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Show Notes
In episode 105 of The Gradient Podcast, Daniel Bashir speaks to Eric Jang.
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Outline:
* (00:00) Intro
* (01:25) Updates since Eric’s last interview
* (06:07) The problem space of humanoid robots
* (08:42) Motivations for the book “AI is Good for You”
* (12:20) Definitions of AGI
* (14:35) ~ AGI timelines ~
* (16:33) Do we have the ingredients for AGI?
* (18:58) Rediscovering old ideas in AI and robotics
* (22:13) Ingredients for AGI
* (22:13) Artificial Life
* (25:02) Selection at different levels of information—intelligence at different scales
* (32:34) AGI as a collective intelligence
* (34:53) Human in the loop learning
* (37:38) From getting correct answers to doing things correctly
* (40:20) Levels of abstraction for modeling decision-making — the neurobiological stack
* (44:22) Implementing loneliness and other details for AGI
* (47:31) Experience in AI systems
* (48:46) Asking for Generalization
* (49:25) Linguistic relativity
* (52:17) Language vs. complex thought and Fedorenko experiments
* (54:23) Efficiency in neural design
* (57:20) Generality in the human brain and evolutionary hypotheses
* (59:46) Embodiment and real-world robotics
* (1:00:10) Moravec’s Paradox and the importance of embodiment
* (1:05:33) How embodiment fits into the picture—in verification vs. in learning
* (1:10:45) Nonverbal information for training intelligent systems
* (1:11:55) AGI and humanity
* (1:12:20) The positive future with AGI
* (1:14:55) The negative future — technology as a lever
* (1:16:22) AI in the military
* (1:20:30) How AI might contribute to art
* (1:25:41) Eric’s own work and a positive future for AI
* (1:29:27) Outro
Links:
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