644 Problem Solving The idea that computers can think has divided computer scientists into two camps — strong and weak AI. The strong AI camp claims that not only can computers eventually learn to think, but they can become conscious of their thinking as well. The weak AI camp claims that computers can never think in the same sense as humans because their thinking process is nothing more than clever algorithms written by a human programmer in the first place. Strong AI proponents claim that the human brain is nothing more than a set of algorithms, known as instinct, that’s already embedded in Strong versus weak AI our brains, so putting algorithms in a computer is no different. Weak AI proponents claim that consciousness is something that only living creatures can have, so it’s impossible for a computer to ever become aware of itself as a sentient being. Neither side will likely persuade the other, but this endless debate does prove that just because someone has earned a Ph.D. in computer science from a prestigious university doesn’t mean that he or she can’t waste time arguing about a topic that no one can ever answer anyway, like politics, religion, or sports. Basically, AI boils down to two topics — problem-solving and machine learning: ✦ Problem solving: When faced with a situation with missing information, the computer can calculate an answer anyway. ✦ Machine learning: The computer can gradually learn from its mistakes so it won’t repeat them again (which is something even humans have a hard time mastering in their lifetime). Problem Solving Computers are great at solving simple problems that have a clearly defined path to a solution. That’s why a computer can calculate the optimum trajectory for launching a rocket to the moon because this problem involves nothing more than solving a lot of math problems one at a time. Although the idea of calculating the trajectory of a moon rocket may seem daunting, it’s a problem that a human programmer can define how to solve ahead of time. Computers don’t need to be smart to solve this type of problem. Computers just need to be fast at following directions. Unfortunately, human programmers can’t write algorithms for solving all types of problems, so in many cases, the computer is left with trying to solve a problem without any distinct instructions for what to do next. To teach computers how to solve these types of problems, computer scientists have to create algorithms that teach computers how to gather information and solve indistinct problems by themselves.
Problem Solving 645 Game-playing Because teaching a computer how to solve a variety of problems is hard, computer scientists decided to limit the scope of the problems a computer might face. By limiting the types of problems a computer might need to solve, computer scientists hoped to figure out the best ways to teach computers how to learn. Solving any problem involves reaching for a goal, so the first test of artificial intelligence revolved around teaching computers how to play games. Some games, such as tic-tac-toe, have a small set of possible solutions that can be identified in advance. Because there’s only a small number of possible solutions to the problem of playing tic-tac-toe, it’s easy to write algorithms that specifically tell the computer what to do in any given situation. The game of chess is an example of a hard problem because the possible number of valid moves is far greater than any human programmer can write into a program. Instead, human programmers have to give the computer guidelines for solving a problem. These guidelines are heuristics. A heuristic is nothing more than a general set of rules to follow when faced with similar problems. Telling a child to look both ways before crossing the street is an example of a heuristic. Telling a child to look left and then look right before crossing the corner of 5th Street and Broadway is an example of a specific direction, which is absolutely useless for solving any problem except that one. Book VII Chapter 4 To teach a computer to play chess, programmers typically use a tree data structure (see Book III, Chapter 5) that the computer creates before making a move. The tree represents all possible moves, so the human programmer simply writes algorithms for telling the computer how to solve each problem by gathering information about that problem. Because games have distinct rules, teaching a computer to play a game also taught computer scientists the best way to teach a computer to solve any type of problem. Artificial Intelligence Of course, the problem with this theory is that teaching a computer to play chess created a great computer that can only play chess. Game-playing taught computer scientists only how to make computers play better games but not be able to solve problems outside a fixed set of rules. Not surprisingly, the one area that has benefited from game-playing research has been using artificial intelligence techniques to create better computer opponents in video games. The next time you play your favorite video game and the computer seems particularly clever, you can thank all the research in AI for making smarter video games.
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