Welcome to CSE473

What is AI Anyway?

AI is a strange field because it's as much of an issue to come up with a definition of the problems we try to solve as it is to solve them. Either you tend to like that or it drives you crazy.

One of the first "definitions" of the term came in the 1940s, from Alan Turing. His definition is totally operational. To decide whether a computer program is "artificially intelligent" you conduct a test in which a human has two screens in front of him, one is connected to a different human and the other to a computer program. He is given some period of time (say several hours) to interact with both entities, and there are no restrictions on the questions he can ask. At the end of that time he has to decide which one is human. If enough people guess wrong enough of the time, then the program is "artificially intelligent."

That's actually a very compelling definition, because it points out that a program doesn't have to think like a human being in order to be intelligent: intelligence is really decided by what a program (or other being) does rather than by how it does it, or to put it another way, IO behavior is everything.

There are two big problems with the Turing test though. The first is this idea that you're communicating through a very narrow communication channel, essentially using Unix talk to communicate. Things that really seem to indicate intelligence---gestures, facial expressions, tone of voice, even manual dexterity---are lost in this definition.

More important, however, is that in a weird sense it's overly narrow in that it equates ``intelligence'' with ``conversational human-like behavior.'' Imagine a big automated factory, where all the machines are servo-controlled. Further imagine that there is a central computer, or more likely a big network of computers, that completely controls the factory's operations. You give it production goals, and the computer does the rest: it orders raw materials, schedules the machines, routes the output, diagnoses and fixes problems with the assembly line, even schedules and performs preventative maintenance on the machines and even on itself. Is that computer intelligent? Certainly not by Turing's definition, since it probably can't tell you what it's doing, much less discuss poetry. But in a very interesting sense it is intelligent with respect to its environment. It can't discuss the weather with you, but it can be very "smart" when it comes to running all aspects of its factory.

It's this "intelligent agent" concept of AI that is very attractive to many people (including all of us at UW) at the moment. There are several implications to taking this view. First of all intelligence is still measured in terms of "IO behavior," but IO behavior is not necessarily conversation. For our agents, IO could be any one of a number of operations. Etzioni's software agents, for example, communicate with the world using Unix commands. One of my students is building an intelligent travel agent that communicates with the human user using (restricted) English language, but that communicates with the reservation database over a TCP/IP channel. The automated factory manager has all sorts of servo controls that constitute what it can do to the world and all sorts of electronic or visual input from the factory that constitutes its sensors.

The second and most important implication of this view of AI is that intelligence should always be measured with respect to a particular domain, or with respect to a particular set of problems to be solved. This is a relief to us, since "general human intelligence" (i.e. building Data the robot) is a long long way off, and it's nice that we can build useful technology in the short term!

What is distinctive about solving problems from an AI perspective?

In my opinion the answers to those two questions are no and yes respectively. One weird thing about AI is that the definition of an "AI problem" tends to change: once we solve a problem, in some peoples' minds it's not AI any more. A great example is the first FORTRAN compilers. Compiling a long program was definitely thought to be kind of mysterious, and definitely required intelligence. But not any more. Likewise chess: one of the first AI efforts was to build a chess-playing program, and now they've succeeded in building machines that play at the grandmaster level, but the curious thing is that nobody (even people who agree with the "IO Behavior" criterion for intelligence) think that these machines are intelligent! That's because we all know and understand the algorithms they use, and they're no longer mysterious. One danger that AI has to watch out for is never getting credit for the cool things it does!

While an "AI problem" is pretty elusive, I think there are techniques and methods that have been developed over the years that are "distinctively AI," and that's what we will focus on in the course. The first is symbolic programming, which is just to say that we tend to look at data and data structures as collections of symbols rather than as, say, matrices of numbers. If you've forgotten your Lisp programming, symbols are essentially equivalent to "enumerated types" in C++. We just prefer to talk about the symbol GREEN somehow referring to "the color green" even though we know it's implemented as a bit in some bit vector. Likewise we love to manipulate lists of symbols like (SALARY JOHN 5000) even though we know it's just a pretty mundane data structure. Which is not to downplay the importance of symbolic programming: for some problems it's a very convenient programming abstraction, and one nice thing about the class is you will get a good chance to add it to your programming repertoire.

The other "distinctively AI" technique is something I'll call "heuristic problem solving" for want of a better phrase. We have long since realized that every problem we want to solve is NP-hard or worse in the worst case, but it seems like in real life the worst case rarely occurs. What's more, finding the absolute optimal solution to a problem (say running errands) is often not so important. So we would be happy with an algorithm that does "pretty well" on most "common instances" of the hard problem. The way we try to implement this idea is to try to formalize our knowledge about a domain, often with rules like "buy the ice cream after waiting in line at the post office, especially on a hot day" and incorporating those heuristics into the problem-solving algorithm. Another hot area of research is in building programs that learn rules like that, thus making themselves more effective problem solvers over time.

What are we going to study in this class?

You can look at the course syllabus (also on the web) for a more specific idea of the topics. Mainly we're going to talk about various ways to build problem-solving agents, but we'll also take a brief spin through Language Understanding, just because it's an interesting topic to study.

How is the course structured?

What are the prerequisites?

Why should you take this class?

Why should I not take this class?