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At a quick glance: 32 is greater than 10. 31/32 is about 0.96875, not near pi ~ 3.141593. 31/10 = 3.1 is a worse approximation of pi than 22/7 ~ 3.142857.
One of the bigger risks of iterative statistical or machine learning fitting procedures is over-fit or the dreaded data leak. Over-fit is when: a model performs better on training data than on future data. Some degree of over-fit is expected. A data leak is when: the model learns things about […]
I have a new “crazy theorists” article up: “Working Through A Trivial Algorithm Whose Analysis Isn’t.” It is my notes on reading through Jonassen and Knuth’s amazing 1978 article analyzing 2 to 3 node search trees. You would think there couldn’t be a lot to that. But there is! I […]
I have a new video demonstrating the Kelly Can’t Fail betting strategy. The idea is: this is a classroom appropriate tool for discussing allocating assets in the presence of risk. The usual Kelly betting on coin-flips is too high variance to expect successful classroom demonstrations. However, the zero variance card […]
Recently I’ve been producing (for my own amusement) example Curta calculations. One motivation was arguing if a proposed solution method for Dudeney’s digits problem was something that could in fact have been easily executed in 1924. This got me thinking, is there an actual difference between calculation and computation? In […]
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It can be bleak out there, but the candor is very helpful, and you occasionally get a win.
Society is once again left holding the bag
How do projects fail at large tech companies? As I’ve said many times, failure means executives aren’t happy with how the project turned out. At healthy companies, that typically means that a sensible engineer wouldn’t be happy either, because the project didn’t work or users hated it. But what actually causes the projects to fail? I’ve seen a lot of projects go wrong - both up close and at a distance - in the last ten years. Here are the main reasons why. Doomed from the start Lots of projects fail because there’s no way they could possibly have succeeded. In American law, some cases get dismissed at “summary judgment”: even if the plaintiff succeeds in proving everything they aim to prove, it still wouldn’t add up to demonstrating enough illegal activity to win their case. At tech companies, some projects are like that: even if the plan goes off without a hitch, the project is still doomed to fail. Some doomed projects begin with over-ambitious plans. For instance, an executive…
Two simple questions to help make Society's Backend better
