Key Takeaways
- Dr. Fei-Fei Li is widely recognized as the “godmother of AI” due to her pioneering work in inventing the AI image dataset ImageNet.
- Li said on a podcast recently that many young people she encounters overthink the small details when they decide on jobs.
- She encouraged young people to focus on the fundamentals, like their passion and belief in a company, when deciding where to work.
Young professionals are making a common mistake by overthinking every detail when they decide on jobs, says the “godmother of AI.”
Dr. Fei-Fei Li, a Stanford computer science professor, is widely regarded as the godmother of AI due to her pioneering work in inventing the AI image dataset ImageNet. Li started developing ImageNet in 2006 based on the idea that teaching machines to recognize images would require large amounts of data. The ImageNet database now holds more than 14 million annotated images and has been used to train countless computer vision models.
On an episode of Lenny’s Podcast released on Sunday, Li said that many young people she encounters “think about every single aspect of an equation when they decide on jobs.” These early-career professionals get trapped in the small details of a job instead of making decisions based on their passion and belief in a company.
“Sometimes I do want to encourage young people to focus on what’s important,” Li said on the podcast.
Related: Looking for a Job? Mark Cuban Has Some Simple Advice for You.
She said that she sees young people “overfocusing on every minute decision” instead of letting themselves be guided by the answers to more fundamental questions, like “Where’s your passion? Do you align with the mission? Do you believe and have faith in this team?”
Li remains on the cutting-edge of AI. Last year, she co-founded World Labs, a spatial intelligence company building AI models that can generate 3D environments. The startup’s first product is an AI generator called Marble that creates 3D worlds from text prompts.
Li said on the podcast that it is a privilege to receive job applications from workers who want to join World Labs. She urges professionals to “focus on the impact” they can make and the kind of work they will be doing when deciding where to work.
Related: These 3 Common Mistakes Are Holding You Back at Work, According to a Former Meta Engineer Known as the ‘Coding Machine’
Li said that she is fearless when it comes to intellectual pursuits, a quality that she looks for in the people she hires.
“I’m an intellectually very fearless person, and I have to say, when I hire young people, I look for that because I think that’s a very important quality,” she said on the podcast. “You almost have to allow yourself to be fearless and to be courageous.”
She brought up the example of how, early in her career, she was close to obtaining tenure as a professor at Princeton University. However, in 2009, she chose to make the leap from Princeton to Stanford University and restart her tenure clock because she was drawn to the Silicon Valley ecosystem. Li has now been working at Stanford for over 16 years.
“I didn’t spend too much time thinking about all the failure cases,” Li said on the podcast. “I don’t overthink.”
Key Takeaways
- Dr. Fei-Fei Li is widely recognized as the “godmother of AI” due to her pioneering work in inventing the AI image dataset ImageNet.
- Li said on a podcast recently that many young people she encounters overthink the small details when they decide on jobs.
- She encouraged young people to focus on the fundamentals, like their passion and belief in a company, when deciding where to work.
Young professionals are making a common mistake by overthinking every detail when they decide on jobs, says the “godmother of AI.”
Dr. Fei-Fei Li, a Stanford computer science professor, is widely regarded as the godmother of AI due to her pioneering work in inventing the AI image dataset ImageNet. Li started developing ImageNet in 2006 based on the idea that teaching machines to recognize images would require large amounts of data. The ImageNet database now holds more than 14 million annotated images and has been used to train countless computer vision models.
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