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The Seed of Artificial Intelligence. SUMEX-AIM.Research ResOurces Information Center, Rockville.,MdNational Institutes of Flealth (DHEW) , Betht?sda, Md-Div. of Research Resources..NIH-00-2071Mar 00N01-RR-9-2114.74p-; Photographs and some other illpstrations maynot reproduce. . 5

MF01/PC03 Plus Postage.*Artificial'Intelligence; *Biomedicine;-ComputerPrograms; Computers; *Computer Science; InforMationNetworks; *Information Systems; Medical Research;*Online'Systems; State of the Art Reviews

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ABSTRACTWritten tu provide an understanding of the ,broad base

of information on,which the artificial intelligence (AI)branch.ofcomputer science rests, this publication presents a general view ofAI, the concepts frdm w.hich it, evolved, its current abilities,' andits promise for research. ThefocuS is on a community of prolectsthat kise the SUMEX-AIM (Staivford Univers,3rty 'Medical Experimerital.

:Computer for Artificial Tntelligence'in Medicine) network, a .nationally-shared computing resource4,eoted entirely to designing Aapplications for the_biomedical scien6es:-Chapters explore the:weaning of AI,.the histpry of computer sFience, the ptocesses ofcomputit,g, the usesof UMEX 1n'biochemistry-0, clinical medicine',psychology, and AI-tool 'building, and the futute applicationsof AI.Appendices provide rinformation,on the.orqabization of SUMEX, 71t8available.facilitifs, and its management, as well as a AireCtory ofSUMEY-AIM project investigatorS and-project-funding soUrceS.. .(FM)

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TheSeeds ofArtificialIntelligence

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A publication ofDivision of Research Resourcr)Nabonal Institutes of I foalthBethesda, Marylahd 20205

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1./.$. DEPARTMENT OF HEALTH-, ,EDUCATION, and OELFARE

'Public Health Service' .National _Institutes of Health

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Acknowledgements

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Tho Nrts of many peoplemado this publication posstble.Special thanks go to Mr. ThomasRindfleisch; Drs. Joshua Leder-berg, Herbert Simon, EdwardFeigenbaum, Bruce Buchanan,

_.113.?1! ........... William Baker, JackMyers, and ifarry Pople: dIKTMr. Edward Post.

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Foreword

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In the past centui y, SCiOnce hilSnot only changed ,QUI conceptitms

j,abolit io world, It has changed itself .1 'von by an explosion of infor motion, specialties in-sciencehave sprung up, inevitably givingrise to subsppcialties: But stayingabreast of new knowledge, even innarrowly speCialized areas, is be-coming increasingly difficult. Oneway to manage the continuingflood of now infoftation may be to 'create entities of intelligence. .

The proposed tool is the intelli:gent machine, a device that mimicsthe expert's reasoning power andcan retain in retrievable form muchof the knowledge currently avail-able to experts in a given specialty.-Most systems of this type are )stiimmature. But some are akee'dymoving into the real world andothers will makt the transitionwithin the next few years. As these

4 activities become apre formalized.,a new branch of apPied sciencewill arise. Most likely it will becalled knowledge engineering.

What systems will be available?Who will they help? How will theywork?

Many answers are contained inexisting books and arlicles. Buttechnical publications'Riffer from

4

the defect Qf their vir toes I hey aretoo detailed, too exhaustive arid,inost minor tont, too focused onsin* areas of rapidly expandingdisciplines To understand this newbranch of computer science, calledartificial intelligence (Al), it is nec-essary to understand tho founda-tion, the broader base, on which itrests.

This publication will plesent ageneral view of Al, the conceptsfrom which it evolved, its currentabilities, and its promise for re-search. The focus is on a commu-nity of projects that use theSUMEX-AIM (Stanford UniversityMedical Experimental Computerfor Artificial Intelligence in Medi-cine) network. .

SUMEX-AIM is a nationallyshared computing resource de-voted entirely to designing AIapplications for the,biomedical sci-ences. It is funded by the NIHsion of Rqsparch Resources,BiotechnolOgy Resources Pro-gram: Although SUMEX-AIM doesnot include all Al projects directedtoward medicie and related re-search in this/country, many of theprograms now using Al techniquesfor medical decision-making weredeveloped using this facility.

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TableofContents

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foreword 4

IntroductionAt tit icial intelligenr,eWhat's in aName? -6

History of ComputingAbacus to ENIAC----and (3eyond 9

Processes of ,ComputingThe Heuristic Mind 20

SUMEX arid the ScienceCommun)tyThe Seeds of Artificial lntellig Ice 24

Biochemistry' 25Clinical Medicine 33Psychology 54Al Tool Building 62

Future of AlPrOspectus 64

Appendix AOrganization and FacilitiesAvailable 6-9

Appendix BManagement 71

Appendix CSUMEX-AIM Pirectory and ProjectFunding 73

%

Introduction

ArtificialIntelligenceWhat'sin aName?

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For contiMos, phaupflots andlinguists have grappled with the'question of defining intelligence.

, Most have approached the issue, by cribing the function of lntelli-vn , or the way it appears in be-

yi An exact defi Rion for thistefort elusive.

its might be expected, machineintelligbnce islrqually, if not more,difficult to define. According to Dr.Margaret Boden in her book Mill-clal Intelligence and Natural Man,

oonipoldr-S aro only I ozioar 01 took,machines programmed le dothings that would require intelli-gence if done by people. Dr. Mar-vin L. Minsky, artificial intelligence(Al) research r at the.Massachu-setts Institute f Technology andadvisor for S EX-AIM, agrees.He says artificial intelligence is thescience of making machines doWings that people need intellizgence to do.

Others take a somewhat differ-

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ent view. Dr. Edward.Feigenbaum,principal investigator of SUMEX-AIM, says the field iS not primarilyoriented toward technology, buttoward investigatingthe nature Ofintelligence as information process-ing, whether the intelligence is ex-pressed by man or machine.

One point of emphasis in current \Al research is to design computerprograms that capture the knowl-edge and reasoning processes ofhighly intelligent specialists. Thepracpcal goal of such work is tomake specialized expertise moregenerally accessible. To do so, .reSearchers are attempting tounderstand how experts go aboutacquiring and using knowledgd.Principles of how knowledge ac-crues and how it is retrieved in log-ical sequence are extracted. Theyare then programmed into thecomputer.

Within the SUMEX-AIM system,the reasoning processes of physi-cians, chemists, and other biomed-ical scientists are being analyzed.At present, the ability of most pro-grams is limited and much lessflexible than the correspondinghuman intellect. In specialized

Dr. Herbert A. Simon; SUMEX-A1Madvisor: sorting out the recipe ofintelligence,

4

areas of medical diagnosis andcheMical str mane analysis, someprograms can already rival humancaPabilities. Still, maily people areskeptical of the computer's potenT..tial.

Nobel Prize winner Dr. Herber tA. Simon, psychologist computetscientist at Carnegie-Mellon Uni-versity and SUMEX-AIM advisor, isconvinced that this potential isgenerally underrated. He sayshuman behavior is based on acomplex but definite Set of laws. Ifthese laws are discovered and re-duced to computer software, Dr.SiMon believes machine intelli-gence comparable to man's will_become a certainty in specificareas of expertise.

To capture these higher level Vfunctions, Al researchers are de-veloping a new approach. It iscalled symbolic computation, a setof methodeby which abstractionscan be expressed and managed inthe computer to solve non-mathematical problems. They em-phasize manipulations of symbolicrather than numeric-information,and they yse largely informal or,heuristic decision-making rtSles

gained from real-wetH experienceWhen used in Al, heoll'itic:,

.tocus the program's attention qnthese parts of the problem that aremost critical and those parts of theknowledge base that are most ml-event. The result is that these pro-gramspul S 10 a lirm of reasoning,rather than a sequence of arith-metie steps.

Use of complex symbolic struc-tures is necessary when construct-ing computer applications fordomains that cannot be well-formulated in mathematical terms--either because they are not fullyunderstood, as in medical diag-nosis, or because the underlyingconcepts are intrinsically non-numerical. "Seldom are thereequations, in the mathematicalsense, that felate megisuremnntsof body parameterS to the diag-nosis of disease," sayis Mr.Thomas Rindfleisch, director of theSUMEX computing facility. "Rather,the precsiss of diagnosis it charac-,terized by a set of strategies hav-ing to do with rules of experienceand judgmental knowledge..Theserules govern the interpretation ofobservations and guide decisions1:

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or example, IN I ERNIS I. adiagnostic computer program inthe SUMOK-AIM network, is fødised on the broadest of medicalkpecialtiesinternal medicine Itanalyzes patient cases by mimick-ing the expel Fs reasoning process"The method used by physicians toarrive at diagnoses requires cornplex information processing whichbears little resemblanceto thestatistical manipulations of mostcomputer-based systems," saysDr. Jack D. Myers, coprincipal in-v'estigator of the project at Me Uni-versity of Pittsburgh. "As a reSult,the focus of reS4arch in this field ofmedical applications has shiftedduring the past few years frommodels of statistical inference tothose using the heuristics of artkfi-cial intelligence."

