The Potential Outcomes of AGI

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:

* Eric’s book

* Eric’s Twitter and homepage