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General AI Charts The Promise of Artificial Intelligence – Again! David Gunning Information Innovation Office (I2O) Defense Advanced Research Projects Agency (DARPA) MIT Washington Seminar Series Artificial Intelligence and Machine Learning Tuesday, October 10, 2017 Approved for Public Release, Distribution Unlimited AI in the News Autonomous Vehicles Image Understanding Language Translation / / / ://www.cbsnews.com CBS Interactive CBS Inc. www.engadget.com :// ://www.roboticstrends.com 2017OathTech Network Aol Tech http Trends Robotics ©2017 Adapted/https https © ©2014 ©2014 >6000 miles without Facebook has 98% Google Pixel Buds real-time operator intervention accuracy translation Approved for Public Release, Distribution Unlimited 2 Commercial R&D ) ) DIUx Experimental ( Experimental - Source: DefenseInnovation Unit Startups Facebook Apple Google Amazon Approved for Public Release, Distribution Unlimited 3 Global Interest in AI Russia China China U.S. / / ©2017 Newsline / Newsline ©2017 TechnologyReview ://www.technologyreview.com ©2017 MIT MIT ©2017 https https://newsline.com “Artificial intelligence is the future, not Research papers published on deep learning only for Russia, but for all humankind. (2012-2016) Whoever becomes the leader in this sphere will become the ruler of the world.” Vladimir Putin – 4 SEP, 2017 Approved for Public Release, Distribution Unlimited 4 Three Waves of AI DESCRIBE PREDICT EXPLAIN Symbolic Reasoning Statistical Learning Contextual Adaptation Engineers create sets of Engineers create Engineers create logic rules to represent statistical models for systems that construct knowledge in limited specific problem explanatory domains domains and train them models for classes of on big data real world phenomena Reasoning over narrowly Nuanced classification Natural communication defined problems and prediction among machines and capabilities people No learning capability No contextual capability Systems learn and and poor handling of and minimal reasoning reason as they uncertainty ability encounter new tasks and situations Approved for Public Release, Distribution Unlimited 5 DARPA Contributions to AI 1960s 1970s 1980s 1990s 2000s 2010s L2M XAI Wave rd CwC 3 Bio-Inspired Deep Big Cog. Arch. Learning Mechanism ANN PAL & IPTO Mind’s Eye Machine PPAML TIPTER, TREC & Link Machine Wave Learning MUC: Image Understanding Discovery Initiative Reading nd Speech Birth of data- 2 driven, Statistical Understanding TIDES GALE BOLT/DEFT/RATS D3M Research (SUR): Speech & Language Language First use of HMM Understanding Understanding DARPA Grand DARPA Robotics I2O Data Project MAC: Autonomous ARPI Planning Challenges Challenges Analytics • MIT, Stanford, & DENDRAL, Command Land Vehicle Initiative Post of the CMU AI Labs MYCIN xDATA QCR • Fleet Command Future Wave Chess, Theorem EXPERT DART Proving, General SYSTEMS Center Management st HACMS 1 Problem Solving Knowledge Knowledge Base DAML & • Shaky the robot Pilot’s Associate Sharing Programs Semantic Web Bob Taylor, Bob Kahn & Steve J. Licklider & the ISO/ Tony Tether Larry Strategic Cross & I2O Original IPTO ITO & IPTO DARPA Roberts Computing SISTO Approved for Public Release, Distribution Unlimited 6 https://www.globalresearch.ca/ Copyright © 2005-2017 GlobalResearch.ca Engineers create sets of The1 st Wave AI of Wave Approved for Public Release, Distribution Unlimited rules to www.mjc2.com © 2015 MJC2 Symbolic Reasoning Symbolic represent knowledge well in represent - defined domains https://turbotax.intuit.com/ ©1997-2017 Intuit, Inc. 7 http://awards.acm.org/ © 2017, ACM, IncInc. MathematicalLogic John McCarthy Stanford Founders of the 1 the of Founders http://awards.acm.org/ © 2017, ACM, Inc. AllenNewell and Approved for Public Release, Distribution Unlimited Carnegie Production Rules st Wave Herbert Herbert http://awards.acm.org/ © 2017, ACM, Inc. Mellon Simon http://awards.acm.org/ © 2017, ACM, IncInc. FramesSociety Marvin Marvin MIT Minsky ofMind 8 Expert Systems (1970s) DEC R1 Commercially-successful rule- based AI “expert system” ://www.shortliffe.net/ http Approved for Public Release, Distribution Unlimited 9 Strategic Computing Initiative (1980s) Autonomous Land Vehicle Pilot’s Associate http://www.tested.com/ Approved for Public Release, Distribution Unlimited 10 Cyc Knowledge Base (KB) (1985 - Today) The Cyc KB is a formalized The Cyc KB contains: representation of a vast • 500K terms quantity of fundamental • 17K relations human knowledge • 7M assertions https://www.cs.us.es/ Approved for Public Release, Distribution Unlimited 11 The 2nd Wave of AI Statistical Learning / ://www.cbsnews.com thrilllist.com CBS InteractiveCBS Inc. Source: ©2014 ©2014 Adapted/https Engineers create statistical models for specific problem domains and train them