Category: Chapters

1. A carefully constructed strategy can’t make up for poor decisions.
2. Just because it’s rare doesn’t mean it’s good.
3. Real men don’t concede.
4. There is no best deck, but there is a worst deck*.
5. Just because it’s big and breathes fire doesn’t mean it’s useful to you.
6. High cost often has an inverse relationship with actual usefulness.
7. Everyone gets mana-screwed once in a while.
8. The strategy that wins is often the simple one.
9. Playing with people is more fun than with the computer.
10. When in doubt, just repeat to yourself, “Untap. Upkeep. Draw.”

* 59 swamps and a Zephyr Falcon

You know how it is. You’re halfway up leading a single pitch 5.9 friction climb and you just ran into its nasty 5.10a crux. It’s a simple overhang, if you were two feet higher you could just pop your foot on your handhold and you’d be on easy street, 5.5 to the top. But there doesn’t seem to be a hold where you need it, and your feet can only find little wisps of rock and small patches of roughness. You lean back hoping to notice the bucket that your hands can’t find, or anything that closely resembles solid footing.

“You’re only making it worse. Just move your feet up and trust it. You gotta keep moving.”

Your belayer is right. The longer you sit there, the more tired you get. The more tired you are, the harder it is to do anything. You fall, because you’re exhausted.

“Lower me, I’m too pumped to get it. Fuck.”

Climbing is about rhythm and balance more than anything. No one is strong enough to hang on the rock forever, and the key is to just keep moving, to keep inching up the face. Everything always looks better when you’re an inch higher. If you sit in a comfortable spot for too long you stop seeing the available handholds, and you get very picky about what you’ll actually trust to hold your weight.

In reality, all of the holds will hold your weight. Rock is strong, way stronger than you’ll ever be. A flimsy little flake might be just enough to swing you up to the biggest bucket of your life, but unless you trust it and make the move you’ll never know.

Momentum is mental. You might be in a tough spot with limited options, but you have to keep climbing. Otherwise you’ll just get tired and fall.

“If you cannot understand that there is something in man which responds to the challenge of this mountain and goes out to meet it, that the struggle is the struggle of life itself upward and forever upward, then you won’t see why we go.”
– George Leigh Mallory

Yesterday was my first day as a part time non-degree student at George Washington University. Our professor was one of the lead programmers for the Apollo program, and informed us that the “Machine Intelligence and Cognition” class we had just sat down to was to be his swan song: the final class he would teach before retirement. He asked us to introduce ourselves.

“My name is Sam, and I work at the United States Patent and Trademark office as a Patent Examiner.”
“You know, Einstein was a patent examiner.”
“I remind myself … every … day.”

We started talking about the history of thinking about the brain, about how religion had stifled independent thought for so long in history. “Why does this pen drop? Because God wanted it to.” He said there was still magic in the world, and that it was his life long task to wipe away the magic and to begin to understand the human brain. He cited the following quote from the famous mathematician and computer scientist Dijkstra:

“The question of whether Machines can Think is about as relevant as the question of whether Submarines can Swim!”

This is a very famous quote in the Artificial Intelligence community, spoken by a person not very famous for his work in AI. However, my professor took this quote and used it in a way that I’d never imagined and still don’t feel entirely comfortable with. Paraphrased, this quote meant that there was still something special about the human brain to Dijkstra, something off limits. Call it a soul, call it the notion that people cannot build something smarter than themselves, but call it magic at some level.

I disagree.

I’m not sure if it was an internal interpretation that I made originally or if it was something my old AI teacher said, but I always interpreted it as much more positive. I looked up the original context in which he made the comment, and I don’t feel I’m overstepping my bounds when I say that Dijkstra wasn’t calling the brain off limits.

When early scientests approached the problem of creating an artificial intelligence, they strove to recreate the brain. All of it. They thought to make something act intelligent, it had to be intelligent like humans were intelligent. That’s not quite the case.

Your inbox is carefully cleaned by statistics. Bayesian filters they call them. They match up words and phrases and create complex models to determine what is spam and what isn’t. The process is pretty neat and exhibits a certain “smartness” to it, but it’s not a human intelligence. In fact, it solves the problem of distinguishing between spam and non-spam about on par with a human and about a million times as fast.

When you approach a problem, most likely you have a set way of proceeding, a way of thinking about it. Dijkstra tells me to throw all of that out. The wheel was a monumental achievement to humans because it provided a simpler solution to the problem of traveling on even ground. If you’re moving things on even ground and you don’t want them to stop themselves, wheels are much easier and more useful than inventing a pair of robotic legs.

Machines that Think is something we’ll always be working towards. Kurzweil says it can be done by 2030, or we’ll at least be able to simulate it. But it’s not necessary to sort your mail.

Watch to see how other people do things, but never forget that you could do it better.

Last night I remembered something I hadn’t thought about in a long time. During my freshman year, a math professor showed me a novel solution to an interesting problem: dating. And he did so with the help of Calculus.

First off, let’s assume that I am capable of comfortably dating and knowing N girls in my life. Not that I will date N girls, but that if I didn’t ever get married and continued to date for the rest of my life, I could reasonably expect to date N girls.

I can only date one girl at a time (it’s me), and the problem isn’t as interesting if you’re allowed to go back to one you’ve already dated. You could just date all N and pick the best one. How can I pick the best girl to marry if I can’t go back in time? Or even more generally, how can make sure I pick from the top of the list, the top A girls? Let’s formalize:

N := number of girls I can reasonably expect to date
A := number of girls who’d make me happy (A < N ) I can compare any girl I am with to any girl I've been with in the past.

Let’s say I could date 10 girls, and that I could be happy with the top 3. If I pick the first girl I date, I’ll only have a 3/10’s chance of happiness. What should I do?

My professor’s answer was to date P girls initially without considering any of them for marriage. These were to provide a baseline, a sort of control group for how crazy or wonderful women you could date would be. Then, after you had accomplished that, you should marry the first woman who is better than anyone else you have ever dated. Simple enough, but how to solve for P?

He used integrals, but for the life of me I couldn’t remember how he did it. I started writing out axiomatic rules (like I did just now) and tried to solve it using statistics and set theory, to no avail. Finally I gave in, started up Visual Studio C++ and wrote in one furious sitting a complete Monte Carlo simulation to solve the problem. It spat out the following table (N=10, A=3) of values for P and their expected success percentage:

0: 0.309 1: 0.602 2: 0.670 3: 0.615 4: 0.581 5: 0.518 6: 0.401 7: 0.283 8: 0.209 9: 0.088

According to my program, the optimal solution if you can date 10 girls and 3 of them would make you happy is to date two girls, get a feel for them, then marry the first girl that beats the rest. Although looking at the numbers, it sways a little to the right, maybe I should take a more finely grained data set, get another decimal point.

My professor might have solved the problem correctly, but I get the feeling there’s a little more to dating than C++.

Update 7/23/06: I found the solution my professor used in solving the problem. Enjoy.

http://plus.maths.org/issue3/puzzle/stopping/solution.html