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EIA2017Italy - Danny Lange - Artificial Intelligence - A Game Changer in App Development

Danny Lange - Artificial Intelligence - A Game Changer in App Development

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EIA2017Italy - Danny Lange - Artificial Intelligence - A Game Changer in App Development

  1. 1. Artificial Intelligence A Game-changer in App Development Danny Lange Vice President of AI & ML Unity Technologies
  2. 2. Head of Machine Learning GM of Amazon ML Retail / AWS Development Manager Analytics / ML Toolkit Computer Scientist IBM Research VP of AI & ML About Danny Lange
  3. 3. About Unity Leading global game industry platform • 16 Billion Downloads in 2016 • Over 2.6 Billion Unique Devices • 1 Billion Monthly Active Players • 40% of top 1000 free mobile games
  4. 4. Windows Phone Windows Desktop Windows Store
  5. 5. From Computer Programming to Computer Training
  6. 6. Typical Business Challenges • Lower prices • Faster delivery • Higher customer service expectations • Demand volatility • High number of products • Supply complexities • More frequent shipments • Transparency and sustainability
  7. 7. A Brief History of Artificial Intelligence Or the end of programming, rule-based systems, and information curation Algorithm & Rule-based Custom HW/SW Reinforcement Learning Commodity HW/SW • Shannon’s Computer Chess (1949) - Minimax Algorithm • Deep Blue vs Garry Kasparov (1997) - Custom HW/SW • IBM Watson Wins Jeopardy (2011) - Curated Data + Q&A • AlphaGo vs Lee Sedol (2016) - Deep Learning + GPU
  8. 8. What Was Before Machine Learning? PROGRAM “ALL-KNOWING PROGRAMMER” DATA RESULTS FEEDBACK Clockwork Universe
  9. 9. What is Machine Learning MODEL LEARNER DATA PREDICTIONS HISTORIC DATA Indeterminism
  10. 10. Machine Learning Explained SQUARE FOOTAGE BED- ROOMS AGE SCHOOL RATING PRICE 2,400 3 12 99 $650,000 3,500 5 7 90 $750,000 1,800 3 23 75 $350,000 2,700 4 1 75 $550,000 GROUND TRUTH PRICE(SQUARE FOOTAGE, BEDROOMS, AGE, SCHOOL RATING) = W1 X SF + W2 X BR + W3 X AGE + W4 X SR SQUARE FOOTAG E BED- ROOMS AGE SCHOOL RATING PRICE W1 W2 W3 W4 INPUT OUTPUT
  11. 11. Types of Machine Learning • Unsupervised • E.g., looking for geometrical closeness (clustering) • Supervised • Ground truth driven learning (most common) • Online • Learning happens as predictions are made • Active • Learner queries for specific examples to improve • Reinforcement • Learning from rewards function (“good dog / bad dog”)
  12. 12. OODA Loop (John Boyd) Heisenberg's Uncertainty Principle • A limit on our ability to observe reality with precision. Gödel's Incompleteness Theorem • Any model of reality is incomplete and must be continuously refined in the face of new observations. Second Law of Thermodynamics (Entropy) - Ludwig Boltzman • Any given system is continuously changing even as we try to maintain order
  13. 13. Making Computers Learn From Exploration to Exploitation
  14. 14. Multi-armed Bandit & Reinforcement Learning Objective: Maximize winnings Exploration vs Exploitation • Gaining knowledge • Max payout with current knowledge Reinforcement Learning • Actions • Rewards X% Y% Z% AGENT ENVIRONMENT ACTION STATE & REWARD
  15. 15. How did the Chicken Cross the Road? Actions Rewards • Fatal penalty (being hit by a car) • Positive reward (collecting gift packet) Exploration Exploitation
  16. 16. Let us move onto the real world
  17. 17. Learning to Become a Gymnast Unity Physics Engine
  18. 18. DeepMind Playing Atari
  19. 19. Learning a Complex Skill Developing an Asset
  20. 20. Navigating by Reading Maps (No GPS)
  21. 21. Network Architecture • Deep Recurrent Q-Network • Dual-stream input • Train network with additional auxiliary losses Actions Inputs Auxiliary signals
  22. 22. Unity Reinforcement Learning API Action State & Reward
  23. 23. Self-driving Vehicles
  24. 24. Shift from Programming to Learning
  25. 25. Why Now? Shift from programming to training • The great AI paradigm shift – Moving from programming to training • Computing power – Cloud Computing and Graphical Processing Units (GPU) • Abundance of data – Big Data, Cloud Storage, and synthetic data • Change is accelerating – Only question of scale • Automation of high-value jobs – Attractive ROI • No turning back – Ahead of the curve; looking around corners
  26. 26. AI/ML in Every Corner of the Organization Some Key Tenets of Applied AI/ML Machine Learning is Becoming an Integral Part of Engineering Infrastructure • Rich set of proven learning algorithms optimized for your organization • Scalable infrastructure that can handle large data sets • Built-in integration with your organization’s data • Easy to use with lots of automation - UI/API, tools, & pipelines • Hosted models with low-latency access • Model management (WWW of data & models) • Experimentation: integrated decision making - A|B testing
  27. 27. Applied AI/ML Architecture MACHINE LEARNING AS-A-SERVICEPIPELINES WEB USER INTERFACE REST API / SDK PREDICTIONSYOUR DATA BATCH REAL-TIME YOUR APPS MODEL MANAGEMENT
  28. 28. AI/ML Cloud Services How to get started • Low hanging fruit: Business problem - “If we just knew…” • Start supervised: Historic data with ground truth • Do not start with Big Data • Use cloud-based offerings • Amazon Machine Learning on AWS • Microsoft Azure Machine Learning • Google Cloud Machine Learning
  29. 29. Bridging from Virtual to Real
  30. 30. Danny Lange Vice President, AI & Machine Learning dlange@unity3d.com @danny_lange dannylange

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Danny Lange - Artificial Intelligence - A Game Changer in App Development

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