4 Viewpoints of AI

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W H E R E S T H E A I ?Roger C. Schank

I survey four viewpoints about what AI is. I describe a program exhibiting AI

as one that can change as a result of interactions with the user. Such a program

would have to process hundreds or thousands of examples as opposed to a

handful. Because AI is a machines attempt to explain the behavior of the

(human) system it is trying to model, the ability of a program design to scale

up is critical. Researchers need to face the complexities of scaling up to programs

that actually serve a purpose. The move from toy domains into concrete ones

has three big consequences for the development of AI. First, it will force software

designers to face the idiosyncrasies of its users. Second, it will act as an impor-

tant reality check between the language of the machine, the software, and the

user. Third, the scaled-up programs will become templates for future work.

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AI Magazine Volume 12 Number 4 (1991) ( AAAI)


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For a variety of reasons, some of which I dis-cuss in this article, the newly formed Institutefor the Learning Sciences has been concen-trating its efforts on building high-qualityeducational software for use in business andelementary and secondary schools. In the twoyears that the institute has been in operation,it has created quite a few prototypes that gobeyond the kind of things that have tradi-tionally been built under the heading of edu-cational software. Behind these new designs

are a number of radically different teachingarchitectures that change the nature of howstudents interact with computers, movingfrom what is called the page-turning archi-tecture to architectures based on simulation-driven learning by doing, story- or case-basedteaching, and Socratic dialogues (Schank1990a; Schank and Jona 1991).

These prototypes have been successful, andfor the most part, the institutes sponsors havebeen impressed with the results, acknowledg-ing that they have never seen anything quitelike them before. Nevertheless, researchers atthe institute have been plagued by a curious

kind of question when they show these pro-totypes to certain audiences. The questionthey keep hearing is the title of this article:Wheres the AI?

At first, I found this question puzzling.Everything they do is AI. But apparently, AIhas a specific definition to many people, andthese programs didnt fit this definition. Myconcern was to figure out what this definitionwas. And while I am at it, maybe it would behelpful if the field of AI itself understood theanswer to this question in a more profoundway. It is not that AI needs definition; it ismore that AI needs substance, a distinction

that I discuss later.

Four Viewpoints on AI

When someone who is not in AI asks wherethe AI is, what assumptions about AI areinherent in the question? There seem to be atleast four prevailing viewpoints that I have todeal with, so this question assumes at least

one of the following four things: (1) AI meansmagic bullets, (2) AI means inference engines,(3) AI means getting a machine to do some-thing you didnt think a machine could do(the gee whiz view), and (4) AI meanshaving a machine learn.

The magic bullet view of AI is as follows:Intelligence is actually difficult to put into amachine because it is knowledge dependent.Because the knowledge-acquisition process iscomplex, one way to address it is to finesse it.

Let the machine be efficient computationallyso that it can connect things to each otherwithout having to explicitly represent any-thing. In this way, the intelligence comes forfree as a by-product of unanticipated connec-tions that the machine makes.

An alternate version of this view is that AIis something that one could, in principle, dis-cover in ones garage. What the form of thisdiscovery might be remains a mystery, butone would drop the discovered item or tech-nique into the machine, and it would becomeintelligent. This view is held, quite firmly, bymany people who write me letters after

having read my name in a magazine article aswell as by many venture capitalists (and, pos-sibly, some connectionists).

The inference engine view of AI wasbrought forth by a variety of people on theWest Coast (for example, the MYCIN [Shortliffe1976] and DENDRAL [Buchanan and Feigen-baum 1978] projects at Stanford Universityand the PROSPECTOR [Duda, Gaschnig, and Hart1979] project at SRI International). Their viewwas, I suspect, an answer to this same ques-tion. When the expert system world began toexplode, AI experts were expert at finding outwhat an expert knew and writing down the

knowledge in rules that a machine couldfollow (this process came to be known asknowledge engineering [Feigenbaum 1977]).Although such expert systems could, in fact,make some interesting decisions, businesstypes who were called on to evaluate thepotential of such systems probably asked,Wheres the AI? The real answer was that theAI was in the ability of AI people to find outwhat the experts knew and to represent the

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It is not that AI needs definition; it is more that AI needssubstance


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information in some reasonable way, but thisanswer would not impress a venture capital-ist. There had to be something that could belabeled AI and that something came to beknown as an inference engine.1 Of course,much of the AI world understood that infer-ence was an important part of understanding,so it made sense that an expert system wouldneed to make inferences too, but to label theinference engine as the AI was both mislead-ing and irrelevant. The business worldbelieved that it needed this label, however, asI discuss later on.

