The Gradient has an interesting interview with Eric Jang, a roboticist formerly with Google, now Vice President of AI, 1X Technologies.
* (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 (https://evjang.com/book/)
* Eric’s Twitter (https://x.com/ericjang11?s=20) and homepage (https://evjang.com/)
Eric's final comments: What the future holds
One example I'd like to like kind of say here is like, it's not really about taking our labor supply and then, you know, swapping it out with with robots.
It's more about like, how can we create a world where there is 10x more labor? And I think people today don't have an answer like so people who are afraid of robots taking over the jobs and such. They don't want their own jobs replaced, but they also don't have an answer to as to how we can 10x the volume of labor supply, right?
And I think if you really frame the question in terms of like, in order to make the world better, You do need more labor. And so the labor pool actually needs to increase. And I guess short of just TEDxing the world population, you do need to just make a bunch of robots to do this. So that's kind of the new vision I have for how my career can fill this.
And as the path to AGI, This is not a direct way to AGI. It's more just like I want to build really, really good systems that can do tasks at a high level of success. And I think this will be a really good stepping stone towards actually building useful AGI systems through the mastery of things like deep learning.
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