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ODSC Presentation "Putting Deep Learning to Work" by Alex Ermolaev, Nvidia

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We will look at the best practices for using deep learning as well as most popular use cases across several horizontal and vertical domains.
Open Data Science Conference West, San Francisco, November 2-4, 2017

Published in: Engineering
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ODSC Presentation "Putting Deep Learning to Work" by Alex Ermolaev, Nvidia

  1. 1. Alex Ermolaev PUTTING DEEP LEARNING TO WORK OPEN DATA SCIENCE CONFERENCE San Francisco, November 2-4, 2017
  2. 2. 2 HUMAN INTELLIGENCE
  3. 3. 3 MACHINE INTELLIGENCE
  4. 4. 4 AI SYSTEMSTRADITIONAL ‘AI SYSTEMS’ = ‘LEARNING SYSTEMS’ Changing its code to improve results Probabilistic Potential for more general purpose Pre-programmed to do same thing every time Deterministic One time use and limited purpose
  5. 5. 5 AI TECHNOLOGIES OVER TIME 1960s 1980s 1990s 2000s 2010s Cybernetics, Control Systems Expert Systems, AT&T, IBM Data Mining, OCR Page rank Machine learning, Netflix prize, PayPal Deep Learning
  6. 6. 6 AI ERROR RATES COLLAPSED AFTER 2012 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 1990 1992 1995 1997 2000 2002 2005 2007 2010 2012 2015 2017 Source: Microsoft Corp Speech Recognition Accuracy
  7. 7. 7 DEEP LEARNING BIG BANG NIPS (2012) ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto Ilya Sutskever University of Toronto Geoffrey e. Hinton University of Toronto Launched from Alex Krizhevsky’s bedroom
  8. 8. 8 GPU COMPUTING AT THE HEART OF AI Performance Beyond Moore’s Law
  9. 9. 9 GPU DEEP LEARNING IS A NEW COMPUTING MODEL Training Device Datacenter TRAINING Billions of Trillions of Operations GPU train larger models, accelerate time to market 10s of billions of image, voice, video queries per day GPU inference for fast response, maximize datacenter throughput DATACENTER INFERENCING Billions of intelligent devices & machines Recognition, reasoning, problem solving GPU inference: real-time accurate response DEVICE INFERENCING
  10. 10. 10 MORE DATA + BIGGER MODEL + MORE COMPUTE = BETTER RESULT 2012 AlexNet 8 Layers 1.4 GFLOP ~16% Error 152 Layers 22.6 GFLOP ~3.5% Error 2015 ResNet 16X Model IMAGE RECOGNITION SPEECH RECOGNITION 2014 Deep Speech 1 80 GFLOP 7,000 hrs of Data ~8% Error 465 GFLOP 12,000 hrs of Data ~5% Error 2015 Deep Speech 2 10X Training Ops
  11. 11. 11 “ Methods that scale with computation are the future of AI.” Richard Sutton, University of Alberta/Google DeepMind
  12. 12. 12 THE ECONOMICS OF AI Source: Kindred
  13. 13. 13 “Mobile computing, inexpensive sensors collecting terabytes of data and the rise of machine learning that can use that data will fundamentally change the way the global economy is organized.” Cloud Services Manufacturing Transportation Healthcare AI IS TRANSFORMING EVERY INDUSTRY Deep Learning Innovation at Unprecedented Pace
  14. 14. 14 EVERY INDUSTRY HAS AWOKEN TO AI 2014 2016 1,549 19,439 Higher Ed Internet Healthcare Finance Automotive Others Government Developer Tools Organizations engaged with NVIDIA on Deep Learning
  15. 15. 15 WHEN TO APPLY DEEP LEARNING? Requirements for Successful Project PROBLEM: big, core, expensive PATTERN: existing, complex DATA: labeled, more data = better model TEAM: data science lead, mixed skills INFRASTRUCTURE: efficient compute
  16. 16. 16 Vision Title hereSound NLP Cyber Title hereFraud Robotics Prediction Title hereRiskRecommender DEEP LEARNING USE CASES Where to apply DL?
  17. 17. 17 DEEP LEARNING FOR VISION Beyond Cats eCommerce Satellite Agriculture Visual Search for eCommerce Carbon monitoring from satellite images LettuceBot only spray weeds
  18. 18. 18 DEEP LEARNING FOR HEALTHCARE Saving Lives Electronic health records Pathology Radiology If >$5000 Charges in time < 1 week in > 5 zip codes Then fraud Electronic health record – predicting future patients Recognizing cancer patterns Bone age assessment in pediatrics
  19. 19. 19 CYBER: MALWARE DETECTION The Unsolved Problem Malware is #1 cyber problem Why DL for Malware? Malware Detection Accuracy If >$5000 Charges in time < 1 week in > 5 zip codes Then fraud WannaCry created by NSA - stolen – becomes ransomware - 300,000 computers locked Old/Known Malware Deep Learning Older ML Signiture -based New/Unknown Malware Most vendors . . . . . . . . . . 99% New malware samples reported to VirusTotal: 1M per Day New computer vulnerabilities found: Few per Year
  20. 20. 20 Body/bullet text no longer has a bullet icon Use 20 pt font No sub-bullets allowed No more than five bullets; one idea per bullet Example of highlighted text Subtitle: 24 pt, one line maximum
  21. 21. 21 FRAUD DETECTION The Evolution of AI Algorithms Rule-based Analytics Machine Learning Deep Learning If >$5000 Charges in time < 1 week in > 5 zip codes Then fraud Low Accuracy High False Alarms 70-85% Accuracy Acceptable False Positives Promises >90% Accuracy Lowest False Positives Automated Feature Extraction
  22. 22. 22 TEACHING A ROBOT TO STAND UP FOR ITSELF New approaches to AI promise to help scientists build machines with greater autonomy. Researchers at UC Berkeley are tapping into the processing power and integrated software of NVIDIA’s DGX-1 to advance robotics using reinforcement learning. DGX-1 will allow them to iterate faster and ultimately build robots that are able to understand and navigate a diverse and changing world on their own.
  23. 23. 23 ACCELERATING DISCOVERIES WITH AI New drugs typically take 12-14 years and $2.6 billion to bring to market. BenevolentAI is using GPU deep learning for NLP to bring new therapies to market quickly and more affordably. They’ve automated the process of identifying patterns within large amounts of research literature, enabling scientists to form hypotheses and draw conclusions quicker than any human researcher could. And using the NVIDIA DGX-1 AI supercomputer, they identified two potential drug targets for Alzheimer’s in less than one month.
  24. 24. 24 DEEP LEARNING FOR TEXT/NLP INFORMATION EXTRACTION SENTIMENT ANALYSIS AUTOMATIC SUMMARIZATION QUESTION ANSWERING SYSTEMS SEQUENCE LABELING Very Recent Developments
  25. 25. 25 DEEP AUTOENCODERS FOR RECOMMENDATIONS Just Published
  26. 26. 26 Vision Title hereSound NLP Cyber Title hereFraud Robotics Prediction Title hereRiskRecommender DEEP LEARNING USE CASES Where to apply DL?
  27. 27. 27

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