It was not too long ago when the first recommendation engines were created, originally to help researchers keep track of articles and information. Now, you probably consult one every single day.

Michael Schrage is a Visiting Fellow in the Imperial College Department of Innovation and Entrepreneurship at MIT, where he examines the various roles of models, prototypes, and simulations as collaborative media for innovation risk management.

He has served as an advisor on innovation issues and investments to major firms, including Mars, Procter & Gamble, Google, Intel, BT, Siemens, NASDAQ, IBM, and Alcoa. In addition, Michael has advised segments of the national security community on cyberconflict and cybersecurity issues, and has written a number of books, the most recent being “Recommendation Engines.”

Michael joins Greg to talk about continuity and patterns, the “search” for advice, trust & exploitation and cat videos.

Episode Quotes:

Where are you getting your best advice from these days?

Who should I trust giving me advice, my best friend, my wife, or these algorithms? That used to be a joke question. Who would you trust advice for a movie or a Netflix series from, your friends or the algorithm? I've literally been at dinners where people say you really got to see so-and-so and said, yeah, Netflix just recommended that two days ago. So you're getting your best advice on restaurants, on travel, on books, on videos from an algorithm, not your friends. What happens to human relationships when your best advice comes from your devices? Not your people.

How did Michael get into this work

What sucked me in to recommender systems, to recommendation engines and the way that they were designed, the way they were architected, the way they were experienced was instead of getting the best answer, I'm getting the best choices. And to me, the real shock is if you're just getting the best answer, then the issue is you need to comply with the best answer. 

What are recommendation engines? 

Recommendation engines are just, they're about the past, present and future of advice. They're the past, present and future of self discovery. I find that fascinating. 

Show Links:

Guest Profile:

• Faculty Profile at Imperial College Department of Innovation and Entrepreneurship at MIT
• Michael Schrage on Big Think
• Michael Schrage on LinkedIn

His Work:

• Articles on Harvard Business Review
• Recommendation Engines
• The Innovator's Hypothesis: How Cheap Experiments Are Worth More than Good Ideas
• Who Do You Want Your Customers to Become?
• Serious Play: How the World's Best Companies Simulate to Innovate
• No More Teams!: Mastering the Dynamics of Creative Collaboration
• Shared Minds: The New Technologies of Collaboration

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