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THINKINGSOLUTIONS1998 MACHINEINTELLIGENCE: T H E D E AT H O F A RT I F I C I A L INTELLIGENCE J O H N B A L L FIRSTWRITTEN SEPTEMBER26,1998 Copyright©1998and2016byJohnBall Allrightsreserved.Nopartofthispublicationmaybereproduced,distributed,or transmittedinanyformorbyanymeans,includingphotocopying,recording,orother electronicormechanicalmethods,withoutthepriorwrittenpermissionofthepublisher, exceptinthecaseofbriefquotationsembodiedincriticalreviewsandcertainother noncommercialusespermittedbycopyrightlaw. FirstEditionSeptember26,1998 ThinkingSolutions,LindfieldNSW SecondEditionApril15,2016 HiredPenPublishing,Seattle,WA TABLEOFCONTENTS PREFACE Whycare? IsAIintelligenttoday? TheproblemwithlanguageinAI Today Thefutureisbright OVERVIEW DESIGNQUESTIONS ASSUMPTIONS THEMODEL—AQUICKSNAPSHOT INTRODUCTION GOALS THEDEATHOFAI WHODOWEMODEL? ROBOTPRODUCTION WHYHASPROGRESSBEENSLOW? DEATHOFPROCESSING DEATHOFTHEPROCESSINGMODEL DOESITADDUP? THEMACHINE SENSES MACHINEINTELLIGENCEMEMORY(MIM) OURBRAIN’SCONSTRUCTION(MICONSTRUCTION) MIOPERATIONS Sleep COMPARISONTODAVIDHUME’SMODEL PATTERNMATCHING,LEARNINGANDUNLEARNING MORECOMPLEXSENSES LANGUAGE LanguageExample LanguageUnderstanding Hutchens’LoebnerPriZeProgram VISIONSYSTEM Recognitionofspelling Recognitionofwords Recognitionoffaces HUMANEXAMPLES:PHYSIOLOGY,PSYCHOLOGY&PHILOSOPHY PHYSIOLOGY FREUD ratman CARLJUNG Jung’sFreeAssociation PLATO PAVLOV’SDOG B.F.SKINNER DAVIDHUME SCIENCE85—THECONFESSION WALDROPINTRODUCTION ROGERSHANK’SDEFINITION THOMASOPOGGIO:MATHEMATICIANOFVISION BRUCEBUCHANAN:MAKINGKNOWLEDGEUSEFUL GEOFFREYHINTON:PARALLELINTELLIGENCE TERRYWINOGRAD:ENGLISHASACOMPUTERLANGUAGE HUBERTDREYFUS:THEBLACKNIGHTOFAI THEROOTSOFMORALITY IMPLICATIONSOFMIMTHEORY HERIDARYVALUE CONCLUSION APPENDIXA.ADDITIONANDMULTIPLICATIONWITHOUTPROCESSING APPENDIXB.ASSUMPTIONS FURTHERREADING P REFACE UPDATED2016 We sit at the threshold of the next generation of computing, in which machines understandusanddowhatwewant.TheyrequirenaturallanguageunderstandingorNLU. My lab now produces programs that solve open scientific problems based on patternmatchingasdescribedinthisbook:forunderstanding,translationandconversation.Ifyou learnonethingfromthisbook,Iwantittobethattheapproachismoreimportantthanthe effort. In the same way that predictions in astronomy weren’t revolutionized by more effort on an earth-centric model, redefining the model to fit the reality of a sun-centric modelwasneeded. This book was first written in 1998 and formed the basis for what subsequently becameknownasPatomtheory,abrain-basedtheoryinwhichbrainsaremodelledwith pattern-matching elements that simply store, match and use hierarchical, bidirectional linksetpatterns.IchuckledwhenIreadsomeofmyexplanationsfrombackthenbecause themodelhasbeenclarifiedthroughthescientificmethodwithextensivetrialanderror. Science progresses as people work through today’s limitations. As in 1998, the biggest technologicalproblemstodayseemtobeinthecognitivesciences,notsomuchinphysics or biology. The branches of cognitive science relating to computational models have proliferated,butremainineffectiveatsolvingcoreproblems,likelanguageunderstanding (NLUcontrastswithartificiallanguages,likeJavaorC#). This book makes you think about the actual problem of machine intelligence by lookingatthefactsofouramazinghumancapabilities:allprovidedbyourbrain. Patom Theory aligns more closely with the brain than the model described on these pages,butthefoundationalconceptforthetheoryiseasiertofollowhere.Theproblemit solvesisthesame,andthemodelhasevolvedtomoreaccuracy.Theprocessingmodelat thecoreofacomputerisstillaninhibitortoemulatingbrainfunction.Thequestionsof parallel processing— a popular computing paradigm—are now dealt with by learned connections within the network, rather than broadcasting from a central controller. Conceptually the model is the same, but the implementation is now working with a simpler design. Similarly, the question of how much fuzzy logic and statistics to use is replacedwiththealternativeapproachofperfectaccuracywithinalandscapeofconsistent ambiguity. WHYCARE? YoumaythinkIregardtoday’sstate-of-the-artincomputersandArtificialIntelligence negatively, but nothing could be further from the truth. Computers today remain remarkablemachinesthathavetransformedourlivesforthebetter.Mostthingsthatcan bedescribedasaseriesofstepshavebeenprogrammedonacomputer,aspredictedbythe founding fathers of AI in 1956. But the things that haven’t been achieved that were expected, like vision and conversational language understanding systems, are not here todaydespitedecadesoftryingbyverysmartpeople. Programmers failed in the 1970s to outperform statistical approaches. Statistical systems won that battle, but remain lacking in human accuracy and worse, their current limitations seem to conjure up fear amongst physicists and entrepreneurs alike, who predictillogicalresults.Imean,Hollywoodlovestopredicttheendofhumanitythrough runawayAI.FirsttheAIbecomesmoreintelligentthanus.Aminuteorsolater,having surpassed the Kurzweil singularity, it creates an automated factory to mass produce machinesthatexcelineveryaspectofroboticsthathumanshavefailedat,todate.And then,Armageddon! ISAIINTELLIGENTTODAY? Toputthesefearsinperspective,today’sAIisnotintelligent.Onascaleof0to100, where0waswherecomputerswereatwhenAlanTuringwroteabouttheTuringtestin 1950, and 100 which is human-level intelligence, we are around a 10. Machines don’t understand,andthereforecannotbeintelligent,regardlessofthespeedofprocessorsorthe sizeofmemory.Basedonthespeedatwhichwecouldmigratetolanguageunderstanding systems,Ipredictmachinescansurpasshumansaround2040assumingwepursueNLUas wedidwithpersonalcomputersandthenphonehandsets. Now what about these murderous robots in our future? Firstly, why would an intelligent machine be able to create robots more effectively than humans today? If creativity comes from the application of problems to new domains, and AI uses a paradigm unlike human brains, why do we think a super-fast machine will get the right answers? If it’s trial and error, and the machine is faster, it still needs to outperform thousands of humans and millions of dead ends. I can’t buy the idea that machines will generalize any time soon, but when they do, they will do it like we do. Secondly, if an intelligentmachinereliesonus,whywoulditnotwanttoworkwithus?Ifitistoostupid tounderstanditreliesonustosurvive,whydowethinkitwillbesmartenoughtobeatus in a competition against us? Wouldn’t we turn it off? How could it stop us? Create an armyofrobots?Nuclearwar? Forthosesciencefictionenthusiastswhoknowthestoryof2001:ASpaceOdyssey, remember that even the HAL9000, a machine that would probably exceed human-level intelligence,wasunabletostopDavidBowmanfromturningitoff.Imean,ifIwereHal,I wouldhavelockedtheexternalhatch,orblockedtheairflowsomehow.Whatadummy! THEPROBLEMWITHLANGUAGEINAI I have used the term ‘Machine Intelligence’ to describe brain-based machines that emulatehumanintelligence.While‘artificialintelligence’orAIisawell-knownterm,it alsobringsitsassociationsofcomputation,programmingand,ifImaysayso,failure.AI hasbeenhypedtodeath,sorry,butithasmany,many,many,many,manytimes.Solet’s welcomeMI. David Hume, an amazing person from our history, developed a model of brain association. He explained sensory experience as impressions, the direct input of experience,andideas,thefaintermemoryofstoredexperience.Ideasarebrainmemories, brokendownimpressionscomprisedofthecomponentparts.Thebrainisfreetocombine ideaswithoutconstraint.Humearguedthatthebestwaytodealwithourcreativeabilityis to condemn all ideas to the flames that have not become ideas from direct sensory experiencedirectly. We need MI to embrace Hume’s teachings. If intelligence comes from the combinations of experience, we need machines to break the world into its constituent meanings,inwhichmeaningcomesfromexperience.Ahorsewithwingsisplausible,but experiencerulesitout.Physicsandanatomyalsoruleitout,butthatisamorecomplex story. Whiletherearesomeideasthathavefallenbythewaysidetoremovethehomunculi from our brain, the text is still, in my humble opinion, a good introduction to a way to approachbrain-basedresearchinanobjectivemanner. TODAY A lot has happened since this book was written in an attempt to make systems act intelligently.WenowhavetheiPhone,Siri,GoogleandMicrosoftCortana.TheInternetis pervasive.WehaveIBMWatson,amachinethatdoesnotunderstandthewordsitmines, butisrelieduponbyexpertstofindinformationwrittenindocuments. Industry uses words to obfuscate for marketing reasons. IBM Watson, for example, call their machine a “cognitive system”, but a search on Google identifies cognition as: “thementalactionorprocessofacquiringknowledgeandunderstandingthroughthought, experience,andthesenses”.Itisunlikelythatalogicalargumentcanbemadetojustify the components of Watson as cognitive because it doesn’t understand words. It doesn’t havesenses.SowhycallaBigDataapplicationacognitivesystem? Thesectiononassumptionsisnearthebeginningofthisbook.Meaningcomesfrom words,andassociationsareverypowerful.Forthatreason,wordsthatareambiguousare removed from the discussion on brains to add clarity. As another example, the Deep NeuralNetworksofProfessorHintonhavebeenrenamed“DeepLearning”toalignwith today’sobsessionofstatisticalgathering.Machinelearningissimilarlypopular.Usinga known word like “learning” makes people think of its meaning. Learning suggests the steps a person’s brain uses to break down meaning. Somehow, it accurately generalizes fromlearning.ThetroubleisthatDeepLearningsimplychangesthestatisticalvaluesin an artificial neural network with setup from people. When applied to the problem of convertingspeechtotext,forexample,thereisnoknowledgeofthemeaningofthewords foundatallbecausethedataisnotavailable. Big data, neural networks, hidden markov models (HMM) and other statistical solutionsareallsimilarmodels.Whentheresultsarewrong,well,whynottrysomething elsetosolvethescience?ToparaphrasethelatefounderofIBM,ThomasJ.Watson,Sr: “Itisbettertoaimatperfectionandmissit,thantoaimatimperfectionandhitit.” THEFUTUREISBRIGHT Today’sAItechnologieshavebeenbuiltoncomputers.Digitalcomputers,machines builttoemulatehumancomputers,areawesomemachinesatwhattheyaredesignedtodo. They run systems for industry. They run the internet. They are at the heart of science, facilitating simulations of complex systems. But they are fundamentally flawed for AI becausetheirstrengthsopposethebiologicalsystem’sdesign. They start with unambiguous information, and carry out sequential steps to process results.AnythingthatcanbecomputedcanbesolvedwithaUniversalTuringMachine, theprinciplebehindeverycomputertoday.Buthumancomputersweren’tdesignedtodo whattheirbrainsdo—see,hear,speakandmove.Theyweredesignedtocalculate.Ilike tosaythat: Today’sdigitalcomputerscompressandduplicateinformation. Brainscentralizeandexpandinformation. Computerscreateaknowledgeacquisitionbottleneck,byforcingaprogrammer todefinethegeneralbeforestoringthespecifics. Brainsstorethespecific,tofindthegeneralthroughexperience. Wecanemulatethebrain’smodeltoday.Therearemanyfeaturesoflanguagethathave been discovered and by looking at the science of language as it applies to meaning and conversation, such as the Role and Reference Grammar, the future promises many new applicationsthatwillaffectourdailylives.Wejustneedtostart. Andwiththestart,wecanbenefitfromthescience.Sincemoderncomputersdeveloped from the 1940s, scientific debates over strategies have raged. Today, many good ideas since the 1950s are shelved, waiting to be re-evaluated in light of today’s amazing improvementsinsoftwareengineering. SomesaythattheHolyGrailofAIisNLU,sowecantalktoourdevicesnaturally.We nowknowthatnotonlydoweneedtounderstandwhatisbeingsaid,butwhatisbeing saidrelatestothemeaningandcommunicationsabouttherealworld.Thisisthestartof theexploitationofbrain-basedtechnology.Wewillallenjoytheride. JohnBall PaloAlto,CA April2016 O VERVIEW September26,1998 Machine Intelligence requires the work of cognitive Scientists to deliver a design. Cognitivesciencecombinesanumberofdisciplineswiththeaimofproducingintelligent machines. The experience of computer scientists, linguists, neuroscientists, philosophers andpsychologistsprovidesabasisforunderstandingthehumanbrain.Usingadesign-byimitationmethod,weshouldbeabletocreateanintelligentmachine. The