I read and reread the paper several times, as well as consulting a lot of adjacent papers, Wikipedia etc.
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I added several hundred Anki cards while doing.
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The early cards were mostly very simple things: facts about TDGammon (which used a similar approach to beat Backgammon), very basic facts about how Go works, and reinforcement learning and Monte Carlo Tree search.
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Later, the facts got more and more complex.
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Of course, I didn't master all the literature around the paper. But I think I made pretty rapid progress coming up to speed.
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Now, just to write one article that wouldn't necessarily have been a good use of time. But a nice thing about Anki is that the information is retained. When the AlphaGo Zero and AlphaZero papers came out, they were very easy to read.
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Verb form: I talk and think of "Ankifying" a paper or book etc.
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Replying to @michael_nielsen
Interesting to wonder when we'd be able to generate a good deck automatically using AI.
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Replying to @danielgross
I've found little use in other people's decks (or Duolingo, which is similar to having an auto-generated deck). One strong, reliable finding in memory research is that depth of encoding makes a huge difference.
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Replying to @michael_nielsen @danielgross
Writing Anki questions is a way to get that depth. ("Depth of encoding" ~ "How rich and complex a set of thoughts you have around an idea when committing it to memory.")
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Given a page of text describing some concepts, and knowlege of what prior "encodings" you have, it _should_ be possible to suggest what concepts you're likely to abstract.
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Replying to @danielgross @michael_nielsen
Constructing a knowledge dependency graph (as
@juanbenet frames it) from what you'd like to know to what you already know seems like a limiting factor0 replies 0 retweets 0 likesThanks. Twitter will use this to make your timeline better. UndoUndo
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