At 15, Fei-Fei Li transitioned from a middle-class life in China to poverty in America. Despite the pressures of her family’s financial situation and her mother’s ailing health, her knack for physics never wavered. She went from learning English as a second language to attending and working at prestigious institutions like Princeton and Stanford. Today, she is among a handful of scientists behind the impressive advances of artificial intelligence in recent times. In this episode, she breaks down her human-centered approach to AI and explores the future of the technology.
Dr. Fei-Fei Li is a professor of Computer Science at Stanford University and the co-director of the Stanford Institute for Human-Centered AI. She is the creator of ImageNet, a key driver of modern artificial intelligence. With over 20 years at the forefront of the field, Dr. Li is focused on AI research, education, and policy to improve the human condition.
In this episode, Hala and Fei-Fei will discuss:
- The current capabilities of AI
- The difference between machine learning and AI
- The training process for AI models
- The gaps in our knowledge about how AI learns
- Why ChatGPT fails at higher-level reasoning like math
- The biological inspiration for vision in computers
- Fears and hopes associated with AI
- The human element of jobs AI can’t replace
- Augmentation of human capabilities through AI
- The three pillars of her human-centered AI framework
- Responsible development and use of AI
- The roadblocks to be aware of when using AI
- Her advice to young entrepreneurs navigating the AI world
- And other topics…
Dr. Fei-Fei Li is a professor of Computer Science at Stanford University and the co-director of the Stanford Institute for Human-Centered AI. She is also the creator of ImageNet and the ImageNet Challenge, a key catalyst to the latest developments in deep learning and AI. Sometimes called the ‘Godmother of AI,’ she is a pioneer in early computer vision research. Dr. Li is the author of The Worlds I See, one of Barack Obama's recommended books on AI. Her work has been featured in various publications, including the New York Times, Wall Street Journal, Fortune Magazine, Science, and Wired Magazine.
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