Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

AI in the Enterprise – Making Corporations Smart Again

1,187 views

Published on

by Danny Lange, VP of AI & ML, Unity Technologies.

Have you noticed how applications seem to get smarter? Apps make recommendations based on past purchases; you get an alert from your bank when they suspect a fraudulent transaction; and you receive emails from your favorite store when items related to things you typically buy are on sale. These examples of application intelligence use a technology called Machine Learning. Machine Learning uses algorithms to detect patterns in old data and build models that can be used to make predictions from new data. Understanding the algorithms behind Machine Learning is difficult and running the infrastructure needed to build accurate models and use these models at scale is very challenging. At Uber and Amazon my teams built Machine Learning services that easily allow business teams to embed intelligence into their applications that can perform important functions such as ETA, fraud detection, churn prediction, forecasting demand, and much more. Lessons learned at Uber and Amazon have implications for the rest of the world.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

AI in the Enterprise – Making Corporations Smart Again

  1. 1. AI in the Enterprise Making Corporations Smart Again 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 software • 5 Billion Downloads Q3 2016 • 2.4 Billion Unique Devices • 700 Million Gamers • 34% of top 1000 free mobile games
  4. 4. 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
  5. 5. What Was Before Machine Learning? Human versus machine PROGRAM “ALL-KNOWING PROGRAMMER” DATA RESULTS FEEDBACK Clockwork Universe
  6. 6. Machine Learning in our Business Human versus machine MODEL LEARNER DATA PREDICTIONS HISTORIC DATA Indeterminism
  7. 7. OODA Loop (John Boyd) Heisenberg's Uncertainty Principle • There is 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 Changing Anything Changes Everything
  8. 8. Multi-armed Bandit Objective: Maximize winnings Exploitation vs Exploration • Gaining knowledge • Max payout with current knowledge Environment • Cost • Adversarial X% Y% Z%
  9. 9. Reinforcement Learning The Autonomous Corporation • Q Learning • Reward Function – scalar value • Objective – find sequence of actions that optimizes Reward Function • The Snake – Progression Learning • Breakaway – Short-term value versus long-term value • Deep Net offers an powerful representation of the cumulative reward • Deep Q and A3C
  10. 10. So Where Does AI Leave Data Science? Operating at the Cohort of One • AI Operates a scale • Extreme scale – 1-1 relationship between company and customer • Extreme speed – better get to know the customer quickly • Optimize and personalize more important than analyze • Data Science should move up the food chain • Data Scientists should become strategic • Start looking around corners • Become customer advocates / ombudsperson
  11. 11. Danny Lange Vice President, AI & Machine Learning +1 425.463.5801 dlange@unity3d.com @danny_lange dannylange

×