Uber started in 2010 to solve a simple problem: how do you get a ride at the touch of a button? Six years and over a billion trips later, they’ve started tackling an even greater challenge: reducing congestion and pollution in the world’s cities by getting more people into fewer cars.

AI Business recently caught up with of the key figures driving this monumental growth – Head of Machine Learning Danny Lange.

In his role at Uber, Danny leads an effort to build the world’s most versatile ML platform to support Uber’s rapid growth. With the help of this branch of AI, including Deep Learning, Uber will be able to provide an even better service to its customers. Previously, Danny was the General Manager of Amazon Machine Learning – an AWS product that offers ML as a Service. Prior to Amazon, Danny was Principal Development Manager at Microsoft where he was leading a product team focused on large-scale ML for Big Data.

Danny called upon his wealth of experience when delivering his hugely popular keynote at The AI Summit in San Francisco on 28-29 September, in which he discussed the topic ‘Bringing Machine Learning to Every Corner of Your Business’.

Uber has proved to be one of the fastest-growing enterprises the modern enterprise has ever seen. So what is AI, and in particular machine learning, bringing to the business? Danny explains:

“At Uber we believe that Machine Learning (ML) can make us a more efficient company and help us to create a better experience for our drivers and riders. While we have already been using ML for a while, developers are taking it to a new level and embedding it into their applications throughout our business.  By lowering the barrier for using ML we believe that we can create more intelligent applications that improve customer experiences”.

Danny explains that AI plays into three key areas of Uber:

“Our core business for drivers, riders, and trips; improved maps; and self-driving cars. While each of these areas are very distinct, some of the underlying ML technologies such as Deep Learning are playing an important role in all of them.

In a period where the demand for AI talent is greatly exceeding the supply, Uber, Danny says, has “employed many bright data scientists who refine and optimize our business through the use of statistics, operational analysis, and ML”.
Danny believes that it is important for an enterprise to leverage ML-as-a-service across its entire organization. It is no different at Uber:

“It is Uber’s vision to bring ML to every corner of the company. It is the sum of all the use of ML that creates the critical mass of smartness that we call AI. It is at that stage that we can maintain a consistently awesome customer experience”.

Looking outward from Uber to the transport industry as a whole, Danny sees wholesale changes in the future, and cites the emergent UberPOOL as a key example of this advancement:

“AI will not only change the vehicles and how they are operated, but also the way they are utilized. The nature of the transportation grid and how it is integrated within society is about to undergo a big change. UberPOOL is one such example, where riders headed in the same direction at the same time can share the journey. It’s an exciting tool to help cities solve congestion and pollution, and riders save money.  UberPOOL requires an advanced set of predictions to match multiple riders in real-time without making trips unnecessary long and avoid detours. We are very excited about the popularity of UberPOOL”.

At The AI Summit in San Francisco, Danny delivered an insightful keynote on ‘Bringing Machine Learning to Every Corner of Your Business’.

Join us at the next AI Summit in New York on 1st December, as the event comes to America's business capital! Find out more here: https://theaisummit.com/newyork/

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