PhD-ing. I think and write about AI safety, cognitive science, history and philosophy of science/technology.
A model of one’s own (or what I say to myself):
My upskilling study plan:
i) Calculus (derivatives, integrals, Taylor series)
ii) Linear Algebra (this video series)
iii) Probability Theory
2. Decision Theory
i) Optimization of individual preferences
4. Computational Complexity
6. Machine Learning theory with a focus on deep neural networks
"Find where the difficult thing hides, in its difficult cave, in the difficult dark." Iain S. Thomas
The Collingridge dilemma: it is difficult to predict the future impact of a technology. However, once the technology has been implemented, it becomes difficult to manage.
The quick answer is that wanting to do alignment-related work does not depend on a Philosophy PhD, or any graduate degree tbh. I'd say, start thinking about what are your interests more specifically and then there might be different paths to impact with or without the degree.
It's more epistemically virtuous to make a wrong prediction than to make no predictions at all.
Why bother with New Year's resolutions when you can just start doing things today (and every today)?
"When they ask me about truth, I say, truth in which axiomatic system?" Teukros Michailides
Helpful post, Zach! I think it's more useful and concrete to focus on asking about specific capabilities instead of asking about AGI/TAI etc. and I'm pushing myself to ask such questions (e.g., when do you expect to have LLMs that can emulate Richard Feynmann-level -of-text). Also, I like the generality vs capability distinction. We already have a generalist (Gato) but we don't consider it to be an AGI (I think).