What we’d like to fund

Besides our in-house research, we are currently funding:

An estimate of the size of the externalities imposed by current AI systems. The team is starting with estimating the time lost to increased authentication challenges, but they hope to also capture positive externalities from e.g. users saving thousands of dollars apiece on professional services like coders and lawyers.
A research agenda with the grand aim of decomposing mere benchmark gains into 1) cheating, 2) memorization, 3) shallow generalization, and 4) OOD generalization.
Research into when slowing down AI progress is desirable, the technical requirements for doing it if it is, how to avoid unaccountable concentration of power in the course of it, and how to stop doing it once it has served its purpose.

We would like to fund:

Work on estimating broader AI externalities.
Work on "centaur evals" (evaluating AI systems by how much they help users).
Work on interaction models and "guardian angels" (models which are actually aligned to and customised for a particular user).
Work on the "virtue vs rules" divide in AI alignment.
Work on the question "How is the generalization gap changing?: how much worse are AIs at new tasks?".
Work on the question "How large are the externalities generated by current systems?".
Work on the question "Are we gradually achieving transformative systems by scaling and 'unhobbling' LLMs?".
Work on the question "Will automated science let AIs create transformative systems?".
Work on the question "Is compute growing fast enough for extreme scaling?".
Work on the question "Can current AIs take over major human jobs?".
Work on the question "Are current systems doing sharp recursive self-improvement? (over inputs, algorithms, new research ideas, their own weights)".
Work on the question "Can interactive AI tools outdo fully-automated ‘set and forget’ AIs?".
Work on the question "Can specialized AIs outdo generalist AIs?".