reality is that these people have been unsuccessful in making progress in what appears to be the challenge of the century— in a century where many of the scientific challenges(atomictheory,astronomy,andtraveltothemoon)havebeenconquered. Creatingmachinesthatinteractwithusnaturallytrulyisthechallengeofthecentury. Spoken and written language is better for communications than windows, mice and keyboards. For machines to move in our world probably means using arms and legs becausetheyarebetterthanwheelstodealwiththingslikestairsanddoors.Andsystems thatcanusessensoryexperience–vision,hearing,balance,touch,tasteandsoonwillalso changehowwedealwithmachines. Machines to-date have been designed with people in mind as the users. A computer revolutiongaveusamousetoaugmentakeyboard,butthatisonlyusefultothehuman driving it. Speech should be faster because we are so fast at talking. We have video cameras and televisions, but they are designed to record and show humans moving pictures, synchronized with sound. Unlike brains that recognize images in real time and the objects that sent the image, the recorded data of the moving image is optimised to reproduceitforhumanconsumptiononanotherdevice,nottounderstandtheimages.The recordingisacompressedversionofvision. Themostextremecaseofahuman-centricdeviceis,infact,thedigitalcomputer,the machine that replaces human computers. Human computers knew what the numbers representedontheirnotepads,digitalcomputersdon’t.Theyknewwhatthestepswereto manipulate the numbers. And they require a human to write the program. Today, all computeroperationsarecontrolledbyaprogramwrittenbyaperson,orconvertedfroma person’shigher-levelprogram. Thesolutiontothechallengewillbeseenwithmachinesthatarenotprogrammed,but learnandinteractbasedonexperience.Andtheywon’tneedustolearnhowtointeract withthem,becausetheyinteractlikeotherpeople. This paper asks a central question: if the human brain and nervous system is a machinethatprovidesalltheobjectiveandsubjectiveobservationsofhumanability,what designandcomponentsarerequiredtobuildamachinethatemulatesitsperformance? Foryears,theworkinartificialintelligenceputforwardbycomputerscientistssince the early Von Neumann and Turing computers are typically based on computational models. The computational model has served the commercial and scientific market for manyyearswithgreatsuccessandtherearemanyproblemsthatarewellsolvedbythese electroniccomputers.Wherewouldwebetodaywithoutcomputersforbanking,finance and air travel? Unfortunately, this computational model is too different to the human process of “thinking”. If it were not so different, why would so many great thinkers be unabletoproduceanartificialintelligence? A way forward is to produce a model that explains the available observations. By meeting the wide-ranging observations and by finding simple models that can emulate these, we can explain the workings of the human brain—and therefore create the seeds requiredtogrowanintelligentmachine. Abetterwaytoapproachthechallengeofcreatinganintelligentmachineistodiscard thecomputationalmodelinfavourofamemory-basedmodel.Thismodelprovidesavast storage capacity and an even vaster number of links between stored items. Through the processofassociation,thememorylinksarecreated. Amazingly,thisnon-intelligentprocessprovidesthebasisforintelligentaction.This modelresultsinthedetectionofarchetypalobjectsthroughtheprocessoflinkingobjects togetherbyassociation.Italsoproduceslanguagethroughamulti-levelpatternmatching facility. It even explains the observations of psychologists, linguists and philosophers throughouttheages. Itisevenmoreexcitingtofindthatthebraincanbemodelledbyasinglehardware designthatisbothsimpleandversatileinapplication.Andthemodelisbasedonjusttwo key elements: memory, and linkage to other memory. Processing, the basis of electronic computingsincethe1950s,iseliminatedasthedominantelement. D ESIGN Q UESTIONS Inordertodesignanything,thequestionmustbeasked:“Whatdoesthishavetodo?” A study of available material indicates that in order to imitate the human brain, our best and only example of intelligence, there are a number of features we must emulate. Thesefeaturesinclude: The ability to recognise objects (visual, auditory, kinaesthetic, gustatory, olfactory)inamatterofsubseconds,despitethefactthatthepartscomprisingthe brainaremillionsoftimesslowerthanthefastestcomputers. Theabilitytothrowaball,despitethefactthatthespeedofthethrowexceeds thebrain’sabilitytocontroltheevent. Theabilitytorun,includingthehugenumberofactivitiesthatmustbemanaged simultaneouslytoensurebalance,controlandpace. The ability to speak intelligible sentences, using correct grammar, context, emotional intensity, and including a huge array of background and other associated knowledge. This ability also despite the fact that the brain is far slowerthanacomputer,andyetacomputerissopooratdoingthistask. Thegenerationofaself-maintainingdatabaseabouttheworld.Thebrainlearns from experience and the environment with no need for anyone to assist in the managementofthedata(withtheexceptionofpsychologicalproblems). Theabilitytolearn,withouttheneedfortraining.Examplesoflearnedabilities includetheacquisitionoflanguage. Andtheneedforanymodelofthebraintoalsoexplainthemanyobservations thathavefedintothesystemsofphilosophicalandpsychologicalthoughtforthe past3000yearsandmore. Thebrainisanamazingorgan—thereismuchwehaveobservedaboutit,andsolittle wehaveimitated,althoughwithsomuchemotionalbaggageandsomuchtheologicaland philosophicalmisinformation,itisnowonder! ThemodelproposedbyWilliamofOckhamisasoundonetouseinthemodellingof thehumanbrain.Thatis:“entitiesarenottobemultipliedbeyondnecessity”.Takingthis basicpremise,themodelprogresseswithacoupleofadditionalobservations: 1. The brain does not calculate in a way similar to a computer (there is no evidence) 2. Thebrainrecognisesobjects 3. Thebrainassociatesobjectswithotherobjects 4. Thebraincanrecallobjectsandtheirassociationsattimes,whenrequired. A SSUMPTIONS Humanity is slow to modify its way given a sufficient number of incorrect assumptions to a better way. Change requiring the eliminations of existing patterns and associations are not often handled well by people—a likely result of our brain’s inner workings. History often finds changes occur best after the death of a strong leader—at leastintheirspecificareaofinfluence. The new theory challenges several long-held assumptions about how we learn and, indeed, what our brain is doing. Brains learn. Whether it is vision, speech, language or how to perform calisthenics, brains learn these skills. Computers are programmed by humans. Ourworldisambiguous.Computersaredesignedtoremoveambiguity. We associate to learn. We manipulate others by using words that associate others to knownideas.Today’sbrainscienceishinderedbyusingsuchterms,especiallycomputer termslikeprocessing,becauselikeitornot,thebrainthenaccessestheassociationsand inhibitscontraryideas. The theory described in this book challenges many of the assumptions we hold concerningthehumanbrain.Thewordsthatimpactourabilitytotalkaboutbraintheory includethefollowing(refertoappendixBformoredetail): Thebrainandnervoussystemisamachinewecancopy. Thereisnomind. Thereisnoconsciousness. Thereisnosubconsciousorunconsciousmind. Dreamsdonothavemeaning. Psychoanalysisisamethodofunderstandinglearning. Humanthoughtisanoutcomeproducedbythebrain—itisnotasingleprocess. Thoughtisnotacomputationalprocess. Computermemorydoesnotworklikehumanmemory. In the tradition of science defined by Ockham’s Razor, these assumptions focus the investigation to construct an intelligent machine and avoid the many limitations put forward by philosophers when explaining why they believe that intelligent machines cannot be produced. They believe that the reason artificial intelligence will fail is its inability to explain the observation of consciousness. The assumptions listed above are merely some of the preconceived limitations retarding the progress towards creating intelligentmachines,yet,theyarecentraltothecorrectpathahead. T HE M ODEL —AQ UICK S NAPSHOT In the theory set forth in this paper, the brain is assumed to be a pattern matching system, with modules in the brain corresponding to areas representing specific memory typesandwithlinkagesbetweenmemoryelementsproducingthemarvellousexperiences oflife.Eachmemoryelementisanareathatcanrecognisepreviouslyexperiencedsensory information and feed that information back through the system to produce a vague imitationoftheoriginalmemory,ifrequired. Thismodelofthebrainleadstoamachineintelligencemodelthatstatesintelligence is a system of Machine Intelligence Memory (MIM) components linked together to produce a large-scale sensory-linked system providing immediate recall of linked informationonastatistical(mostorleastlikely)basis. I NTRODUCTION Therehavebeenmanywhoclaimthatintelligentmachinesarejustaroundthecorner. Therehavebeenlargeprojectsaimedatcreatingspeakingmachines,inferencemachines and machines with networks of elements that look like neurons to some degree. In the 1980s, Japan set about the production of the so-called 5th generation project to revolutionisethecapabilityofcomputers. Theseclaimshaveallbeenshowntobeoutrageouslyinaccurate.Regrettably,thelarge amountsofmoneyandeffortappeartohavebeenputintoprojectsthatdidn’thaveaclear result. They were pure research projects into areas where at best the description of the projectscanbecalledguesswork. This book considers a different approach. It outlines what is known about our best intelligent model—human beings. By using a human being and creating a “thought experiment” (Einstein had great success with this approach), we can venture into the worldofintelligentmachinesandseewhatmakesthemtick. So, can we use what we know about brains, and the human brain in particular, to createanintelligentmachinetoday?Inourquestforprogress,humanshavealwaysbeen successful in building machines once we have a good vision of our goal. Provided the machineisdescribedinsufficientdetail,itismerelyaquestionofeffort(readmoneyin today’seconomicclimate)toproducetorequiredoutcome.Themodelputforwardinthe followingpagesshowsmanyofthecomponentsrequiredtocreatemachineintelligence. Shouldthismodelbefollowed,anintelligentmachinewillbetheresult. Rarelydoesanunclearstrategyproduceaclearoutcome! Thequestionoffundinginacapitalistsocietyisadrivingforce.Thefirstcompanyto produceanintelligentmachinewillchangethefaceofcomputingforever—relegatingthe desktop computer to the failed automation tool of the 1950s to the 1999s. This would representatrulydarkperiodinhumanhistory. With the construction of intelligent machines, the Internet will become a haven for machinesthattalk,sellandprovideotherservices. Theendofthedesktopcomputerisnear.Thenewmachinesofthefuturewillspeakin nativehumanlanguagesandprovidethetypeofcompanionshippreviouslyonlypossible throughtheexploitationofslavesandservants. ANewWorldisuponus!Readontoseewhy… G OALS Asimplemodelexplainseverything.Whereisthesimplemodelforbrainfunction? The Greeks in the centuries before Christ dreamed of demons and Gods who were able to outperform humanity in many ways. Their champions of intellect—men like Socrates,PlatoandAristotle—documentedthewaytheworldworkedthroughamethodof observationandcommonsense. Someoftheirmoreobviouserrors,suchastheAristotelianmodelofa“Earth-centric” universe, took many, many years to disprove. Galileo underwent torture for his sin of creatingabetter/moreaccuratemodelofthesolarsystemthanthechurchwasawareof. Platowasabletoprove,inhisinfamousstyleofquotingSocratesthathumansdon’t learn. They merely remember what they knew from a previous life. It would be many centuriesbeforeJungbegantalkingaboutthecollectiveunconsciousinasimilarwayto explainhisobservationsaboutpeople’sinnatecapabilities. Is human intelligence a more complex entity than the scientific study of astronomy, physicsorchemistry?Hugeadvanceshavebeenseeninthosesciencesintheyearssince Galileo—especially since the start of the 20th century. The composition of the atom has beendescribedtoalevelofdetailthatishardtoimagine—letalonebelievetobetrue.The universe has been described in terms of its visible history, its size, it age and the vast variety of its celestial bodies. We live in a universe that exceeds the imagination of the greatestmindsofsciencefictionbyitssheervarietyandsplendour! Whythen,hasthestudyofintelligencebeensopooratdescribingthehumanbrain— and