I'm a philosophy graduate from Oxford who's been working as a programmer for five years. I'm missing some of the required mathematical background right now, but I think I could be a good fit for AI safety research. I'm trying to figure out the next steps I should take.
- Andrew Ng's machine learning course (https://www.coursera.org/learn/machine-learning).
- Decent understanding of single variable derivations.
- Some basic statistics (probability distributions, Bayesian inference, confidence intervals, linear/logistic regression).
- Python (not an expert but have used it full time for ~1 yr).
- Applied to MLAB bootcamp, rejected (I'm a bootcamp graduate so the timed-algorithm-based application process was always a long shot).
- Applied to a local computer science masters, rejected due to 'insufficient training' (trying to follow up and get more data).
- 80K 1:1 career advising. It was a really helpful experience and they suggested I take a closer look at this path.
- Some local networking. I'm in Montréal and have a friend who works at MILA, he has been pointing me towards a few events and putting me in touch with some folks in the ML space.
- Reading LessWrong, trying to have some of my own ideas on alignment, however simple/flawed. I've had the 'narrow melt-all-GPUs AI' thought summarized here: https://astralcodexten.substack.com/p/practically-a-book-review-yudkowsky. I've thought a bit about how strongly typed systems might help, ekmett's livestreams from when he was working with MIRI are on my to-view list: https://www.twitch.tv/ekmett.
- Multivariable calculus, chain rule, backpropagation.
- More statistics, e.g. enough to understand all of this ARC paper.
- Aforementioned ekmett livestreams.
I'm interested in this because my impression from struggling with papers in the space (like the ARC one above) is that the work to be done requires mathematical and statistical foundations but actually leans more towards something like philosophy in terms of methodology. I think I can pick the mathematics and statistics up, even if I do it quite a bit more slowly than a lot of the readers on here. And I enjoy the methodology of/could be pretty good at philosophy (1st class degree from Oxford).
I am looking for advice on what I should do next (and why). Some hypothetical candidate actions: "quit your job, read and implement papers x, y, z, apply to these openings" (a Daniel Ziegler style opportunity https://80000hours.org/podcast/episodes/olsson-and-ziegler-ml-engineering-and-safety/), "take these online courses", "apply to this course that I suspect will consider applicants with your background", "try and get a job in problem space x". "I actually think you have misconceptions about the space and wouldn't be a great fit" would be disappointing but also valuable feedback if applicable.
I'm in my late 20s and can relocate, retrain, etc. If your suggestion is extreme, please suggest it anyway and I can decide if I'm willing/able. Thanks all!