Nvidia GTC Highlights Diverse AI Career Paths

At Nvidia’s global AI conference, researchers, entrepreneurs, and business executives pointed to how they entered the field of AI.

Romi Mahajan, Contributor

September 23, 2022

2 Min Read
Nvidia GTC Highlights Diverse AI Career Paths

A far-reaching and captivating panel discussion at Nvidia GTC 2022 featured five leaders who pursued AI career paths, each from a different background. The discussion highlighted the ubiquity and the “portability” of AI as a driver for career opportunities in every industry. 

Led by Nvidia head of strategic initiatives Louis Stewart, the panel discussion focused largely on the different pathways each executive took to get to their current work in AI.

For Chelsea Sumner, healthcare AI leader at Nvidia, her interest in AI came via medical research and her desire to democratize medicine. Sumner suggested that people interested in AI should “stand on the shoulders” of mentors to learn. She added that newcomers should be willing to fail and fail often.

Adobe product marketing manager Carrie Gotch, meanwhile, discussed the value of early internships. These internships not only stoked Gotch’s interest in AI but brought together interdisciplinary teams to create intelligent feedback loops for AI. 

Dr. Laura Leal-Taixe from the Technical University of Munich cited a hands-on course she took when studying abroad in the U.S. Her work in autonomous vehicles and computer vision has gone from fringe to mainstream. She noted the importance of diversity in AI and made a clarion call to anyone interested in the field to “try, try, try.”

Related:Data Scientist Careers Are Evolving — Here's Why

Lockheed Martin’s vice president of AI, Justin Taylor, said AI is crucial but only with the right end goal in mind – in Taylor’s case, safety and security. Taylor’s entry point to AI came as an already established leader in technology.

Finally, eminent scholar and entrepreneur Dr. Jay Lee focused on the importance of data in AI. Understanding data’s context and relevance is key and requires discipline and domain knowledge. AI without that discipline and domain knowledge is unproductive, Dr. Lee said. 

Contrary to popular notions, the panel said people interested in AI career paths do not need to pursue computer science from an early phase of their education and professional work.

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About the Author(s)

Romi Mahajan


Romi Mahajan is a marketer, author, investor, and advisor. Romi spent a decade at Microsoft, has been chief marketing officer of five companies, and has advised over 40 companies across the technology, financial services, fintech, media, agency, and marketing spaces. He has authored two books in marketing and over 1,000 articles on the intersection between business and technology. 


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