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
31 published records
01 / prediction
“Maybe it thinks in alien concepts and you can’t really monitor the million-line pull request because you can’t really understand the whole thing and you can’t give labels.”
02 / prediction
“One is that as these models get smarter, they are going to be able to operate in domains where we just can’t generate enough human labels, just because we’re not smart enough.”
03 / evaluation
“Of course that’s why we pioneered, and what DeepMind is sort of famous for, using games as a proving ground. That’s partly because it’s efficient to research in that domain.”
04 / belief
“The systems that are around today are not dangerous, in my opinion, but in a few years they might have potential.”
05 / belief
“Obviously, society is adding more data all the time to the Internet and things like that. I think that there’s a lot of scope for creating synthetic data.”
06 / belief
“Perhaps chaining thought, lines of reasoning, together and using search to explore massive spaces of possibility. I think that’s kind of missing from our current large models.”
07 / belief
“If you improve the models, then I think your search can be more efficient and therefore you can get further with your search.”
08 / belief
“I would say that’s still not a perfectly understood mapping, but it’s an interesting one that we’re getting better and better at.”
09 / uncertainty
“There’s a perception that maybe other labs are more compute-efficient than DeepMind has been with Gemini. I don’t know what you make of that perception.”
10 / belief
“I think if it’s not properly grounded, the system won’t be able to achieve those goals properly.”
11 / belief
“In my view, the only sensible approach when you have huge uncertainty is to be cautiously optimistic and use the scientific method to try and have as much foresight and understanding about what’s coming down the line and the consequences of that before it happens.”
12 / belief
“I think the next versions of this over the next year, 18 months, we’ll maybe have some contextual understanding of the environment around you through a camera or a phone or some glasses.”
13 / belief
“I think at the stage we’re at now, there are huge amounts of world-class engineering that have to go into building the frontier systems.”
14 / belief
“As these systems become more powerful and more general and more capable, I think one has to look at the access question.”
15 / belief
“Actually my thesis, and that paper particularly that started that area of imagination in neuroscience, was showing that first of all memory, at least human memory, is a reconstructive process.”
16 / belief
“I think we’re getting to the stage where our systems could help the best human scientists make their breakthroughs quicker, almost triage the search space in some ways.”
17 / belief
“We have those implicitly internally in various safety councils that people like Shane chair and so on. But it’s time for us to talk about that more publicly I think.”
18 / belief
“I’m hoping we’re going to see a lot more of this kind of transfer, but I think things like getting better at coding and math, and then generally improving your reasoning.”
19 / belief
“I think what we’ve got to do in the next few years, in the time before those systems start arriving, is come up with the right evaluations and metrics.”
20 / belief
“I think if a capability like that was discovered through red teaming or external testing, independent testers like government institutes or academia or whatever, then we would have to fix that loophole.”
21 / belief
“If ChatGPT and chatbots hadn’t gotten the interest they ended up getting—which I think was quite surprising to everyone that people were ready to use these things even though they were lacking in certain directions, impressive though they are—then we would have produced more specialized systems built off of the main track, like AlphaFold and AlphaGo, our scientific work.”
22 / evaluation
“I think the history of human endeavors has been such that once you know something’s possible it’s easier to push hard in that direction, because you know it’s a question of effort, a question of when and not if.”
23 / recommendation
“I think that’s actually one of the areas where a lot more research needs to be done, the kind of mechanistic analysis of the representations that these systems build up.”
24 / preference
“I think that’s valuable because those ideas and those algorithms should also work when you have some knowledge too.”
25 / evaluation
“The good news is that with the popularity of the recent chatbot systems, I think that has woken up many of these other parts of society to the fact that this is coming and what it will be like to interact with these systems.”
26 / evaluation
“I think we get some grounding through the RLHF feedback systems because obviously the human raters are by definition, grounded people.”
27 / evaluation
“I think we’re still in the nascent stage of this, of data curation and data analysis and actually analyzing the holes that you have in your data distribution.”
28 / recommendation
“Part of the issue is that with these very general systems, there’s so much surface area to cover about how these systems behave. So I think we are going to need some automated testing.”
29 / recommendation
“I think that maybe in the next three, four, five years, we would also want air gaps and various other things that are known in the security community. So I think that’s key and I think all frontier labs should be doing that because otherwise for rogue nation-states and other dangerous actors, there would obviously be a lot of incentive for them to steal things like the weights.”
30 / evaluation
“I think we don’t know how long AGI is going to be. We always used to say, back even when we started DeepMind, that we don’t have to wait for AGI in order to bring incredible benefits to the world.”
31 / prediction
“I will say that when we started DeepMind back in 2010, we thought of it as a 20-year project. And I think we’re on track actually, which is kind of amazing for 20-year projects because usually they’re always 20 years away.”