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The Question
What skills should young people like me be investing in?
About This Conversation
In the midst of the AI hype bubble, how do we spot real AI capabilities and snake oil marketing? Many AI companies promise that they will help us predict the future. Is this true? Can AI really predict the future? Turns out, in this capacity at least, it might be more snake oil than success.
In this episode, I sit down with Arvind Narayanan, a Princeton Computer Scientist and co-author of AI Snake Oil. We discuss how to spot snake oil as well as how to prepare for a workplace of integrated AI. What skills should young people like me be investing in? And how do we plan for our futures?
Conversation Map
A guided path through the source-provided topic list for this episode.
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Why algorithms struggle to predict human behavior
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The risks of using AI in healthcare, hiring, education, and criminal justice
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How generative AI is changing programming and other professions
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Will AI replace jobs or transform them?
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The future of entry-level work and professional training
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What students should learn in an AI-shaped economy
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Artificial general intelligence and the future of work
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How to separate genuine expertise from AI hype
Recurring Questions
Recurring Question
"What books have had the most impact on you?"
For Young Listeners
"What advice do you have for teenagers?"
Resources Mentioned
- Editor Peter Choi's Youtube (Ideas I Guess): youtube.com/@ideasiguess
Guest Bio
Arvind Narayanan is a professor of computer science at Princeton University and the director of the Center for Information Technology Policy. He is a co-author of the book AI Snake Oil, the essay AI as Normal Technology, and a newsletter of the same name which is read by over 75,000 researchers, policy makers, journalists, and AI enthusiasts. He previously co-authored two widely used computer science textbooks: Bitcoin and Cryptocurrency Technologies and Fairness in Machine Learning. Narayanan led the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information. His work was among the first to show how machine learning reflects cultural stereotypes. Narayanan was one of TIME's inaugural list of 100 most influential people in AI. He is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE).
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