Dwarkesh Podcast / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
20 published records
01 / evaluation
“With driving, because of the dynamics of how it's set up, it's very hard to make a mistake, correct it and then learn from it because the mistakes themselves have significant ramifications.”
02 / prediction
“Eventually it’ll be learning from observing what people do from the kind of natural feedback that you receive when you're doing a job together with somebody else. This is also the kind of stuff where the prior knowledge that comes from these big models is tremendously valuable, because that lets you understand that interaction dynamic.”
03 / evaluation
“It's very hard to do because robotic experience consists of time steps that are very correlated with each other.”
04 / uncertainty
“I don't know the answer to that question, but it's also a tricky question to answer because not all arms are made equal.”
05 / uncertainty
“We're already trying to figure out what are the real things this thing can do that could allow us to start spinning the flywheel. But in terms of stuff that you would actually care about, that you would want to see… I don't know but single-digit years is very realistic.”
06 / belief
“" Representing your context in the right form, that captures what you really need to achieve your goal—and otherwise discards all the unnecessary stuff—I think that's a really important thing.”
07 / uncertainty
“I don't know if there was an episode like this in the training set, but just for fun I took one of the shorts and turned it inside out.”
08 / uncertainty
“Probably it requires as much experience as the language stuff. But because we don't know the answer to that, to me a much more useful way to think about it is not how much data do we need to get before we're fully done, but how much data do we need to get before we can get started.”
09 / commitment
“There's the question of where the software will be, and then there's the question of how many physical robots we will have.”
10 / evaluation
“For the specifics of how we make that happen, that's a very long conversation that I'm probably not the most qualified to speak to. But in terms of the ingredients, the ingredient here that is important is that robots help with physical things, physical work.”
11 / evaluation
“Those things can also be addressed with scaling. But we have to identify the right axes for that, which means figuring out what data to collect, what settings to collect it in, what methods consume that data, and how those methods work.”
12 / evaluation
“One theme here that is important to keep in mind is that the reason that those building blocks are so valuable is because the AI community has gotten a lot better at leveraging prior knowledge.”
13 / recommendation
“All of that kind of propagates back into the actions they take and leveraging all these other data sources. So what I think is actually the key here to leveraging auxiliary data sources including simulation, is to build the right foundation model that is really good and has those emergent abilities.”
14 / evaluation
“Meaning the things that we take for granted—like picking up objects, seeing, perceiving the world, all that stuff—those are all the hard problems in AI.”
15 / recommendation
“The scope will have to start out small because there will be certain things that these systems can do very well and certain other things where more human oversight is really important.”
16 / evaluation
“In a sense, what you said is quite right in that a very powerful AI system can simulate a lot of stuff. But also at that point it almost doesn't matter because, viewed as a black box, what's going on with that system is that information comes in and capability comes out.”
17 / uncertainty
“I'm probably not prepared to tell you what percentage of all labor work can be done by robots, because I don't think right now, off the cuff, I have a sufficient understanding of what's involved in that big of a cross-section of all physical labor.”
18 / evaluation
“We care about this because we see this as a very fundamental aspect of the AI problem.”
19 / evaluation
“Everybody who's deploying an LLM is of course going to look at what it's doing and it's going to use that to then modify its behavior. It's complex because it comes back to this question of representations and figuring out the right way to derive supervision signals and ground those supervision signals in the behavior of the system so that it improves on what you want.”
20 / evaluation
“when I started working in robotics in 2014, I used a very nice research robot called a PR2 that cost $400,000 to purchase. When I started my research lab at UC Berkeley, I bought robot arms that were $30,000. The robots that we are using now at Physical Intelligence, each arm costs about $3,000.”