the subsequent implementation of non-human intelligent machines? A most likely reasoncomesbacktotheissueGalileohadtoovercome—thehumanmindisverypoorat explainingideasthatcontradictourbasicbeliefs. In the case of human beings, we seem reluctant to attribute intelligence to other animals. Yet, it is likely to be the case that the easy things to model in an intelligent machine will be the higher human functions. The functions of a dog: balance, climbing anddescendingstairsonfourpointylegs,enjoyingthecompanyofhumansandlearning tricksaresomeoftheamazinglycomplextasksthatdogsappeartodowithoutthinking. Wedon’tusuallyattributeintelligencetodogs,andyetdogsappeartobeconscious,able to make complex decisions over which animals are food to eat and which humans are friends and which are enemies. If we took a dog and added the language and planning abilityofhumans,adogwouldprobablybealsoman’sbestfriendatschool! Weare,inmanyways,God-like(althoughmorethanahundredyearsago,Nietzche pointedoutthatGodisdead).Whatelsedowecompareourselveswith?Althoughfewin the scientific world today accept the full doctrines of religion, we nevertheless typically fall back on reincarnation and the Christian beliefs of heaven and hell when our lovedonespassaway(thisistrueinAustralia—I’mlesssureofothercountries). Thegoalofthisbookistoignorethepre-conceivedideasofthepastandlookatwhat humans do and how we seem to do it. Why should we move on from being the most intelligent species on the planet, to a species that can take advantage of our natural surroundings to produce even more intelligence? In the same way that we overlook the bestwaytorunourplanetduetopreconceivedideasandpowerstructures,weoverlook theobviouswithmachineintelligence.Untilnow,thatis. Whatfollowswillcovermanyoftheideasknownaboutourbrain.Howwelearn,how wechange(unlearn),howwerepresentideasinourbrain,howourlanguageisbased,and manyotherfactsoverlookedbymostresearchers. T HE DEATH OF AI Artificial Intelligence (AI to its students) has been a research area since the 1940s during the birth of mechanical computers. Today, these mechanical computers are commonly called computers. Although the first computers were people who manually computed figures for companies, the computers developed in the 1940s produced the abilitytorepresentknowledgeassymbolsinadditiontotheirnumericalrepresentations. AIworkersbegantomakeoutrageousclaimsaboutthenewAImachinesthatwould change every aspect of our lives in the next few years. Compare this to the time when astronomersfirstnoticedthatthestarswerealongwayaway.Therewerenoclaimsthat wewouldsoonbevisitingthem.Why?Justbecausetheycouldobservethemdidn’tmean thattheyhadthetechnologytogothere.JustbecauseAIresearchershadamachinethat couldaddandmultiplyquickly,shouldn’thaveledtotheconclusionthattheywouldsoon haveamachinethatwillunderstandtheworldarounditthroughvision,touchorhearing. As I stated in the assumptions: thought is not a computational process. Basing the developmentofanintelligentmachineonaprocess—likeAI—thatdoesnotmimichow our brain works will and has led to failure. An understanding of how our brain works enablestheconstructionofcomponentsthatwilldothejob.Thereisnospeculation—we knowthatthebraincandocertaintasks.Ifweproducemachinesthatdothesetasks,the machinewillstarttobehavethewaywedesignit. W HO DO WE MODEL ? The first question to consider is what will make an intelligent machine? As psychology observes how people work, and people are our best source of intelligent behaviour, then it makes sense to use their input. Note that we will comply with the documented observations of psychologists but not their theories. There are useful observations made by the likes of Freud and Jung, for example. However, their theories soundtodayliketheramblingoflunatics—inlinewithotherexamplesfromtheturnofthe century.Similarly,philosophyprovidessomeusefulobservations. Cognitivescienceisamulti-disciplinaryscienceintendedtoassistintheproductionof intelligent machines. It adds the field of linguistics and neuroscience, the study of our physical brain and nervous system and its interactions with our body. Computer science thenallowsustomodeltheresultingconcepts—althoughinthemovefromAItomachine intelligence,newmachinedesignsarerequiredthatdifferfromacomputer’sdesign.We are no longer interested in modelling the human calculators adding large columns of numbers.Wewanttoproduceamachinethatcansee,hear,speak,moveandfeel—some ofthebasiccomponentsofhumanintelligence. Whoelsedowemodelinthequesttoproducemachineintelligence?Therehasbeen anexplosionofmaterialtoimprovehumanity.Therearethemanagementtextbooksthat considerhumanbehaviour.Moreimportantlytherearethenewagethinkers.Mensuchas AnthonyRobbinsexplaintheworkofpsychologistsandtherapiststothemasses—these provide models and working examples of how to manipulate our brain. Our machine intelligence has no better starting place than here. In fact, some of the difficulties in producingintelligentmachinesinthepasthavebeenthisevidentlackofappliedresearch material. R OBOT P RODUCTION Canweproduceahuman-likerobotin1998?Theanswerisno,althoughagreatmany science-fictionwriterswouldhaveusbelieveotherwise.Canweproducearobotwiththe capabilitiesofsimpleinsects?Againourcurrenttechnologicalcapabilitydoesnotprovide for this. Can we produce a machine that drives a car—or reads a book and produces commentaryonit?No. Ihavebeenintriguedwiththeconceptofproducinguseful“computing”toolstoassist humanitysinceatimebeforeIsawStanleyKubrik’s2001:ASpaceOdyssey(writtenby Arthur C. Clarke). The idea of interacting with the HAL 9000 computer was quite a pleasing concept to me, as much from the mental challenge of talking with it as to the simplificationoftasksthatsuchacomputercouldmaketoourdailylives. Withsuchaninterestintheidea,IbecameastudentofCognitiveSciencewiththeaim offurtheringmyknowledgeinthefieldandcreatingsuchademonstrationsystem.After completing my university studies, other commitments took me away from the study of machineintelligence. Imagine my surprise to find that little has changed, nearly 15 years later. Why has there been little progress in machine intelligence—an area that promises so much and indicatesgreatfinancialrewards? The answer lies in our understanding (or lack of understanding) of two technical challenges: How does our human vision system work and how can we understand language? W HY HAS PROGRESS BEEN SLOW ? There appear to be two approaches to the challenge of machine intelligence: one comesfromthefieldofcomputerscienceandonecomesfromthefieldsofneuroscience, linguistics,philosophyandpsychology. Those trained in the computer technology have been brought up to understand the principles of computation and ordered programming to solve problems. Brute force approacheswhereamachinerepeatedlyperformsaspecificoperationuntiltheproblemis solved are commonplace. This approach is the counter-example of a computer that can manipulatevisionandspeech. Thosetrainedintheothercognitivesciencesusuallysufferfromalackofknowledge ofhowacomputeroperates.Understandingofthebrainstructureisoflimitedassistance, as there is not currently a good roadmap to follow from our understanding of the brain. Turningtothemodelsofexistingcomputersandtheirassociatedprogramsagainresultsin somebasicallynegativepremisesbeingcreated. There are also some solid theories about language, from such scientists as Noam Chomsky,whichprovideagoodprocessingmodeloflanguage.Sadly,thismodeldistracts frommodelsthatprovidethesamefunction,buthavethecapabilitytogofurther. The secret to creating a scientific advance in an area where there is clearly no road forward requires two skills: the understanding of the existing systems without any commitmenttothemandtheabilitytodiscardmanyofthoseideasinthequestforanew model. D EATH OF PROCESSING Why are we so obsessed with processing? Of course our Von Neuman architecture computerworksbytakinginformationfromastore(misleadinglycalled“memory”)puts itintotheCPUregistersandthenisabletoprocessit.Thisprocessingcanbeanoperation suchascompare,add,subtractandsoon.Itisprocessingbased! Whywouldweassumethatourbrainprocessesanimage?Partlythereissupposition that our brain combines images from the two eyes to form a distance measurement of sorts.Thisenablesustohuntpreyeffectively.Butwhydoweassumethatprocessingis involved? What if, rather than process all the information we receive into lines, movements, shadows,andsoon,wesimplycomparedtheimagetoallpreviouslyseenimages? What if our brain is a parallel machine that is able to compare large numbers, vast numbers of previously seen images and find the best matching ones in the presented image? What if, in order to recognise a block, the computer merely compares all previouslyseenobjectsforamatch? Itcan’tbedone?Ifthisisso,itisonlybecausewehaven’tdesignedtherighttypeof computers.Itwouldbefarsimplertodesignasystemtorecognisepreviouslyseenimages than to design an image construction system—a parsing system for vision, if you will. Although Chomsky would not like this approach, what if the computerised parsing systems created to understand language were counterproductive to intelligent vision progress? D EATH OF THE P ROCESSING M ODEL The death of the processing model can be useful here, as well. By using pattern matching, rather than processing as in our vision system, perhaps a better approach is possible. Whatifwehadasystemcapableofstoringwordswithreferencestowhosaidthem andlinkstowhattheobjectsare?Thisissimilartothelearningenvironmentofthechild. Theeyesareabletomatchwordstoobjects.Forexample,thiscanbeseenwhenachild locatesanobjectwhengivenitsnamebyaparent. Now,whatifoursystemwastostoreallwordsandsentencesutteredwithreferences to the people who said them, the way they are said, and the objects that are discussed linkedtothewords.Thememorywouldquicklybecomearepositoryforaccesstoobjects (vision),names(auditory),grammar(theorderinwhichthewordsareheard).Syntaxis impliedbythemanipulationofobjectsbeinglinked.Ifthesentence,“putthehammerin theatom”isstated,theknowledgeofthesizeoftheobjectsindicatestheissuewithit. D OES IT ADD UP ? Addingwiththealternativearchitectureshowssomeinsightintothemodel.Let’sadd 1 and 4. As Appendix A demonstrates, the processing model, while helpful to solve problemsusing1950stechnology,isnolongerimportantinthequesttosolveproblemsof addition.Simplyusingamemorysystem,suchasthatproposedwithMachineIntelligence Memory(MIM)—wherecomponentsarelinkedtogethertoproducealarge-scalesensorylinked system providing immediate recall of linked information—enables equivalent results to processing problems without the need for computational capability in the machineintelligence. Itisunlikelythatthehumanbrainhasacomputationalcircuitinanywaysimilarto themoderncomputer,sowhyassumethatanintelligentmachinewillrequireone. T HE M ACHINE Whatdoesanintelligentmachinecomprise?Clearlytheremustbethebasicsensesof ahuman.Thestudyofphilosophyhashighlightedthefactthattheworldweexperience throughoursenses,theworldofphenomenology,isonethathumanscaneasilytalkabout usingourlanguagecapability. S ENSES Thereareanumberofhumansensesthatwillprovidethebasisforlearning.Learning by example is one of the easiest ways of enabling a machine to work in our world. Unfortunately,today’scomputersdon’tlearnbyexample.Thishasresultedinthetedious processofattemptingtoprogramwhatweobserve. For example, have you ever attempted to explain to someone, who is blind, simple instructionsondealingwiththephysicalworld?