![]() ![]() In my research field, the same technical answers keep coming up: matrix factorizations, log-linear models, regressions, stochastic gradients. But for example if it has anything to do with mining or the earth, I know there’s a good chance the answer is ORE, and I can work backwards to figure out the pun. Clues often have tricky or misleading wordplay. But I know the inventory of answers that keep coming up, which makes it much easier to connect them to questions. ![]() I don’t mean I can fill in everything immediately. The most important thing is that - and this will sound weird, but stay with me - I know the answers. Knowing one fill word can help me recognize another and another, in the same way that knowing a property of one distribution can imply a whole chain of relationships. And just as in puzzles, these facts are interconnected. In the same way, knowing lots of facts about math doesn’t necessarily help in machine learning, but knowing specific facts about specific branches of math can help immensely. Wayne GRETZKY may have been as great a hockey player as Bobby ORR, but only one of them is a short sequence of high-frequency letters. For both research and crosswords you need to know trivia, obviously, but it has to be the right trivia. The best way to reduce a solution space is to know something. It usually means that your assumptions are wrong. In machine learning it’s not uncommon to prove that your problem has no solution, yet humans do it unconsciously thousands of times a day. Sometimes, though, this helps the other way: when the letters that are blank can’t possibly be filled with anything correct, you know you need to revisit your assumptions. Fixing as much as possible from the start and then working in the space that’s left keeps you focused. If you know the first letter of a word, you can cut down the number of possible solutions dramatically. Solving a problem is much easier when you have a partial answer. When doing research it’s easy to get emotionally attached to your ideas, but we need to be open to modifying or even abandoning them when it turns out that our great idea isn’t actually how the world works. If I’m solving on paper I like to use a pencil with a big, solid eraser. I have occasionally done a Saturday puzzle in pen, but I always end up with some Es that look an awful lot like they began life as As. Walking, napping, eating lunch, they all help in research too. Anyone who has done crosswords knows the bizarre thrill of coming back to an impossible section after a few hours and seeing the answer instantly. Paradoxically, stepping away from a problem can be the most useful tool for persisting. The back of your mind is as important as the front. It’s not a virtue or a character trait, it’s experience and confidence that give you the optimism to persist, even when nothing is obvious. Inspiration, perspiration, etc, etc, we’ve all heard that before, but what I think is important is to recognize where persistence comes from. Even if it’s the flashes of inspiration that get remembered, nothing hard happens without persistence. I sit with the clues, start working around the few breaks I get, and then start to unravel whole sections. It’s that I don’t stop after that initial pass. What has changed is not so much that I can fill in more words at a glance (I can, but only a few). Today I can do a Saturday puzzle without too much trouble. Every single square was just as blank as when I started. I scanned the full set of clues and knew absolutely nothing. I remember the first time I saw a Saturday New York Times puzzle. You can’t solve hard crosswords, or pursue research, without confidence and persistence. But lately I’ve noticed that many of the habits and attitudes I developed as a solver are also relevant to research. ![]() Apart from doing a puzzle before grad school exams to warm up my neurons, I never saw much of a connection between puzzles and my day job as a professor of data science. I’ve been doing crossword puzzles daily for many years. ![]()
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