Nathan Lambert: belief

Transcript context

…I don’t think I can possibly understate the importance of the thing that doesn’t get talked about almost at all by robotics folks or anyone, and that is safety. All the interesting complexities we talk about regarding learning, all the failure modes and failure cases—everything we’ve been talking about with LLMs where sometimes it fails in interesting ways—all of that is fun and games in the LLM space. In the robotic space, in people’s homes, across millions of minutes and billions of interactions, you really are almost allowed to fail never. When you have embodied systems put out there in the real world, you just have to solve so many problems you never thought you’d have to solve when you’re just thinking about the general robot learning problem. I’m so bearish on in-home learned robots for consumer purchase. I’m very bullish on self-driving cars, and I’m very bullish for robotic automation, like Amazon distribution— …where Amazon has built whole new distribution centers designed for robots first rather than humans. There’s a lot of excitement in AI circles about AI enabling automation— …and mass-scale manufacturing, and I do think that the path to robots doing that is more reasonable. It’s a thing that is designed and optimized to do a repetitive task that a human could conceivably do but doesn’t want to. But it’s also going to take a lot longer than people probably predict. I think the leap from the AI singularity to scaling up mass manufacturing in the US because we have a massive AI advantage is one that is troubled by a lot of political and other challenging problems. Let’s talk about timelines specifically: timelines to AGI or ASI. Is it fair, as a starting point, to say that nobody really agrees on the definitions of AGI and ASI?…

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