“Canyoupleasewalkovertothemiddle of the room and pick up that piece of paper?” you ask. “No, the middle of the room, behindyou,yes.No,totheleft,notyourleft,myleft.That’sitthreemoresteps—okstop. Itisjustinfrontofyou—noonthefloorbelowyou,infrontofyourfeet…” You get the point—the information required to verbally describe a visual image is large—andthemessagewemustsendisneithersimple,norarewewellpracticedatsuch long-windeddescription.Infact,iflanguagewereintendedtodescribesuchvisualthings, wewouldnodoubthavewordsfor“twostepsforwardontheground”and“behindyouat waistlevelthreepacesaway”. Therearemanyhumansenses—theobviousonesincludevision,hearing,touch,taste, smell,andbalance.Wealsohaveadditional“senses”thatarefacilitatedbythekeysenses. Wehavedepthperceptionaspartofourvisualcapability,wehaveanideaofwhereour limbsareatanymomentthroughourtouchsense,wecansenseaccelerationthroughour senseofbalanceandwecandetectwordsspokentousaspartofoursenseofhearing.In fact, our basic transducers—our I/O sensors to use some computer jargon—are able to providesomeveryusefulcapabilitywhencombinedinourbrain. Howdoweorganiseourintelligentmachinetodeveloptheseothercapabilities? M ACHINE I NTELLIGENCE M EMORY (MIM) Machine Intelligence Memory (MIM) differs from the so-called memory used in computers. Memory in a computer provides the ability for a CPU to take out for examination a unit of data—usually only a few characters in length—to process or compare. While this is handy for the task of adding up sums of numbers, it is a foolish designforcomparingonedigitisedimagewithseveralthousandotherimages. MIMhastwokeytaskstoperformandthesetasksandthefunctionperformedenable a machine to become intelligent in real time. It is possible to produce an intelligent machine without MIM, however a machine that takes six hours to respond is hardly intelligentinmostpeople’seyes.Thetwotasksareasfollows: 1. To identify and store new and recurring patterns in the information being received. 2. Torecognisepatternspreviouslystored. These two steps are so important for learning that machine intelligence is hard to imaginewithoutit.Why?Becauseifthesebasicelementscanidentifyandmatchpatterns in their experience, MIM can perform some amazing feats that explain many psychological,linguisticandneuroscientificobservations! It is important to consider that in itself, this process is not intelligent. It is merely a mechanical means for storing information without need of consideration. That is, by findingapatternintheexternalworld,theinformationneededtoidentifyitasapattern againinthefutureisstored. O UR B RAIN ’ S C ONSTRUCTION (MI CONSTRUCTION ) Our system will use what I call machine intelligence (MI) To construct our MI machine,weconnectsensestoMIM,individually(seeFigure1).Althoughinitiallythere maybeaneedtoincludesomebasiccomponentsintheseMIMstodetectkeyitems,ina similarwaytothatusedbythehumaneyeandvisualprocessingcentresinthebrain,the MIMconceptallowsforthistonaturallyoccur. Figure1:Thesenseslinktotheir ownpatterns—andthentoanoverallpatterndevice.Forexample,burnttoastis distinguishedfromtoastwithvegemiteduetoitscolour,smell,tasteandtexture. Atthispointitisimportanttobegintolookforhigherlevelsofpatterns.Amachine that recognises patterns in the external world is not intelligent. MIM must additionally monitorthematchesbeingmadebetweentheindividualsenses.Thisissimilartotheway that our brain forms associations with objects observed at a similar point in time. The typesofpatternsthatcanbemadethiswayinclude: 1. Thelinkbetweenthesmellofanoilyragandthelookofit; 2. Thelinkbetweenthesoundofagunfiring,theflash fromthemuzzleandthe feeloftherecoilforce; 3. Thelinkbetweenawordandthesightofanobject;and 4. Thelinkbetweenthetopviewofanobject,theleftviewandthebottomview. Theselinksprovideforsomeusefulcapabilitiestobemade—andlearningbecomesa question of experience. As experiences are made using multi-sensory input, patterns are matchedthatformwhatisthebasisforobjectrecognitionandreal-worldknowledge. Itappearsthathumansaredrivenbytwoforcesandhavecertaincapabilities.Humans are driven by their ability to match objects and experiences previously learned. And humans associate between certain events that occur in close time proximity. Our brain usestheobjectsandassociationsforthebasisofconversationandaction.Ourbrainisable to directly locate fragments of sensory experience and through association change the currentstateofmind. Simplyput,humansuseassociationsandanorderedcollectionofobjectstospeakand move quickly—without thinking. In other words, rather than combining information throughaprocessofanalysis,humanshavepre-preparedprogramswhicharewiredinto ourbrains.Theseprogramsaresimplyrun—whetheritbetospeakortothrowarockata rabbitinordertoreceiveahigh-proteindinner. MIO PERATIONS When the intelligent machine is in operation, the MIM memory will automatically start detecting and storing patterns. At a higher level of MIM, one that monitors many sensesforexample,theMIMwillstorelinks(patternsdetected)betweeneventswithout theneedforanalysisorthought.Ifthoughtisrequiredfordeterminingwhattolink,the age old issue would be raised—who thinks for the brain’s thinker? The homunculus returns. The model presented here—the one that has MIMs to remember what has happened, is a model that does not require thought. The system just automatically rememberspatternsbystoringthem. SLEEP For the model proposed, the system builds up a large number of similar database objects.Tooptimisethesystemandavoidtoomuchdatabeingstoredunnecessarily,the system must be shut down on occasion in order to compare recently stored objects and determinewhethertheyshouldbestoredinthelongertermordiscarded.Inotherwords, thereisbenefitinaperiodofinactivity,similartosleep,inordertooptimisethesystem both in terms of future performance by discarding similar objects and in terms of total storagecapacityrequired. In the human and mammalian example, the system is shut down for a period called sleeptime.Duringthistime,thereareanumberofactivitiestakingplace,suchasdreams, REMsleepandsoon. Wedonotknowwhyhumansdream;however,wedoknowthatmachineintelligence will need a period of inactivity similar to dreams. Interruption of the consolidation of memory may well leave vivid traces that cannot be attributable to experience. It is interestingtonotethatneurosurgeonshaveatechniquetodeterminewhattissuetoremove insurgerythroughaprocessofbrainstimulation.Intheprocess,thesurgeonwillstimulate the brain with a probe. When the probe affects a specific part of the cortex, patients experiencevividmemoriesindicatingthelinkagesbetweenthatpartofthebrainandthe experiences(Hume’sideas)thatwerestoredafterbeingperceivedasimpressions. C OMPARISON TO D AVID H UME ’ S MODEL DavidHumearguedthatthebraintakesininformationdirectlyfromthesenses.That informationisclearasitisreceiveddirectlyfromthesenses—hecalledthisdirectsensory experienceanimpression. Oncetheseimpressionsarestoredinthebrain,theyarelessclearandhecalledthese memoriesideas.Asrecalltakesplace,ideascombinewithotherstoproducenewoptions. Humeindicatedthatcomplexideas—thosecomprisingmanysimpleideas—arevalidonly iftheycorrespondtoimpressions.Ifthebrainneverexperiencedacomplexidea,butonly experienced a series of simple ideas, then the complex idea is false and should be committedtotheflames. I will use the terminology of Hume for impressions and ideas to ensure consistency withphilosophy. P ATTERN MATCHING , LEARNING AND UNLEARNING Thequestionofthebrain’spatternmatchingcapabilityisclear.Ifahumanwalksinto aroom,thereisanalmostinstantaneousrecognitionoftheobjectsaround.Thepatternsof avisualenvironmentareeasyforthebraintorecognise,althoughtherecallofthenames is clearly a separate process. Pattern matching is recognition, not recall. The computer gamethatdrawsa3-Denvironmentonascreenforinteractionisnotmachineintelligence —MIduplicatesintelligentbehaviour.Itdoesn’tcreateimages(recallofMIM)forvideo projectionanymorethanahumancanprojectamoviefromhereyes. Learningistheprocessofmatchingandstoringpatterns.Therecognitionisgearedup tocombinethesenses—thesoundofafireislinkedtothesightofafireandislinkedto thesmellofafire.Themoresensestherearethatareconnectedtoanobject,theeasier recognitionwillbeastherearemorepathways(links)totheobject. Whybasemachineintelligenceonthisidea?Becausepeoplerelyonassociationandit isatechniquethatenablesamachineto“learn”.Computersbecomeintelligentmachines by learning to connect experiences—not by clever human programmers learning how to write complex programs to describe a machine’s version of reality. Of course, there is more to human memory than just recognition, and that will be explained soon. Without thisrecognitioncapabilityinourMachineIntelligence,thetaskbecomesdifficult,indeed. Unlearning.Whatdoesthatmean?Welearnbylinkingtogetherobjectsthathavebeen recognised—these objects can be from any of the senses. This follows from childhood observations,wherechildrenlearnthebasicsoftheenvironment—blocks,toycars,puppy dogs,icecubes.Eachisexploredintermsoftaste,viewpoints,smell,weight,temperature, colour, texture. Who hasn’t seen a child pick up a stick from a garden and put it in her mouth? These multi-sensory experiences enable the associations with the object to produceausefulmemoryforfuturereference. Unlearning is the process of disconnecting those associations. Unlearning is one of thoseskillshumansdonotlearntodo(exceptincertainstressfulsituations).Withoutan ability to unlearn, prejudices are formed and remain—despite information that is to the contrary. Without the ability to unlearn, childhood problems remain throughout life. Without the ability to unlearn, we all exhibit irrational behaviour at times for no clear reason. Without the ability to unlearn, we are all distinctly human, and all distinctly intelligent. Ourcomplexitycanstillbeexplainedintermsofasimplemodel.Thismodel,through theinteractionwithitscomponents,enablesthecomplexbehaviourseeninhumans—the behaviour which is unique to each individual based on her experience and the way in whichtheelementsareconnectedinthebrain! M ORE COMPLEX SENSES The sense of touch has a number of capabilities—temperature, texture, vibration, pressure, movement, duration, steady-intermittent, intensity, weight, density, location… Clearly, each of our senses is a summary description of a number of more specific attributes.Eachsensealsodetectspatternsatdifferentlevels—thisabilityisnotsomething humansconcentrateon.Itjusthappensasaresultofthewiringofourbrain. Is this all there is? A collection of senses connected to a group of pattern matching machines?Ofcoursenot!TheremustbethecapabilitytoconnecttheMIMsthatmatch differentattributestogethertoformasinglesensetoidentifyobjects.Similarly,thehigher levelMIMthatfindspatternsintheattributeMIMscannotfunctioninisolation. These attribute MIMs are consolidated within the sense to identify objects. Higher level MIMs again connect objects together to identify the different sensory input in one place.Thisisfinefortheidentificationofsingleobjectsfromtheirconstituentattributes. Keep in mind that this process is automatic—MI must simply connect patterns detected from lower level MIMs to the higher level. Our brain appears to have the capability to connectmanyotheremotionalattributeswithobjectsorsequentialprocedures.Thisisalso automatic and such people as Anthony Robbins explain what techniques can be used to unlearn these unsupportive links and replace them with supportive ones through such psychologicaltechniquesasNeuro-LinguisticProgramming,NLP. Butwait,there’smoretothesensesthanmeetstheeye.Thisusefulcapabilitytodetect patterns from our senses and connect them to form objects has a useful offshoot— language. The capability of a sense isn’t simply what we experience—it is also what we concludefromit.Theseconclusionsincludethebasisforlanguage,inference,logicandso on. L ANGUAGE Language is a special feature of higher level pattern matching. By the process of recognition of word sounds detected, which automatically link to the visual, auditory, kinaesthetic, olfactory, or gustatory objects, the sequences of words are stored by the brain.ThepatternsinwordusearedetectedinatleasttwoareasbyMIMs. Therearepatternsofwordusagethatrelatestotheorderinwhichwordsarespoken. Theorderensuresthatthesamemeaningcanbeattributedthenexttimethesameword orderisused.Thereisanimportantdifferenceinthegrammaticalmeaningof: 1. Hithim. 