How Uber uses AI and ML for it's various problems. This was presented in IBM Index conference 2018 in San Francisco

1. Ankit Jain
Finance Data Science
Using Artificial Intelligence in
Logistics at Uber

2. Who am I?
Ankit Jain | Data Scientist
Finance Data Science
Primarily work on trip forecasting at Uber
Previously
Data science at BofA, Facebook, and a few VC
backed startups

3. Why I’m here
● Discuss the applications of AI in logistics & forecasting
● Data and signals are our eyes and ears into the world (past, present, and
future)
● But extracting information from them to drive business efficiency is not
straightforward

4. “AI is going to affect most aspects of our lives. It's
similar in impact to other revolutions that have
occurred in human history, like the agricultural
revolution, the industrial revolution and the
computer revolution.”
-- Zoubin Ghahramani, Chief Scientist, Uber

5. There are mainly two types of Logistics businesses:
● On Demand
● Scheduled
Types of Logistics Business

6. Objective
A key aim of any logistic business is :
● Minimize Estimated Time of Arrival (ETA)
● Improve Reliability
At minimal costs AI

7. Data
● Location Data
● Anonymous User (Driver/Rider) Activity data on the platform
Huge amounts of data is processed everyday.

8. AI PROBLEMS

9. Space
FORECASTING
Problem Space
Time Forecasts
MaMap Data@2018 Google at Uber

10. Forecasting- Problem Types
Long-Term Forecasting
Uber uses forecasting to make optimal business decisions. Long term forecasting
is useful for financial planning and onboarding new drivers
Short-Term Forecasting
Logistic businesses care about forecasting at a far more granular level though.
Knowing supply/demand imbalances before they happen ensures marketplace
stays efficient
Real-time Forecasting (anomaly detection)
Having minute-by-minute forecasts of all major metrics allows us to immediately
detect outages and issues

11. Pricing
● AI enables smarter and more efficient pricing for any
logistic business by anticipating trends in the market
and adjusting accordingly.
● Few input features to the ML algorithm:
○ Origin and destination of the trip
○ Day of the week and hour of the day
○ Weather, holidays etc.
○ Type of vehicle
○ Current/expected supply and demand gap

12. Dispatch and ETA
● ETA: Some factors which affect ETA are:
○ Current Traffic Conditions
○ Origin and Destination locations
○ Past traffic conditions of same hour and day of the
week data
○ Weather
● Dispatch is essentially efficient matching of drivers and
riders. It is based on a combination of factors including
○ ETA, Driver/Rider ratings etc.
○ For carpool, it is based on % route overlap for trips

13. Food Delivery
● Restaurant/Dish Recommendation
○ Past user history
○ Popularity
○ Offers
● Estimated Time to Delivery
○ Pick-up Time
○ Food Preparation Time
○ Delivery Time

14. Self-Driving Cars
● One of the most challenging problems
● Lots of data recorded by sensors
● Extremely high accuracy requirement
● System should be able to handle
uncertainty
● Three major use cases of AI:
○ Perception (Computer Vision)
○ Prediction
○ Motion Planning

15. And...
● Customer Support
○ Help customer support agents respond to tickets
● Destination Prediction
○ Predict the destination of riders based on current location and time of the
day
● Fraud
○ Payment Fraud
○ Collusion Fraud (Driver/Rider, Driver/Restaurant)

16. “The future is not some place we
are going, but one we are creating.”
-- John H. Schaar

17. Thank you
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 contains 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.
Ankit Jain | ankit.jain@uber.com
Data Science | Finance Data Science
We are hiring!
Machine Learning Engineers
Software Engineers
Data Scientists
https://www.uber.com/careers

18. Proprietary and confidential © 2017 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 contains 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.