"In final tom INTERNIST willamplify intelligence," Dr. Feigen-baum says. It will supply ewertadvice to the general Practitionekand physician's assistant, ac-celerating and improving theirwork. "NI equally important out-

( owe ()I 1w5wow!) such rs this it!.;111v11,\X 1\llV1 is elmcitnq. organ',mg, and polishing a body of knowleskje that rarely sees the light otdray." he says "It is the knowledgethat underlies the exper tise ofpiactice, the knowledge that isnor malty transmitted by a kind ofOSMOSIS process from master toapprentice. 'that knowledge willnokbe codified, taught, used, andcritiqued." In essence then, a keygoal of artificial intelligence research in the SUMEX-AIM com-munity is to capture in computerprograrrts the knowledge andproblem-solving abililies of ex-perts. After studying this process inmany specialized areps of exper-tise, Mr. Rindfleisch says, it may ul-timately be possible to capture in

\ computer programs something ofprocess of creativity and dis-

covery itself. Programs.then wouldpossess the ability to detect pat-terns that establish order fromchaos, to draw connections be-tween seemingly unrelated ideas,and to establish the principles forsolutions tO new classes ofproblems.

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4

HistoryofComputing

AbacustoENIACand Beyond

Boethius (left) and Pythagoras: afanciful battle between arithmeticcalculation and the abacus.

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In the millennium before Christ,'amid the great cities and con-quests of Greece and Home,dreamers and theorists were layingthe groundwork"for today's thinkingmachines. Like seed crystals in asupersaturated solution, thesevi,ionaries drew from nature, as-sembling conclusions from obser-vations about the universe..Theirefforts brought important advancesin mathematics, astronomy, andmedicine.

f)tuch of the early work in for-mulating the laws of mathematicsmay appear to have little connec-tion with the computer of today. Mteach step forward in this elaboratesciesce was indispensable to theultimate arrival of the computer.

'Pythagoras, a 5th century 'B.0philosopher known as the founderof Greek mathematics, was theharbinger. He, first described the"mystical significance of numbers"and established the relationshipbetween musical harmony andmathematics.

Perceiving in the skies a regu-larity similar to khat of music,Pythagoras studied movements ofthe heavenly bodieS, or as he

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callmI it, "the music of thespheres I in became? the first torealize the impoonlice of yoometric _shape, which governs all naturefrom crystalline rock to the humanbody. In so doing, Pythagoras setthe direction of mathematicalthought for centt ii es to come.

Mathematics was soon iegardedas exact. It became the corner-stone of all science. For centuriesscholars,believed that its logic wasinfallible. BUt in the 19th centurythe first inklings of doubt surfaced.Two mathematicians, one in Hun-gary and the other in Russia, es-tablished irrefutably that it was im-possible to prove Euclid's postulateof parallels, which states that nomote than one line parallel to agiven straight line can passthrough a given point. Alternatetheories sprang up, threatening toscatter the focus of science. Math-ematics, the mainstay of scientificcertainty, was suddenly unceitain.If there are two or more geomet-ries, which is right?

After much thought and delibera-tion, Jules Henri Poincaré, a 19thceptury philosopher and mathe-matician, found the solutign. He

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answeied simply that the questionmeaningless Poiricare, de

scribed as one of the most eminentscientific thinkers of his generation,asked, "Is the meter more truethan the foot? Are Cartesian coordilates false arid polar coordinatescorrect?" One geometry cannot be

'more true than another, just moreconvenient, he concluded.

Through his philosophy, Poln-care provided the flexibility neces-sary for science to advance froman age of scientific complacency.Few realized the significance ofPoincare's study of mathematicaltruth. Even fewer guessed that, in2 decades, absolutes of classicalscience such as space, time, andsubstance would.becokie approx-imates, and the most respected as-tronorner would agree that, if mancould look deep enough intospace, he would see the back Ofhis head.

But the human mind is capableof much more than just abstraction.Driven by the social pressures ofwar, business competition, and

o , labor-saving machines weredeveloped. The first mechanicalaid to calculation was the abacus.

I he Phoenician wont AllAR, thename of a flat slab covered in sandIII which figures could he drawn,provide(I the root tor the I nglr.diword ()tiring Greek and I tornantimes, the piunitiabocus was aflat wooden boaid with counter s ItdevOoped into the now fainiliai uirangement of beads threade(I Onwires or laid in grooves

With teithadvent of arithmeticsigns in the 15th century, the popu-larity of the abacus began todecline in Europe. John N9pierfurther reduced the labor of longmultiplication and division with theinvention of logarithms Multiplica-tion and division.were then facili-tated by adding or subtracting the"logs" of numbers.

Before this technique could bewidely used, accurate tables oflogs and antilogS had to be corn- 4.piled and.printed. Despite valiantefforts by mathematicians to Makethese tables accurate, thedrudgery of figuring, printing, andcopying the numbers led to errors.Often, mistakes were handeddown from generation to genera-tion as mathematicians built, all toofaithfully, on the wobbly shoulders

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mathematical tables was !soondeveloped MI ';kiin log deviceknown as the IIrI i tile, whichconsists of two 0(1 scaiosmetliuted side-by side in a mannerto permit slutilig them easily backail(' for th Whereas the modernoliqital computer counts, tbe analogdevice measures quontities. 114nslide rule scale is arranged so thatnumbers fall at distances corre-sponding to their logarithms. Ls-sentially, multiplication is accom-plished by adding iwo lengthstogether. Division is done by sub-tracting two lengths.

As \ni,ith all analog devices, theaccuracy of slide rules is limited bythe accuracy of measurement.Their use did not solve the proplemcaused by incorrect tables, butrather introduced a lack of preci-sion. The solution, of course, wasto produce reliable tables.

1[11812 this thought occurredto Charles Babbage and JohnHerschel, tVvo young mathemati-cians, while they wew checkinglogarithm tables for órrorl..As re-counted by

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ings, ho exclaimed- "I wish to (lodthat those calculations had b( enexecuted by steam." I lerschel re-plied: it is quite possible." And sooccurred the idea that was to dom-inate Babbage's life---elimination oferr or tIll ough mechanized calcula-tion.

Because his ideas were so ad-vanced and his standards so high,Babbage experienced one disap-pointment after Mother. In manyways the 19th century inventor'swork belongs more to oOr timethan to his own.

Babbage's first project, the Dif-ference Engine, was to be a largo,complex adding machine designedfor compding mathematical tables.Unfortunately, the machine wasdoomed to fail. The mechanical ,tolerances required for the)na-chine to work exceeded capabili-ties of the time. The accuracywithwhich gears could be out was in-adequate. Clocks, the nearest me-chanical cousins to the DifferenceEngine, were still laboriously fittedtogether by hand.

Undaunted by this challenge,Babbage designed new machinetools. He hired and trained a tech-

Scientific American illustrates use ofthe Hollerith Tabulator in the 1890census: the' era of data-processingbegins.

assistant nut these piepara.tains est money arid the initialsum pfuvided by the British .freaslily soon dwindled away Fiveyeais after beginning the project,Ilabbago was asking the gover n-mont for morn money Ills reque!Owas graitiod. Rut again, ihoamotint was not --and could nothave beenenough.

After almbst a decade of workand some £35,000 of governmentarid personal monies, the project

was rOlandoned. If completed, theengine would have !wen uIllaikable piece of work 2 tons ofbrass, stool, and pewter, cut totolerances never before attempted0.'mbittered by failure, Babbage,

a man of considerable wealth, hay-ing In/willed 1,100,000 fun» hisfather, devoted Much time andmoney to insulting and slanderingfigureheads of the scientific andpolitical establishment whom, heblamed for the engine's failure. But

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ho did not Fibandon his gOal After111:33 Ilabbage elaborated on a"gigantic-idea" which he had firstconceived while Working On the Difforence Engine. If built, this mas-sive device, dubbed the ArialyticalEngine, worild have been the firstgeneral-purpose computing machino.

Babbage's scheme contained,for the first time. most of the mis(r)tial features of tho modern com-puter An arithmetic unit called "themi)l" was designed to carry out ad-dieron, Subtraction, multiplication,and divisiorvA memory unit was tohave room tor 1,000 numbers,each 50,digits longa capacitybeyond technology until the firstelectronic computer appeareda hundred years later.

lnstructipns and data were to befed into the machine on punch,cards, which had been invented in1800 by Jos6ph Jacquard for usewith his automatic loom. After cal-culations were completed, resultingnumbers would be printed up to 29

The Analytical Engine was asfarsighted and intricate in designas it wasimpossible to build. Once

The Groat Brass Brain: prodictingtidos accoratoly and officiontly in1914.

Ilabbage's ambition hadtranscondod his time I von withtoday's tochnotogy the 011(11111,would be difficult to constr (RA hoclitiso of the mochanical tole!ances required ;ilill Ilabbage's offorts were not altogother.in vain

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tits enthusiasm spread to otheis,notably Her man I tollerith, who dosigned tho first machine devoted todata piocessing

Hollerith's machine, which usedpunch cards, was the easy winnerin a contest staged by the U.S.ConSus Office to pick an efficientsystem for tabulating the 1 90census. His device completed thetest in half the time needed by htscompetitors, whose entries usedmanual methods.

Data in the form of "yes" or "no"answers were translated ontopunch cards, which were compiledin a machine that electromechani-cally sensed positions of holes.Cards passed under a set -ofbrushes that transferred a pulse ofelectricity through each hole to ametal cylinder.

