mgallagh@uhasun.hartford.edu (Michael Gallagher) (05/20/91)
Greetings, all. As I have read this newsgroup for awhile, I have grown very interested in the field of Artificial Intelligence. I realize that it is not an area one just can start as a straight discepline on the undergraduate level. In fact, this university only offers one intro AI class; an overview of the different areas of the field. Therefore, my question is: What is/are the best ways to get into the field of AI?? What are the best starting points, background(s) to have, etc. Any info/advice/etc would be appreciated. -M. Gallagher
gal2@quads.uchicago.edu (Jacob Galley) (05/22/91)
In article <622@ultrix.uhasun.hartford.edu> mgallagh@uhasun.hartford.edu (Michael Gallagher) writes: > ...Therefore, my question is: What is/are the best ways to get into >the field of AI?? What are the best starting points, background(s) to have? I am in the same position as you. You should look around in the philosophy and psychology departments, and if semantics and meaning interest you, check out linguistics. Basically, I'm finding that the best way to prepeare myself for CogSci/AI (I'm not even that sure what the difference is yet) is to build my own major, out of a loosely structured program here called "Philosphy and Allied Fields." Good luck! -- -- Jacob Galley gal2@midway.uchicago.edu University of Chicago.
clin@eng.umd.edu (Charles Chien-Hong Lin) (05/26/91)
In article <1991May22.154503.10564@midway.uchicago.edu>, gal2@quads.uchicago.edu (Jacob Galley) writes: > In article <622@ultrix.uhasun.hartford.edu> mgallagh@uhasun.hartford.edu (Michael Gallagher) writes: > > ...Therefore, my question is: What is/are the best ways to get into > >the field of AI?? What are the best starting points, background(s) to have? > > I am in the same position as you. You should look around in the philosophy > and psychology departments, and if semantics and meaning interest you, check > out linguistics. Basically, I'm finding that the best way to prepeare myself > for CogSci/AI (I'm not even that sure what the difference is yet) is to build > my own major, out of a loosely structured program here called "Philosphy and > Allied Fields." > > Good luck! > > AI, to me, is viewed as a conglomeration of different things. For folks in philosophy, one might worry about what is intelligence. In psychology, one might worry about the same thing but perhaps in a different manner. Anyway, I'm not suited to explain AI in that fashion. I'm better suited to explain it from a computer science viewpoint. From this viewpoint, it would be better to get into computer science in general. AI, at least the way its practiced among computer scientists, is the use of computer science techniques to simulate human intelligence. So, unless you know what a graph or tree is, knowing AI without knowing CS in general won't be too useful. Now whether CS techniques will do the job of simulating intelligence, that's a different question. I take a more pragmatic view. If it appears to do things more "intelligently", well good enough. I don't think it has to AI has to produce things that are exactly intelligent aw we understand it, just as a plane does not imitate a bird except that it flies. Probably you should go ahead and take the course. It will give you an overall feel of what AI is. The name AI often suggests ideas that do not bear out in reality. Until you take a course, and see what it's really like, you won't get an accurate view of AI. If you're still interested, try doing a project with a professor who does work in AI. If you are interested in the computing aspects of AI, then a degree in computer science would be the way to go. If you prefer linguistics or semantics without so much of the programming, then philosophy/psychology/cog. sci may be the way to go. Personally, I'm not to clear on what cognitive science is myself. Let me briefly mention some of the topics in AI. Planning -- Start with some initial state (a bunch of blocks lying on a table) and some operators (how to move a robot arm). Attempt to come up with a plan that achieves a goal state. (Have the blocks piled up). Planning might be useful for having, say, a robot try to carry out a task without help. Expert Systems -- most commercial of the topics. Emulate the behavior of an expert by a lot of if-then clauses ("If coffee cup dropped, ground is wet"). Neural networks -- Use neural networks to simulate the brain. Basically, a network with inputs, and some weighted factors that produce a desired output. Networks are "trained" more than "programmed" in the usual sense of the word. Natural language -- Try to understand human sppech. Need to know stuff like context-free grammars. Uncertain Reasoning -- Not exactly a subdiscipline. Concerns of how one should reason given that you are uncertain of your knowledge. Uses probability among other methods. Automated theorem Proving -- Most mathematical of the group. Attempts to find efficient ways of proving mathematical theorems. Machine vision -- Getting a computer to "see" or identify objects. Robotics -- How to get a robot to move around on uncertain terrain. How to get robots to figure out how to carry out certain tasks. Machine Learning -- Trying to get a machine to "learn". One version is to use a bunch of logical clauses and decide what are meanignful rules to learn. Computational Machine Learning -- Stuff like identifying finite automatas. Deals with efficient methods of doing so, but domains are usually mathematical objects. Commonsense reasoning -- How do people learn common sense ideas. Qualititative physics -- How do you represent the idea that if an object falls, it breaks, etc. This is only a brief overview, but covers most of the major topics. Some I'm sure I've left out; others overlap, or do not yet constitute a distinct subfield of AI. Even some of the terminology is suspect. When I say "learning", I do not mean learning as you might think of "learning". Like I said, one way is to have a bunch of logical clauses, etc. Sometimes you want to prove something. If it is proved, then maybe some of the rules that were deduced can be kept around. Makes for more efficient access for the next thing to be proved. You see that there is a lot of CS flavor in this. Basically CS has come up with a bunch of mathematical techniques, and since those are pretty well understood, they try to apply it to the task of AI. It's tough to tell if this is the right approach, but with nothing else more innovative at the time being, that's the technique being used. To me, AI should gear itself toward making things that are practically useful (more or less), and not toward completely philosophical ideas, but I haven't really given the topic much thought. -- ____ _ / | __|_| clin@eng.umd.edu | | | harles | in "University of Maryland Institute of Technology" | _| \_____/ |_|\___/