S4: #037: Kevin Lyman on Deep Learning, Startup Competitions, and the Woz

Our guest in this episode is Kevin Lyman, an extremely talented entrepreneur and computer scientist based out in San Francisco. He’s worked at companies including Microsoft and SpaceX, and he’s even founded several companies as well.

Now he’s applying his ability to create leading products to the healthcare sector- and he’s the COO and Lead Scientist for Enlitic, a startup that develops deep learning products to make doctors faster and more accurate. He’s going to help us break down some of the details and capabilities of Artificial Intelligence, and explain the differences between machine learning and deep learning. Don’t worry - you don’t have to be a computer scientist to understand it, but with so many terms floating around for a set of technologies that is on track to be the most significant development of our lifetime - we need to peel back the curtain a little bit to understand them a bit better. Then we get into how all this is making an impact on healthcare, and how people and organizations are collaborating to create and implement the solutions.

Today's Topics:

• The data problems and opportunities facing health organizations
• The differences between AI, machine learning and deep learning
• Enlitic's approach to gets critics on board to support their products
• The regional differences that help drive the different responses to AI solutions
• Presenting to Steve “The Woz” Wozniak at Cube Tech Fair – and winning €1 million in the process

Links and Resources Mentioned:

• Forbes Article: “U.S. Falls Behind China & Canada In Advancing Healthcare With A.I.” 
• Enlitic Founder Jeremy Howard
• Contact Enlitic: info [at] enlitic.com
• Kevin Lyman on Linkedin
• Kevin Lyman on email: kevin [at] enlitic.com
• Kevin Lyman on Twitter
• Company website: https://www.enlitic.com
• Digital Health Events
• Join Digital Health Today

Video:

Enlitic Lung Cancer Screening Demo

Chest X-Ray Population Screening Demo

The wonderful and terrifying implications of computers that can learn I Jeremy Howard

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