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.

1. AI in the Enterprise
Making Corporations Smart Again
Danny Lange
Vice President of AI & ML
Unity Technologies

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. 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. 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. What Was Before Machine Learning?
Human versus machine
PROGRAM
“ALL-KNOWING
PROGRAMMER”
DATA RESULTS
FEEDBACK
Clockwork Universe

6. Machine Learning in our Business
Human versus machine
MODEL
LEARNER
DATA PREDICTIONS
HISTORIC DATA
Indeterminism

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. Multi-armed Bandit
Objective: Maximize winnings
Exploitation vs Exploration
• Gaining knowledge
• Max payout with current knowledge
Environment
• Cost
• Adversarial
X% Y% Z%

11. 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

12. 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

13. Danny Lange
Vice President, AI & Machine
Learning
+1 425.463.5801
dlange@unity3d.com
@danny_lange
dannylange