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Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4

Yoshua Bengio, along with Geoffrey Hinton and Yann Lecun, is considered one of the three people most responsible for the advancement of deep learning during the 1990s, 2000s, and now.

Lex FridmanhostYoshua Bengioguest

Oct 20, 201842mWatch on YouTube

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EPISODE INFO

Released: October 20, 2018
Duration: 42m
Channel: Lex Fridman Podcast
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

Yoshua Bengio, along with Geoffrey Hinton and Yann Lecun, is considered one of the three people most responsible for the advancement of deep learning during the 1990s, 2000s, and now. Cited 139,000 times, he has been integral to some of the biggest breakthroughs in AI over the past 3 decades. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep4-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 *PODCAST LINKS:*
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SPEAKERS

Lex Fridman
host
Yoshua Bengio
guest

EPISODE SUMMARY

In this episode of Lex Fridman Podcast, featuring Lex Fridman and Yoshua Bengio, Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 explores Yoshua Bengio on credit assignment, world models, and AI’s future Yoshua Bengio discusses the gaps between biological and artificial neural networks, focusing on long-term credit assignment, memory, and the need for richer world models. He argues that current deep learning is too shallow in abstraction and overly dependent on passive supervised learning, and that progress requires new training frameworks emphasizing causality, active agents, and disentangled high-level representations. Bengio connects these technical issues to broader themes like symbolic vs. neural approaches, generalization to new distributions, and the importance of machine teaching. He also touches on AI safety, societal impacts, bias and ethics, how science actually progresses, and his own motivations and persistence through the AI winter.

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