2. Himhit. Most English speakers will understand both sequences. ‘Hit him’ is an order to probablecruelty.Thelatter,althoughpoorEnglishusage,meansthatthemanhasorwill hit. The point is that the grammar of language is important and is a product of pattern matching.Inthiscase,thepatternsarethewordsequencesdetected—automatically. AMIMmonitoringauditoryinputwillresultinpatternsforhearingandwillgenerate the capacity to recognise good grammar—meaning whatever is heard is, by definition, goodgrammar—atleastinitiallywithouttheabilitytounlearncomingintoplay. Ofcoursegrammarisimportant,butasthehugenumberofexamplesdemonstrating thatwordorderdoesnotuniquelydefinemeaning,moreisnecessary.Noticethataswords are recognised by the grammar-matching machine, that there are links to the objects referenced themselves. These links produce patterns that a MIM can monitor. The sequencesinthiscaseprovideadifferentpattern—apatternofobjectrepresentationand themanipulationofobjects.ThisMIM,whichisoftenreferredtoasthedeepstructuresby linguists such as Chomsky, produces the effect of an inner world that can be communicatedbetweenindividualsthroughlanguage. Let’s explain this another way. Language is triggering a sequence of objects in the brain—these create a pattern that represents an artificial environment. This environment conveysamessageofactionorsimpledescription.Theprocessoflearning,MIMpattern detection,enablessequencestobefound. Of course we all know that sequences of grammar can never be complete since grammarisaninfinitelyvaryingchoice.Rememberhowever,wehavethelinksbetween objectsatthedeeperlevel(notsimplegrammar).NoamChomsky,thewell-knownlinguist toldussoandabasicconsiderationoflanguagebacksthisup.Thedeeperlevelprovides generalisation—patternsofobjectsexistinthisdeeperlevel.Forexample,acatandadog are both pets, potentially. In order to know that fact, we have a link between them—or rather,aMIMhasnoticedthepatternthatmanydogsarepets—sothegeneralisationthat maybestoredisthatdogsarepets. Through a process of generalisation, objects that represent the act of playing games oftenfollowobjectsthatrepresentpets.Backintheworldofgrammar,theactofplaying gamesareacollectionofphrases.Phrases? Patternsareimportantatmorelevelsthansimplegrammarandthedeeperlevel,called (say)syntax.Therearecollectionsofsentencesthatformphrases—aMIMmonitorsthose. Similarly,aMIMmonitorsstories—conversations.TheseMIMsenablepatternsinstories to be generalised to further understand the world. By knowing what happened in a particularstory,similarsequencescanbeputintoactioninlife.Welearnfromexample andapplytheexamplesfrequentlyandbydefaultasthealternativeisgenerallythought,a processofworkavoidedbythebrain,ifatallpossible. Language is an interesting example of the complexity of linkages available in the human brain. The lack of linkages, the lack of memory and the lack of performance of slowmodern-daycomputersresultsinthepoorresultofacomputerinreal-worldhuman intelligenceprocessing.LanguageisthedriverforthecreationofMI. LANGUAGEEXAMPLE Exampleofasimplelanguagestructurewillhelptounderstandtherequirementsofa functioning brain. Let’s consider the sentence: ” The cat sat on the mat.” It should be a simpleexercisetomapthisintoitscomponentmeaning.Shouldn’tit? LANGUAGEUNDERSTANDING Languageunderstandinghassomedifficultchallenges,indeed.Therearehundredsof rulesofgrammar–manyaredifficulttowritedownandonlythelargesttomesarecapable ofwritingtheentiregrammarforEnglishintobookform. Sadly,thisdoesn’tassistinthedevelopmentofcomputerlanguageunderstanding,asa language parser merely breaks a sentence into its component parts. If the sentence is incompleteorunheard,thenthesystemfailsutterly.Itiscommontohearquotesthatmost humans only hear a small part of what is said to them – yet understanding can be quite highdespitethesmallnumberofwordsheard. Thedeepermeaningofwordsmustbetakenintoaccountinordertoensurethatthe sentences said or written are possible in the real world. Ah, this leads to real world understandingandknowledge.Anotherchallenge!Orisit? To process natural language according to the textbooks is a multi-step and complex procedure. Luckily a computer can do this with recursive calls! There is a considerable amount of complexity in grammar, much of this is caused by the nature of word combinations used in history. Perhaps, as Chomsky claims, this is due to the deep structuresoflanguageandauniversalgrammar. Perhapsallthisprocessingrequiredinordertounderstandlanguagecanbeunderstood a different way? Perhaps there is a method similar to our first technique that can be employed. HUTCHENS’LOEBNERPRIZEPROGRAM JasonHutchensisaresearcherattheUniversityofWesternAustralia.Asaparticipant to the Loebner prize, the contest to compete to match the Turing Test, Jason’s program wassuccessfuloneyear. The program uses a similar basis as the MIM model—in this case a model called a ‘third-orderMarkovmodelonthewordlevel’.Theprogrameffectivelytakesdiscussions with people through the keyboard, and adds the word links to its database. When the program is building a response, it uses previously experienced word sequences. These wordsequencesarebuilttousetheleast-probabilityresponse,inordertocreatetheleast likely combinations. The conversations are hard to follow, although they follow grammaticalrules,andarethereforenativelanguagesentences. It is interesting that the resulting conversation is better than many other examples peopleareabletoproduce.Let’sexaminehowthismodelrelatestotheMIMmodel. In the case of MIMs, word sequences are monitored and linked together. Of course, the MIMs also link the other components together as well, such as visual data, auditory data, kinaesthetic data, and so on. In this case, the MIM is limited to only one sense, writtencodedwords(bluntly,itisblind,deafandinsensitive).Nevertheless,theprogram isabletocapturethegrammaticalstructureoflanguage. This is a small and limited application of the MIM theory, and its success is initial supportforthecomprehensiveMIMmodel. V ISION S YSTEM The human vision system performs a simple task – to identify to the owner what is goingonaroundhimorherfromadistanceusingthereflectedlightfromvariousobjects. Emulatingthisonacomputerhasprovendifficult. Thetrickappearstobe,basedonthewealthofcomputersciencetextbookswritten,to solve the puzzle of how the brain makes so many calculations in less than a second. In fact, in around the time it takes for the nerve impulses to travel through 8 neuron groupings,wecanrecogniseascene.Thistakesaroundaquarterofasecond. For a sophisticated computer, taking into account the lines we see, the shadows, the colours,theambientlight,theunusualangleatwhichweseeanobjectandsoon,thereis a heck of a lot of calculation required to compute what objects we are seeing. Further, thereisaheckofalotmoreinformationweneedtocompareinordertounderstandwhat thephysicalrelationshipsarebetweentheobjects–canaballholdupaglass–orarewe seeinganopticalillusion?Givenabout6hours,agoodcomputercandeterminetosome degreewhatthepicturerepresents. Many computer textbooks tell us that the computation that is required by our serial processing computer must somehow convert images. This conversion duplicates the paralleltechniqueusedbyourbraintounderstandimages.Buthowdoesthebrainwork? Theprocessingmustbedifficultwithmorethan1,000,000inputsfromourretina. RECOGNITIONOFSPELLING Thetaskofrecognisingspellingisonethathascausedmuchconfusion.Howdoesthe MIMmodelexplaintheissueofspellingrecognition?Infact,somepeoplehavetheability to recognise whether a word is correctly spelled, despite the fact they cannot spell the wordthemselves!Theissueofrecognitionversusrecallisanimportantone. DavidHumespokeofimpressionsbeingthedirectsensoryexperience(theeffectof the transducers in the body sending signals to the body’s nervous system). Ideas are further defined as the recollection of impressions—they are less real/strong than impressions,andthebrainmanipulatestheseinextremelyfasttime. According to Hume, therefore, the recognition task is one in which direct sensory details are compared with information previously stored—i.e. with ideas. In the case of ourMIMmodel,theMIMscontaintheideascapturedduringpatterndetectionovertime. Asnewimpressionsaremade,theyarecomparedwiththeexistingideas.Newideasare formed either immediately or during a period of inactivity of the system when large volumesofmaterialcanbeeliminatedorcompressed. Thehumanbrainismuchbetteratmemoryrecallwhenunusualobjectsarecombined with multi-sensory experiences, rather than single-sensory information. How does this relatetotheissueofspelling? Thereareanumberoflevelsofpatternmatchingrequiredtounderstandspelling.Ona pageofwrittentext,therearelettersdrawn,therearegroupsoflettersformingwordsand groups of words forming sentences. There are higher levels of categorisation leading finallytoacompletebook/letter/document.Tounderstandspelling,thebrainmustbeable toidentifyalltheselevelsofobject. Intermsofthespecificwordsinthesentences,thereissomerelieffromthedifficulty of reading individual letters, as the words must be used in context and following a grammar. MIMsmanagethisasfollows:thelettersarepatternsstoredastheMIlearnstowrite. Theprocessoflearningsendsthemulti-sensoryexperiencetothemovement,visual,and kinaesthetic part of the MIM. Reading requires the impressions on the page to be convertedintotheimagesthatcompriseastory. Afewlettersinmostwordsissufficienttorecogniseasingleword. Thisisagreatadvanceoverthepreviousmodel—asnolongerdoweneedtorelyon eachandeverylettertocreatemeaning. RECOGNITIONOFWORDS Therecognitionofwordsiscriticaltosupportanintelligentmachine. RECOGNITIONOFFACES The recognition of faces is something that humans do remarkably accurately. Although people can forget the name of a person, they are able to recognise faces very quickly.TheMIMmodelrequirestwosteps,thatprobablyrequiretimetocomplete—first theimagemustbestoredthenoncethereisanobjectdefinedfortheimage,afurtherlink tothesoundofthename,thespellingandsoforthcanbecreated. Howdowerecogniseaface?TheMIMmodelassumesthatitisabletodetectpatterns inthesensoryinformationpresented.Thequestionastohowthatinformationispresented is critical for the success of the pattern-matching engine. If the MIM is able to quickly detecttheface,usingaseriesofallpreviouslyseenfaces,thentheprocessofpullingout theappropriateonesortheappropriatesub-selectionoffacesenablesothercontextualdata toprovideahigh-quality,high-reliabilityfit.Providedtheinformationthatcannotbetrue is eliminated, the search volume is significantly reduced to the point where detailed statisticalevaluationcanbemade. Visual recognition of faces is therefore simple to implement with the MIM model, providedthatitisnotlimitedtosimplytheinformationforthevisualsense.Humansrely on all senses, and for example, how often can a man recognise his girlfriend by her perfumealone? H UMAN E XAMPLES :P HYSIOLOGY ,P SYCHOLOGY & P HILOSOPHY Any brain theory needs to explain observation. That’s what science is about: explainingobservations,predictingoutcomesandenablingtechnologytoexploitit.This section looks at a variety of human observations and explains them in terms of brain theory based on intelligent memory, rather than on the computer model of memory interactingwithahuman-writtenprogram.Comparetheseexplanationstocontrasttoday’s approachwiththealternative. Bytheway,whilesomehavetheorizedthatmachinescouldwritetheirownprograms, in practice that has not happened in the same way that AI has not happened. Having a machinethatlearnshowtoworkintheworld,ashumanbrainsdo,isimportanttoremove theneedforprogrammers. P HYSIOLOGY Physiology is a term used by the motivational speaker, Anthony Robbins, among many others. Anthony describes the effect of the human emotional state as influencing human behaviour. By being in the appropriate emotional state, you are able to perform feats not possible in other emotional states. For example, by being in an extremely positiveandconfident,Anthony’sstudentscanwalkoverhotcoals. This observation is explained in terms of the MIM model as follows: the MIMs are constantly capturing information about the environment—linking visual objects to auditoryobjects,togustatoryobjects,tofeelings—infacttoanyobjectcurrentlyactiveat thesametime. AsMIMsarelinkingactualsensoryinformationtogether(Humecalledthesesensory experiences“impressions”),thebrainisabletolearnthatanappleissweet,arosesmells niceandapillowissoft,nottomentionthelanguagethatidentifiestheseobjectsthrougha linkageprocess.ButHumealsoidentifiedthatthebrainhasarecollectioncapability.He calledthisability“ideas.”Ideasaresimilartoimpressions,onlyweaker. MIMs, over time, store impressions for future use—the data that is stored becomes Hume’sideas.TheywilllinkwithotherideasinMIMsthroughtheautomaticprocessthat is designed into the MIM. Should the intelligent machine be in an emotional state of excitement, the ideas linked at that time will be connected to that emotion. Later, if the machineintelligenceisinastateofdespair,otherideaswillbelinked.Itislikelythatthe events will be quite different in the different environments, as the MIMs will link activitiesthathappenwhentheappropriateemotionalstateisheld. The point is humans are often compelled to act in inappropriate ways due to their emotionalstate.Inthiscase,asMIMsenabletheactionsthatfittheinput—andtheinput