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Lauren Ipsum: A story about computer science and more

22 Dec 2014  | Carlos Bueno

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"You're welcome, Laurie. Good luck! Maybe our paths will cross again." The Wandering Salesman headed off, cowbells and pans rattling, to a place he'd never been before.

And so did Laurie...


The Field Guide to Userland: Chapter 0
You might have wondered whether this or that part of the story is real. Can you really make a Fair Coin out of an Unfair Coin? Can you really use ants to find shorter paths? Yes, you can do both—and a lot more besides. In this guide, you'll find out how some of the places, people, and things Laurie encounters in Userland connect with our own world.

Jargon. In the real world, jargon doesn't look like a mouse-dog, or even a dog-mouse; it looks just like an ordinary word! Computer scientists (and really, all scientists) love inventing new words. We call these specialized words jargon or argot. Jargon can be good, because it saves time when you're discussing things with your colleagues. It can also be bad, because it excludes nonexperts from the conversation.

When you're just starting out with programming, all the jargon, argot, and weird new words can be intimidating, but don't let that stop you from learning. Jargon can be silly, or powerful, or dangerous, depending on how you use it. But no matter what words you use, if you always obfuscate instead of explicate, no one will understand you!

Wandering salesman. The Traveling Salesman problem is a classic computer science question. Given a group of cities, your goal is to find the shortest Hamiltonian path, a route that lets you visit each city only once. If you have a large number of cities, it would take a very long time to calculate that path, even for a computer. So the Wandering Salesman might be on the road for quite a while!

Instead of calculating the exact answer, the salesman looks for a path through all the towns that's close enough to being the shortest. A very interesting aspect of the Traveling Salesman problem is that humans are pretty good at solving small examples by hand. There is much discussion about what algorithm people use in their heads while solving it.

The Upper and Lower Bounds. In Userland, the Upper Bounds are a mountain range, as tall as anything can be. The Lower Bounds are valleys as low and deep as anything can be. nothing can be higher and nothing can be lower than these two bounds.

In the real world, finding an upper and lower limit on a problem can simplify things a lot. Say you have to guess a person's age. It's unlikely that anyone is over 150 years old, and no one can be younger than 0 years old. So, 150 is the upper limit and 0 is the lower limit. You can narrow down the bounds even more with simple questions. Is the person still in school? If so, then he is probably younger than 30. Can the person drive a car? If so, then he is probably older than 15.

Mile Zero. You sometimes see signs that mark a point on a highway, like "Mile 14." Now, if there is a Mile 14, then there must be a Mile 13, and a Mile 12, and so on. Everything has to start somewhere, and Zero is where it starts. Possibly the most famous Mile Zero is in key West, Florida. It's at the beginning of US route 1, which goes all the way to Canada. If you look carefully and are very lucky, you might find a Mile Zero near where you live.


Note: This is only a sampling of the Field Guide that pertains to Chapter 0.


Used with permission from No Starch Press, Copyright 2014, this article was excerpted from Lauren Ipsum: A Story About Computer Science and Other Improbable Things, written by Carlos Bueno. Available now in bookstores, the book can also be purchased online.


About the author
Carlos Bueno is a former Facebook engineer who currently works at the database company MemSQL. He writes articles about computer science, programming, and the structure of the Internet.


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