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Lex Fridman PodcastDileep George: Brain-Inspired AI | Lex Fridman Podcast #115
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Episode Details
EPISODE INFO
- Released
- August 14, 2020
- Duration
- 2h 10m
- Channel
- Lex Fridman Podcast
- Watch on YouTube
- ▶ Open ↗
EPISODE DESCRIPTION
Dileep George is a researcher at the intersection of neuroscience and artificial intelligence, co-founder of Vicarious, formerly co-founder of Numenta. From the early work on Hierarchical temporal memory to Recursive Cortical Networks to today, Dileep's always sought to engineer intelligence that is closely inspired by the human brain. Support this channel by supporting our sponsors. Click links, get discount:
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EPISODE LINKS: Dileep's Twitter: https://twitter.com/dileeplearning Vicarious Research: https://www.vicarious.com/science PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 4:50 - Building a model of the brain 17:11 - Visual cortex 27:50 - Probabilistic graphical models 31:35 - Encoding information in the brain 36:56 - Recursive Cortical Network 51:09 - Solving CAPTCHAs algorithmically 1:06:48 - Hype around brain-inspired AI 1:18:21 - How does the brain learn? 1:21:32 - Perception and cognition 1:25:43 - Open problems in brain-inspired AI 1:30:33 - GPT-3 1:40:41 - Memory 1:45:08 - Neuralink 1:51:32 - Consciousness 1:57:59 - Book recommendations 2:06:49 - Meaning of life CONNECT:
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SPEAKERS
Lex Fridman
hostDileep George
guestNarrator
other
EPISODE SUMMARY
In this episode of Lex Fridman Podcast, featuring Lex Fridman and Dileep George, Dileep George: Brain-Inspired AI | Lex Fridman Podcast #115 explores Dileep George explains brain-inspired AI, inference, and human-like vision Lex Fridman and Dileep George explore how insights from neuroscience can guide the design of artificial intelligence, focusing especially on perception and vision. George critiques brute‑force brain simulations like Blue Brain and instead advocates for explicit computational theories that explain how neural microcircuits implement inference and world modeling. He describes his Recursive Cortical Network (RCN), a brain‑inspired vision system that uses feedback, lateral connections, and probabilistic inference to solve tasks like CAPTCHAs with very little training data. The conversation broadens to concept learning, grounded language understanding, memory, GPT‑style large language models, brain–computer interfaces, and what it really means to “understand” the brain if our goal is to build AGI.
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