After forming the Tabulating Ma-chine Company in 1896, which was'one of several businesses that

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later for builtMachine!; for sof Ong such cards,comparing 0110 to the other, andprinting &ILI I includn mere In-ter [nation for business use, Hol-lerith increased his punch cards tothe siie of the dollar bill of his time,which later became an industrystandard

lis inventions opened the doorto an era of computing machines,ushered in by the first efficientkey-driven calculating machine.Called a oomptometer, it was, built .by Dorr E. Felt from A macaronibox.

The riing popularity of calculat-ing aids and machines in businesscharacterized Ft shift in attitude to7ward the kind of work people coujdor should do. Calculating machinessoon entered into head-on compe-tition with people hired as "rapidcalculators" by businesses trying tokeep pace with expanding mar-kets. BeSides the tedium associ-ated with mental calculation, healthwas also a coriideration. Mentalcalculators, as experts in the tradewerR called, often complained that,their -6Veninto wore haunted byunending processions of figures .

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,clerk, was forced to changecareers because the "monotonousgrind of clerical work" had de-stroyed his health. At the turn 'ofthe century, Burroughs entered thecoMpteMeter field and from hisearly venture grew one of today's.Major manufacturers of digitalcomputers, Burroughs Corporation.

As the calculating marhinesgained acceptance, more andmore applications were found. Onecalled Milli9naire, developed in1893 and widely useclaibe business,found immediate and key uses inscience. Percival Lowell beganusing Millionaire in 1905 to searchfor a "Planet X" located some-where beyond Neptune. Calcula-tions were completed in 1914, butthe planet, later named Pluto, wasnot sighted until 1930, 14 yearsafter Lowell's death.

At ttle same time that manufac-turers were converting to massproduction techniques, Spanish in-ventor Leonardo Torres y Quevedowas demonstrating a theory thatheralded the oncoming age of theprogrammed machine in industry.

Vannevar Bush and the 1930's differ-ential analyzer: "I was trying,to solvesuch problems of electric circuitry asthe one connected with failures andblackouts in power networks. I hadbeen thoroughly stuck because Icould not solve thetough equationsthe investigation led to."

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"roues combined electromechani-cal calculating techniques withprinciples of automata, dernonstr at-ing that such machines can per-form any desired sequence ofarithmetic operations.

Torres' electromechanicalArithoMeter, exhibited in 1920,realized theories of automata thathe had pioneered 7 years earlier.Arithmetic problems were typed inby theboperatct, and the Arithome-ter printed the answers on a type-writer. Torres became the first per7son to use a system of time-sharing when he linked severaltypewriterb to one Arithometer.

One of his other inventions wasa remote-controlled guidance sys-tem that successfully steered aboat through Spain's Bilblo harbor,dramatizing the fact that machinescould perform tasks formerly re-served for human intelligence. Tor-res later built the first decision-making device-z--a chess-playingmachine that matched a rook and'king against a human opponent'sking.

In 1914 Scientific American an-nounced the arrival of ."a greatbrass brain" which computed

ocean tides On the basis of 37 fac-tors, displaying the i et;ults on Owl':During the first World War, shipsused information from the machineto maneuver into shallow water-and elude German U-boats.

After World War I, VannevarBush Z7if the Massachu,setts Insti-tute of Technology:invented the dif-ferential analyzer, an analog deviceassembled from gears, cams, anddifferentials that methanicallycompleted the various functionsnecessary to solve a differentialeqUetion. Busfts machine wasapplied to many different :tasks, re-pladng devices such as ':netweykanalyzers" built by utility com-panies in the 1920s to analyzeload reOrements. These ma-chines produced scale models ofpower networks, but they could notpredict large power surges thatmight cause blackouts. The differ-ential analyzemas the first ma-chine with such a capability. Itssuccess seemed to indicate thatbig, general-purpose analog com-puters would dominate scjentificcalctflatIon in the future.

In the 1930's servomecha-nismsautomatic devices that

controlled other machines bynlonitoring their output -came intouse. Oil refineries and syr up-production plants were among thofirst to use these "machines thatboss other machines." As controlproblems were rethiced, more'andmore applications were found forthe servomechanisms. Steam tur-bines, airplanes, and chemicalprocesses were soon included inthe domain of the new device.

As machines surprised societywith newfound abilities, theircreators took to flights of fancy,building'robots in exaggeratedhuman forms. Inventors built tin-can contraptions that walked,talked, and responded to me-chanical commands. The robots'lifelike actions were an elaborateillusion, as they were controlled bysimple automatic devices or, re-motely, by human operators. Assuch, they were no More thannovelties, commonly used in prod-uct and company promotions .orfairs.

Willie Voce lite, built by Westing-t house in 1931, was one of these.Willie had a stovepiPe head, ex-pressionless face, and cauliflower

A.tlektro and Sparko en route to the1939 New York-World's Fair: tin-cancontrapOons that walked, talked,and responded to mechanicalcommands.

B.EN1AC, the world's first electroniccomputer, begins operation in 1946:an unwieldy collection of vacuumtubes ,and relays that could only beprogrammed by manually changingplug-and-socket connections and bysetting switches.

oars At the inauguration of passenger air ser Vico between NewYolk and San Francisco, Williemade i speech, wished Over yenebon voyage, helped star t the MI-9nes, and after his official dutieswere completed, relaxeciwith acigarette in the company of alovely model hired for the occarsion.

Eight years later, Willie's metalcousin, Eloktr 0, a stocky, tough-looking robot, appear0 at the NewYork-World's Fair with fUs faithfulcompanion Sparko, the first robotdog. Elektro walked, talked,counted on his fingers, puffed ciga-

rettes, arld collki distinguish hetwoen red and green with the aid ofa photoelectricx:ell. Sparko barked,wagged his tail, sat up, andbegged.

In the late nth tift, engineersturned thr*ir cellectiN're genius toproblems raised by the secondcoming of world war. The its.Army sot out to improve differential

Nralyzers'used at Maryland'sAberdeen Proving Grounds to cal-,Wale firing tables for artillery bat-

- teries. Modifications increased\speed and accuracy by a factor of80, allowing the machinelo pro-cluce one trajectory every 15 alio-

utos as compared to the 20 hoursneeded by a skilled mathematician.ft it the machine was limited by itsdesign to processing dif fer entialequations: it could only calculatethe,functions of vectors.

"ThorQ exist problems beyondour abil4 to solve, not because oftheoreti al difficulties, but becauseb,,of instil ticient means of mechanicalcomputation," Howard H. Aikenisaid of the analyzer in 1937. He,then proposed a new kind of cal-culating machine.

In 1938 IBM began building aforerunner of the device for Har-vard University. It was called theAutomatic Sequence ControlledCalculator (ASCC). After its com-pletion in 1944, the ASCC,nicknamed Mark 1, became the.first automatic, general-purposedigital calculator.

Mechanical switches called re-lays routed electrical signals in theASCC. During its 15 years of use,ASCC pfoved to be a reliable and

, effective machine, but its morethan three-quarters-of-a-millionparts and 500 miles of wiring mademaintenance expensive and diffi-cult.

flw calculate); was mainly usedby the lf S Navy.for ballistics andship design .;cienu4, and industrylater used the machine to gondatoastionomical tables and specifi-cations for lens design It was alsoused in military studies at WrightPatterson Air Force Base and inr esearch for the Atomic I nergyCommission.

A year before ASCC das fin-ish9d, John Mauchly and J. Pies-per Eckert, Jr., of the University ofPennsylvania, proposed the nextlogical step in mechanized calcula-tion. First described as an elec- .tronic difference analyzer, thescientists predicted their new cal-culator Would execute all functionsin computing firing tables, produc-ing each complete table in only 2days, The device promised to getaround a major failing of the differ-ential analyzer by allowing input or.such data as atmospheric resist-ance defined by numbers ratherthan by mathematical formulae.

Built in secrecy at the Universityof Pennsylvtinia, the new device,which ultimately became known asENIAC (Electronic Numerical-lnte-Orator and Calculator), was moved

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People were necw;sary to genmato) firing tables on differ

arlalyzer, and the tiuriii r oleslowed production Cpmpletion ofone table, on the average, toyk 2or 3 months.

.VVith the now machine, lengthyohd repetitive calculations for oach60-second trajectory could becompleted in just 30 seconds. ButENIAC was not completed until1946, and the huge device, com-posed of some 18,000 vacuumtubes and 1,500 relays, was neverused for ballistic computations. Itdid find wide-ranging applicationsin scientific calculation, however.Until the early 1950s ENIACTIab-bled in weather predictionatomicenergy research, cosmic ray,)studies, and thermal ignition!

Germany may have entered thefield of electronic computers aheadof 'America, although little is knownabout the true dimensions or oper-ation of these machines.. The most

fccessful version, Z4, was de-stroyed in an Allied bombifig raid.Designed by Konrad Zuse and builtat the Gergran Aircraft Research

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At the war's end, Zuse could riotcoavince Allied intorrogator9 thathe had any scientific expertise tootter, arid his research came to asudden stop. Not until the midfittiesdid he resume his work, this timeas owner oT a computer man-ufacturing'\compamk which waslater absorbedby a large Germanelectronics firm.

As technology flourished duringthe 1940s, a major breakthrough inthe burgeoning field of computerscience occurred. Althou9h the/exact source of the concept isuncertainJohn von Neumann,Mauch ly, Eckert, or British-mathe-matician Alan M. Turingit wassuggested that instructions couldbe stored as numbers in the ma-chine itself. The idea raised themechanized kingdom severalrungs on its evoliilionary ladder. ,For the first time, logical choices ofprogram sequences would bemade inside a machine.