Thus, it came to pass that business peoplewho read about AI and assumed that AI andexpert systems were identical began to expectinference engines in anything they saw on acomputer that had AI in it. Many otherpeople in AI were left in a quandary, however,about how to explain that what they did wasalso AI, which led to questions such as, Whatis AI anyway? This question was never easy toanswer without discussing the nature of intel-ligence, a subject best left undiscussed in abusiness meeting.

I term the next view the gee whiz view.This view maintains that for a particular task,if no machine ever did it before, it must beAI. Two important types of programs to dis-cuss within this conception of AI are opticalcharacter readers and chess-playing programs.Are these programs AI? Today, most peoplewould say that they are not. Years ago, theywere.2 What happened?

The answer is that they worked. They wereAI as long as it was unclear how to make them

work. When all the engineering was done,and they worked well enough to be used byreal people, they ceased to be seen as AI.Why? The answer is threefold: First, geewhiz only lasts so long. After a while, peopleget used to the idea that a computer can dosomething they didnt know it could do.Second, people tend to confuse getting amachine to do something intelligent withgetting it to be a model of human intelligence,and surely, these programs arent intelligentin any deep sense. Third, the bulk of the workrequired to transform an AI prototype into anunbreakable computer program looks a lot

more like software engineering than it doeslike AI to the programmers who are workingon the system, so it doesnt feel like you aredoing AI even when you are.

The fourth view of AI is one that I myselfhave espoused. It is a long-term view. It saysthat intelligence entails learning. When yourdog fails to understand his environment andimprove on mistakes he has made, you referto him as dumb. Intelligence means getting

better over time. No system that is static, thatfails to change as a result of its experiences,looks smart. Real AI means a machine thatlearns. The problem with this definition isthat according to it, no one has actually doneany AI at all, although some researchers havemade some interesting attempts.3 Accordingto this view, there is no AI, at least not yet.Thus the question, Wheres the AI? is a toughone to answer.

Knowing Something Helps,Knowing a Lot Helps More

Lets consider the SAM-FRUMP-ATRANS experience.In 1976, a group of my students and myselfbuilt SAM, a program that summarized, para-phrased, translated, and answered questionsabout newspaper stories (Cullingford 1978,1981). The program was slow and cumber-some and was capable of reading only a few

stories in a few domains. Nevertheless, it wasthe first program to perform this task, and wewere proud of it. No one asked where the AIwas because no one had ever seen anythinglike it. It qualified as AI based on the gee whizcriterion.

In 1978, we attempted to improve on SAMin some ways, thus came FRUMP (DeJong1979b). FRUMP was very fast in comparison toSAM. By this time, we had even faster machinesof course, but the major speedups wereaccomplished by limiting inferencing andattempting to understand only the gist of anygiven story. We were able to blur the distinc-

tion between inferencing and parsing in away that made interesting theoretical claims(DeJong 1979a).

We were excited by FRUMP. It worked onroughly 50 different domains and succeededon about 75 percent of the stories that werein these domains. The branch of the UnitedStates Department of Defense that had spon-sored this work wanted to use FRUMP for a par-ticular task. We were put in the strangeposition (for a university research lab) ofhaving to hire people to code domain knowl-edge into FRUMP. After a year of this coding,FRUMPs robustness had only slightly improved,

and the sponsor was annoyed because it hadactually wanted to use the program. Further, Iwas concerned about having to hire moreand more non-AI people to make a piece ofsoftware really work. I had always thoughtthat our research money was for supportinggraduate students to do research. I wasuneasy about actually trying to develop aproduct. And it didnt interest me all thatmuch to try. What did it mean to be doing

the geewhiz view

maintainsthatif no

machine everdid it before,

it must be AI.


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AI, I wondered. It meant building theoriesand programmed examples of these theories,it seemed to me, in the attempt to understandthe nature of the mind as part of the ultimatequest for the learning machine of the fourthviewpoint given earlier. In 1978, I was notinterested in building products.

