Oct 15, 2017 • 52M

Luis Perez-Breva on Artificial Intelligence Myths

 
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Bob Zadek
Bob talks about the issues that affect our lives on a daily basis from a purely libertarian standpoint. He believes in small government, fewer taxes, and greater personal freedom.<br /><br />America has lost its way, but it cannot and does not need to be reinvented. Our founders were correct about their approach to government, as were John Locke, Adam Smith and the other great political philosophers who influenced them. The country’s first principles are economic and social freedom, republicanism, the rule of law, and liberty. Bob believes we must take the best of our founding principles and work from them because a country without principles is just a landmass.
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The talk of artificial intelligence is everywhere, but the truth is, most of us don't really know what AI is. In his op-ed in Business Insider, Luis debunks 5 major AI myths that have been manufactured due to the dominant and often misleading dystopian narrative about AI in the future:

Myth 1: AI is going to kill our jobs
Myth 2: Robots are AI
Myth 3: Big Data and Analytics are AI
Myth 4: Machine Learning and Deep Learning are AI
Myth 5: Search engines are AI

Bob and Luis focus on the first myth, which seems to arrive dressed in new garb every couple of decades. Historical figures ranging from Queen Elizabeth I (1589) to John Maynard Keynes (1930) have predicted that new inventions would give rise to mass unemployment, and the dire warnings keep coming. Today's predictions come with the added warning of impending doom. But is there anything to fear (other than fear itself)? Luis is a serial innovator, who currently directs the MIT Innovation Teams Program, MIT’s flagship hands-on innovation program jointly operated between the Schools of Engineering and Management. He joins the show to clarify the difference between automation, robotics, and true artificial intelligence. His new book Innovating: A Doer’s Manifesto for Starting from a Hunch, Prototyping Problems, Scaling Up, and Learning to Be Productively Wrong (MIT Press, 2017) also lays out a framework for thinking about innovation, which cannot be captured in any template or formula. Get ready to use your brain.

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