I’m interested in having a dialogue. Things I’m interested in:
Deep learning theory, from high level abstract stuff like singular learning theory and tensor programs, to mechanistic interpretability[1], to probably relevant empirical results in ML which seem weird or surprising which theories seem like they have to explain in order to succeed.
Alignment strategy. I have many opinions, and lots of confusions.
What caused the industrial revolution? I don’t know much about this (though from my understanding, nobody knows much about it), but I am curious about it.
I’m also very curious about the history of science, and am always open to having someone talk to me about this, even outside dialogues. If you see me randomly on the street, and I have my headphones in, and am on the other side of the street from you, but you know something about the history of science that you want to tell someone, come to the other side of the street, get my attention, and tell me so! I may not necessarily in that moment want to talk, but I will at least schedule a meet-up with you to talk.
Topics in theories of optimization & agency. Ranging from the most concrete & narrow topics in mathematical programming, to broad MIRI-esque questions about the core of consequentialism, and everything in between.
Lots of other stuff! I think I’m usually pretty interested in peoples’ niche interest they really want to talk about, but I haven’t ever subjected that statement to pressure under the kind of adverse selection you get when putting out such a general call.
A side comment: I really love the dialogues idea. The dialogues I’ve read so far have been really good, and they seem like a fast way of producing lots of really high-quality public ideas and explainers. Hopefully Plato was right that the best way to transmit and consume philosophy is via watching and having dialogues, and the ideas on LessWrong get better.
I’m interested in having a dialogue. Things I’m interested in:
Deep learning theory, from high level abstract stuff like singular learning theory and tensor programs, to mechanistic interpretability[1], to probably relevant empirical results in ML which seem weird or surprising which theories seem like they have to explain in order to succeed.
Whether or not, and under what conditions interpretability (and deep learning theory more generally) is good, and more broadly: how can we do interpretability & deep learning theory in the most beneficial way possible.
Alignment strategy. I have many opinions, and lots of confusions.
What caused the industrial revolution? I don’t know much about this (though from my understanding, nobody knows much about it), but I am curious about it.
I’m also very curious about the history of science, and am always open to having someone talk to me about this, even outside dialogues. If you see me randomly on the street, and I have my headphones in, and am on the other side of the street from you, but you know something about the history of science that you want to tell someone, come to the other side of the street, get my attention, and tell me so! I may not necessarily in that moment want to talk, but I will at least schedule a meet-up with you to talk.
Topics in theories of optimization & agency. Ranging from the most concrete & narrow topics in mathematical programming, to broad MIRI-esque questions about the core of consequentialism, and everything in between.
Lots of other stuff! I think I’m usually pretty interested in peoples’ niche interest they really want to talk about, but I haven’t ever subjected that statement to pressure under the kind of adverse selection you get when putting out such a general call.
A side comment: I really love the dialogues idea. The dialogues I’ve read so far have been really good, and they seem like a fast way of producing lots of really high-quality public ideas and explainers. Hopefully Plato was right that the best way to transmit and consume philosophy is via watching and having dialogues, and the ideas on LessWrong get better.
Which I think of as being strategically very similar to deep learning theory, as it tries to answer many very similar questions.