By 1982, though, I had become interestedin trying to build AI products. Why? Probablythe main thing that had changed was AI. Theexpert system hype had hit, and the venturecapitalists were everywhere. I spoke loudlyand often against this overhype and ran apanel that year at the National Conference onArtificial Intelligence (held at Carnegie-MellonUniversity in Pittsburgh) on the coming AIwinter that was likely to result from ridicu-lous expectations.4 At the same time, I hadbegun to think more and more about theFRUMP experience. I realized that AI peoplereally had to produce something that workedat some point, that we couldnt simply writepapers about ideas that might work or workedon a few examples and satisfied the sponsorswho were pouring real money into AI. Withthe increase in money coming from the com-mercial world, the die was cast. We had toproduce something then, I figured, or wemight not get another chance. Thus, I, too,started a company, called Cognitive Systems,with FRUMP as the intended product.

Why FRUMP never was the product of Cogni-tive Systems is a business discussion not rele-vant here. What Cognitive Systems did windup building was something called ATRANS,which was a program that read international

bank money transfer messages (Lytinen andGershman 1986). The program is workingtoday in various international banks. It parsessentences that all have similar content abouthow and where money is to be transferredfrom country to country. In essence, ATRANS isa single-domain (and a narrow domain atthat) FRUMP. A version of this program is alsoused by the United States Coast Guard. At aDefense Department meeting three years ago,ATRANS was identified as one of only three AIprograms actually in use for defense.

Why am I telling you all this? Because thereis 1 important fact to know about ATRANS. It

took something like 30 person-years to makeit work. This number is in addition to any ofthe SAM-FRUMP work. There is an importantlesson to be learned here.

To make AIreal AIprograms that dosomething someone might want (which is,after all, the goal of AI for those who fund AIand for many of those in AI), one must do agreat deal of work that doesnt look like AI.Any of Cognitive Systems programmers

would have been justified in complainingthat they had come to work there to do AI,and all they were doing was working on end-less details about determining various abbre-viations for bank names. They also asked,Wheres the AI?

The lesson to be learned from ATRANS issimple enough. AI entails massive softwareengineering. To paraphrase Thomas Edison,AI is 1-percent inspiration and 99-percentperspiration. AI people will never build anyreal AI unless they are willing to make thetremendously complex effort that is involvedin making sophisticated software work.

But this discussion still doesnt answer theoriginal question. Sponsors can still honestlyinquire about where the AI is, even if it is inonly 1 percent of the completed work.

From Three Examples to ManyThe answer to Wheres the AI? is that its inthe size. Let me explain.

For years, AI people specialized in buildingsystems that worked on only a few examplesor in a limited domain (or microworld).Sometimes these systems didnt really workon any examples at all; it just seemed plausi-ble that they would work. The practice of get-ting AI programs to work on a few examplesis so rooted in the history of AI that it israrely discussed. There are many reasons forthis practice, but the simplest explanation is

that until the creation of the various venturecapital backed companies in the early 1980s,almost all AI programs were Ph.D. theses thatproved the concept of the thesis with a fewexamples. It was almost never anyones job tofinish the thesis.5 Often, it wasnt clear whatthis finishing meant anyway because thesetheses were rarely directed at real problemsfor which there was a user waiting. In anycase, even if there was a ready use for the pro-ject, no one wanted to tackle the inherentlyuninteresting task of doing all that softwareengineeringat least no one in an AI labwanted to. And even if someone did want to

do it, there was no one who wanted to payfor it or who seriously understood how muchit would really cost to do the other 99 percent.

Nevertheless, there were otherwise intelligentpeople claiming that Winograds (1972) SHRDLUprogram that worked on 31 examples hadsolved the natural language problem or thatMYCIN (Shortliffe 1976) had solved the prob-lem of getting expertise into a computer.6

Prior to 1982, it is safe to say that no one had

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smaller domains. But the chess work didntscale up at all, so the reasonableness of doingsuch work is simply a question of whetherthis work was needed by anybody. If therehad been a need for chess programs, thenchess would have been seen as a success of AIbut probably not as actual AI. In the end, AIwas never supposed to be about need, however.In the 1960s and 1970s, most AI people didntcare if someone wanted a particular programor not. AI was research.

