FWIW, both linear algebra and multivariable calculus are required for students at UC San Diego, which is a large public institution.
(Although it’s a little tricky as our university has sub-colleges, not all of which require both).
Thanks! I’m a little worried about my own signal-to-noise ratio for now, so I’ll do that later on if I feel my short-form quality improves.
Meant for this to be a reply.
Yeah, Netlify was really easy to set up, so I recommend them!
I don’t think I’ll be able to migrate blog comments because I’m still not sure what I’ll do for comments on the new blog, actually.
I don’t want to use Disqus because it’s bulky, other options cost money, so maybe a self-hosted option...
Long-term, I’ll probably do some more organizing to put posts into sequences / other things to improve readability.
Right now, the focus is on updating/editing old posts, so that the main page is a set of polished essays that work as standalones.
This seems reasonable.
See also Yudkowsky in Inadequate Equilibria for a similar sentiment:
Try to spend most of your time thinking about the object level. If you’re spending more of your time thinking about your own reasoning ability and competence than you spend thinking about Japan’s interest rates and NGDP, or competing omega-6 vs. omega-3 metabolic pathways, you’re taking your eye off the ball.
I don’t think the author disagrees all that much with you. I’m reading his claim as something more like “the default attitude (some) people have towards reading does not set them up for good learning”.
In the essay, he acknowledges the role that effort and metacognition play in making the actual learning happen. The actionable parts I found useful were at the end where he was hypothesizing about improved mediums, e.g. an online textbook with spaced repetition built in to facilitate recall.
I think I agree that the generalizations you cited were hasty, especially as there is no formal review on those matters. I, too, find that I can get value out of books and can probably do a better job than just giving a brief summary.
Let me try to pick out the parts that I liked and see if you disagree with those:
The main part of the thesis that I found interesting was the analogy between books and lectures. In both cases, there is the potential of transmissionism as one naive way of thinking about how information gets absorbed.
It’s clearer, however, that lectures in an educational setting aren’t enough. Without problem sets, examples, and other applications, most of the “real learning” doesn’t happen. (i.e. “mathematics is not a spectator sport”) Good professors seem to recognize this and supplement their teachings accordingly.
Then, the author points out that a similar acknowledgment for books is not the norm. There is still room for improving the medium, and one example he gave was the spaced repetition enhanced online book. There is an undercurrent of “you as the reader need to put in effort to get value out of your reading”, which I agree with. It’s a different state of mind when I’m reading for insight vs reading for fun. In the first case, I might re-read passages, skip around, stop for a bit, take some notes, etc. etc. whereas in the second case, I’m probably just reading rather quickly from cover to cover.
I see. Thanks for providing the additional info!
I’ve read over briefly both this article and the previous one in the series. Thank you for putting these together!
What I’m curious about in quant trading is the actual implementation. Once you, say, have a model which you think works, how important is latency? How do you make decisions about when to buy / sell? (Partially echoing Romeo’s sentiment about curiosity around stop losses and the actual nitty-gritty of extracting value after you think you’ve figured something out.)
Heads-up: nowadays, when people talk about neural networks for games, they really mean deep learning combined with reinforcement learning.
Back to your question: When you don’t have a log of games, you typically have some other way of assessing performance, e.g. assigning a “score” to the state of the game, which you can quantify and optimize.
For a specific well-known example, I think this paper on training to play Atari games with deep reinforcement learning goes over a lot of the actual math / implementation details.
I’ve looked a little bit at the RAISE website, and I’ve looked at the overview of curriculum topics, and I’m finding it a little...sparse, maybe? (I haven’t actually looked at the class materials on grasple though, so maybe there’s more stuff there.) I’m wondering how realistic it would be for someone to start engaging with MIRI-esque topics after learning just the courses RAISE has outlined.
At least for the prerequisites course, these are all topics covered throughout the first two years of a typical undergraduate computer science degree. And that doesn’t seem like quite enough.
EX: TurnTrout’s sequence of essays on their journey to become able to contribute towards MIRI-esque topics seems to span a much greater gamut of topics (linear algebra, analysis, etc.) at greater depth, closer to what one might cover in graduate school.
I guess, to operationalize, I’m curious about:
1. What target audience RAISE has in mind (technical people looking for a refresher, people who have had zero real exposure to technical subjects before, etc. etc.) for their materials.
2. What degree of competence RAISE expects people to come out of the curriculum with, either best-case or average-case.
3. In the best case, how many units of material do you think RAISE can product? In other words, is it enough for students to study RAISE’s material for a 6-month long curriculum? 1 year long?
(Of course, it’s also much easier from my position to be engaging/critiquing existing works, than to actually put in the effort to make all of this happen. I don’t mean any of the above as an indictment. It’s admirable and impressive that y’all have coordinated to make this happen at all!)