maywellincludetheMIMconditionofemotionaldespair—themostlikelybehaviourto produceistheonepickedupbytheMIMsinthestateofdespair. F REUD SigmundFreudisoneofthebetterknownscientistsofthenineteenthcentury.Freud made many observations of a certain type of patient and identified neuroses. Whether neurosesareanactualthing,orifitisanothertypeofmind/epicycle,isn’timportant.What is important is that he made a number of observations about human behaviour that any modelofintelligencemustalsoexplaininordertobeconsistent. Remember that Freud put forward a number of statements to describe the human brain.Forexample,thedescription,“unconscious”describestheobjectiveandsubjective human experience of the “ideas” that comprise “thought”. The question is, however, is thereanunconscious,orsimplyaneffectcausedbythedesignofthehumanbrain?Isn’tis equallyvalidtoidentifytheexperienceasthatcausedbythemanylinkagesexistinginthe brainwhenexperienceisbuiltup? For example, when a child is hit after opening his parents’ bedroom door, his brain mayhavealinkcreatedbetweenthepainofthehitandtheopeningofthedoor,through theprocessofassociation.Later,whentheboyappearsonanationalgameshowandhas to choose between a door and a box, he chooses the box. This can be because his “unconsciousbelievesdoorsarebestleftclosed”ormoresimply,hehasmorepainlinked toopeningthedoorthantoopeningthebox. Psychoanalysismaybeabletoexploretheboy’sbrain’slinkagestodiscoverthathe hadthisbadexperiencewithhismotherandfather,butreallythereasonforhissubsequent actionscanbeexplainedinthesimpletermsofthestrongerfeelingslinkedtothedoor. ThepointisthatMIMsenablethesearchingofinformationtobebroughtforwardas possible solutions. Consider the human brain, an organ that has not been specifically designed and is therefore imperfect. It is reasonable to assume that the feedback that is subjectively experienced is the result of MIM-like searching of memory, rather than another component of the brain—called the unconscious, for example, which has many other meanings given over time. The unconscious is a misleading, incompletely defined termandmustbeignored. It was highlighted earlier in the review of language that language is built up from a group of pattern matching systems. Grammar is generated by following the patterns created by sequences of words. Provided words occur with some frequency, the brain generalises that the words necessarily follow that sequence. At the same time that the sequences of words are generated, links are also created between the various sensory objects.Forexample,thesentence“Thedogsatonthemat”hasasequenceofwordsthat followsthehumangrammarforEnglish.Theprocessoflearningthissentencealsohasa linkofavastnumberofdogsinasittingposition(visual)withavastnumberofobjects thatarefloormats(visual).Notethatthereisahugeamountofadditionaldetailavailable fromthissentencebasedonpreviousexperience.Forexample,thedogimageislinkedto specific dogs, their smell, their behaviours, experiences with the emotional attachment, cartoondogs,howtheyact,whattheysoundlikeandsoforth.Thelanguageisindeedrich duetothebrain’samazingnumberofavailablelinkages. Freud identified parapraxis—a slip of the tongue or pen. These so called Freudian slipsallowsomeonetosaywhattheyarethinking,despitethefactthatitisundesirableto sayso.Howdoesthelanguagemodeladdressthisfact? Consider the parapraxis described in Sophie’sWorld where a worker is toasting his boss(whomhethinksisaswine).Hesays:“Here’stotheswine!”Thiswasnotwhatthe workermeanttosay. How does the language model deal with this? Language verbalisation requires the patternscreatedthroughtheprocessoflearningtobeproducedbackwards.Thatis,rather than hearing words, linking them to their meaning—meaning is created and linked to wordsinthegrammar. Asthebrainisabletoproducespeechwithoutin-depththought(whateverthoughtis), thegenerationofwordsforatoastfollowsapathlikethis.Firsttheenvironmentlimitsthe typesofsentencesavailabletobeusedtoasmallselectionofformalphrases.Oneofthese phrasesis“Here’stoX”whereXisavastarrayofoptionsfrommemorysuchas: Charlie,theworld’sbestsoccercoach. Us,mayweliveinpeaceandharmony Stalin,themanwhofreedusfromslavery. Theexamplesgoonandonfromourlifetimesupplyofexperiencesandlinkstoother experiencesthatcouldbehelpfulshouldanimmediateanswernotspringtomind(ifone exists). SothequestionishowtofillinX.Theworkerhasthefirstofthewordscompletedby followingtheflowofthestoredgrammar(thepreviouspattern).Nextadescriptionofthe personbeingtoastedisrequiredperpreviousexamples—theautomaticselectionismade withoutneedfordeliberationandthustheswinepopsout! There is no need to postulate complex, or metaphysical explanations to this observation.Apattern-matchingmachineismorethancapableofdoingthisaction. ConsiderFreud’sdeepermodels.Freudwroteoftheconsciousasbeingthatwhichwe focus on, the preconscious that which we can recall if we try, and the unconscious, that whichwecannotaccessbecauseamechanismtorepresstheideahasstoppeddirectaccess toit. Freuddevelopedthismodelbasedonhisobservationsofpatientswhorepresenteda partofsocietynotnormallyclassedasabnormal.HowdoestheexplanationfromFreudfit intotheMIMmodel? Clearly, as information is fed into the system, patterns are stored. The process of gathering information is fed into a combining part of the brain—as mentioned even a reptilerequiresaunifiedviewoftheworldinordertoadoptquicklytotheenvironmentto survive. As the combining part of the brain processes information, it is stored in the memory—orinthecaseofourmachineintelligence,inMIMs. CanweexplainFreud’stheorybetterbyusingMIMtheory? WhatiftheinformationavailabletothereptilianbrainthroughimpressionsisFreud’s conscious?Ananimalactingasasingleentityisimportanttosurvival,asthosehappyto loselimbswilllikelydiewhenattacked.Theconsciousisthenanevolutionaryadvantage becauseeachbrainconnectstheanimaltoasingleworldview. Similarly, the preconscious and unconscious are both MIM details in that the consciouscanbetheactivesetofpatternsatapointintime,whichtheMIMsassociations arenotactive,butconnected.ProvidedtheinformationisavailableintheMIMs,itcanbe broughttothesurfaceasanactivememory. MIMs contain links to many other areas. For example, the association of a woman, links to the association of your mother, which links to the association of your father. Strongassociationsarenevermorethananactivationaway. To use this information, it must be identified as a good fit for the problem under revieworthebrainwouldneversolveproblems.Forrecognitionofmemories,information must be fed through a similar system to the one that experienced it in the first place, becauseitwouldotherwiseneedsometypeofencodingtorepresenttheoriginalsensory experiences.Memoriesbasedontheactualphysicalsourceareeasiertoimaginethanone convertedintoamemorythroughsomekindofcomplexalgorithmanddecoder. RATMAN It is interesting that some of the famous theories of Sigmund Freud centre on the experiences of the so-called “rat man”. The rat man heard of a torture involving rats, wheretheygnawtheirwayoutthoughthevictim’sbody. Freud identified that the rat man had a sexual experience with his governess at a young age, and that through that experience, he associated fear of his father through perceived punishment and hostility. Freud worked with the rat man for 11 months to furtherunderstandhisintricatelinkagesofmemories.Asaresult,hecuredtheratman’s neuroses,byexplainingtheratman’sirrationalreasonsforfeelingthewayhedidabout somethings. Freudbringshistheoriestoplayinordertoexplainthemeaningtheratmanassociates withhismemories. In terms of the MIM theory, all existing memories are the basis for new memories. Only when there is no match in the database, or when the result of the memory search conflictswithotherknowledge/information,doesaneedtodetectnewpatternscomeinto play. Inthecaseoftheratman,hispreviousexperiencesresultinaccessesofonepartofhis memory (such as memory of his lady) does an association come into play—fear for her safety.ThetraceofassociationsthatconnectsthetwomemorieswasFreud’spsychology andthebasisformuchofhistheories. If children associate memories of their father with punishment, and later in life associatesomeothermemorywiththeirfather,therewillbealinktothepunishmentand thefeelingssurroundingit. Another of Freud’s founding moments came in the work of his colleague, Josef Breuer. In the case of Anna O, Josef talked through Anna’s previous experiences in an attempttocureherfromparalysis,hallucinations,andothernervousconditions.Through the process of talking, Anna was able to recall events that initially linked together memoriesthatcausedherundesirableconsequences.Anexamplewasthesightofadog drinkingfromaglasssickenedAnna.Later,shebecamehydrophobic,andcouldnotdrink untilthepreviousmemorywasexposed. In terms of the MIM model, the associations between memories are for the benefit of recognition, not for recall in general. So should a machine build up memories such as Anna, a memory may trigger a stronger association with a feeling, for example. If the feeling is one of repulsion, it is unlikely that the MIM’s controlling element will take action to create that event, unless there is an alternative action that has a worse feeling associatedwithit.ThatdescribesAnna’sproblem,andisnomorethantheapplicationof linkages between memories. The theory of Freud is one based on his dealings with neurotics,howevertheworldhasmoreextremecasestoconsider.CarlJunglookedinto thosecases. C ARL J UNG JungcanbecharacterisedasoneofFreud’sdiscipleswhoexpandedonFreud’snarrow definition of psychology and added areas which met his greatly expanded range of observation.AsJungexperiencedpatientsinafarmoreadvancedstateofdysfunctionality (he worked in insane asylums), his observations expanded his theory to include such people. Oneofhisthemesisthatofthearchetype—thedefiningforcethatcharacterisesour individualandourcollectiveunconscious.Thearchetypesinclude:themother,thefather, thehero,thewiseoldman,etc.etc. HowdoestheMIMmodelexplaintheobservationofJung? ThemodelisbasedonMIMsdetectingpatternsintheworld.Asachildgrowsup,he or she notices people, what they do and so on. Some of these people fit into categories suchasthosementionedabove.Yourmother,orthepersonactingasyourmother,hasa setofcharacteristics.Somethingswillorwon’tbedoneforthechild.Asstoriesofother livesareconveyed,theywillincludedescriptionsofotherpeople’smothers,theirwayof behavingandsoforth. TheMIMs,asapatternmatchingsystem,willhaveavastamountofdetailaboutthe mother figure. Through the various linkages that have developed over time, it will be knownthatthemotheris(typically)onewhowilltakerisksforherchildren,acook,the keeperofthehouse,theauthoritarianfigureforyoungchildren,theonewhotakescareof daddy,andmanyotherthings.Throughtheprocessofbuildinguptheknowledgebaseof themother,asetofattributeswillbeavailable.Theseattributescanbecalledanarchetype —or alternatively as the concept applies to most other objects known in detail, the attributesaremerelythat—attributes. Fromthedawnofphilosophy,archetypeshavebeenakeytalkingpoint—theMIMisa waytoenablethatprocesstoautomaticallygenerateitself. JUNG’SFREEASSOCIATION CarlJungexperiencedtheproblemsthatexistinhumansinafarmoreextremeway thanFreud.Again,whileFreudwastalkingwithhousewivesfromwealthyfamilies,Jung was working in asylums with far more disturbed people. There is a great difference between a housewife feeling depressed and unfulfilled and a person who takes every opportunitytomutilatetheirbody. Freud related his observations to a number of characteristics that fit his model of humanpsychology. Jungcreatedatooltoassistwithunderstandingthesourceofaperson’sproblemusing freeassociation.Infreeassociation,thepsychologistsayswordsandthepatientresponds with their first association. Through this process, the psychologist is able to understand somethingaboutthepatientandenablea“healing”processtotakeplace. Althoughahealingprocessmaybenomorethanchangingtheperson’sassociationto somethingmoreuseful,theidentificationofthememorycausingtheproblemisvital. Howdoesthemodelpresentedheredealwiththisobservation?WhatisJunggetting atwhenheislookingintotheassociations?Asthebrainisconstantlycreatingassociations with pattern experienced/matched, at some point it is possible that an association is createdthatdisempowerstheindividual. Jung,throughaprocessoftrialanderror,identifiesifanywordsareassociatedtoan experiencethatisdisempowering. The MIM model