That's an interesting recommendation. I'm unfamiliar with buying reusable masks, do you know a good way to decide whether to buy a small, medium or large mask?
Great post!
I think I'd argue something similar but distinct at the beginning. My impression is that people only quit anki for one reason, and that reason is that they don't like it. All "how to stick with anki" advice is only useful insofar as it makes anki more fun or enjoyable. I genuinely look forward to my reviews (almost) every day. Sometimes I do a lot, sometimes I do a little.
The 20-card/day limit is probably more useful at the beginning, when it can be tempting to try to add in tons of new cards. But more reviews can be fun too! I don't have a hard limit, and I think I do something like 40-50 reviews/day (and often at the end I still want more!) There's definitely such a thing as too many reviews, but that threshold is different for everyone. I'd recommend everyone reading this to test their limits, but as soon as you get annoyed, move back into easy-mode.
Also, I'd like to second @Random Developer that knowing to DELETE cards with little provocation is essential to making anki fun. If your cards are annoying, you will start to associate that annoyance with anki more generally. If you are annoyed with anki, you're more likely to drop it.
From my own experience, the closest I came to deleting anki was when I was trying to learn a bunch of esperanto quickly, doing hundreds of reviews/day (and getting many of them wrong) and became annoyed with it. I tried to push through, but I started to not want to do my other reviews, either. One of the best decisions I ever made was giving up and deleting that entire deck. I think I would have slowly faded away from anki in general if I had stuck with it for a few more weeks. "You must not dread anki; you must not treat it like a chore. If a card causes you to avoid reviewing, gouge it out and throw it away. It is better to give up on one card than to lose the benefits of spaced repetition forever."
One example of a web of interrelated facts that I have concerns molecular simulations, with bold/italic denoting things that I have in my anki deck, or would make good cards.
One interesting thing about moleculaes bouncing around is that a nanosecond, which sounds really short, is actually a decently long time. Consider that molecules at room temperature are typically moving at about the speed of sound (340 m/s) and a typical chemical bond length is about 0.1 to 0.2 nanometers. This means that a typical molecule (if nothing bumps into it) will go 1700-3400 bond-lengths in a nanosecond! Of course, molecules in liquid, which are jammed pretty close together, won't move that far without interruptions- they'll bump into each other, switch direction and bump into others many times over the course of a nanosecond. This means that the typical timestep (the when integrating the differential equations of motion) for a molecular dynamics simulation has to be much shorter. In practice, for a molecular dynamics simulation that simulates all the atoms of a system, is about a femtosecond. With these timesteps, it becomes possible to simulate about a microsecond of simulation time per day of all atoms of a medium-sized protein moving around on a modern GPU like an A40. This is a big reason for why we can't just simulate a protein folding to crack the protein folding problem. Protein folding takes about a second or on the order of a million GPU-days if you were to simulate it.
One thing that's useful for me is to draw analogies. For instance, the earth is about as big compared to the kilogram as benzene ( kg) is small.
That's true. The specific energy of antimatter is also actually double the "maximum" if you don't count the mass of the matter (1 gram of antimatter + 1 gram of air produces about 2 grams worth of energy). Funny enough, this is analogous to combustion fuel. The reason combustion fuel (on the order of 50 MJ/kg for most hydrocarbons) seems to be able to store much more energy than, say a high explosive (on the order of 5 MJ/kg) is because high explosives contain their own oxidizers, while combustion fuel uses the air as an oxidizer.
I'll have to push back on this. I think if there's one specific program that you'd like to go to, especially if there's an advisor you have in mind, it's good to tailor your application to that program. However, this might not apply to the typical reader of this post.
I followed a k strategy with my PhD statements of purpose (and recommendations) rather than an r strategy. I tailored my applications to the specific schools, and it seemed to work pretty decently well. I know of more qualified people who were rejected from a much higher proportion of schools who spent much less time on each application.
(Disclaimer: this is all anecdotal. Also, I was applying for chemistry programs, not AI)
Another way to assess the efficacy of ML-generated molecules would be through physics-based methods. For instance, binding-free-energy calculations which estimate how well a molecule binds to a specific part of a protein can be made quite accurate. Currently, they're not used very often because of the computational cost, but this could be much less prohibitive as chips get faster (or ASICs for MD become easier to get) and so the models could explore chemical space without being restricted to only getting feedback from synthetically accessable molecules.
If this happens, it could lead to a lot of AI researchers looking for jobs. Depending on the incentives at the time and the degree to which their skills are transferable, many of them could move into safety-related work.
I really like this idea, especially the part about doing it on Baffin Island. A few questions/comments/concerns
I find it interesting how nobody seems to make Mr. Humman's arguments about chess, but plenty of people seem to make his arguments concerning ASI.
Now, if I were arguing with Mr. Humman, I'd get him to try to play against stockfish (or to play against me, and cheat using stockfish) to let him know what it really feels like to play against a superior mind. I feel like it would be pretty hard to continue arguing his point about chess after Stockfish punched his lights out for the 5th time in a row (or however long it takes to sink in). As a side-note, I do find it interesting that chess engines were only briefly mentioned here. Nor was a large lookup-table-type chess engine.
Now, it might be that there are no Mr. Hummans (with respect to chess) these days, but I wonder if there ever were any. That is, someone claiming that a machine would never be able to reliably beat a human at chess. Was this a notion that more than a handful of people actually held? Was it shattered by the advent of chess engines or something else?