What podcasts say about Ryan Greenblatt
Every statement, with the speaker, the exact quote and the moment it was said.
What Ryan Greenblatt has said on podcasts
59 statements · 32 positive · 20 negative · 2 mixed · 5 neutral
Automated AI research could compress four or five years of progress into one year.
“Maybe my sort of median expectation is something like four or five years of AI progress in a single year.”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 1:13
Automated AI R&D could produce four or five years of progress within one year.
“Maybe my sort of median expectation is something like four or five years of AI progress in a single year.”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 1:14
Full AI R&D automation may arrive around 2030–2031, with all-human-job capability around 2033.
“I would say that I expect full automation of ARD, perhaps somewhere around 2031. 2030, and then getting to the beats. All humans on the job milestone. Maybe I expect median around 2033”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 3:10
Full AI R&D automation may arrive around 2030–2031, with all-job superiority around 2033.
“I expect full automation of AR&D, perhaps. somewhere around like 2031, 2030, and then getting to like the like beats all humans on the job milestone. Maybe I expect median around 2033”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 3:10
Video-editor automation will precede automation of all human jobs.
“the milestone for automating your video editor is earlier than the milestone of being able to automate all human jobs”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 4:07
Video-editor automation may occur around the time of full AI R&D automation.
“the video editor automation maybe occurs more like around full automation of AR&D”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 4:17
Many small-scale AI R&D tasks can be containerized, verified, and reinforced-trained.
“there's this whole class of containerizable, verifiable, small scale R and D tasks that we can aggressively rl the AI's on”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 5:19
Training on verifiable AI R&D tasks will transfer fairly well to broader AI R&D.
“my expectation is that the transfer for ARD will look pretty, pretty good, but not amazing.”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 9:33
Skills trained on small AI R&D tasks will transfer fairly well to broader AI research.
“My expectation is that the transfer for AR&D will look pretty good, but not amazing.”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 9:33
Machine learning research is structurally shallower than mathematics research.
“I think ML is a very shallow domain relative to math.”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 11:36
Machine learning and most domains are relatively amenable to iterative hill-climbing research.
“I think ML and most other domains are much more amenable to sort of hill climbing.”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 12:46
Machine learning and most domains are more amenable to incremental optimization than deep abstraction.
“ML and most other domains are much more amenable to sort of hill climbing.”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 12:46
More compute substantially helps AI research.
“I think compute is just really helpful for doing AI research.”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 15:31
Current AI systems can competently match mediocre machine-learning researchers.
“when I look at AIs right now, I think it's already the case that they can pretty competently match humans who are mediocre at ML research”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 16:28
Current algorithms could train a GPT-3-compute model somewhat better than GPT-4.
“right now we'd be able to train a version of GPT-3 that's probably somewhat better than GPT-4”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 18:50
GPT-3-era compute with current algorithms could produce a model moderately better than GPT-4.
“right now we'd be able to train a version of GPT3 that's probably somewhat better than GPT4. Is basically what we'd see, probably a moderate amount better than GPT4.”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 18:50
Five years of AI progress may require roughly eight years of algorithmic progress.
“to get five years of AI progress, you're probably going to need around, I would say, maybe eight years of algorithmic progress very roughly”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 19:07
Additional expert-human data effort has not been a major driver of AI R&D progress.
“scaling up the amount of effort spent on getting expert human data has not been hugely important for AIR&D in general.”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 20:20
Increasing expert-generated human data has not been a major driver of AI R&D progress.
“scaling up the amount of effort spent on getting expert human data has not been hugely important for AI R and D in general”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 20:20
Better RL environments mainly reflect improved design knowledge and AI labor, not more human experts.
“The reason why RL environments today are much better than they were in 2024 is not that much because we have hired way more human experts to make RL environments. It is instead much more, because we better know what RL environments we even want to make and how we should structure them. And also we're using huge amounts of AI labor to build RL environments.”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 21:18
AI systems can be trained to learn rapidly and adapt across varied environments.
“you could train an AI to be really, really good at learning on the fly”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 25:14
Broadly trained AI systems could quickly learn to work as TSMC engineers despite lacking TSMC-specific data.
“those AIs could then be put on the job at TSMC. And then even though TSMC is not literally in their data distribution, their data distribution is really wide and the AIs are extremely good on their data distribution, such that it transfers to picking up being good at being an engineer at TSMC and learning that on the fly”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 26:00
AI systems can understand unfamiliar codebases faster than humans.
“AIs can understand a new code base much faster than humans can”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 27:40
Advanced AI can understand a large codebase in substantially less than an hour.
“The model will get some understanding of the code base very fast in the course of maybe significantly less than an hour, potentially much less than an hour”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 28:00
AI systems have substantially improved on non-verifiable domains.
“the AIs have improved a bunch at non verifiable domains”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 30:41
Choosing and designing large frontier-scale experiments is AI R&D’s least verifiable component.
“The least verifiable. Probably making calls on large experiments.”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 34:06
Choosing and interpreting large experiments is AI R&D’s least verifiable component.
“The least verifiable? Probably making calls on large experiments.”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 34:06
Training AI systems to detect training-code bugs should be relatively easy.
“training AIs to find bugs is going to be one of the easier... tasks to train AIs on”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 37:57
Training AI systems to detect training bugs is relatively verifiable, though sometimes compute-intensive.
“I think that this is a pretty verifiable task. It's not arbitrarily verifiable because maybe often to demonstrate the bug, you might need to do a moderate scale compute experiment”
Open the episode · Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032Listen at 38:20
Highly capable AI R&D alone could radically transform the world.
“for the world to be radically transformed, it is sufficient for the AIs to be really good at R&D”
Open the episode · Ryan Greenblatt – What happens once AI can automate AI research?Listen at 44:15
Statements are attributed to the speaker as said on the episode and reflect their view at the time, not PodLume's. They are not advice.