Bringing ML to Every Corner of Your Business

Dr. Danny Lange Head of

May 25, 2016 About Danny Lange, Ph.D.

Uber

Amazon/ AWS

Microsoft Research

IBM Research UBER’s Mission

“Transportation as reliable as running water, everywhere, for everyone.” Incredible Growth and Scale

• 6 continents, 70+ countries, 425 cities, 8,000+ Employees • We completed 1st billionth ride in December 2015... 2nd billionth in June • 5+M Uber trips happen around the world each day • Uber has over 50 million riders globally • uberPOOL accounts for 20% of Uber trips globally • 10% of millennials who ride with Uber in the U.S. have already changed their car ownership behavior • 100+K of driver partners sign up each month • Over 1.5 million active drivers globally

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 The Future is Here Machine Learning is already at work

Today, the most innovative companies on earth - companies disrupting entire industries - rely on Machine Learning to drive business processes and user experiences.

The future of business innovation has (AI) at its very core.

Machine Learning, a subfield of AI, has broken out of the research labs and is fast becoming the cornerstone of business disruption. A Brief History of Artificial Intelligence Or the end of programming, rule-based systems, and information curation

• 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

Algorithm & Rule-based Reinforcement Learning Custom HW/SW Commodity HW/SW What Was Before Machine Learning? Human versus machine

Clockwork Universe Feedback “All-knowing Programmer”

Data Program Results Machine Learning in our Business Human versus machine

Indeterminacy

Historic Data Learner

Data Model Predictions

Decision-making - Manual (query) - Automatic (programmatic) Push decision-making to the edge OODA Loop

● John Boyd, US Air Force ● Father of the F16 figther plane ● Discourse on Winning & Losing ● Observe-Orient-Decide-Act ● Gödel's Incompleteness Theorem • Any model of reality is incomplete and must be continuously refined in the face of new observations. ● Heisenberg's Uncertainty Principle • There is a limit on our ability to observe reality with precision. ● Second Law of Thermodynamics (Entropy) - Ludwig Boltzman • Any given system is continuously changing even as we try to maintain order Changing Anything Changes Everything Machine Learning with Experimentation (A|B Testing) and Feedback

Historic Data Learner

Experiments

Data Model Predictions Action-1

FEEDBACK Got it! But how do we get there? Some Key Tenets of Uber ML

• Rich set of proven learning algorithms optimized for our business • Scalable infrastructure that can handle our large data sets • Integration with business data • Easy to use with lots of automation - UI/API, tools, & pipelines • Model management (WWW of data & models) • Experimentation: integrated decision making - A|B testing

Machine Learning is becoming an integral part of Uber’s engineering infrastructure Machine Learning at Uber

Drivers Maps Self-Driving Cars Riders Routes Trips ETA UberEATS The food you want, from the restaurants you love, delivered at Uber speed

• ETD = distance * avg_speed + cooking_time • Multiple models: -Pre-order -Post-order -On-preparation • Ranking recommendations • Search ranking How to get Started The cloud makes it easy to start small

• 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: - Machine Learning - Azure Machine Learning - Google Cloud Machine Learning

- Big ML Thank you

Danny Lange

Head of Machine Learning @ Uber +1. 425.463.5801 [email protected]

@danny_lange Proprietary and confidential © 2016 Uber Technologies, Inc. All rights reserved. No part of this document may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval systems, without permission in writing from Uber. This document is intended only for the use of the individual or entity to whom it is addressed and containsdannylange information that is privileged, confidential or otherwise exempt from disclosure under applicable law. All recipients of this document are notified that the information contained herein includes proprietary and confidential information of Uber, and recipient may not make use of, disseminate, or in any way disclose this document or any of the enclosed information to any person other than employees of addressee to the extent necessary for consultations with authorized personnel of Uber.