on big data Approved for Public Release, Distribution Unlimited 12 TIPSTER Text Understanding Program (1990s) Text Retrieval Evaluation Conference The TREC Corpus (TREC) ://www.cs.bilkent.edu.tr/ http http://slideplayer.com/ Approved for Public Release, Distribution Unlimited 13 Statistical Language Learning (1990s) • Statistical language processing • Word tagging • Parsing with probabilistic grammars • Grammar induction • Syntactic disambiguation • Semantic word classes • Word-sense disambiguation 2017 The 2017 The MIT Press / © ://mitpress.mit.edu https Approved for Public Release, Distribution Unlimited 14 DARPA AI Under Tony Tether (2000s) Cognitive Computing DARPA Grand Challenges DARPA Autonomous Vehicle Grand Challenge 140 miles of dirt tracks in California and Nevada Approved for Public Release, Distribution Unlimited 15 Key Enablers of 2nd Wave AI Better machine-learning algorithms Big data and cheap storage Power-efficient processing Extensive industrial opportunities https://quid.com/insights/the-future-of-artificial-intelligence Patents in Machine Learning 2004 - 2013 Approved for Public Release, Distribution Unlimited 16 Machine Learning Techniques (2000s to Today) Approved for Public Release, Distribution Unlimited 17 Deep Learning Breakthrough (2012) ImageNet © 1987 – 2017 Neural Information Processing Systems Foundation, Inc. Krizhevsky, A., Sutskever, I. & Hinton, G. ImageNet classification with deep convolutional neural networks. In Proc. Advances in Neural Information Processing Systems 25 1090–1098 (2012). cs.stanford.edu/ This report was a breakthrough that used :// http convolutional nets to almost halve the error rate for object recognition, and precipitated the rapid adoption of deep learning by the computer vision community Approved for Public Release, Distribution Unlimited 18 Deep Learning Architecture Each “feature map” performs a local Fully-connected layers analysis over the whole input space perform global analysis convolutions subsampling 0 1 approx. 30,000 8 cells in total for 9 >99.5% accuracy input 20 feature maps 12x12 1000 each 24x24 feature maps 28x28 fully convolutions subsampling connected Machine-learning “programmers” design the network structure with experience and by trial and error Approved for Public Release, Distribution Unlimited 19 Neural Nets are Trained with Data Computed outputs Data inputs non-linear cell weights cell inputs function (learned) (from previous layer) = POS (SUMPRODUCT( W1:W16, V1:V16)) Approved for Public Release, Distribution Unlimited 20 Image Captioning Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Deep Learning, Nature, Vol. 521, (pp. 436‐444). Deep learning, Y. LeCun, Y. Bengio, G. Hinton - Nature, May 2015 http://www.nature.com/ © 2015 Macmillan Publishers Limited Approved for Public Release, Distribution Unlimited 21 Classification of Skin Cancer Example images processed by the CNN The deep learning CNN outperformed the average of the dermatologists at skin cancer classification Dermatologist-level classification of skin cancer with deep neural networks, A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau & S. Thrun - Nature, February 2017 http://www.nature.com/ © 2015 Macmillan Publishers Limited Approved for Public Release, Distribution Unlimited 22 http://www.nature.com/ © 2016 Macmillan Publishers Limited AlphaGo Approved for Public Release, Distribution Unlimited 23 DoD Applications Data Analytics and Machine Learning Anti-submarine warfare Continuous for Intelligence Analysis Trail Unmanned Vessel (ACTUV) ://geimint.blogspot.com/ Google http Approved for Public Release, Distribution Unlimited 24 Challenges with 2nd Wave AI “Panda” <1% targeted “Gibbon” distortion (99.3% confidence) + = www.tensorflow.org www.tensorflow.org Manifold separation process can be exploited Approved for Public Release, Distribution Unlimited 25 Challenges with 2nd Wave AI Statistically impressive, but individually unreliable Fei - Fei , Li , Karpathy a young boy is holding a Credit: Andrej baseball bat Approved for Public Release, Distribution Unlimited 26 Andrew Ng on the State of AI When asked what can today’s AI systems do, Andrew responds: “Anything a human can do in less than a second” 2017 Coursera 2017 Coursera Inc. Today’s AI systems, especially with / © deep learning, are solving the machine perception problem ://www.coursera.org https Approved for Public Release, Distribution Unlimited 27 The 3rd Wave of AI perceive abstract explainable model learn reason Approved for Public Release, Distribution Unlimited 28 The Need for Explainable AI AI System Watson AlphaGo User ©IBM ©Marcin Bajer/Flickr • We are entering a new • Why did you do that? age of AI applications Sensemaking Operations • Why not something else? • Machine learning is the • When
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