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
15 published records
01 / evaluation
“These are quite big areas. They don't measure things like understanding streaming video, for example, because these are language models and people can do things like understanding streaming video.”
02 / belief
“I think we can sort of get there, but we have this issue of a reference machine, which is unspecified.”
03 / belief
“There is no one thing that would do it, because I think that's the nature of it.”
04 / uncertainty
“I don't know. At the moment, it looks to me like all the problems are likely solvable with a number of years of research.”
05 / belief
“Maybe we've sped things up a bit, but I think a lot of these things would have happened before too long anyway.”
06 / commitment
“Speaking of the early years, it's really interesting that in 2011, you had a blog post where you said — “I’ve decided to once again leave my prediction for when human level AGI will arrive unchanged.”
07 / prediction
“I think the next landmark that people will think back to and remember is going much more fully multimodal.”
08 / commitment
“It should preserve them because if it's making all its decisions based on a good understanding of ethics and values, and it's consistent in doing this, it shouldn't take actions which undermine that.”
09 / preference
“We need to make sure it understands humans ethics well, at least as well as a very good ethicist because that's important.”
10 / prediction
“What will it take to align human level and superhuman AIs? It's interesting because the sorts of reinforcement learning and self-play kinds of setups that are popular now, like Constitution AI or RLHF, DeepMind obviously has expertise in it for decades longer.”
11 / evaluation
“Large language models have a certain kind of sample efficiency because when something's in their context window, that biases the distribution to behave in a different way and so that's a very rapid kind of learning.”
12 / preference
“If you want a model that writes creative poetry, then that's fine because you want to be able to be very free to suggest all kinds of possibilities and so on.”
13 / evaluation
“I think that powerful machine learning, powerful AGI, is coming in some time and if the system is really capable, really intelligent, really powerful, trying to somehow contain it or limit it is probably not a winning strategy because these systems ultimately will be very, very capable.”
14 / preference
“The way I think about it is this: to have a profoundly ethical AI system, it also has to be very, very capable.”
15 / prediction
“It's difficult because you'll never have a complete set of everything that people can do because it's such a large set. But I think that if you ever get to the point where you have a pretty good range of tests of all sorts of cognitive things that we can do, and you have an AI system which can meet human performance and all those things and then even with effort, you can't actually come up with new examples of cognitive tasks where the machine is below human performance then at that point, you have an AGI.”