Earlier, programming ENIAC andZ4 had been extremely tedious: inENIAC. by changing plug-and-

socket connections arid by settingswitches; in Z4 by instr fictionspunched into discarded 3!)rilliimovie film. The concept ofsoftware programming provide(tthe basis for the next generation ofcomputers.

The first machine with a com-pletely "logical design," which vonNeumann described at the tir9e ofENIAC's construction, was to becalled EDVAC (Electronic DiscreteVariable Automatic Computer).While EDVAC Was still under con-struction 011948, ENIAC, afterspecial wiring modifications, be-came the first computer to embodythe stored-program concept. UsingENIAC's new capabilities,-vonNeumann and severalmeteorologists completed the firstcomputer-based weather forecast.Computations for the hydrogenbomb were begun on ENIAC andcomplerd on its successor MANI-AC (Mathematical Analyzer,Numerical Integrator and Com-puter). MANIAC was one of manystored-program computers that fol-lowed in the wake of the new pro-gramming concept, although eachdiffered considerably in design.

I.DVAC, I mAC, .10IINNIAC(which was named for v.onNeumann), ;I-AC, SWAC, andNOliC were the first few to appiiar. .

As computers became increas-ingly power foil, these machinesmoved into nokv areas. In his bookCybernetics, Norbert Wiener ex-plored the potential uses of au-tomata. In 1948 W. GreyWalter en-tered the field.of cybernetics (theComparative study of automaticcontrol systems) with an elec-tromechanical "tortoise" built tostudy simple reflex motion:"Thesemachines are perhaps the simplestthat can be said to resemble ani-mals," Walter wrote,. "Crude thoughthey are, they give an eerie im-pression of purposefulness,Inde-pendence, and spontanerty."

Von Neumann, decidedly a ,"software scientist,"'hoped to useautomatic machines such as themodern computer to draw conclu-sions,about complex natural or-gantsms. He built on the idea ofthe Universal Turing Machine, ad-vanced by Turing in 1936. Turingdescribed, in theory, a machine -that could don y calculation withinthe\icealm oMman intellect. The

A mode4integrated circuit: putting40-times the memory of ENIAC on a \chip the size of an aspirin.

Universal luring Machine, whichcontains ideas later built into al1general cowling) machines, pro-vides a standaid for measuring thecomplexity of a computer.

Around 1950 the computeremerged as general tool. It h4dbecome applicable not only to mill-/lary use, but also to functions ingovernment, industiy,science, education, and social sci-ence. The coThputor's spectaculargrowth in capability, applications,arid numbers surprised mostpeople. In 1954, using cathode ray

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ntiot;, expe s in the field had estimated that only some )() COMpanies would eventually find usefor computers.

Eluis with the developrpent in thelate fifities, of computer kinguages,which simplified pi ogi anmung, andwith the introduction of integratedcircuits in the midsixties, compres-sing the equivalent of 1,400 tran-sistors, resistors; and diodes ontosilicon chips an eighttvot an inch

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Sql1,110, !RI iez,tiaining widespread application of cominitersHew off Private tuisinoss begantizong computers to process Orders,inventories, and payrolls. Com-puters set copy for newspapersand processed checks tpr banks.Airlines used computers to makeand record seat reer vations.

Even in medicine, an alea bestcharagerized as inexact and highlysubjeCtive, computers-fitted intovery specific and important niches.Applications have included suchareas as electrocardiogram analy-

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515 systems, aids for managin,clinical routines, and instrumentdata collection. Statistical cluster-ing techniques were applied todiagnostic programs and were oneof the first diagnostic approachesto prove useful in medicine.

Just as those new methods ofnumerical calculation'were beingtested, the first inklings of a newapproach to computing were intro-duced.,The first application of thisapproach----symbolic comPutingwas the establishment of databanks stocked with patient informa-tion. It was an application of sym-.bols to algorithnis, matchingnames to numbers, and it carriedalong the guarantee that ifpattern-matching was properlyapplied, the answers would befound.

But medical diagnosis, on thesame level as practiced by thephysician, required much more inthe way of programming tech-niques. To grapple successfullywith the problem, prototypes ofhigh level analysis and symbolicrepresentation were developed.Many of these resulted from earlywork in applying artificial intelli-

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genCe (Al) to biomedwal problemsAl Stanford liruversity. under the

direction of Nobel PIII0 winner Dr.,fostrua I gletfierg.ilfid Dr I dw.udiolyenbaum, a team of sc,ientistsbegan the development in 1966 ofDENDRAL., a chemistry programwhose offspring now rivals exper tsin figuring out the structures of cer-tain organic molecules. DENDHALwas the unlikely outgrowth of asystem c;illed the Advanced Com-puter for Medical Research(ACME), which had been sup-ported by the Biotechnology Re-sources Program(BRP) of the MNDivision of Research Resources(DRR) since 165. OriginallyACME was dedicated to real-timeanalysis of data gained during --biological and clinical research.The computer was oriented entirelytoward numerical calculation, orbatch processing of biomedical re-

,search data.

By the midsixties, principal'in-vestigator Dr. Lederberg believedthat ACME had proved its worth asa data analyzer. At the urging of Dr.Feigenbaum, he proposed t9at thesystem host a new type of com-puter science application, artificial.

intelligom1 he first attempts to use ACMI

for Al were under taken al Stanford,and DLNDItAl was the first majoreflor I In the begirming tfie machine's size and speed wet e suffi-dent. Rut as DE-NDRAL grew, andother research was added, moreand more computing time andmemory space were required. Bythp early 1970s, Stanford's Alneeds completely outstripped themachine's capacity and DENDRALappeared to be doomed.

During this period Dr. WilliamBaker of the BRP arrived for a sitevisit. In light of ACtIviE's inadequatecapacity and the interest in Al atStanford and elsewhere around thenation, Dr. Baker suggested thatACME be dropped in favor of asystem devoted to the develop-ment of this new type of comput-ing. Stanford applied for a grant,another site visit was made; andthe NatiOnal Advisory ResearchResources Council of the N11:I rec-ommended funding. A machinedesigned to handle symbolic com-putation was installed and, throughthe use of a nationwide communi-cations network called ARPANET,

"All these deraye7a thousandth ofa second here, a millionth of a

. second therewe'll have to get thedarn thing fixed."

a natiorlal computer communitywas established. An advisor ygroup, Drs. Ledorberg, Feigen-baum, and Baker, recommendedthat 40 percent of the computer'scapacity be made available toStanford researchers, another 40percent spread among the nationalcommunity, and the remainder as-signed to development of the newsystem.

In 1973 SUMEX-AIM was formedas a community resource for thedevelopment of AI techniques.From .ils inception, the resourcehas Veen supported by the NIHDivi4ion of Research Resource&Bi technology esources Pro--

am. Drs. Le rberg and Feigen-aum directed t e network that

/was to become a major medium forthe development of projects likeDENDRAL among a national groupof biomedical researchers. In mid-1978 Dr. Lederberg left to becomepresident of The Rockefeller Uni-versity. He remains an advisor ofSUMEX-AIM. Dr. Feigenbaum isnow principal investigator of theresource, which currently includessome 20 autonomous projects,each targeted for application

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loday's Al expel ts believe thatcomputers will find many non-professional and small businessapplications as the potentials of thefield become better under stood.

computeis may somedayregulate heating and cooling sys-tems, notify fire or police depart-ments in emergencids, and cto taxstatements, among other chores.At work, letters will be typed, or-ders placed, bills paid, and filessearcired by computer. At neigh-borhood shopping centers, payrollrecords will be maintained andstock automatically reordered ac-cording to need and profitability.

Some scientists characterize this, revolution as a race to put more

and moro function, processingppwer, and storage capacity ontoeach semiconductor chip. Sincethe midsixties, the amount of func-tion on a chip.has risen by a factorof 10,000. The cost of a chip,meanwhile, has remained approx.-imately constant at $5 to $50. ,

BV the end of the century, com-puter buffs predict, this trend ofbroadening applications Will be in

full stride, as w I tt -; trOnd towardMil riaturizafion, ich made cornouting available to the general ptib-lic. A single silicon chip, measuringonly a few millimeters sguare,,willbe able to follow 20 million instruc-tions per second, using 10 millioncells of internal mummy storage.

And just as imagination andhardware have gone hand in handsince the early 1900's, scientistspredict that programming tech-

cniques and the science softwarewill keep pace with deve

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AIn the course of 30 years theProcesses computer has graduated (win

vacuum tubes and rnecharucallays to silicon chips, each one nolarger than a pencil maser But the

mputing . transition from [NIAC, one of tilefirst electronic number-crunchersof the late for ties, to the "thinking"machinevf today required morethan advances in hardware. It re-quired advances irlovzgramrning

/concepts. -Over the last few decades, there

.z/Ifas been an increasing emphasison the design of knowledge-basedsystems. At the lowest level, thoseprograms differ from traditionalprograms in two key ways."Theyemphasize manipulations of sym-bolic rather than numeric informa-tion, and they use largely informalor heuristic decision-Inaking rulesgained from real-world experiencerather than mathematically provedalgorithms. At a higher level, thesetools of symbolic processing areused to construct understandablelines of reasoning in solving prob-lems and to interact wp.humanusers.

