mikes@tekecs.UUCP (Michael Sellers) (09/05/86)
> I find it very interesting that there is so much excitement generated over > parallel processing computer systems by the AI community. Interesting in > that the problems of AI (the intractability of: language, vision, and general > cognition to name a few) are not anywhere near limited by computational > power but by our lack of understanding. [...] > The field that was once A.I. is very quickly headed back to > it's origins in computer science and is producing "Expert Systems" by the > droves. The problem isn't that they aren't useful, but rather that they > are being touted as the A.I., and true insights into actual human thinking > are still rare (if not non-existant). Inordinate amounts of hype have long been a problem in AI; the only difference now is that there is actually a small something there (i.e. knowldege based systems), so the hype is rising to truly unbelievable heights. I don't know that AI is returning to its roots in computer science, probably there is just more emphasis on the area(s) where something actually *works* right now. > Has everybody given up? I doubt it. However, it seems that economic reality > has set in. People are forced to show practical systems with everyday appli- > cations. Good points. You should check out the book "The AI Business" by ...rats, it escapes me (possibly Winston or McCarthy?). I think it was published in late '84 or early '85, and makes the same kinds of points that you're making here, talking about the hype, the history, and the current state of the art and the business. > So what is one to do? Go into cog-psych? At least psychologists are working > on the fundamental problems that AI started, but many seem to be grasping at > straws, trying to find a simple solution (i.e., familly resemblance, primary > attribute analysis, etc.) The Grass is Always Greener. I started out going into neurophysiology, then switched to cog psych because the neuro research is still at a lower level than I wanted, and then became disillusioned because all of the psych work being done seemed to be either super low-level or infeasable to test empirically. So, I started looking into computers, longing to get into the world of AI. Luckily, I stopped before I got to the point you are at now, and found something better (no, besides Amway :-)... > What seems to be lacking is a cogent combination of theories. Some attempts > have been made, but these authors basically punt on the issue, stating > like "none of the above theories adequately explain the observed phenomena, > perhaps the solution is a combination of current hypothesis". Very good, now > lets do that research and see if this is true! And this is exactly what is happening in the new field of Cognitive Science. While there is still no "cogent combination of theories", things are beginning to coalesce. (Pylyshyn described the current state of the field as Physics searching for its Newton. Everyone agrees that the field needs a Newton to bring it all together, and everyone thinks that he or she is probably that person. The problem is, no one else agrees with you, except maybe your own grad students.) Cog sci is still emerging as a separate field, even though its beginnings can probably be pegged as being in the late '70s or early '80s. It is taking material, paradigms, and techniques from AI, neurology, cog psych, anthropology, linguistics, and several other fields, and forming a new field dedicated to the study of cognition in general. This does not mean that cognition should be looked at in a vacuum (as is to some degree the case with AI), but that it can and should be examined in both natural and artificial contexts, allowing for the difference between them. It can and should take into account all types and levels of cognition, from the low-level neural processing to the highly plastic levels of linguistic and social cognitive interaction, researching and applying these areas in artificial settings as it becomes feasable. > [...] My real opinion is that > without "bringing baby up" so to speak, we won't get much accomplished. The > ultimate system will have to be able to reach out, grasp (whether visually or > physically, or whatever) and sense it's world around it in a rich manner. It > will have to be malleable, but still have certain guidelines built in. It > must truely learn, forming a myriad of connections with past experiences and > thoughts. In sum, it will have to be a living animal (though made of sand..) This is one possibility, though not the only one. Certainly an artificially cogitating system without many of the abilities you mention would be different from us, in that its primary needs (food, shelter, sensory input) would not be the same. This does not make these things a requirement, however. If we would wish to build an artificial cogitator that had roughly the same sort of world view as we have, then we probably would have to give it some way of directly interacting with its environment through the use of sensors and effectors of some sort. I suggest that you find and peruse the last 5 or 6 years of the journal Cognitive Science, put out by the Cognitive Science Society. Most of the things that have been written in there are still fairly up-to-date, as the field is still reaching "critical mass" in terms of theoretical quantity and quality (an article by Norman, "Twelve Issues for Cognitive Science" from 1980 in this journal (not sure which issue) discusses many of the things you are talking about here). Let's hear more on this subject! > Ted Inoue. > Cornell -- Mike Sellers UUCP: {...your spinal column here...}!tektronix!tekecs!mikes INNING: 1 2 3 4 5 6 7 8 9 TOTAL IDEALISTS 0 0 0 0 0 0 0 0 0 1 REALISTS 1 1 0 4 3 1 2 0 2 0
craig@think.COM (Craig Stanfill) (09/06/86)
> I find it very interesting that there is so much excitement generated over > parallel processing computer systems by the AI community. Interesting in > that the problems of AI (the intractability of: language, vision, and general > cognition to name a few) are not anywhere near limited by computational > power but by our lack of understanding. [...] For the last year, I have been working on AI on the Connection Machine, which is a massively parallel computer. Depending on the application, the CM is between 100 and 1000 times faster than a Symbolics 36xx. I have performed some experiments on models of reasoning from memory (Memory Based Reasoning, Stannfill and Waltz, TMC Technical Report). Some of these experiments required 5 hours on a 32,000 processor CM. I, for one, do not consider a 500-5000 hour experiment on a Symbolics a practical way to work. More substantially, having a massively parallel machine changes the way you think about writing programs. When certain operations become 1000 times faster, what you put into the inner loop of a program may change drasticly.
rggoebel@watdragon.UUCP (Randy Goebel LPAIG) (09/07/86)
Mike Sellers from Tektronix in Wilsonville, Oregon writes: | Inordinate amounts of hype have long been a problem in AI; the only difference | now is that there is actually a small something there (i.e. knowldege based | systems), so the hype is rising to truly unbelievable heights. I don't know | that AI is returning to its roots in computer science, probably there is just | more emphasis on the area(s) where something actually *works* right now. I would like to remind all that don't know or have forgotten that the notion of a rational artifact as digitial computer does have its roots in computing, but the more general notion of intelligent artifact has concerned scientists and philosophers much longer than the lifetime of the digital computer. John Haugeland's book ``AI: the very idea'' would be good reading for those who aren't aware that there is a pre-Dartmouth history of ``AI.'' Randy Goebel U. of Waterloo