“If you optimize only for a single objective such as improved profit, there will be fallout and dire ramifications. But if you establish standards that incorporate humanist objectives as well, science can help you achieve them.”

Eric Siegel is a former Columbia University professor and leading consultant, who is always finding new ways to make machine learning and data analytics more engaging and understandable for the masses.

His passion for machine learning and data analytics is manifested in his accomplishments:

• Founder of the long-running Predictive Analytics World and the Deep Learning World conference series, which he started back in 2009
• Author of the bestselling “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die”, which has been used in courses at hundreds of universities

Eric has appeared on prominent media outlets such as: Bloomberg TV and Radio, Business News Network (Canada), National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet.

Furthermore, his books has been reviewed and featured in: Businessweek, CBS MoneyWatch, The European Business Review, The Financial Times, Forbes, Forrester, Fortune, Harvard Business Review, The Huffington Post, The New York Times, Newsweek, Quartz, The San Francisco Chronicle, Scientific American, The Wall Street Journal, The Washington Post, and WSJ MarketWatch.

In an era where technology advancements dominate economic growth, impact our personal lives and influence both international and domestic policies, we gather insights from Eric on the path forward for machine learning and artificial intelligence for the near future.

EARLY LIFE

Tell us more about your family background and share with us on what it was like growing up.

I grew up in Burlington, Vermont, a small, progressive city in the cold northeast of the U.S., where Bernie Sanders was my mayor and Ben & Jerry (now famous internationally for their ice cream) were two guys running a single, small shop.

My mother was a teacher and father a doctor. They were extremely caring respectively, so I was very privileged and well-supported. I was encouraged to express myself openly and freely, which led to my participation in many musical and theatrical performances through high school and college. This actually helped my tech career, since I have become a professional speaker – delivering an engaging keynote is very much like delivering a very specific kind of theatrical monologue.

As for my tech interests, I was intrigued by computers from the age of 9. My buddy and I would ride our bikes at age 10 to the university bookstore, where we could access a Texas Instruments personal computer, and teach ourselves the BASIC programming language.

By age 11, in 1980, my family got an Apple ][+, which had no hard-drive (of course) and used the household television as its screen. Having a computer at home to hack on for hours a day gave me the lucky opportunity to learn and experiment in those years.

How did your upbringing shape the person you are today?

With the independence and trust I was given and very few rules – my heart and mind were free to develop real passion for certain kinds of technology and the confidence to share that passion. I came to love machine learning most of all. But I also developed the impulse to express my opinion when I feel technology isn’t being applied properly.

How would you sum up your childhood?

A peaceful, forgiving, and supportive environment and a lot of fun!

JOURNEY

How and when did your interest in machine learning come about? And why did you decide to become a university professor at Columbia?

My original interest in machine learning, which fully blossomed in 1991, was really an infatuation with the technology itself. After all, the ability to learn from experience (data) makes it the most fascinating type of engineering to me.

Being a professor gave me the opportunity to explore the area more deeply and also develop methods to most effectively teach the subject matter to newcomers.

But ultimately, technology has got to be useful, not only interesting. In my career as a consultant, beginning in 2003, my focus had turned to how to most effectively deploy machine learning.

Why did you write the book ‘Predictive Analytics’ and created the ‘Predictive Analytics World and Deep Learning World conference series’?

I wrote Predictive Analytics to demonstrate why the field – aka machine learning — is intuitive, powerful, and awe-inspiring. It’s a book about the most influential and valuable achievements of computerized prediction and the two things that make it possible: the people behind it and the fascinating science that powers it.

While there are a number of books that approach the how-to side of ML, this book serves a different purpose (which turned out to be a rewarding challenge for me): sharing with a wider audience a complete picture of the field, from the way in which it empowers organizations, down to the inner workings of predictive modeling.

The Predictive Analytics World and Deep Learning World conference series continue my efforts to focus on the commercial application of machine learning. The conferences cover best practices and lessons learned in its real-world deployment.

