Episode Summary: This episode’s guest is Uri Sarid, PhD, CTO for MuleSoft, Inc. Sarid speaks about where he believes the future of machine learning (ML) applications in industry might go – he thinks applications might stay small and niche-based, and will develop based on how well each serves its individual purposes. He also gives his perspective on how companies may adapt to deal with these disparate ML technologies, and expands on his belief that finding ways to connect technologies will be an important path in the development of machine learning applications and platforms across industries.

 

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Expertise: Computer software applications and platforms for consumers and enterprises

Brief Recognition: Prior to becoming CTO at MuleSoft, Inc., Uri Sarid was most recently Vice President of the NOOK Cloud at Barnes & Noble, where he architected, led, and released the flagship digital content and user platform for NOOK. Previously, he was VP of Engineering at eMeter (acquired by Siemens), the leading provider of enterprise software for the SmartGrid. Sarid has also held CTO and VP of Engineering positions at companies including Loyalize, Aptana, Accomplice, Noosh, and digiGroups. Sarid began his career with research and teaching positions at Stanford University, Lawrence Berkeley Lab, and the University of Notre Dame. He holds a PhD in Theoretical Physics and Astrophysics from Harvard University and a BS with Highest Distinction from the University of Arizona, and is also an author on 7 patents.

Current Affiliations: CTO of MuleSoft Inc.

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Interview Highlights:

(1:35) What do you think might get this field (ML) itself to congeal?

(5:20) What you’re mentioning here, the development of what would be the backbone that would allow for so many types of programs to communicate…it feels to me like we’re pretty far from that today, but give me your thoughts…

(7:20) It seems to me as though it’s a different way of creating a product…it sounds as if you’re of the belief that more people will have this as a thought in the front of their mind – thinking how can we make (technologies) seamless and stream into other business processes right away…

(9:45) Going into how businesses are thinking about and incorporating big data and machine learning technology today…where do you see those trends picking up in the last number of years?

(11:08) In terms of applications that you’ve seen pick up…where else have you seen that sort of pick up…where do you see (companies) using those ‘weapons’ of machine learning and artificial intelligence and really drawing some value from them?

(13:48) How do companies go about building an application network in the first place?

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