The correct AI question had to do with thegenerality of a solution to a problem, andthere was a good reason. It is trivial to build aprogram to do what, say, Winograds (1972)SHRDLU program did for 31 sentences. Justmatch 31 strings with 31 behaviors. It wouldtake a day to program. People believed thatWinograds program was an AI programbecause they believed that his program did itright. They believed it would scale up. Theybelieved that it would work on more than 31sentences. (In fact, so did he. See Winograd[1973]). At the time, when I was asked myopinion of Winograds work, I replied that itwould never work on a substantially largernumber of sentences, nor would it work indifferent domains than the one for which itwas designed. I did not reply that his programwas not AI, however.

The fact that a program does not scale updoes not necessarily disqualify it from beingAI. The ideas in Winograds program were AIideas; they just werent correct AI ideas in myopinion. What then does it mean for a pro-gram to have AI ideas within it? After all, thisquestion is a key one in our search to find thelocation of the AI.

So to summarize the argument so far, an AIprogram exhibits intelligent behavior, but anon-AI program could as well. An AI programshould scale up, but many do not. And an AIprogram has AI ideas in it. Further, an AI pro-gram is not intended to accomplish a particu-lar task but rather to help shed light onsolutions for a set of tasks. This summary was,more or less, the standard view of AI withinAI prior to 1980.

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exactly what constitutes intelligent behavioris not exactly agreed on. With my scaleupmeasure, the idea in looking at a chess pro-gram would have been to ask how its solutionto the chess problem scaled up. Or to put itanother way, were people writing chess pro-grams because they wanted computers to playchess well, or were they writing them becausethey scaled up to other problems of intelli-gent behavior?

It would seem that no one would want achess program for any real purpose, but thisquestion, after all, is a marketing one. If thetoys made money, well, fine: It doesnt matterhow they work; it matters that they work. Butif we care about how they work, there areonly two possible questions: First, we need toknow if the solution to making them worktells us anything at all about human behavior.Second, we would want to know if it tells ussomething that we could use in any programthat did something similar to, but more gen-eral than, playing chess.

Brute-force, high-speed search through a

table of possible prestored moves is unlikelyto be anything like the human method forchess playing. Chess players do seem tounderstand the game they are playing and areable to explain strategies, and so on, after all.Thus, the only other question is whether onecould use these same methods to help tell ussomething about how to solve problems ingeneral. In fact, the original motivation towork on chess in AI was bound up with theidea of a general problem solver (for example,Newell and Simons [1963] GPS system). Thedifficulty is that what was learned from thiswork was that people are really specific prob-

lem solvers more than they are general prob-lem solvers and that the real generalizationsto be found are in how knowledge is to berepresented and applied in specific situations.Brute-force chess programs shed no light onthis issue at all and, thus, are usually deemednot to be AI.

Thus, the scaleup problem can refer toscaleup within a domain as well as to scaleupin the greater domains that naturally embody

an AI program is not intended to accomplish a particular

task but rather to help shed light on solutions for a set of tasks.


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smart people and quite good with Lisp (theprogramming language used by many AIresearchers), and maybe they could pull it off.When I heard about the plan that the venturepeople had for them, however, I knew theywere doomed.

The plan went like this: If the expert systempeople were given a hard problem, enough ofthem could build a program that could solvethis problem within reasonable constraints.But no one solution would have been a prod-uct. For a certain amount of money, theycould build a program to solve problem A,but they wouldnt be a great deal closer tosolving problem B. Venture capitalists wouldnever invest in a custom software develop-ment company. They want a company thatmakes one thing and then sells that thing100,000 times. A company that makes onething and sells it once isnt much of aninvestment. What the expert system peopleknew how to do was build custom software.But there isnt much money in doing it.What they were asked to do instead was builda shell, an environment for developing soft-ware. This project made sense to the ventureguys, and the AI guys had to go along. Theproblem was that although the shell might begreat to sell, first, it would be wrong, andsecond, where was the AI?

