[Edited] How important do you think it is to have ML research projects be lead by researchers who have had a lot of previous success in ML? Maybe it’s the case that the most useful ML research is done by the top ML researchers, or that the ML community won’t take Redwood very seriously (e.g. won’t consider using your algorithms) if the research projects aren’t lead by people with strong track records in ML.
Additionally, what are/how strong are the track records of Redwood’s researchers/advisors?
Additionally, what are/how strong are the track records of Redwood’s researchers/advisors?
The people we seek advice from on our research most often are Paul Christiano and Ajeya Cotra. Paul is a somewhat experienced ML researcher, who among other things led some of the applied alignment research projects that I am most excited about.
On our team, the people with the most relevant ML experience are probably Daniel Ziegler, who was involved with GPT-3 and also several OpenAI alignment research projects, and Peter Schmidt-Nielsen. Many of our other staff have research backgrounds (including publishing ML papers) that make me feel pretty optimistic about our ability to have good ML ideas and execute on the research.
How important do you think it is to have ML research projects be led by researchers who have had a lot of previous success in ML?
I think it kind of depends on what kind of ML research you’re trying to do. I think our projects require pretty similar types of expertise to eg Learning to Summarize with Human Feedback, and I think we have pretty analogous expertise to the team that did that research (and we’re advised by Paul, who led it).
I think that there are particular types of research that would be hard for us to do, due to not having certain types of expertise.
Maybe it’s the case that the most useful ML research is done by the top ML researchers
I think that a lot of the research we are most interested in doing is not super bottlenecked on having the top ML researchers, in the same way that Learning to Summarize with Human Feedback doesn’t seem super bottlenecked on having the top ML researchers. I feel like the expertise we end up needing is some mixture of ML stuff like “how do we go about getting this transformer to do better on this classification task”, reasoning about the analogy to the AGI alignment problem, and lots of random stuff like making decisions about how to give feedback to our labellers.
or that the ML community won’t take Redwood very seriously (e.g. won’t consider using your algorithms) if the research projects aren’t lead by people with strong track records in ML.
I don’t feel very concerned about this; in my experience, ML researchers are usually pretty willing to consider research on its merits, and we have had good interactions with people from various AI labs about our research.
[Edited] How important do you think it is to have ML research projects be lead by researchers who have had a lot of previous success in ML? Maybe it’s the case that the most useful ML research is done by the top ML researchers, or that the ML community won’t take Redwood very seriously (e.g. won’t consider using your algorithms) if the research projects aren’t lead by people with strong track records in ML.
Additionally, what are/how strong are the track records of Redwood’s researchers/advisors?
The people we seek advice from on our research most often are Paul Christiano and Ajeya Cotra. Paul is a somewhat experienced ML researcher, who among other things led some of the applied alignment research projects that I am most excited about.
On our team, the people with the most relevant ML experience are probably Daniel Ziegler, who was involved with GPT-3 and also several OpenAI alignment research projects, and Peter Schmidt-Nielsen. Many of our other staff have research backgrounds (including publishing ML papers) that make me feel pretty optimistic about our ability to have good ML ideas and execute on the research.
I think it kind of depends on what kind of ML research you’re trying to do. I think our projects require pretty similar types of expertise to eg Learning to Summarize with Human Feedback, and I think we have pretty analogous expertise to the team that did that research (and we’re advised by Paul, who led it).
I think that there are particular types of research that would be hard for us to do, due to not having certain types of expertise.
I think that a lot of the research we are most interested in doing is not super bottlenecked on having the top ML researchers, in the same way that Learning to Summarize with Human Feedback doesn’t seem super bottlenecked on having the top ML researchers. I feel like the expertise we end up needing is some mixture of ML stuff like “how do we go about getting this transformer to do better on this classification task”, reasoning about the analogy to the AGI alignment problem, and lots of random stuff like making decisions about how to give feedback to our labellers.
I don’t feel very concerned about this; in my experience, ML researchers are usually pretty willing to consider research on its merits, and we have had good interactions with people from various AI labs about our research.