Does A.I. progress/innovation have a speed limit?

What could happen next, and will we see it coming?

Excerpt: "A conversation about the factors that might slow down the pace of AI development, what could happen next, and whether we’ll be able to see it coming.

Ajeya Cotra: I'm interested in something you mentioned in your recent paper and on Twitter — the idea that the external world puts a speed limit on AI development. With applications like self-driving cars or travel booking agents, you need to test them in real-world conditions so they encounter failures they can learn from. This creates a speed limit in two ways: first, you have to wait to collect that data, and second, people may be reluctant to be guinea pigs while the system works out its bugs.

That's why we're currently creating narrower, more specialized products with extensive code to handle edge cases, rather than letting AI learn through trial and error in the real world. Is that a good summary of the concept?

Arvind Narayanan: That's mostly correct, with one clarification: I don't think we'll always need to manually code for edge cases. The handling of edge cases can be learned, but that learning will often need to happen within real organizations doing real tasks — and that's what imposes the speed limit."