The first issue first: Why would it be wrong?The assumption of the venture capitalists wasthat given the right tool, any programmercould build an expert system. This idea was amarketable one, so it worked in their terms.Of course, it wasnt an idea that had a lot ofreality in it. Building a complex AI system iscertainly made easier by having a good envi-ronment for programming, but knowing

what to program is the real issue. So one isleft addressing the second issue, namely,where is the AI? This issue was addressedfrom a business point of view with the con-cept of an inference engine. The idea was thatthere was a piece of magic that was the AIand that this magic, plus a software develop-ment environment that made it easy to buildthese things, was salable. And it was, at leastinitially. The problem was that little of a

The Great Expert SystemShell Game

At the beginning of the last section, I saidthat the answer to the title question was inthe size. When building FRUMP, my studentsand I realized that the difference betweenFRUMP and something somebody wanted toactually use involved a great deal of engineer-ing of some rather dull information. To buildATRANS, we had to bite this bullet. Little bylittle it became clear to me that to build an AIprogram that someone wanted, an idea thatwas virtually a contradiction in terms in the1970s, someone would have to stuff a machinewith a great deal of knowledge. Smart is nice,but ignorant tends to obviate smart. Gradual-ly, it was becoming clear that AI would haveto actually work and that making it workmight mean paradoxically, using our owndefinitions, not doing AI at all.

This situation was all brought to a head bythe great expert system shell game. Whenthe venture capitalists discovered AI, theybrought more than just money to the table.

They also brought with them the concept ofa product. Now, it took me a long time tounderstand what the word product was sup-posed to mean, so dont assume (if you are inAI) that you understand it. In fact, I am stillnot sure I understand it in a profound way. Ithought, for example, that a conceptualparser (for example, ELI [Riesbeck and Schank1976] or CA [Birnbaum and Selfridge 1979])might be a product. It isnt. I thought thatFRUMP might be a product. It wasnt. An expertsystem shell is a product. Unfortunately, itsnot always a profitable one (which is, not sur-prisingly, the standard measure of goodness).

I dont intend to explain here what a productis. What is important to understand is whythe venture capitalists insisted that their AIpeople build expert system shells.

When the expert system folks went intobusiness, I was skeptical that they could buildanything that anyone really wanted. After all,no one had ever done that in AI before, andas I have said, it was kind of out of the boundsof AI to even try. But I figured, they were

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really complex nature could be built withthese shells. Or in other words, a program-ming environment plus an inference enginedoesnt make up all there is to building anexpert system, which might not have beenthe thing to be building in the first place.

Where was the AI? It wasnt in the infer-ence engine at all. These inference engineswere, after all, pretty simple pieces of softwarethat tested to see if the logic of the rules thatthe knowledge engineers wrote came up withany conclusions. The AI in complex expertsystems was in the organization and represen-tation of knowledge, the attempt to under-stand the domain under study, and thecrystallization of what was important in thedomain and how experts in the domain rea-soned. Now, I was saying at the time (see, forexample, Schank et al. [1977]) that the AI wasalso in collecting the actual experiences of theexperts and indexing them so that remindingand, hence, learning could take place, but the

expert system folks were in no mood to payattention. Those that did were usually notinvolved in the shell game. To put it anotherway, the AI was where it always wasin theattempt to understand the intelligent behav-ior in the system being modeled.

The fact that no expert ever experiencesanything in his(her) domain of expertisewithout learning something from the experi-ence and changing in some way was easilyignored.7 The hope was that static systemswould model an experts reasoning ability atany moment. But experts dont remain expertsfor long if all they do is blindly apply rules.

The act of creating a shell with which non-AIpeople could write rules to be run by an infer-ence engine was, in a sense, an act of notdoing AI. What had been left out was the skillthat AI people had been learning all this time,the skill of figuring out what was going on ina domain and getting a machine to model thehuman behavior that occurred in the domain.What had been left out were the AI ideas.

Some companies tried to remedy this situa-tion by training the users of these new shellsto become knowledge engineers. But AI iskind of a funny subject. For years, we havebeen taking graduates from the best institu-

tions in the country and teaching them AI.Often, even after a few years, they just dontget it. What dont they get? They dont getwhat the issues are, how to attack an issue,how to have an idea about an unsolved prob-lem, and how to build programs that embodythese ideas. AI is a way of looking at complexproblems, and often, it is difficult to learn todo. It seemed to me that it was hopeless to tryto teach this area to programmers in a few

weeks when years often didnt help.Thus, my claim is that although some

expert systems might well have been AI, fewof those built with inference engines werelikely to be unless one changed the then-current definition of AI. The change is easyenough to describe. AI had been the creationof new ideas about how to represent and usecomplex knowledge on a computer. Now, thedefinition was changing toward AI being pro-grams that utilized these AI ideas in someapplication that someone wanted.