allows for arbitrary and automatic associations to be formed, so should an experience produce a constant focus on this experience, the entity may not functionwellinthefuture. P LATO The ancient Greeks developed many of the more enlightening aspects of human development. Philosophy, the science Socrates believed should provide rulers for the world,developedstronglyaroundtheyear400B.C. Plato, Socrates’ star pupil, described the world as being comprised of objects—with thehumanrepresentationofapureform.Formsencapsulatethepureformofanentity— thedogginessofadog,thetreenessofanoaktree,thefemininelookofahumanfemale. WhatdoesthismeanintermsofMIMs? MIMs capture patterns in the environment as presented to the sensory apparatus. Providedthesensoryapparatusprovidestherighttypeofpatternsforfurtheranalysisin real time, the manipulation of objects automatically defines Forms—not the way Plato suggested, as he put the chicken before the egg, but in a way that allows for the same observationandconclusionasPlato.NotethatPlato’sFormsarenottoodissimilartothe archetypesdetailedbymanyotherphilosophers. MIMs capture all the patterns it experiences. That is, it captures patterns that are presented. Whether it reduces the information into component parts, or keeps the entire detailforlateruseisunimportant,asaMIMoperatesasablack-box. Forexample,aMIMwillcontaineitheraverylargenumberofimagesofaspecific object. In the case of a milk carton, it will contain the front-image, the side image, a numberofin-betweenimages:thetopimage,thebottomimageandsoon.Thesewillall belinkedbyassociationtothesoundofthewords“milkcarton”.Thefeel/weightofthe cartonwillalsobelinked.Andthetemperature/coldnessofthecartonwillbelinked.And many other linkages will exist. The fact that some information is redundant is unimportant.Whatisimportanthowever,isthefactthatassociationlinksalargenumber ofdifferentviewsofthesameobject. How does a single entity comprising many views connect with Plato’s Forms? As therearelinkagestomanyviewsoftheoneobject,thereareequallylinkagestotheviews ofsimilarobjects.Insomeways,theshapeofthemilkcartonissimilartotheshapeofa loafofbread,andtoacartonofbiscuits,andmanyothers.Theselinkagesareallcreated automaticallyastheknowledgebaseisbuiltupthroughsimplyassociations. Assumingthatthesystemautomaticallydetectsbasicpatternsinthelargevolumeof materialstored,andthensimplifiesthenumberofassociationstothecommonlyoccurring patterns, then the system will comprise a collection of basic forms comprising objects. These basic forms will be the patterns comprising, say, a dog. All dogs have similar characteristics,andalthoughsomedonotcomplywithallofthem,thereisneverthelessa coregroupofcharacteristicsthatPlatowouldwellcallForms. ThefactthatFormscanexistonlybytheMIM(orthehumanbrain)simplifyingthe patterns is irrelevant. The point is that MIMs can/will automatically produce simplified modelsthatresultintheproductionofobjectswhichareidenticaltoPlato’sconceptofa Form. P AVLOV ’ S D OG The Russian scientist, Ivan Pavlov, is famous for his experiments in classical conditioning.Henotedthatbyringingabellatthesametimeashefedadog,acurious event would happen. A dog naturally salivates when shown food—this requires no conditioningandiscalledanunconditionedresponse.Itisinnate.Afterconditioningthe dogforawhile,thatisringingthebellwhileadogisshownandgivenfood,hefoundthat ringingabellwithoutgivingfoodstillresultedinthedogsalivatingasiffoodweredue. Thiswascalledaconditionedresponse—thesalivationbecamelinkedtotheeventthat happened at the same time. The brain of a dog—and other animals such as humans— appearstoautomaticallylinktogetherevents.Inthiscasethedoglinkstogetherthepattern of ‘bell ringing’, ‘food bowl with food in it’ and ‘eat food-goody’ automatically as the eventshappen.Theeventsmusthappensimultaneouslyfortheeffecttotakeplace. Later,thebellbyitselfislinkedsufficientlytotriggerinthedog‘eatfood-goody’as theanticipatedoutcome.Thisisnotintelligence—merelyanautomaticresponseofMIM patternmatching. Humans and dogs share a common ancestor, and our brains have remarkable similaritiesinmanyareas.Itisworthexploringthedetailedanalysisofthiseventinterms oftheMIMmodelpresentedhere,sincethesimilaritiesbetweendogsandhumansallows ageneralisationtobemade. The dog has a “memory” that connects the sight and smell of food to the digestive process. The patterns that represent food: thousands of images, smells, tastes, feelings, sounds, textures and so on all are linked with the response—salivation and the dog’s body’spreparationtoeat. Asabellissoundedatthesametimethatthedogidentifiesthefoodsource,thebrain links these two objects together. That is the basis for the MIM model. Over repetitive trials, the link becomes stronger—an event that has been shown to be a function of the connectionsbetweenneuronswhenlearningtakesplace.Thestrengthoftheconnectionis akeyelement. Astimegoeson,thebellbecomeslinkedtoonething—thefood.Asthereisnoother exampletodefinethebell,thesoundofthebellisanintegralpartoftheeatingexperience. Whatthismeansisthateitherthebellorthesightofthefoodislinkedtothesameobject —theonethatcausestheDog’sbodytopreparetoeat! B.F.SKINNER Skinner upgraded the complexity of the Pavlov stimulus-response conditioning. He introduced the concept of operant conditioning (effective conditioning). Operant conditioning,forexample,takesabirdandtrainsittodotricks,likespinningaroundto theleftuponseeingalight. Thebirdisfirsttrainedthatwhenitseesalight,itwillreceivefoodataspecialfeed bin. Next, the bird is set on a table. When the bird turns to the left a bit, the light is immediately turned on and the bird takes the food. Over a period of time, the bird becomesconditionedtoturnaround360%totheleftwhenitseesthelight. Skinnersaidthatthisindicatesthatthestudyofbehaviourismisontherightpath.I suggest that this indicates, in terms of the machine intelligence model as follows: the initialtrainingusesthePavlovmethodtotrainthebirdtolinkthelighttofood.Next,the bird’sbraindetectsthatwhenitmovestotheleftitwillgetalightandthusfoodandthus pleasure.Thenewpatterndetectedisthatafullturntotheleftof360degreesislinkedto thefood/light. Notverycomplex… D AVID H UME David Hume is recognised as one of the final figures in the philosophical field of empiricism. Hume claimed that there is no “I” as the human “I” is a complex idea that changeswithtime.Intheassumptionsofthispaper,itwasidentifiedthatthereisnobasis forthisconceptanditwouldremainoutsidetherealmsofthemachineintelligencedesign. As it is possible to design a machine that behaves as if it is coming from a single source of intelligence, there is no need to actually design the machine to have a central pointofcontrol.Thereisno“I”neededinamachineintelligence. Further David Hume identified that the brain has a psychological property of generalising observations to causal reality. However, simply because the brain believes thatobservationsleadtoabeliefincausality,doesnotmeanthatthereisanactualcausal connectionbetweentheobservation. David Hume’s observation, that identifies science as effectively a description of the world,isquitecorrect.Followingthatlineofthought,toproduceamachineintelligence requirestheconstructionoftransducerstoconvertfromtheworldtosensoryinputstothe machine,MIMstoidentifypatternsintheenvironmentandtoassociateeventsthathappen at about the same time. This process will produce, among other things, a grammar for languageandawaytounderstandtheworld. S CIENCE 85—T HE C ONFESSION Ifweembracethetheorythatthehumanbrainworksasapattern-matchingmachine rather than a processing machine, we can see why the current approach to creating an intelligentmachineisnotworking.Artificialintelligencereliesheavilyontheideathatthe brainworkslikeacomputer—aprocessingmachine—soeffortstoadvancethefieldare relyingonanoldparadigm. Consider some of these quotes from leaders in 1985, many of whom continue to influence the field today. Articles from Science85 highlights the trouble caused by the adoptionofassumptionsthatarenotcorrect.M.MitchelWaldropwritesaboutthekeyAI researchers. Thecentralassumptionmaybesummarisedthisway: 1. Computersaremachinesthatadd/multiplyandsubtractbinarynumbers. 2. Theneuronsinthehumanbraininsomewaysrepresentbinarynumbers. 3. Therefore,thebrainmanipulatesbinarynumbersinawaysimilartoacomputer. 4. Asthebrainmanipulatessymbolsinawaythatissimilartoacomputer,itmust have a complex process for breaking down complex images, sounds, and thoughtsintoaformthatenablestheslowbraintoprocesscomplexsensorydata toenablethoughttosystematicallyunderstandtheworld. W ALDROP I NTRODUCTION “Doing AI research is a bit like doing problems in physics or mathematics. First turn a set of abstract speculations into a computer program (write down the equations); then make the program perform (solvetheequations).”,writesMrWaldrop. Itisquiteatruestatement—andthemostlikelyreasonthatAIhasfailedtoproducean intelligentmachine.Theassumptionhereisthatthehumanbrain“processes”information —inthesenseoftakinginput,manipulatingit,andproducingoutput.Whilethecomputer certainlydoesoperatethisway,whyassumethebraindoes?Whatifthebrainrecognises input delivered from the senses, compares that information in a statistical way to all previousexperiences,andselectsthemost(orleast,tobemoreinteresting)likelyoption thatdoesn’tincludeacontradiction. Forexample,saytheinputisavisionofastripedtailmovingbehindatree,withalow growlbeingheardthroughamicrophoneandthefeeloflightrainhittingyourhead.The growlwillbematchedbyanumberofobjects,thisisreducedbythenumberoftimesthat type of tail was experienced at the same time, although the rain does not reduce the numberofoptionsatall.Adragondoesn’tmatchthetypeoftail,soitisn’tconsideredfor furtheranalysis—atigerdoesmatchalltheinformation—henceitisselected. This process doesn’t require processing—just matching based on previously learned/experiencedpatterns. R OGER S HANK ’ S D EFINITION ““It can be summed up in one word,” says Yale University’s Roger Shank, a pioneer in natural language programming. “Process. Seeing whatthestepsare,seeingwhattheinputsare,andprovidingalgorithms togetfromplaceAtoplaceB.” Curious,isn’tit?Asahumanbeing,itishardtodescribetheprocessweusetotiea shoelace,orderamealatarestaurantordecidetotakeaspecificactionwhenpresented withalternatives—andyetwecandothesethings.Doesourbrainhaveasetofcomplex built-inalgorithmsthataresocomplexthatwecannotreproducethem,despitebeingable todescribethebreakdownoftheconstituentsofmattertoatomsandtheconstructionof theuniverseintosolarsystems,stars,galaxiesandsuper-galacticclusters. What if we learn a series of steps by simply linking together these steps, based on repetition?Andwhatif,byexperiencingthesepatternsagain,weareimmediatelyableto connectalltheresultsofsuchapattern? Considerthis,whenasmallpieceofmusicisheard,thebraincaninstantlydetectwhat themusicis,whenitwasheard,whosangit,whattheylooklike,andsoon.Thisisn’ta processing ability; it is a linking ability. The fact that the links are already established enables very fast access—a great difference to the process of inference or induction. Processingwouldrequiretheinformationtobepresentedfirst,compressedintothebraincodethatmakesourobservationsofthebrainsocomplex,andthenprocessedtoconnectit tootherinstancesofinformationthroughthesecomplexalgorithmsthatShankspeaksof. The fact is that the neurons in the brain are slow. That we are able to find links so quickly,indicatesthatthebrainsimplylinkingobjectstogether(associationisoneofthe betterformsoflearningandthisiswelldocumented).Thebrainrapidlyconnectssensory input to the previously linked objects to provide recall. These objects can be sights, sounds, feelings—any submodality for that matter. Human memory works better when thereismorethanonesenseinvolvedinthememory.Theseinturnarelinkedintimeand space to other images, sounds, and feelings to represent larger objects. In humans, language provides a further rich structure in which to understand and manipulate the world. T HOMASO P OGGIO :M ATHEMATICIAN OF V ISION “Vision can be described as ‘reverse optics,’” he says. “You start with the two-dimensional image on the retina. And from that you have to reconstructthe3-Dobjectthatcausedtheimage. Really? Doweneeda3-Dconstructioninordertounderstanda2-Dimage?Thatseemstobe drawingalongbow,indeed! Why does Poggio consider this the case? Perhaps it has to do with the fact that humans can manipulate visually recognised