ACHIEVEMENTS

Which achievements/milestones are you most proud of and why?

The Predictive Analytics World conference series has thrived since we launched it in 2009. As the leading vendor-neutral, cross-vendor commercial (non-academic) event, it plays an important, central role in the industry. It’s always so exciting to bring together the leading innovators for these meetings.

My book’s wide adoption was also very rewarding to me, after investing a great deal of effort to describe the technology so it could be understood by all. Hundreds of universities have adopted it and it lead to 120 keynote invitations across industry sectors, including ad tech, marketing, market research, e-commerce, environmentalism, manufacturing, financial services, insurance, news media, healthcare, pharmaceuticals, government, human resources, restaurants, travel, real estate, construction, and law.

What do you think are the key ingredients to your success?

First principles. When I started out as an independent predictive analytics (aka machine learning) consultant in 2003, today’s high demand wasn’t there yet. It hadn’t become a trend. But I knew that optimizing operations by way of per-individual or per-unit predictions, which is what you get from machine learning, was clearly valuable and would have an important place in the world.

I also have always enjoyed working to make technology understandable to newcomers and non-technical folks. And that’s a valuable and yet often underdeveloped skill! After all, business leaders and decision makers need to understand the fundamentals if a technology is going to be deeply integrated into the daily operations of a business.

PERSONAL (LIFE)

What is your life motto (Or core values) if any?

Geek out! Get really into it. As much as you can for as many minutes of every day, focus on your love for the details of what you’re doing rather than the outcome or recognition that you may also be hoping for.

To you, what are the most important things in life? Why so?

I’m all for the cliches: Family, friends, health, and happiness. But after those I would put experiencing a personal connection to work, if you can find it. There’s a lot of gratification there, when work is meaningful to you.

What’s worth mentioning on your life’s bucket list that you have not done?

I’d like to shift my work life to invest more time into ethical technology. Also, zero-gravity in a parabolic airplane trip. As for space travel, thank you but no thank you!

What are some things that many people don’t know about you?

I took 10 years of acting classes. I love actors, their work, and their process. I can’t stop watching interviews with them. The craft of acting has solved aging: The older an actor gets, the deeper their work. Most other performing arts don’t work that way.

What kind of legacy do you hope to leave behind?

I hope that my work shows people the joy that can be found in doing good work.

What are some life lessons you will take to your grave?

People are generous and thoughtful – except for when they aren’t. When they aren’t, they are subconsciously acting out the same strength-testing that kids put one another through on the playground. When people aren’t seeing your valid point, or aren’t responding to it, keep this in mind so that you can persevere.

VIEWPOINTS

You sometimes publish op-eds on analytics and social justice. How do you see that analytics and machine learning can advance the moral objectives of society?

If you optimize only for a single objective such as improved profit, there will be fallout and dire ramifications. But if you establish standards that incorporate humanist objectives as well, science can help you achieve them.

AI/ML is playing a more significant role in the advancement of new technologies around the world, with endless new applications across many industries within reach. Where do you see the development of new technologies based on AI/ML in the next 3 to 5 years?

The main development will be existing technologies’ growing deployment, rather than the development of new technologies. These things are growing rapidly: computer power, data aggregation, and familiarity with machine learning’s potential.

As a result, machine learning’s penetration across company functions will continue to increase. And so will its consumer-facing deployment, including for certain self-driving capabilities and our digital experience. Machine learning fortifies healthcare, prevents fraud, cuts costs, and streamlines manufacturing. This vast applicability makes ML “the new electricity,” as Andrew Ng has put it.

As a professor who used to teach in a University, you got to interact with countless students on a regular basis. In your opinion, how important is it to get more of the younger generation to learn AI/ML?

This is critical. It should be taught in high school. Machine learning is increasingly central to how society is run, and yet curriculums are very slow to adapt to it.

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