Where was the AI in the expert systemshells? It was in the assertion that rules wouldeffectively model expertise and in the programsthat attempted to implement this assertion.The only problem was that for complexdomainsthat is, for AI-like domains ofinquirythe assertion was wrong.

The Larger the Better

One of the real issues in AI, as I mentionedearlier, is size. When we talk about scaleup,we are, of course, talking about working onmore than a few examples. What every AIperson knows is that a program that works on5 examples is probably not one-tenth the sizeof one that works on 50. Outsiders imaginethat it might be the same size or, possibly,one-half the size. But what is really the case isthat for real domains, the size changes workthe other way. Once you have to account forall the myriad possibilities, the complexity isphenomenal. It is critical, if AI is to mean

applications of AI ideas rather than simplythe creation of these ideas, that size issues beattacked seriously. No one needs a programthat does 5 examples. This approach workedin 1970 because AI was new and glossy. Itwill not work any longer. Too much moneyhas been spent. AI has to dive headlong intosize issues.

Now, as it turns out, although this issuemight seem to be annoying and, possibly,dull as dust, the truth is that it is size that isat the core of human intelligence. In myrecent book, Tell Me a Story (Schank 1990b),I argue that people are really best seen as

story-telling machines, ready to tell you theirfavorite story at a moments notice. Peoplerarely say anything that is new or somethingthey have never said before. Looked at in thisway, conversation is dependent on the art ofindexing, finding the right thing to say at theright time.8 This problem is pretty trivial forsomeone who only has three stories to tell.Much like our speech-recognition friend earlier,it is simply a problem of differentiation.

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have real experiences, contradictory view-points, exceptions, confusions, and the abilityto have an intuitive feel for a problem. Get-ting at these issues is critical. It is possible tobuild interesting systems that do not knowwhat the experts know. Expertise can be cap-tured in video, stored and indexed in a soundway, and retrieved without having to fullyrepresent the content of the expertise (forexample, the ASK TOM system [Schank et al.1991]). Such a system would be full of AI ideas,interesting to interact with, and not whollyintelligent but a far sight better than systemsthat did not have such knowledge available.

Software engineering is harder than youthink: I cant emphasize strongly enoughhow true this statement is. AI had better dealwith the problem.

Everyone wants to do research: One seri-ous problem in AI these days is that we keepproducing researchers instead of builders.Every new Ph.D. recipient, it seems, wants tocontinue to work on some obscure smallproblem whose solution will benefit somemythical program that no one will ever write.We are in danger of creating a generation ofcomputationally sophisticated philosophers.They will have all the usefulness and employ-ability of philosophers as well.

All that matters is tool building: Thisstatement might seem odd considering mycomments about the expert system shell game.However, ultimately, we will not be able tobuild each new AI system from scratch. Whenwe start to build useful systems, the secondone should be easier to build than the first,

and we should be able to train non-AI expertsto build them. I dont mean that these toolswill allow everyone to do AI on their personalcomputers. It does mean that certain standardarchitectures should evolve for capturing andfinding knowledge. From this point of view,the shell game people were right; they justput the wrong stuff in the shell. The shellshould have had expert knowledge about var-ious domains in it, knowledge that is avail-able to make the next system in the domainthat much easier to build.

These five issues are real and important tothink about. They are practical points, not

theoretical ones. A little practicality mighthelp the field get to the next level of theory.

OK, But What Do YouReally Think?

AI depends on computers that have realknowledge in them. Thus, the crux of AI isin the representation of this knowledge, the

content-based indexing of this knowledge,and the adaptation and modification of thisknowledge through the exercise of thisknowledge. What I really think is that case-based reasoning (Riesbeck and Schank 1989;Jona and Kolodner 1991) is a much morepromising area than expert systems ever wereand that within the area of case-based reason-ing, the most useful and important (andmaybe even somewhat easier) area to work inis case-based teaching. Building real, large,case bases and then using them as a means bywhich users of a system can learn is a problemwe can attack now that has enormous importfor both AI and the users of such systems.

Case-based teaching depends on solvingthe following problems: the indexing ofmemory chunks, the setting up of tasks basedon indexing, the matching of student state toan index, the anticipation of the next question,knowledge navigation, problem cascades,Socratic teaching, and button-based interaction.

