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Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement
Manage episode 295254144 series 2906499
Content provided by Kanjun Qiu. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kanjun Qiu or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Jacob Steinhardt (Google Scholar) (Website) is an assistant professor at UC Berkeley. His main research interest is in designing machine learning systems that are reliable and aligned with human values. Some of his specific research directions include robustness, rewards specification and reward hacking, as well as scalable alignment.
Highlights:
πβTest accuracy is a very limited metric.β
π¨βπ©βπ§βπ¦βYou might not be able to get lots of feedback on human values.β
πβIβm interested in measuring the progress in AI capabilities.β
36 episodes
Share
Manage episode 295254144 series 2906499
Content provided by Kanjun Qiu. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kanjun Qiu or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Jacob Steinhardt (Google Scholar) (Website) is an assistant professor at UC Berkeley. His main research interest is in designing machine learning systems that are reliable and aligned with human values. Some of his specific research directions include robustness, rewards specification and reward hacking, as well as scalable alignment.
Highlights:
πβTest accuracy is a very limited metric.β
π¨βπ©βπ§βπ¦βYou might not be able to get lots of feedback on human values.β
πβIβm interested in measuring the progress in AI capabilities.β
36 episodes
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