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!)
If you want a proof-based approach, Linear Algebra Done Right is the typical go-to that’s also on the MIRI page. I went through maybe the first 3/4ths of it, and I thought it was pretty good, in terms of number of exercises and helping you think about manipulating vector spaces, etc. in a more abstract sense.
Otherwise, I’ve heard good things about Gilbert Strang’s MIT OCW course here: https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/.
In general, I think that 3B1B’s videos are really good for building intuition about a concept, but trying to do exercises off of the pedagogy in his videos alone can be quite challenging, especially as he often assumes some mastery with the subject already. (EX: In the eigen-stuffs video, he doesn’t actually explain how to find the eigenvalues of a matrix.)
Thus, I think it makes more sense to stick to a traditional textbook / course for learning linear algebra and using 3B1B as supplementary stuff for when you want a visual / different way of looking at a concept.
Also, it might be worth checking in to see what you want to learn linear algebra for. I suspect there are more domain specific resources if, for example, you cared about just the useful parts of linear algebra used in machine learning (dimensionality reduction, etc.).
I think the overarching thing to do is to simply write more. To that end, there are a lot of ways to make this happen. For example: participating in NaNoWriMo, committing to writing a sequence of articles about topic X, engaging in discourse online, summarizing research papers for a larger audience, writing guest articles for a blog, or journaling.
As for improving your actual workflow, I think that the Typical Writing Class you take in school largely gets it right. For example, writing outlines / summaries before starting the actual writing, asking people to proofread, have several drafts / edit, and try to imagine reading it from the reader’s perspective w/o the extra context you have as a writer.
(I think most of the dissatisfaction I had with the skills taught to me during class had a lot more to do with the context of “Oh man, I have to write this thing using this technique for school and not of my own volition?” rather than the skills themselves not being very good.)
Do you have any thoughts written up anywhere on what a game you’d design would look like? I’ve been following all of your MTG / digital CCG articles with a lot of interest; I’d be really curious which aspects of game design you’d like to incorporate.
I am wondering if there are commonalities between what different self-help things are doing. For example, it seems that a lot of self-help is focused on changing our default actions, ala debiasing, so there is a train of thought that starts with cognitive biases and goes from there.
A related question I’m wondering about, which seems related to this is “Why does self-help work? What is it doing?”
Not 100% related to the question, but maybe the discussion here in the comments could spark more (or another question).
Oh, right, yes, I didn’t think about that.
That is a good example where the above advice I listed, as well as the following models, break.
Oh, right. I think an implicit thing here is “for people you want to be close to, this makes sense to do”.
In other cases, as I sorta skimmed over, having simplified models, relying on norms/roles, etc. etc. is usually enough to get by.