I will not bother to explain each of theseproblems because I have done so elsewhere(Schank 1991). The major point is that eventhough getting machines to tell what theyknow at relevant times is a simpler form of AIthan the full-blown AI problem, it is notsimple. Even the kind of practical, develop-mental form of AI that I am proposing is fullof enough complex problems to keep many atheoretician busy. I would just like theoreti-cally minded AI people to stop countingangels on the head of a pin.

So where is the AI? It is in the size, theideas, and the understanding of what is sig-

nificant that contributes to the behavior ofintelligent beings.

Notes

1. Inference engine is just another name for a pro-

duction system; see Davis and King (1977) and

Waterman and Hayes-Roth (1978).

2. For example, in Computers and Thought, one of

the early seminal AI books, there appears a chapter

entitled Chess-Playing Programs and the Problem

of Complexity (Newell, Shaw, and Simon 1963). In

the entry for optical character recognition in the

Encyclopedia of Arti fic ial Inte llig ence, Hull (1987)

states: The reading of text by computer has been

an AI topic for more than 25 years (p. 82).

3. For example, see Carbonell (1986); Carbonell and

Gil (1990); DeJong and Mooney (1986); Hammond

(1989); Lenat (1983); Mitchell (1982); Mitchell,

Keller, and Kedar-Cabelli (1986); Quinlan (1986);

and Sussman (1975).

4. Another panel on this same subject that I also

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DeJong, G., and Mooney, R. 1986. Explanation-

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Hammond, K. 1989. Case-Based Planning: Viewing

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Jona, M., and Kolodner, J. 1991. Case-Based Rea-

soning. In Encyclopedia of Artificial Intelligence, 2d

ed. New York: Wiley.

Klein, G., and Calderwood, R. 1988. How Do

People Use Analogues to Make Decisions? In Pro-

ceedings of the Case-Based Reasoning Workshop(DARPA), ed. J. Kolodner, 209218. San Mateo,

Calif.: Morgan Kaufmann.

Lancaster, J., and Kolodner, J. 1988. Varieties of

Learning from Problem-Solving Experience. In Pro-

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tive Science Society, 447453. Hillsdale, N.J.:

Lawrence Erlbaum.

Lenat, D. 1983. The Role of Heuristics in Learning

by Discovery: Three Case Studies. InMachine Learn-

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Michalski, J. Carbonell, and T. Mitchell, 243306.

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Lytinen, S., and Gershman, A. 1986. ATRANS: Auto-matic Processing of Money Transfer Messages. In

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McDermott, D. 1981. Artificial Intelligence Meets

Natural Stupidity. InMind Design, ed. J. Haugeland,

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McDermott, D.; Waldrop, M.; Schank, R.; Chan-

drasekaran, B.; and McDermott, J. 1985. The Dark

participated in, entitled The Dark Ages of AI, was

held two years later at the 1984 National Confer-

ence on Artificial Intelligence. For a transcript, see

McDermott et al. (1985).

5. See McDermotts (1981) essay lamenting this fact

and its effect on the progress of AI.

6. Dreyfus (1979) criticizes these and other overly

ambitious claims of progress made by AI researchers.

7. Now we have some evidence that experts doindeed learn from and use their experiences in daily

reasoning and decision making (see, for example,

Klein and Calderwood [1988]). For example, both

expert and novice car mechanics were found to use

their past experiences to help generate a hypothesis

about what kind of problem a car might have (Lan-

caster and Kolodner 1988; Redmond 1989). Archi-

tects and mechanical engineers were observed

using old design plans while they created new ones

(Goel and Pirollo 1989).

8. For further discussion on indexing and the

indexing problem, see Jona and Kolodner (1991),

Riesbeck and Schank (1989), and Schank et al.

(1990).

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New York: Academic.

Roger C. Schank directs The

Institute for the Learning Sci-ences at Northwestern Universi-

ty, where he is also John Evans

professor of electrical engineer-ing and computer science, psy-

chology, and education. The

institute embodies Schanksconcepts for conducting lead-

ing-edge interdisciplinary research in human learn-

ing, providing software solutions for education,and accomplishing the successful transfer of AI

technologies from the university environment to

the real world. Schank holds a Ph.D. in linguistics

from the University of Texas at Austin and is theauthor of over a dozen books, including Tell Me aStory, The Cognitive Computer, and the newly

released The Connoisseurs Guide to the Mind.

Articles

WINTER 1991 49

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