Symbolic computation is neces-sary in certain domains, such as

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medical diagnosis, because cornprelmnsive mathematical formulations do not exist. 1 or example, therelatioriship of a symptom suchas "burning pain in the upperztbdor»en" =to disease diagnosisrequires the manipulation of sym-bolic information.

Projects currently in SUME X-AIM include areas of medicine,biochemistry, and psychology. Thekey goal of an Al program is to ex-plain conclusions and allOw theprofessional to interadt in the deci-sion process.

As a result, Al programs dependlargely on decision-making strate-gies composed of heuristics, orrules based on Judgmerit and ex-perience, which are expressedsymbolically. These strategiesstarkly contrast with numeric corn--putation, which is largelygorithmic, following a m e-metically fixed s rocedureswhen evaluating functions ortabulating results, However, thetwo classes of computation are nottotally dissimilar. .

Clinical flowcharts are al-gorithms used by diagnosticianswhen deciding how best to man-

age a patient. Offen these fixedprocedures are designed by expertptlysicions for use by pat aniedicscharged witp per for mins mrtainroutine tasks. As suet!, data, arerepresented symbolically. Becauseclinical algorithms are relativelysimple, computers are seldomnecessary.

But automated record-keepingand data banks, more intricateexamples of the clinical algorithm,require the computer. In these sys-tems, patient !lames and historiesand other relevant information aremanipulated as symbols, and areconnecIto numeric data thatgive spoc1fivalues to theinformationfor example, patientage: 21. Pattern-matching al-gorithms can be used to locaterecords of similar individuals or

groups of patients to prochicestatistical summaries.

Although the oaf bust systemsseldom did mow than maintainrecords, there have been recent at-tempts to create programs that cancomplement this function by ana-lyzing the stored infer illation.ARAMIS (American Rheumato4yAssociation Medical InformationSystem) is one of the most suc-cessful projects in this category. Inaddition to search and statisticalfunctions, the data bank .offersanalysis of prognosis as it relatestd,a, specific type of patient. Pro-gram6 systematically search thedata base to locate case reportsand summarize the outcomes ofvarious alternative treatments,matching recorded case historieswith descriptionsof current pa-.

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tionts. In systews such as this, theanalysis of alternatives and the de-cisitv about the best theiapy aresolely up to the physician..

More complex decision-makingprograms attempt to assist thephysician in evaluating the bestti eatment strategy. The dedsioncriteria used in such firograms takevarious forms. Some decision'rulesmay have rigorous statistical jus-tification, while others may be onlyapproximate {Liles based onhuman experience and judgment.These latter strategiesiare calledheuristics. Each type can be effec-tive in providing solutions to prob-lemg.

In statistical approaches to diag-noses;the decision criteria have

The ARAMIS data bank" meetingneeds in thetstudy and practice ofrheumatology. (Abbreviations: Al -ar-tifiCial intelligence, ARA-AmericanRheumatism Association,CCC=cooperating clinical trialscommittee of the ARA, SLE-sys-temic lupus erythematosus,SCCS=scleroderma cooperativecriteria study, JRA =Juvenilerheumatoid arthritis, Canadian RA -Canadian Rheumatism Association,UDB-uniform data base Mr rheu-matic disease, FDA=Food and DrugAdministration, VA-Veterans Admin-istration)

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been codified to a certain degree.Baye's theory of probability is oneexample. Flsentially, Bayesiananalysis relates specific patientdata to different disease signs ex-hibited by selected groups of pa-tients In establishing these rela-tionships, it is sometimes possibleto compute the most likely cadsefor symptoms obserwki in a pa-tient.

One of the earliest such pro-grams,-developed in the 1960's,was used to diagn9se congenitalheart disease. In some casestudies, the program reacheddiagnoses with accuracy compara-ble to those rendered by two expe-rienced physicians. As researchershoned and polished the program,applications for other diseaseareas were discovered. Todaymany types of diagnostickrogramsusing Bayesian analysis are in op-eration. But Bayes' theory is justone of several techniques used inmedical decision analysis.

Another displays sequences ofsteps representing various possi-ble actions and events. Sequencesof this type resemble tree-shapednetworks. Nbdes or junctions in the

_,. ,,iAtree are of two nf MS. t (Aversionnodes, the clinician chooses fi(mi aset of possible Rctims. One actionmight be deciding to perfor m a cottain test. At chance nodes, thepossible responses ofpe patientto some action that ha6,been takenare represented. When performinga diagnostic test, the patient's tresponse----whether he developscomplications, for example----is amatter of statistical likelihood. Byusing the dedsion Vein, a cliniciancan come to a more infbrmed con-clusion about the range of alterna-tive strategies.

Modifying the tree by attachingpatient-oriented values to decisionnodes makes the simulation more-realistic. For exampld, a definitivediagnosis might not be pursued ifthe required tests were expensiveand painful, if the health of the pa-tient were not threatened by thisinaction, and if rendering a defini-tive diagnosis would not sirifi-canny improve tis health.

.

iThe effort to develo these ap-plications into program using arti-ficial intelligence began n the early1970's. The intent was to focusprimarily on the use of symbolic

reasoning tee;hniques. 1 he Objec-tives have been to capture thejudgmental or heuristic knowledgeof expel ts for decision-making, andto constf uct I oasoned and ()Wain-able solutions

ifor diagnostic prob-

mles. Generla y the logic built intothese prograr s is composed of sixmajor elemer*,_

Plan-Gene te-and-Test. ln thisframework;'- ie program uses .heuristics t elect the generalarea in whit the answer is likelyto be found; t generates plausi-ble Solution within these boun-daries, and; sts conclusionsagainst ob rved data, appro-priately rev IN conclusions untilone that be. fits the data is un-covered. tDomain-SAcific-Knowledge.Much of th power thatdecision-m' ing programs holdiis derived f m specific rules andknowledge:- bout the target areaof applicati n. Such knowledgebases enc e factual informa- .lion about e domain and theheuristic ri, s used by expertsto rapidly fird solutions to prob-

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Flexible Knowledge Base. Ifchosen pr operly, the knowledgebase is small enough to b"e han-dled adequately by the com-puter, but large enough to bemeaningful to the prospectiveuser. Once the basic program iso Irating, knowledge can be

d, removed, Or changed byusing an explicit and flexible en-coding of the knowledge.Line-of-Reasoning. Specialistsin the target area of an applica-tion must be able to flollow thelogic used by the program whenit generates conclusions. Al-though not strictly necessery,specialists should also agreewith the route chosen. To ac-complish these goals, computerscientists in SUMEX-AIM teamup with experts in target fields tolearn the mechanics of reason-ing. Human logic is then trans-lated into computer language inthe form of symbolic rules.Multiple Sources of Know!-edge. Often several practitionerslend their expertise to the designof Al programs. Textbook knowl-

dgeis usually incorporated aswtI. Having access to knowl-

t_oti"s%'%,fro

edge representing varied points(-4 view ran speed HO plerosslocating a solution and ieducethe chance of overlooking alternative 9Q1itions.Explanation. The program mustbe able to explain the lino ofronsoiling thalled to its conclu-sions. It not, the user cannotunderstand the basis for theprogram's conclusions. Also,thrqugh the explanatory function,flaws'in the program's logic canbe located and fixed without ex-tensive study.

Over the last decade, computerscientists have used these ele-ments to build many types of pro- 7grams. Some include the ability tolearn. Others emulate crketivity.Those in the SUMEX-A.IPtietworkare devoted to expert problem-solving in medicine, biochemistry,or psychology.

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TheSeeds ofArtificialIntelligence

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A typical strategy in some Al re!Watch IS to choose a problem thatis tightly foctismi and easy to concoptualize, such as a game Ibisapproach offers cm tain advantages, most notably that ideas rtribe tested with minimal experiso oftime and money

These games are called toyproblems because they serve Flopractict.al use An example is themissionaries'_dilemma, a puzzle inwhich three missionaries want tocross a river, but their efforts arestymied by an equal number ofcannibals. A boat thtit holds asmany as two people is available,but the missionaries must never beoutnumbered, or they will becomethe main course of that evening'scookout.

Projects in SUMEX-AIM gen-erally shun Joy problems. ''Theiruse leads to sterility in that youquickly figure out the solution, butare not faced With the additionalchallenges that a messy world pro-vides," Dr. Feigenbaum says. "Weseek our inspiration from programsdirected at diagnosing disease orassisting biologists in planningDNA manipulation experiments,

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ho key to designing a socces'.ful Al pioject, he says, is to pick aproblem limited enough to be congamed, Nit not so simple Phil Oreprogram designed to solve It call.not be expanded into a practicaltool. Most of the time, projects inSUMLX-AIM are restricted to asubsection of an inteVed area ofapplication. When that segment isadequately covered, boundariesare carefully extended.

An equally important criterioncalls for an association betweenthe project and at least one expertfrom the target field of application.The collaboration must be a dedi-cated one, according to Dr.Feigenbaum. "You cannot have thekind of inspirational meeting ofminds needed f9r a project to suc-ceed if the specialist and pro-grammer meet every once in awhile," he says. "It takes aquarter-time to half-time effort bythe expert that stretches over anumber Of years."

