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1 Carnegie Mellon University, University of Pittsburgh
2 Carnegie Mellon University, University of Pittsburgh Artificial Intelligence (AI) and Deep Learning (DL) Overview Paola Buitrago Leader AI and BD Pittsburgh Supercomputing Center ECSS Symposium 19 February 2018
3 Outline Definition of AI and DL History/ Milestones Academia Course Enrollment Conferences Academic Production Open Source Tools Technical Performance Vision Natural Language Processing Theorem Proving Challenges and opportunities 3
4 AI and DL Definition 4
5 AI and DL What s AI? Activity devoted to making machines intelligent Intelligence Quality that enables an entity to function appropriately and with foresight in its environment 5
6 AI and DL What s AI? Activity devoted to making machines intelligent Intelligence Quality that enables an entity Including: Logistic regression Knowledge databases Naive Bayes to function appropriately and with foresight in its environment Classification trees Random forest K-means 6
7 AI and DL - What s DL? [Ref1] 7
8 AI and DL - What s DL? [Ref1] 8
9 AI and DL - Representation Learning - Deep Learning Model [Ref2] 9
10 AI and DL What s AI? Activity devoted to making machines intelligent Intelligence Quality that How does it relate to DL? enables an entity to function appropriately and with foresight in its environment Deep Learning Representation Learning Machine Learning Artificial Intelligence 10
11 AI Milestones 11
12 AI Milestones Skin Cancer Othello Checkers Chess Jeopardy! Atari Imagenet Go Switchboard Poker Pac-Man [Ref3] 12
13 AI Milestones Skin Cancer Othello Checkers Chess Jeopardy! Atari Imagenet Go Switchboard Poker Pac-Man Poker: Libratus CMU Powered by Bridges NIPS2017 Best paper award Science paper 13
14 AI in Academia 14
15 AI in Academia - Course Enrollment [Ref3] 15
16 AI in Academia - Course Enrollment Bridges support of large ML courses Spring Deep Reinforcement Learning and Control: ~200 students Spring Deep Reinforcement Learning and Control: ~500 students 1536 GPU Utilization for CMU , Deep Reinforcement Learning and Control, Spring Homework 1: Implement Q-learning using deep learning function approximators in OpenAI Gym. Homework 2: Implement Linear Quadratic Regulation (LQR) and iterative Linear Quadratic Regulator (ilqr) K80-hours P100-hours 1024 GPU hours per day /01/17 3/08/17 3/15/17 3/22/17 3/29/17 4/05/17 4/12/17 4/19/17 4/26/17 5/03/17 5/10/17 5/17/17 5/24/17 5/31/17 Date [Ref4] 16
17 AI in Academia - Main Conferences Main conferences NIPS - Neural Information Processing Systems - NIPS Dec 2018, Montreal, Canada CVPR - Conference on Computer Vision and Pattern Recognition - CVPR Jun 2018, Salt Lake City, USA ICML - International Conference on Machine Learning - ICML Jul 2018, Stockholm, Sweden ICRA - International Conference on Robotics and Automation - ICRA May 2018, Brisbane, Australia IJCAI -International Joint Conference on Artificial Intelligence - IJCAI Jul 2018, Stockholm, Sweden ACL - Association for Computational Linguistics - ACl Jul 2018, Melbourne, Australia AAAI - Association for the Advancement of Artificial Intelligence (conference) - AAAI Feb 2018, New Orleans, USA 17
18 AI in Academia - Conference Attendance [Ref3] 18
19 AI in Academia - AI Published Papers [Ref3] 19
20 AI in Academia - AI Published Papers [Ref3] 20
21 AI in Academia AI/ML Published Papers by Organization [Ref4] 21
22 AI in Academia - AI/ML Published Papers to 2017 by Organization [Ref4] 22
23 AI Frameworks 23
24 AI Platforms - Open Source Software [Ref3] 24
25 AI Platforms - Open Source Software [Ref3] 25
26 AI Technical Performance 26
27 AI Technical Performance - Vision Object Detection Large Scale Visual Recognition Challenge Steel drum Image Classification Task Output: Scale T-shirt Steel drum Drumstick Mud turtle Output: Scale T-shirt Giant panda Drumstick Mud turtle 1000 object classes images 27
28 AI Technical Performance - Vision Object Detection Large Scale Visual Recognition Challenge Classification Results (CLS) Classification Error [Ref1] 28
29 AI Technical Performance - Vision Object Detection [Ref3] 29
30 AI Technical Performance - Vision Visual Question Answering [Ref3] 30
31 AI Technical Performance - Natural Language Processing Machine Translation [Ref3] 31
32 AI Technical Performance - Natural Language Processing Question Answering [Ref3] 32
33 AI Technical Performance - Natural Language Processing Speech Recognition [Ref3] 33
34 Summary 34
35 Summary - AI Challenges and Opportunities Computational complexity Larger and larger data. Big Data challenges. Cycles to train: Model and data parallelism ( Architectural decisions HW/SW AI model Data architecture Adoption Bring new fields to explore AI techniques Diversity Include everyone s voice 35
36 Q&A 36
37 References Some of the material and slides for this lecture were borrowed from: [Ref1] - Ruslan Salakhutdinov class on Deep Learning at CMU (Fall 2017). Material available at [Ref 2] - Deep Learning Book (2016). Ian Goodfellow and Yoshua Bengio and Aaron Courville [Ref 3] - Artificial Intelligence Index Organization. Material available at [Ref 4] - ML and NL publications by Marek Rei. Material available at 37
38 Recommended Resources Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016) Deep Learning Book Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer. Machine Learning: A Probabilistic Perspective, by Kevin P. Murphy. Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009) The Elements of Statistical Learning David MacKay (2003) Information Theory, Inference, and Learning Algorithms 38
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