Seems like you’ve spent a lot of time thinking about AI safety! I think it’d be valuable if you shared links or listed references to things you read that helped you develop your thoughts, since that would let people trace back and connect your writing to the broader literature. As it stands I mostly have to guess to what extent you are thinking about various things within the context of the existing literature vs. coming at the ideas fresh with less engagement with existing writing on particular topics.
It’s Pasha Kamyshev, btw :) Main engagement is through
1. reading MIRI papers, especially the older agent foundations agenda papers
2. following the flashy developments in AI, such as Dota / Go RL and being somewhat skeptical of the “random play” part of the whole thing (other things are indeed impressive)
3. Various math text books: category theory for programmers, probability the logic of science, and others
4. Trying to implement certain theory in code (quantilizers, different prediction market mechanisms)
5. Statistics investigations into various claims of “algorithmic bias”
6. Conversations with various people in the community on the topic
Seems like you’ve spent a lot of time thinking about AI safety! I think it’d be valuable if you shared links or listed references to things you read that helped you develop your thoughts, since that would let people trace back and connect your writing to the broader literature. As it stands I mostly have to guess to what extent you are thinking about various things within the context of the existing literature vs. coming at the ideas fresh with less engagement with existing writing on particular topics.
It’s Pasha Kamyshev, btw :) Main engagement is through
1. reading MIRI papers, especially the older agent foundations agenda papers
2. following the flashy developments in AI, such as Dota / Go RL and being somewhat skeptical of the “random play” part of the whole thing (other things are indeed impressive)
3. Various math text books: category theory for programmers, probability the logic of science, and others
4. Trying to implement certain theory in code (quantilizers, different prediction market mechanisms)
5. Statistics investigations into various claims of “algorithmic bias”
6. Conversations with various people in the community on the topic