The seed from which SUMEX-AIM grew embodies this type ofcollaboration. Known as DEN-

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A.The missionarie dilemma: use ofsuch toy prob ms In Al research Isnot produc ,

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A technician in the Stanford massspectrometry laboratory: generatingdata for Ilse VENDRAL.

lAt it booan Ii too; when Iiieigenbaum tol(1 1)1 Joshuaoderberg, then chairman of the

Stanford genetics depar tmont,about his interest in modeling sci-entific ffintight with Al techniquesChemistr y was (:trusel) as thotaeget field for tWO major reasons.Hist, fnuch knowledge in the fieldalready existed in machine-readable Mon. Second, chemistrywas tho field in which Dr. Leder-berg was expert. When the projectgrew in scope, Dr. Cart Djerassi,Stanford professor Of organicchemistr y, was recr uited.

Applying Al to science was inevi-table, according to Dr. Feigen-baum. "As the computer grew inpower and the cost of its use de-creased, more and more spe-cialties looked to the computer forassistance in information process-ing," he says. "But very few spe-cialties in medicine and other fieldsof science could be modeled byformulas and calculations, whichare the-traditional means of exploit-ing the computer.-

Programs of this type must em-ploy processes similar to thosepresent int human reasoning and,

lheiefore. must he expressedsymbolically, I I ergenbilumJo achieve this, it is necessary todeyekw techniques by which symbols can be reptesented andmanipulated Rut when 1)1 NI)f-1,41vva!; COIICPIVed 5011111 1!) year!; ago.

Intelligence was truly afledgling discipline.

BiochemistryDENDRAL

-The project was initially begunas a prototype to demonstrate thatcomputerized symbolic reasoningcould be successfully, applied tomolecular strycture pralltnis inchemistr y. The program illustrargswell the evolution of Al work.

In solving problems, DLNDRALuses instrument data from a massspectrometer (MS) and a nuclearmagnetic resonance (NMR) pec-trometor, together with othiar con-straints on structural features in themolecule. These constralbts de-scribe configurations of atoms andprovide limits within which the an,-sWers, structural candidates for anunknown compound, must fit. Suchconstraints eliminate the produc-

lion of itilde',HrdWh101, 1),1!.,0t1 (n) /WWI( ,1) or

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quicklinalized the power provIdedby supplying severi-d soutcw.; ofknowledge when ;II mlyringmolecular str witures litan earlycase run on.D1..NDRAt., con-straints based on organic chomis

y principles alone would haveadmitted 1 25 million plausiblecandidate structures fOr a singlecompound under study. The scien-tists r esponded by adding informa-tion from proton NMR analyses,from which the program could infera few additional constraints. "Theset of 'plausible candidates wasthen reduced to onothe nightstructure!" Dr. Feigenbaum recalls."This WIS not an isolated result butshowed up dozens of times in sub-sequent analyses."

The original DENDRAL programwas restricted to a small number ofmolecular families for which theprogram had been giveh a special-ist's knowledge, "namely thefamilies of interest to our chemist-collaborators,-Dr. Feigenbaumsays. "Within these areas, DEN-

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1)1 Bruce Iluchanim, a memberof the DLNDHAL team, explainsthe general approach of DI_ N-DHAL "There are three phasesplan, generates, and test," he says"In approaching a problem, DEN-DHAl, makes some rough guessesas to what the solution should look

hat is the planning phase.The generation phase works withinthe established constraints of theplan to develop plausible solutions.

. Filially, each plausible solution istested."

Testing is accomjVhed in twosteps, which folloW. a "model-drivenstrategy." First, the computer gen-erates sets of instrument data thatwould be expected to describeeach candidate structure. Thesesets are then compared to actualdata about the compound. Theclosest fits are retained and rankedaccordingly. Having enough knowl-edge about the characteristics of acertain type of compound to domodel-driven analysis drasticallyreduces the amount of data that

mut,t exammod, since the dataare use(i mainly to verify pos,;ihlearroA/011;

1)11\JIMI1\1'; 11trmary limitationwas its restriction to only a smalluubset of organic molecules, thesaturated, aliphatic, monoluncholm! compounds. Work carriedout alter DENDIIM's early suc-coss has focused on thestructure-generation aspects of theplan, generate, and fest paradigmFrom this paradigm, the structuregenerator, called CONGLN forCONstrained structure GENera-tion, has been extracted. CONGENis the segment of the main pro-

sgram that is not closely tied to spe-cific instrumental data and is,therefore, of greatest use.

"Chemists have many sources ofdata for both planning and testing,so the use of DENDRAL as awhole, which would restrict them toNMR and mass spectral data,would be a hindrance," Dr. Bu-chanan says. "That is why, in thelast 3 years, almost all the effort onOA project has gone into develop-ing CONGEN, since it has thewkiest possible applicability."

Now under the direption of Stan-

2 6

101(1 t;tlertil;;Is s Carl Djerassiand Denni:i niitli, the 1)1 NDliAlproject has evolved into one of thebest known mut nwst successfulapplIcations of artificial intelli-gence the CONGI:N program andrelated subprograms aid efiernistsin determining the molecular struc-ture of unknown organic com-pounds. Because tho molecularstructure-tf a compound must beknown before its other propertiescan be studiedproperties relatedto pharmacology or toxicology, forexampleDENDRAL promises animportant contribution to biomedi-cine. Some investigators have al-ready capitalized on this offer.

Durinthe past 5 years theCONGEN program has been usedsuccessfully by chemists workingon biomedical problems at Stan-ford and other institutions. Abouttwo dozen scientists use the pro-gram each year when solvingquestions about the structures ofcompounds. Invesfigator affiliationsare split about 50-50 between uni--versities and private industry. Theprogram has been exported toseveral laboratories in the UnitedStates. The British government is

J.

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now supporting work at the Uni-versity of Edinburgh aimed at link-ing industrial researchers in theUnited kingdom with CONGEN. Acopy of the program now runs onthe,Edinburgh computer. A col-

, league at the Australian NationalResearch Organization is alsospeaiheading an effort to makeCONGEN available in that country.

More recent research effortshave been directed to extendingCONGEN's representation of struc-ture even further. The program willsoon include principles of molecu-lar stereochemistry, or three-dimensional representation of

'structures. Stereochemistry is ab-solutely essential in understandingstructures and interactions ofmolecules in chemical and bio-chemical *terns, Dr. Smith ex--plains. This new work is pointedtoward a system of computer-based planning and testi% whichincoiporates chemical ancispec-troscopic data from several differ-ent techniques.

As the forerunner of Ar§ shift toknowledge-basecLanalysis, DEN-DRAL holds a special place in'computer history. It demonstrated

CONGEN printout currently one ofthe most successful applications ofartificial intelligence, this progrdmhelps chemists determine themolecular structure ()Organic com-pounds.

2 V

the superiority of domain-specificknowledge as a means to achieveexpert performance and in sodoing raised impor tant issuesconcerning knowledge representa-tion, acquisition, and use.

But, more important than its ob-vious contributions, the programdemonstrated that Al concepts andprogramming techniques were ad-vanced enough to produce usefultools, although each could dealwith only one limited specialty. Thisexample of competence, accordingto Dr. Feigenbaum, vastly im-proved the credibility of Al andpaved the way rcir other such sys-tems. "For us, the DENDRAL sys-tem has been a fountain of ideas,many of which have found theirway into our other projectS," Dr.Feigenbaum says.

Meta-DENDRAL

The project in SUMEX-AIM mostclosely associated with DENDRAL,as might be expected by its dame,is meta-DENDRAL. Developed byDr. Buchanan, professor of corn- .puter science at Stanford, t e pro-gram learns rules,about a s cific

0')

type of compound by examiningdata from a set of examplesThese r tiles can then be used tointerpret data concerning unknownorganic compounds. Both DE_ N-DRAL and meta-DE:NORM_ usethe same rule-based logic. Criteriasot up by expert chemists guidemeta-DENDRAL's generation andselection of rules.

Dr. Feigenbaum says the pro-gram was evolved from DENDRALfor two reasons. First, it was de-cided that DENDRAL has laid afoundation firm enough to pursuethe deeper study of scientifictheory formation. Second, it wasrecognized that acquiring expertknowledge of a specific domain.was the bottleneck in building pro-grams targeted for real-world use.

Meta-DENDRAL was originallyintended to complement the parentprogram. Its job was to formulate .rules for interpreting data frommass spectrometer analyses. Insuch analyses, molecular frag-ments are sepatated according tomass and electrical charge.Meta-DENDRAL's output is sets ofrules that deScribe how moleculesfragment when studied with mass

28

5;p( wtt mliotr y (MS) MetaN1DIt At also includes evidence

suppor ting each fragmentation ruleand a serrnmar y of contr adicter yevidence. Constraints, fed in bychemists, guide generation of pilesalong desired lines.

The program, like DENDRAL,uses the plan-generate-testframework. The process includesthree steps: interpret the data andsummarize evidence; generate aset of plausible candidates; testand refine the set of plausiblerules.

