A Q & A with Pedro Domingos: Author of ‘The Master Algorithm’

27Sep15

by Jennifer Langston, University of Washington

Pedro Domingos, University of Washington professor of computer science and engineering, is the author of “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.”

A popular science romp through one of today’s hottest scientific topics, the book is an essential primer on machine learning. It unveils the deep ideas behind the algorithms that increasingly pick our books, find our dates, filter email, manage investments and run our lives — and what informed consumers and citizens ought to know about them.

Domingos, who will speak at Seattle’s Town Hall at 7:30 p.m. on Sept. 22, answered a few questions about the book.

What is machine learning, and how might a person encounter it in a typical day?

PD:  Machine learning is the automation of discovery — computers learning by themselves by generalizing from data instead of having to be programmed by us. It’s like the scientific method on steroids: formulate hypotheses, test them against the data, refine them — except computers can do it millions of times faster than humans.

Google uses machine learning to decide which Web pages to show you, Amazon and Netflix to recommend books and movies, Twitter and Facebook to select posts for your feed. Siri uses learning algorithms to understand what you say and predict what you want to do. Spam filters use it as well. Retailers use it to decide which goods to stock and how to lay out their stores. If you receive a credit card offer, chances are a learning algorithm picked you. At many companies, when you apply for a job, a learning algorithm screens your resume. Online dating sites use machine learning to match their users — there are children alive today who wouldn’t have been born if not for machine learning. In other words, machine learning is involved in pretty much everything we do these days.

Why is it important for someone who isn’t a computer scientist to understand principles of machine learning?

PD: Learning algorithms make a lot of decisions on your behalf every day. As we just saw, they can determine not just what goods you buy but also whether you’ll get a job or even who your lifetime companion will be. If these algorithms are a black box to you, you have no control over where they will take you. Think of a car as an analogy: only engineers and mechanics need to understand how the engine works, but you need to know how to drive it. In the future cars will drive themselves, but you’ll have to know how to drive learning algorithms — and right now you probably don’t even know where the steering wheel or the pedals are.

Your book talks about what different “tribes” in machine learning research might contribute to curing cancer, and what their approaches lack. Why focus on that question?

PD: Curing cancer is one of the most important problems in the world — perhaps themost important problem — and machine learning has a big part to play in solving it. What makes cancer hard is that it’s not one disease, but many. Every patient’s cancer is different, and it mutates as it grows, so there’s no one-size-fits-all solution. The cure for cancer is a learning program that predicts which drug to use for which cancer by looking at the tumor’s genome, the patient’s genome and medical history, etc. But none of the current approaches to machine learning is able to solve the problem all by itself, so it’s a great illustration of both what each approach brings to the table and what it’s missing.

What is the difference between the algorithms that Netflix and Amazon use to recommend products you might like? Why is it important for consumers to be aware of these differences?

PD: Like every company, Netflix and Amazon each use the algorithms that best serve their purposes. Neflix loses money on blockbusters, so its recommendation system directs you to obscure British TV shows from the 70s, which cost it virtually nothing. The whole machine learning smarts is in picking shows for you that you’ll actually like even though you’ve never heard of them. Amazon, on the other hand, has no particular interest in recommending rare products that only sell in small quantities. Selling larger quantities of fewer products actually simplifies its logistics. So its recommendation system is based more on just how popular each product is in connection with the products you’ve bought before. The problem for you if you don’t know any of this is that you wind up doing what the companies want you to do, instead of what you want to do.

If you know — even just roughly — how the learning algorithms work, you can make them work for you by deliberately teaching them, by choosing the companies whose machine learning agrees best with you and by demanding that the learning algorithms let you explicitly say things like “This is what I want, not that,” and “Here’s where you went wrong.”

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