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Lex Fridman PodcastIan Goodfellow: Generative Adversarial Networks (GANs) | Lex Fridman Podcast #19
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Episode Details
EPISODE INFO
- Released
- April 18, 2019
- Duration
- 1h 8m
- Channel
- Lex Fridman Podcast
- Watch on YouTube
- ▶ Open ↗
EPISODE DESCRIPTION
Ian Goodfellow is the author of the popular textbook on deep learning (simply titled "Deep Learning"). He invented Generative Adversarial Networks (GANs) and with his 2014 paper is responsible for launching the incredible growth of research on GANs. He got his BS and MS at Stanford, his PhD at University of Montreal with Yoshua Bengio and Aaron Courville. He held several research positions including at OpenAI, Google Brain, and now at Apple as director of machine learning. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep19-sb See below for timestamps, and to give feedback, submit questions, contact Lex, etc. *CONTACT LEX:* *Feedback* - give feedback to Lex: https://lexfridman.com/survey *AMA* - submit questions, videos or call-in: https://lexfridman.com/ama *Hiring* - join our team: https://lexfridman.com/hiring *Other* - other ways to get in touch: https://lexfridman.com/contact *OUTLINE:* 0:00 - Introduction 5:40 - On cognition/consciousness 7:04 - Self-awareness 14:00 - Deep learning 23:10 - Limits of machine learning 26:44 - GANs 42:32 - Semi-supervised learning 49:34 - Data augmentation *PODCAST LINKS:*
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SPEAKERS
Lex Fridman
hostIan Goodfellow
guest
EPISODE SUMMARY
In this episode of Lex Fridman Podcast, featuring Lex Fridman and Ian Goodfellow, Ian Goodfellow: Generative Adversarial Networks (GANs) | Lex Fridman Podcast #19 explores Ian Goodfellow on GANs, AI Limits, Security, and Future Intelligence Ian Goodfellow discusses the strengths and limits of deep learning, emphasizing its hunger for data, lack of generalization, and role as just one component in larger AI systems. He explains how he conceives deep learning as learning multi-step programs rather than static representations, and outlines how cognition and limited forms of self-awareness can plausibly emerge from current architectures. A major portion of the conversation focuses on generative adversarial networks (GANs): what they are, why they surprisingly work so well, their evolution since 2014, and their applications in generation, semi-supervised learning, privacy, and fairness. Goodfellow also highlights adversarial examples and security as critical open challenges, and speculates about what it might take to achieve more general, human-level AI systems.
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