In the first step, meta-DENDRALcites each piece of MS data as ahighly specific point of fragmenta-tion, then sums up the evidencesupporting such fragmentation andthe configurations that wouldcause these atoms to separate.The next step is a heuristic searchfor general rules thar govern thefragmentations. The search beginswith the single most general ruleand proceeds toward more de-

jiailed specifications. This processcontinues until the program de-cides that the rules being gener-ated are becoming too specific.Meta-DENDRAL also includes a

. Dr. W. Todd Wipke, principal inves-tigator of the SECS project: design-ing syntheses faster and without thebias of past experience. .

criterion for deciding whether anemerging [UR? IS too goner al

In the final stage, the programtests candidate rules, comparing111;isitive and contradictory evi-dbnce. Those with a negptive barancie are disregarded. Rules withiedundant features or suppor tedby the same evidence are merged.

The end resultis a rule-set ofcomparable quality to those thatcould be OonerEite'd by human ex-perts, according to Dr. Buchanan."In some tests, meta-DENDRAL

kricreated rule-sets that we had '-ffeviously acquired from our ex-perts during the DENDRAL proj-ect," he says. "In a more stringenttest, involving a family of com-pounds for which the mass-spectral theory had not been corn-pletely worked out by chemists, theprogram discovered rufe-sets foreach subfamily."

These rules were judged by ex-perts to be "excellent." A paperdescribing them was published inthe American Chemical SocietyJournal in 1976.

Emphasis during the past year ,has been to make meta-DENDRALmore-efficient. A major overhaul

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was accomplished, largely rem-(Jiff Illing UM mettkdc; by whIchtho program Works:',With West)changes, the ability ttl9eneraterules concerning a different type ofdata, carbon 13 nuclear magrieticresonance, was incluct,, . Severalpapers were published r,.. 1979 Onthe rules generated irI Mrs area.

SEORe

The SECS (Simulation.andEvaluation of Chemical Synthesis)project is aimed at describing thelogical principles used,,when con

Dstructing molecules. evelopedprimarily by Dr. W. Todd Wipke, achemist and computer scientist atthe University of California, SantaCruz, SECS is Intended to promotethe development of new andmodified drugs, as well as syn-thetic compounds modeled afterthose that occur 'naturally. In par-ticular, the project is concentratedon assisting the chemist to designand select syntheses of biologicallyimportant molecules. Dr. Wipkesays the computer offers severaladvantages over conventionalMethods.

2 9

1.10ng chemists shouldbe able to (10!;icIfiand without the bias of pa,;t experience," ho explains. :Many morepossible syntheses will be consid-ered because of the system's ex-tensive library of chemical reac-tions, which is larger than anyperson can remember. And thecomputer can better process andrecord the many structures that willresult."

Through on-site terminals ortelephone links, investigators fromuniversity, industrial, and privatelaboratories are now using SECS.Versions of SECS are available byacdessing SUMEX-AIM, or at theUniversity of Pennsylvania MedicalSchool, the International ADPNetwork Computers, or Merck &Company, Incorporated, amongothers. Dr. Kenneth Williamson ofMount Holyoke College usedSECS to build three-dimensionalmodels of some 50 compoundsparticularly important in nuclearmagnetic resonance spectroscopy.Other scientists have successfullyused the program to design chemi-cal syntheses. One chemist usedSECS to develop procedufes for

ailMeAdin1111

making synthetic morphine.1 hese users have given us a lot

01 suggestions for improving theprogram," Dr. Wipke says. "Somehave contribrad new reactionsand quite a few people from indus-try have actually contributed laborto thbe projectquite sophisticatedlabibr. In one case, an organic ,chemisf from Hoffinan-LaRoche'who had worked in the fieki of . rheterocyclic chemisio/ for 10 years3pent a year endowing the pro-gram with his knowledge ofchemistry."

The scientist's lack of experiencein computer science was nqt aproblem because S6CS uses aspecial language called ALCHEM,which was develop d by Dr.Wipke's group, He says it is com-posed of declarati es that describehow the environment of moleculesinfluences chemical reactions.

"A chemist can understand thelanguage and rotild a reaction withonly about 5 minutes of explana--tion;-" he says. "To actually use thelanguage well takes only a coupleof days."

When working on a problem, theprogram studies data about the

30

natur al tar got molecule and conttucts a three dimow;ional model

for diSplay on 0 graphics tem mina!!lased on the analysis, SI CSdraws from its kriowledge base toselect reactions that could be usedin the last step of the synthesis andthen backtracks through the re(wired peocursorst. het system stimulates thechemist's own creativity," Dr. Wipkesays. "It presents many differentand unbiased approaches to thesynthesis." The chemist guides thecomputer through the process bypointing out the most interestingtechniques.'

"This is a unique feature of ourproject in terms of Al research.Usually programs are designed tofind one good way to accomplish atask. We are interested in findingall the good syntheses, and thatinvolves dealing with plans, plansthat have many branches and "many contingencies," he says.

There is another feature thatsets the project apart from othersin the field, according to Dr. Wipke."Our program is interactive. We aretackling the problem of synthesisfrom the viewpoint of how best to

-s

tl!e the heinist and the compoteily; a team and to have each teammember doin() the tasks tot whichthat memhet is hest !railed. A 10 ofAl has heel] diiected at how tomalio the computei do the wholething with very little emphasis onpresenting intermediate results tothe user in a form that allowst.search process to he guided, inter-rupted, stopped, or redirected."

Dr. Wipke and colleagues,mostly synthetic:organic chemists, .are currently expandipg the pro-gram to include mbre complexstrategies for designing syntheses."Essentially, the prograffwill havea more preciely directed search,and it will be more selective inwhat it generates," he says.

But before these strategies canbe piq into the computer, they mustbe exPlicitly defined, which is often,difficult to accomplish. For in-stEince, strategies based on princi-ples of symmetry are learned from.experience ratheethan fromtextbooks, Dr. Wipke explains. Forthe computer to recognize a sym-metrical design, these principlesmust be'dissected and reassem-bled In the form of software.

Unlike the cur rent version ofSF.C.,S, which uses Al CIII M toexpress r ules eonceinipg chemicalreactions, strategies., will be wr ittenusing mathematical equations. Ex-pressing knowledge in this formwill allow the Wipke team to buikiae explanatory function into theprogram. If questioned by thechemist as to why a certain reac-tion was chosen fakthe synthesis,the computer will brPable to reply,citing strategies of chemistry.

In final form the strategy portionof the program will complement thepart that deals only with reactions."The current program deckles whatto do by consulting a list of goals,"Dr_ Wipke says, "and that goal listwill be created by this higher levelreasoning process which picks outthe strategies applicable to thesituation and explains why. Theprogram will then select ways toimplement the strategies and, fi-natty, decide hew to modify themolecule's structure. This multi-step procedure allows a view of theproblem free from human bias."

Dr. Wipke hopes to demonstratethat computer-based synthesistechniques can also be applied te

SECS printout: helping the chemistdesign and select syntheses ofbiologically important compounds.

4

the study of metabolism. nursed ontechnology from the Sf:CSgram, a new compute! programcalled XENO has been developedto predict metabolic pathways forxenobiotic compoundS--chemicalsnot normally found in the body. Theobjective is to predict plausiblemetabolites of a given xenobiotic."What you put in is t6e chemicalstr ucture of the foreign compound,"he says. "What you get out is thechemical stRicture of the metabo-lites."

Predictions of plausible metabo-lites result from knowledge of howcompounds are activated by en-zymes. Many of the mechanismsinvolved in these processes areknown and more are being discov-ered. j

In addition, the metabolite'sstereochemistry is predicted. Acompound may exist in two forms,each the mirror irpage of the other.s.One may be active while the otheris not, or they may botfvbe activebut produce different effects.

"Stereochemistry in metabolismis a new frontier," Dr. Wipke says."In tApast, instruments were notsensitive enough to explore this

angle using the amount of motabolito that was obtained

In recent months problefli;been submitted to the progrwn totest its ability-to predict metabo-lites. Dr. Wipke says XLNO hasbeen fairly successful at identifyingmetabolites found in laboratorystudies, and also predicts metabo-lites that have not been found.When discrepanbies occur, Dr.Wipke says, they sometimes canbe traced to errors in the knowl-edge base. "The computer maypredict more metabolism than isactually going on in living sys-tems," he says. 'That's really nOttoo bad, because the metabolitesthat can be isolated will always beincluded in the set of metabolitespredicted by the computer. Theprogram defines a set of candi-dates to look for."

Dr. Wipke and colleagues havenow foCused on expanding theknowledge base, particularly to in-clude models of more species.Only the rat and the mouse arecurrently described in detail.

An index of biological activitiesassociated with metabolitesforexample, nrcinogensis slated

*/

31-

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for inclusion Iho functrtni willapply pattern r ecognition to Awnpounds not listed in the index as ameans of classifying moiatwlites

The XENO project is not the onlyspin-off from SECS. In 1978 theSECS program led to developmentof a daughter project that extendscomputer-assisted synthesis intophosphorus chemistry. Under thedirec3ion of Dr. Wipke, Drs. GerardKaufman and Francois Ctioplin atthe University of Strasbourg inFrance created a knowledge basecomposed of reactions pertainingto phosphorus. In analyzing sev-eral compounds and searching theappropriate literature, the new sys-tem found most of the existingsyntheses and, more Importantly,suggested new techniques thatappear to be equally geod or bet-er, according to Dr. Wipke.

OLGEN

Experiment-planning in themanipulation of DNA is the goal ofMOLGEN, a Stanford project beingconducted in collaboration withScientists at the University of NewMexico (UNM). Program develop-

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dward I eigenbatscientists sPeter I riodland, anDoug utlag MO1.to advise geneticists about the dosign of labor ator y experiments.These include methods used toanalyze and modify nucleic:acids.

