Sports analogies to deliberate practice

Being an athlete and training for a sport confers skills that are easily transferred to many kinds of work. Many employers like athletes because they know how to be part of a team (unless it's a solo sport), and they understand what it's like to work towards a goal.

The nice thing about sports is that training and the results of that training are tangible and easy to see. When you train for a sport, you aim to be consistent in your performances. You also pace yourself so that you peak at competition time. Each sport is different. For instance, playing basketball requires you to peak several evening a week for games, but a marathoner might only run a race every few weeks. However, the ideas are the same. Pacing is important because you don't want to get injured. This is just like in a knowledge worker type job. If you work too hard, you'll burn out. So pace yourself.

The reason that athletes become really good is because athletic training is a form of "deliberate practice." The idea is that you figure out what are the key things you need to work on so that you can perform at the highest level during competition. For a hockey player, that might mean trying to take shots from many different angles, off different feet, in awkward positions, so that during a game, you can score. For more information about deliberate practice, check out this post about deliberate practice at Psychology Today and this discussion of Geoff Colvin's book on the subject.

Thought of the day: Can you teach a computer good taste?

I don't know much about artificial intelligence or learnabilty, but it's fun to think about what a computer might be able to learn. One characteristic that separates a novice from an expert is "good taste". (I first heard of this idea from a Paul Graham essay.) Supposedly, "good taste" is something that is not a characteristic that is easy to acquire quickly, unlike simple "knowledge." Then it would be a interesting challenge to see if a computer could learn good taste. If it's hard for humans, it would be probably be even harder for computers. Scientists already have enough trouble in machine vision research, getting computers to see things that humans find obvious.