By Patrick Henry Winston, Karen A. Prendergast
What's the base line on man made Intelligence? "The AI enterprise bargains a finished precis of the industrial photograph, current and destiny, for synthetic Intelligence within the machine undefined, drugs, the oil undefined, and digital layout. AI's brightest and top - financiers, researchers, and clients - study present initiatives, speculate on traits in manufacturing facility automation, examine examine in Japan and the united states, and word the professionals and cons of funding possibilities. Contents: "Expert platforms. Amplifying services with specialist platforms, Randall Davis (MIT). XCON: a professional Configuration approach at electronic gear company, Arnold Kraft (DEC). DIPMETER consultant: a professional emblem research procedure at Schlumberger, James D. Baker (Schlumberger). CADUCEUS: An Experimental professional method for scientific analysis, Harry E. Pople, Jr. (University of Pittsburgh). The Low highway, the center highway, and the excessive highway, John Seely Brown (Xerox). "Work and Play. Inventing the longer term, Alan Kay (Atari). The Engineer's Apprentice, Aryeh Finegold (Daisy platforms Corporation). The Programmer's Apprentice, Charles wealthy (MIT). clever Advisory platforms, Roger Schank (Cognitive platforms, Inc. and Yale University). usual Language entrance Ends, Larry R. Harris (Artificial Intelligence Corporation). "Robotics. "Intelligent Robots: Connecting conception to motion, ). Michael Brady (MIT). clever Robots: relocating towards Megassembly, Philippe Villers (Automatix, Inc.). clever Robots: delusion or truth, Paul M. Russo (GE). "Today and the next day. the issues and the Promise, Marvin Minsky (MIT). An funding chance? Frederick R. Adler (Adler & Company). Financing the longer term, William H. Janeway (F. Eberstadt& Co., Inc.) From the Blocks global to the company international, Patrick H. Winston and Karen A. Prendergast (MIT). find out how to study extra.
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Additional resources for Artificial Intelligence Business: Commercial Uses of Artificial Intelligence
The results suggested that Rl would soon solve problems correctly 90 percent of the time. This was very encouraging and indicated that the program was ready to be distributed to its user community for more extended testing. But when it was placed in that setting, users criticized system performance 40 percent of the time. Performance suddenly plummeted to the 60 percent level. What happened? Some of those problems were mistakes on the part of the users resulting from incorrect data or a Randall Davis 28 HEARSA Y-111 PROSPECTOR PUFF TEIRESIAS INTERNIST MACSYMA META-DENDRAL DENDRAL PROGRAMMER'S s;]5TI APPRENTICE ::: : : ABEL HARDWARE DEBUGGING SOPHIE STEAMER MYCIN R1 DIP ADVISOR Figure 5.
0] This will aid in determining the identity ORGANISM-1. 1] the morphology of ORGANISM-! 2] the gram stain of ORGANISM-! 2) that the identity of ORGANISM-1 is pseudomonas [RULE050] MYCIN was using rule 50 and already had certain information about the patient. If the physician adds this fact, then MYCIN will know whether this· rule will fire. If it does fire, MYCIN will have more information about the topic that it is dealing with. COLI? I used rule 084 to conclude that the identity of ORGANISM-!
By starting all over again, retrieving all of the rules that have anything to say about negative gram stain and trying each in turn. This is known as backward chaining. The inference engine starts with the thing it is trying to accomplish and moves backward through the set of knowledge-base rules. When it finally runs out of rules, it asks the physician a question. Answering How and Why Questions Another featur~ of MYCIN-type expert systems is that their behavior is transparent. A physician can ask why the question the program asks is relevant.