Tom Cunningham
Tom Cunningham
@testingham.bsky.social
Economics Research at OpenAI.
A new post: On Deriving Things

(about the time spent back and forth between clipboard whiteboard blackboard & keyboard)

tecunningham.github.io/posts/2020-1...
January 31, 2025 at 7:14 PM
Choosing headcount? Increasing headcount on a team will shift out the Pareto frontier of a team, and so you can then sketch out the *combined* Pareto frontier across metrics as you reallocate headcount.
October 25, 2023 at 3:48 PM
Choosing ranking weights? You can think of the set of classifier scores (pClick,pReport) as drawn from a distribution, and if additive it's easy to calculate the Pareto frontier, and if Gaussian then the Pareto frontier is an ellipse.
October 25, 2023 at 3:48 PM
Choosing launch criteria? You can think of the set of experiments as pairs (ΔX,ΔY) from some joint distribution, and if additive it's easy to calculate the Pareto frontier, and if (ΔX,ΔY) are Gaussian then the Pareto frontier is an ellipse.
October 25, 2023 at 3:47 PM
New post: Thinking about tradeoffs? Draw an ellipse.

With applications to (1) experiment launch rules; (2) ranking weights in a recommender; and (3) allocating headcount in a company.
October 25, 2023 at 3:47 PM
The most interesting mechanisms: (1) AI can find patterns which humans didn't know about; (2) AI can use human tacit knowledge, not available to our conscious brain.
October 6, 2023 at 8:07 PM
It requires formalizing the relationship between the AI, the human, and the world. Interestingly there are a number of reasons why the AI, who only encounters the real world via mimicking human responses, can have a superior understanding of the world. Can describe this visually:
October 6, 2023 at 8:06 PM