The Importance of Machine Learning in a Machine Learning Environment

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Adam: The broader question is how do you take some problem in society and put it in a machine learning context. Like the idea of well if we somehow had this training feedback on what people actually valued then we could throw a machine learning at it, he says. Adam: Some of these ideas suggest to me that actually it could look pretty different but I don't know if that's true.

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What are focused research organizations? Which kinds of research projects lend themselves to the FRO model? Researchers in academia frequently complain about the incentive structures around funding and publishing; so how do FROs change those dynamics? Why must FROs be time-limited, especially if they're successful? Who's in charge in an FRO? How does "field-building" help to improve science? What effects might large language models have on science?

Adam Marblestone is the CEO of Convergent Research. He's been launching Focused Research Organizations (FROs) such as E11 bio and Cultivarium. He also serves on the boards of several non-profits pursuing new methods of funding and organizing scientific research including Norn Group and New Science. Previously, he was a Schmidt Futures Innovation Fellow, a consultant for the Astera Institute, a Fellow with the Federation of American Scientists (FAS), a research scientist at Google DeepMind, Chief Strategy Officer of the brain-computer interface company Kernel, a research scientist at MIT, a PhD student in biophysics with George Church and colleagues at Harvard, and a theoretical physics student at Yale. He also previously helped to start companies like BioBright and advised foundations such as the Open Philanthropy Project. His work has been recognized with a Technology Review 35 Innovators Under 35 Award (2018), a Fannie and John Hertz Foundation Fellowship (2010), and a Goldwater Scholarship (2008). Learn more about him at adammarblestone.org.

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