I don’t yet have actual words committed to text editor for this year’s little A-to-Z yet. Soon, though. Rather than leave things completely silent around here, I’d like to re-share an old sequence about something which delighted me. A lon while ago I read Edmund Callis Berkeley’s **Giant Brains: Or Machines That Think**. It’s a book from 1949 about numerical computing. And it explained just how to *really* calculate logarithms.

Anyone who knows calculus knows, in principle, how to calculate a logarithm. I mean as in how to get a numerical approximation to whatever the log of 25 is. If you didn’t have a calculator that did logarithms, but you could reliably multiply and add numbers? There’s a polynomial, one of a class known as Taylor Series, that — if you add together infinitely many terms — gives the exact value of a logarithm. If you only add a finite number of terms together, you get an approximation.

That suffices, in principle. In practice, you might have to calculate so many terms and add so many things together you forget why you cared what the log of 25 was. What you *want* is how to calculate them swiftly. Ideally, with as few calculations as possible. So here’s a set of articles I wrote, based on Berkeley’s book, about how to do that.

**Machines That Think About Logarithms** sets out the question. It includes some talk about the kinds of logarithms and why we use each of them.

**Machines That Do Something About Logarithms** sets out principles. These are all things that are generically true about logarithms, including about calculating logarithms.

**Machines That Give You Logarithms** explains how to use those tools. And lays out how to get the base-ten logarithm for most numbers that you would like with a tiny bit of computing work. I showed off an example of getting the logarithm of 47.2286 using only three divisions, four additions, and a little bit of looking up stuff.

**Without Machines That Think About Logarithms** closes it out. One catch with the algorithm described is that you need to work out some logarithms ahead of time and have them on hand, ready to look up. They’re not ones that you care about particularly for any problem, but they make it easier to find the logarithm you do want. This essay talks about *which* logarithms to calculate, in order to get the most accurate results for the logarithm you want, using the least custom work possible.

And that’s the series! With that, in principle, you have a good foundation in case you need to reinvent numerical computing.

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