{"id":220,"date":"2013-06-25T19:15:57","date_gmt":"2013-06-25T19:15:57","guid":{"rendered":"http:\/\/morganredfield.com\/blog\/?p=220"},"modified":"2022-03-22T01:10:51","modified_gmt":"2022-03-22T01:10:51","slug":"use-the-right-metric","status":"publish","type":"post","link":"https:\/\/morganredfield.com\/blog\/2013\/06\/use-the-right-metric\/","title":{"rendered":"Use the right metric"},"content":{"rendered":"

In the field of artificial intelligence, there\u2019s a fairly simple (compared to other AI techniques) method called A* (pronounced A-star) that\u2019s used to solve a lot of problems. It can be used in any situation where you\u2019d want to find the shortest path from where you are now to some goal. This is a very common problem, and shows up everywhere from calculating the best airline routes to video game NPC AI. It\u2019s useful in any kind of route planning, whether it\u2019s a route through space or through some other set of sequential decisions.<\/p>\n

A* works by comparing the state of the world now with the state of the world if a certain decision were made. Since it has to choose which decision to make before it knows what the result of a decision is, it uses a heuristic to approximate the result of different decisions and then chooses between those decisions. The heuristic approximates the distance to the goal after each given decision. There\u2019s a bit more to it than that, but you can read about it on Wikipedia or something if you\u2019re interested. What I find really intriguing about A* is the importance of the heuristic.<\/p>\n

It turns out that A* doesn\u2019t always work well. If the heuristic isn\u2019t chosen well, then A* will do very poorly at finding a way to the goal. It may find a route that is much worse than what\u2019s possible. Whatever heuristic is chosen needs to never over-estimate the true distance to the goal. AI researchers will spend a lot of time thinking up good heuristics for their application, often much more time than they spend implementing the path-finding A* algorithm.<\/p>\n

What AI researchers realize that a lot of other people don\u2019t is that the way you estimate the distance to your goal has a huge impact on how fast you reach it. I\u2019ve been noticing recently that the standard heuristic is very poor in a lot of areas in life where people are trying to achieve something. I first noticed that in the field of education when I was having trouble learning about AI in the first place.<\/p>\n

When I took my first few algorithms and AI classes, I got very high grades. I\u2019m good at school and test-taking, so I got 4.0s in most of the classes that I took. Unfortunately, I was often unable to apply the information or implement the algorithms after the class ended. The heuristic I\u2019d been using to see if I learned, my grade, was often over-estimating my progress. I was eventually able to truly learn and implement the different algorithms that were important, but I had to completely ignore grades while doing so. Instead, I just focused on small projects that included the algorithms. These small projects (what homework aspires but often fails to be) were a truer measure of progress than grades ever were.<\/p>\n

Since noticing that grades were such a poor metric for education goals, I\u2019ve been on the lookout for other places that this appears in people\u2019s daily lives. Here\u2019s what I\u2019ve noticed so far. Can you think of any that I\u2019ve missed?<\/p>\n