraditionalML vs DL
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  TraditionalML vs DL
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  ➔ 1958 Percentronunit - Frank Rosenblatt ➔ 1986 Backpropagation - Geoffrey Hinton ➔ 1986 RNN - Schuster & Pallwal ➔ 1989 LeNet Backpropagation to multi-layer perceptron - Yan LeCun ➔ 1997 LSTM - Sepp Hochreiter and Jürgen Schmidhuber ➔ 1998 LeNet-5 Convolutional neural networks - YanLecun ➔ 2009 GPU for deep learning - Andrew Ng ➔ 2011 Demonstration of ReLu for deep neural networks - Yoshua Bengio ➔ 2012 AlexNet wins ImageNet ➔ 2012 Dropout technique - Geoffrey Hinton ➔ 2014 Generative adversarial networks - Ian Goodfellow & Yoshua Bengio ➔ 2015 CNN beats human error in ImageNet ➔ 2016 AlphaGo - Goole DeepMind ➔ 2016 Detectic metatastic cancer beats human pathologist ➔ 2017 Capsule networks - Geoffrey Hinton
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  Meta Learning Transfer Learning Deep Reinforcement Learning Supervised Learning Unsupervised Learning Semi- supervised Learning Deep Learning
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  Meta Learning Transfer Learning Deep Reinforcement Learning Supervised Learning Unsupervised Learning Semi- supervised Learning Deep Learning
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  Meta Learning Transfer Learning Deep Reinforcement Learning Supervised Learning Unsupervised Learning Semi- supervised Learning Deep Learning
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  Meta Learning Transfer Learning Deep Reinforcement Learning Supervised Learning Unsupervised Learning Semi- supervised Learning Deep Learning
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  Meta Learning Transfer Learning Deep Reinforcement Learning Supervised Learning Unsupervised Learning Semi- supervised Learning Deep Learning
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  Meta Learning Transfer Learning Deep Reinforcement Learning Supervised Learning Unsupervised Learning Semi- supervised Learning Deep Learning
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  Meta Learning Transfer Learning Deep Reinforcement Learning Supervised Learning Unsupervised Learning Semi- supervised Learning Deep Learning
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  2016 Commercial success Transfer learning Supervised learning Unsupervisedlearning Reinforcement learning Andrew Ng, NIPS 2016 Drivers of success in industry
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  Meta Learning Transfer Learning Deep Reinforcement Learning Supervised Learning Unsupervised Learning Semi- supervised Learning Deep Learning convolutional neural networks Autoencoders Attention models Policy networks Value networks Deep Q-learning Long-short term memory . .. Gated recurrent units Generative adversarial networks Multi-layer perceptron Recurrent neural networks � � � � � � � �� � �� �
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  Data ProductUsers
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  Data ProductUsers
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  Data ProductUsers
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  Data ProductUsers
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  Data ProductUsers Positive feedback loop
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  Data ProductUsers Positive feedback loop
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  Otober, 2016 March,2018 January, 2018 March, 2017April, 2018May, 2017
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  THANK YOU