Open Philanthropy recommended a grant of $85,000 to support Michael Specter’s work co-teaching a course with Professor Kevin Esvelt and developing a seminar for fellows of MIT’s Knight Science Journalism program. Both courses will be held at MIT.
We sought applications for this funding to support the development of courses on a range of topics that are relevant to certain areas of Open Philanthropy’s grantmaking.
Grant investigators: Luke Muehlhauser
This page was reviewed but not written by the grant investigator. Massachusetts Institute of Technology staff also reviewed this page prior to publication.
Open Philanthropy recommended a grant of $13,277,348 over four years to the Massachusetts Institute of Technology (MIT) to support research led by Neil Thompson on modeling the trends and impacts of AI and computing. Thompson will use this funding to hire new staff and expand his lab work.
This follows our November 2020 support and falls within our focus area of potential risks from advanced artificial intelligence.
Grant Investigator: Catherine Olsson and Daniel Dewey
This page was reviewed but not written by the grant investigators. Massachusetts Institute of Technology staff also reviewed this page prior to publication.
Open Philanthropy recommended a grant of $1,430,000 over three years to the Massachusetts Institute of Technology to support research by Professor Aleksander Madry on adversarial robustness as a means to improve AI safety.
This falls within our focus area of potential risks from advanced artificial intelligence.
Grant investigators: Luke Muehlhauser
This page was reviewed but not written by the grant investigator. Massachusetts Institute of Technology staff also reviewed this page prior to publication.
Open Philanthropy recommended a grant of $550,688 to the Massachusetts Institute of Technology to support research led by Neil Thompson on modeling the trends and impacts of AI and computing. The research will consist of projects to learn how algorithmic improvement affects economic growth, gather data on the performance and compute usage of machine learning methods, and estimate cost models for deep learning projects.
This falls within our focus area of potential risks from advanced artificial intelligence.
The grant amount was updated in October 2021.