MOLGEN is mainly foc(rsed onorganizing experimental tech-niques and determining the orderin which they should beapplied toachieve specified goals, Dr. Brutlagsays. "The enormous volume ofdetailed knowledge makes it likelythat good experiments am beingmissed," Dr. Feigenbaum says."We believe that an intelligentplanning assistant can offer help inanticipating the results of combin-ing experimental methods in manyways."

Dr. Peter Friedland saysMOLGEN makes near,expert deci-sions when selecting physicalmethods, such as electron micro-scopy or enzymatic modification, toanalyze molecular structure. Even-tually the program will be ex-panded to include the design of

del the direc:0 Ii. Kedes andn, compute!Stefik andbiochemist IV

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synthesis experiments in whichmethod; for buildiN molecules willbe described I midi() c ial analyses

,uhieti as well, ,ilk)wing ttnprOgr am to klentify the products of

:nucleic acidsThe success of MOl. GEN as an

experiment designer depends onthe quality of its knowledge base.Much effort has boon expendedlosupply the base with explicit infor-mation about DNA str uctures,restriction enzymes, a hierarchy oflaboratory techniques, and a grow-ing collection of genetics-orientedstrategies for discovering informa-tion about various aspects of DNAmolecules. Most common ana-lytical and manipulative methodshave already been pul fn the base.

Results of the research includesome special-purpose programs inthe area of molecular genetics.The most useful are highly refinedversions of previou.sly existingstrategies. Many of these concerndetermination of the sequences ofnucleic acids in DNA. Modificationsare focused on technical aspectsof the prodrams; those leading to'impftived efficiency, for &ample,and those addressing human en-

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DNA's spiral ladder of heredity: help-ing chemists manipulate themolecule through well-planned ex-periments is the tOCUS of MOLGEN.

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gineering concerns to make iteasier for icientists not familiarwith c4Anputors to use the progrfims.

In addition to its applied,or ienta-MOLGEN includes an Al re-

search dimension: use of theknowledge domain of moleculargenetics to create a generally ap-plicable problem-solving program.lhe system is designed to allowgeneralization into domains be-yond genetics in future,researchand application.

"Integrating the many diversesources of knowledge is a centralproblem in constructing MOLGENbecause the expert-planning pro- ecess requires a blend of biological, .genetic, chemical, topological, antiinstrument knowledge," Dr,Feigenbaum says. "The expert'sknowledge Of experimental strate-gies must also be representI antiput to use."

PROTEIN STRUCTUREPROJECT

fluilding computer models of,pro-tein structures from cryStallo- -graphic data, particularly electrO

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density maps, is 1,he goal Of thePRO II IN SHIM:flit IF 1)10)0(1 at:Aanforo I lectr On dt.msrly nhipsarc data f ()presenting the sti tic-tures in three dirnensivs. Unfor to-nately, these maps well liallycrude and ambiguous As .1 result,the program depends largely enbackground ir\for [nation, such asthe amino acid s'equence in a pro-tein, for guidance and support informing hypotheses about thecompound's three-dimensionalstructure.

f3ecause the shape of amolecule exerts a major effect onits performance, accurate analysesand representations of molecularstructure are seen by medical re-searchers as essential-to under-standing the biological function of.these complex molecules.

Interprating electron densitymaps is the prt of a proteinchemist, which the system's logicscheme attempts to capture .through the use of heuristicrules. Due to(the size of proteinMolecules, which often containmany thousands of atoms, theplan-generate-and-test strategyusedby,DENDRAL cannot be em-

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ployed !lather, the :;\;!Aern precestogether hypothows try ( rcei rIm ntint.) :,11(Te!;!>ively on !.pecrfW atex;of the protein 1 he project is underthe direction of Drs. I ergenbaurnand Rober t kngelmor 0 of StanfordUniversity with assistance born Mr.Allan -Fer y, it the t fniversity of Calanima (1 Ir vine, and the strongcollabor ition of Dr. Stephen itemat the C San Diego.

Clinical Medicine

INTERNIST

Heuristic search tethnigues areused in all SUMEX-AIM projects,although each differs according tothe purpose.of the project. Drs.Jack D. Myers and Harry E. Pople,mentors of INTERNIST at the Uni-versity of Pittsburgh, reasoned thatthe best way to design a computerprogram for solving difficult prob-lems is to simulate the mentalprocesses used-by people. Theyare primarily interested in buildinga Program thal will aid skilled vpe-cialists in solving cOmplicated prob-lems concerning internal medicine.Spin-offs from the program might

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be used tIy physicians assistantsor ill rur al health care clinics, milltar y outposts, and spacecraft. tobe effective, the program must beable to diagnose several diseasesif they are present in a tingle pa-tient, and it must render diagnosesquickly to reflect the current statusof the patient.

Drs. Myers 6nd Po le analyzedthe diagnostic routlnq followtd bythe expert clinician ahd establisheda set of criteria:

pbservations foci into the corn-PUtcar must evoke Ihe appropri-ate.hypotheses of disease.Hypotheses must generate a listof manifestations that would bepresent in the patient if the diag-npsis is correct,The computer must be able torank models of disease accord-ing to their probability of beingcorrect and must be able to delcide when the weight of evi-dence is sufficient to permit rea-,sonably Confident judgments-1-he program must be able todroup hypotheses into Mutuallyexclusive,subsets correspondingto different diagnoses, in order tohandle cases in which more than

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Ono disease may bo presentSince beginning their work in

1970, pls. Myers and Pople havedeveloped an operative system,and in so doing have par tiallyachieved these objectives.INTEIINISTUccepts descriptionsof disease manifestations in anyorder and asks for more infor mo-tion, stich as historical items,symptoms, signsl and laboratorydata. These facts are not enteredin a specific order, but rather asthey are gained through tests andobservations. As factsjaccumulate,nodes of recognition are triggeredand a pattern begins to develop.

Now, with a specific direction,the computer fits the data togetherlike pieces of a jigsaw puzzle. Aninterlocking web of programmeddata isiset up, beginning with cate-gories uch as liver disease lead-ing into specifics like hepatitis A.After sufficient data have been fedinto the computer, disease modelsare developed. The models arethen compared and ranked.

"The computer holds the profilefor each disease in its memory andif the model fits that standard pro-file very closely, it could make a

3 4

di gnosis,- Dr Myers says "If thatis 1 possible, it will set out askingquestions to obtain fur ther )nfor malion, so that one or more of themodels can tnconfir med."

A second generation programdubbed INTERNIST II, which mayspeed up the diagnostic procedure,is now being designed. Althoughexperimental, the new programhas shown promising results, rais-ing hopes that it will lead to-a moreefficient workup of clinical prob-lems when the program is applied.

Dr. Myers predicts that within 5years INTERNIST might be diag-nosing disease on a practical,rather than experimental, basis.When.completed, the 'system owillbe able to assist physicians work-ing on difficult cases and .paramedics serving in reMote or.medically underserved areas.

"The computer's assessment ofa patient's condition will be re,garded as evidence to help thepractitiondr form a diagnosis," hesays. "The program is intended to .serve as a conwltant, not as a re-placement for the physician."

Currently, more than three-fourths of the knowlelge appli-

Drs. Jack Myers (poin4) and Harry-Pople VERAYST: "No one canpossibly emorize all the data inmedicine."

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cable to intern' medicine, 000 ofthe broa(1est )ecialties, has beentranslated ir o symbolic data str tic-tures and stored in the computermemory. Over the past 2 years IN-TERNIST's ability to translate thisvast store of knowledge into accu-rate diagnoses has been proved,using a variety of difficult casestudies that were published inmedical jour nals or occurred inPittsburgh teaching hospitals. "Inthe great majority of cases theprogram has been effective ia sort-ing out the pieces of the puzzleand coming to a correct diagnosis,"Dr. Myers says. '`The knowledgebase is too incomplete for a com-prehensive test in a clinical situa-tion, although it is used on anad hoc basis at Presbyterian-University Hospital, Pittsburgh, forclinical guidance."

Within 1 year, the knowledgebase is expected to reach a "criti-cal stage of completeness," ac-cording to Dr. Myers. Soon after,field trials of INTERNIST arescheduled to begin at Presbyterian-University Hospital. If successfulthere, a half-dozen other healthcare centers will take part in the

testing At each institution, Dr.Myers estimates, 20 caw analyseswin be run each day. During thetrials, physicians' reactions to thesystm and their patter n of use willbe retorded. On the basis of thisinformation, INTERITST will be re-vised, if necessary, to improve ser-vice to future users.

In the past year, Drs. Myers andPople have devised a programcalled ZOG, which makes it possi-ble for a physician only casuallyacquainted with computer scienceto master the use of INTERNIST,reportedly within 5 minutes. Testsshow that ZOG, developed atCarnegie-Mellon.University, is veryversatile and easy to use. Dr.Myers says ZOG is important be-cause the computer must be easyto operate if it fk. to bridge theever-widening gly between what isknown in medicine and whatphysicians are able to remember.

"No one can possibly memorizeall the data in medicine," he says."There's just too much kngwledgeand that pool of information is con-stantly increasing. The computerhas a perfect memory and is ad-mirably suited for a large knowl-

4

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