objects. There is a sense in which an object hasmorethan2dimensions.Doesthatmeanthatthebrainhasa3-Dvisualrepresentation ofanobject?Notnecessarily! Whatifthebrainstoresanumberof2-Dimagesofthesameobject—inawaysimilar tothatofamovie.Inamovie,multiplestillframesshownat25framesperminuteappear to give a naturally moving image to the human eye. If the brain stores a few thousand images of an object, and can access all of these images at once to give the brain the knowledge of what the total shape of the image is, why would a 3-D representation be required? The brain is good at storing objects through association. In the case of vision, an object that has continuous images through time with changes in images could easily be storedasmultipleimagesofthesamething. Rememberingobjectswouldbenomoredifficultthanmatchingoneofthepatternsof the object, and then using the other stored information about the object that has been learnedthroughassociation. B RUCE B UCHANAN :M AKING KNOWLEDGE USEFUL “So one of the critical research problems in AI,” says Buchanan, “is findingefficientmeansofbuildingnewknowledgebases.” Howdowecreateaknowledgestore?Andhowshouldwecreateaknowledgestore? Thesequestionscertainlyhavevitalimportanceinthesearchformachineintelligence. Today, there are a number of methods for creating databases. On a commercial basis, databases are created for efficiency and to minimise total storage capacity for financial reasons.FortheconstructionofAIsystems,databasesareusedthatspecifywhattypeof informationneedstoberecordedinordertocarryoutaprocedure,suchasorderingfood, recognisingaroomormovingaroundaroom. Intheexamplewearemostinterestedin,inthehumanbeing,theredoesn’tappearto be a set database that dictates what we can learn or do. One day we may have a set procedure to follow to get to work, and the next day we may do something completely different to get to work. One day, we may think that there are no more than 2 types of goldfish,andyetthefollowingdaywemaydiscoverthereareanadditional15.Oreven thatsomegoldfishhavelungs,wingsandcanfly.Thebrainhasnoapparentpreconception aboutanyobject. Sowhatmethodmakessensetobuildnewintelligentdatabases?Howshouldwedo it? An obvious method is one that is self-generating based on association and pattern matching. The associations take place automatically whenever two patterns are active at anyonetime. The model discussed earlier, with MIMs providing non-intelligent pattern matching and association creation, provides the capability to produce a database without the need forapredeterminedstructure.Anytimetwoormoreobjectsareactiveatthesametime, theybecomeassociated.Forthisreason,dinnermayinvolveflamingkebabs,magnumsof drink and gallons of ice cream—without the need to categorise dessert, entree, or main course.Retrospectively,thesecategoriesbecomedefinedasicecreamisadessert. Followingthroughtheassociationsanotherway,itbecomesapatternthattheorderof foodsinamealisentree,mainandthendessert. The fact that savoury foods come first, followed by sweets is another pattern that emergesfromtheassociationsbetweendifferentmealsovertime. G EOFFREY H INTON :P ARALLEL INTELLIGENCE “Untilweknowhowtodotheproperprogramming,”saysHinton,“we wouldn’tknowwhatkindofparallelsystemtobuild.” The chicken and egg story is one that has baffled scientists for eons. The MIM structure is one that benefits from its parallel design. The fact that memory and associations are based on predetermined patterns, and that knowledge is one in which largenumbersofassociationsareconsideredsimultaneously,demonstratesthatthetypeof parallelsystemtodesignisoneinwhichmemorycanbesearchedsimultaneously. Geoffreyisfocussedonprogramming,whichisaverydifferentparadigm.Towritea program to learn is a difficult thing, indeed. To write a program to match patterns and connect/createassociationswhentwoormoreeventshappensimultaneouslyisadifferent matter. Thefactthatitisdifficulttowriteagenericprogramtolearn,builddatastructuresthat require dynamic change, and provide the ability to reconstruct the data elements retrospectively,isadifficulttasktoperformwithCOBOL,C++orLISP. T ERRY W INOGRAD :E NGLISH AS A COMPUTER LANGUAGE “Theideaisthatlanguageandthoughtcanbemodelledbysuchthings as formal logic,” he says. “But I think that is grossly oversimplified.” Winograd feels that a great deal of what goes on when people talk happensatadeep,perhapsunconsciouslevelwedon’tyetcomprehend. “Whatpeopleactuallydohasverylittleincommonwithformallogic,” hesays.“Andwhat’smissingisthesocialdimension.Onceyoutakeinto accountwhatyouareusingawordfor,whatpartitplaysindiscourse, thereisnoboundarytothemeaningofthatword.” Winogradhighlightsthefactthatlanguagehaslittletodowithformallogic. In fact, language builds up automatically as children experience the world. The process of linking the sound of words to objects creates meaning to those words. The orderofwordscreatesgrammar.Thelinkagebetweenthemeaningsofthewordscreates storieswhichprovidepatternsofmeaning.Thesepatternsofmeaningrepresentcontext. Languageistheassociationofwords,linkedtotheirmeaningsandwiththemeanings linked to create understanding. A vast network of intertwined words, images, sounds, feelingsandothersubmodalities. H UBERT D REYFUS : T HE B LACK N IGHT OF AI He maintains that computers will never be able to think because scientists will never come up with a suitably rigorous set of rules to describehowwethink. Dreyfus makes the case for intuition. He argues that perception, understanding and learning are not just a matter of following discrete rules to compute a result; they’re holistic processes that make possible our status as human beings living and breathing and interacting in the world. This view is supported by recent psychological studies in categorisation.Wheredowedrawthelinebetweenavaseandacup?It depends on the context. How do we know that an A in Old English typefaceisthesameasanAinTimesRomantypeface?Certainlynotby counting serifs. If these really are intuitive processes, then Dreyfus is right:Thequestforlogicalrulesisasenselessone. The argument of Dreyfus is that “intuition” is more than discrete rules. He has providedaworthyobservation.Itistruethattheideaofarigoroussetofrulestodescribe howwethink,maybesimilartotryingtofindarigoroussetofepicyclesthatexplainthe motionoftheplanets.However,itisnotclearwhatitmeanstothink—forthatreasonthe assumptionisthatthereisnosuchthing. Itistruethatignoringtheissueofthinkingmakestheideaofsettinguprulestothink alsoirrelevant.Consideringthefactthatintuitionisdefinedasthe:“directperceptionof truths,facts,etc.,independentlyofanyreasoningprocess.”Thatisanimportantstatement. Ifthereisnosuchthingasareasoningprocess,allactivitywhichpeoplemayclassifyas thought,becomesintuitivethinking. The model explained above, in which MIMs detect patterns in the sensory information presented, becomes a machine in which its processes are based around complexinterconnectionsofobjects.Whenlookingfortheworst,butcomplete,fittoaset ofconditionstheMIMmodelenablestheselectionofasolutiontoaproblem,withoutthe needforcomputationorreasoning.Rather,theMIMmodelproducesoutcomesbasedonly onexperience,notonacomputationalbasisoramethodbasedonreasoning.Reasoningis ratheraprocessthatcanbeestablishedthroughtheMIMmodelasanexplanationafterthe eventfordecisionsthatneedtobejustified. Some of the experiments that have been performed on commissurotomy patients (thoseinwhichtheircorpuscollasumhasbeensevered)indicatethatifapartofthebrain compels a motion, the language part of the brain will explain it. This is despite the fact that the language part of the brain had no access to the part of the brain that made the decisiontomove.Itcouldbesaidthatthebraincanexplaineventsithasnoknowledgeof, but explain the observed events in terms that are not inconsistent with all observable information. The MIM model is one which produces a holistic result. The system uses all the availableconnectedinformationinordertoproduceanumberofpossibleresults—thenit isaquestionofwhichonetochoose.Furtherinformationmustbeaddedtothesystemin ordertoproducearesultwhenprovidedwithinsufficientinformation. Let’sconsidertheissueofcategorisation:whatisthedifferencebetweenavaseanda cup?Thevisualimagemaynotidentifyit—northesubmodalitiesofcolour,texture,size, temperature.Itdependsonotherfactors. Where do these other factors exist that enable us as human beings to make the distinction which we normally can make? When we are taught the word “vase”, we are probably seeing an image of a cup with flowers in it. Over time, a number of vases are seenandaddedtoourmemoryforfutureuse.Similarly,anumberofcupsarestoredwith informationabouthowtheylook,feel,smell,whatsizetheyare,whatcolourtheyare,and soon. Aswegrowolder,wehavemoreoftheseimagesstoredinourbrain—withthesound, spelling,andfeelingsabouttheobjectsstoredthroughassociationaslinkedobjects. Sowhentheneedtodifferentiatebetweenavaseandacupismade,thebrainisable to use the network of connections for vase to compare the information presented. A comparisonwiththenetworkofobjectsforthecupwillenableadecisiontobereached basedonprobability.Ifthecupisshownwithflowersinit,thevasewillcertainlywin.If the vase is shown with cappuccino in it, the cup will win. It comes down to the associationswhichmeansitcomesdowntowhatisbeingcalled,context. InthecaseoftheoldeEnglishAversusthemodernA,itmakessensethatthebrain doesn’thaveaprocesstocounttheserifs.However,thereisalotofinformationavailable aboutthelookofthepagethatindicatesthattheobjectsbeingconsideredareletters.There willbemanyassociationsthatwillhelptoconfirmthis. Someoftheassociationswillbethelookofthebook—thousandsofdifferentbooks will be linked together in the visual MIM area to identify the book. Similarly, the olde English A will be matched most closely by the saved patterns for A’s previously seen. TheremaybeathousanddifferentAsstoredinatypicaladulthumansbrain.Thismeans thatrecognitionisn’tamathematicaloracomputationalchallenge,ratheritisapatternmatchingproblem. Wecanknowthattwoletterareidenticalbecausewehaveexperiencedthefactbefore —therewouldbenowayforarandomlyconstructedalphabetortypefacetobeidentified priortopreviousidentificationorinstruction.Havingsaidthat,onastatisticalbasis,which isthebest-fitoutcomefortheMIMcomponent,thebrainisabletoidentifyanAthathas neverbeenseenbefore,providedthatitlooksmostlikeanotherApreviouslystored!The bestfitapproachisusedfrequently,whichishowwecannavigatethroughforestswithout gettingconfusedbythenumberoftreesweseethatdifferfromeveryotheronewehave experienced. T HE R OOTS OF M ORALITY Piaget postulated that younger children are self-centred and do not yet havethecognitiveskillstounderstandthepurposeofsociety’srulesnor theabilitytousetheminareasonedapproach. Butaroundtheageof10,children’scognitiveskillsreachthepointwhere they can interpret society’s rules and become aware of the effects of violating them. They also begin to use their emerging intellectual capabilitiestoreachhigherlevelmoraljudgements,whichincludemotive andintent. HowcanweusethistofurtherexplaintheMIMmodel?Piagetobservedadifference in behaviour between children of different ages. In the MIM model, there are levels of pattern matching. Prior to having patterns for the constituents for sounds the level of MIMsthatdetectpatternsinthatlevelcanhardlygenerateanyusefulpatterns. Similarly, in children, it makes sense that as the brain develops more complexity through increased numbers of patterns and associations, that higher level of pattern can subsequentlytakeplace.Itmaywellbethatatageten,therearesufficientexperiencesthat enable the detection of pattern related to society’s rules that were not available prior to that. Toprovethiswouldrequireanexperimentinwhichcertaincontrolchildrenwerenot allowed to experience situations in which society’s rules were not made available and compare the children after a year or two. That kind of research becomes entwined in ethicalissues. I MPLICATIONS OF MIMT HEORY The MIM theory described above radically changes the view of the human brain, intelligenceandourviewonthecomplexityoflife.Whatanamazingconcepttoconsider! And the next 20 years will see vast changes in machine performance as these ideas are convertedintousefultoolstohelphumans. H ERIDARY V ALUE The machine intelligence discussed here will have some properties that may prove similar to biological life forms. If the MI is able to link a number of high-level actions, such as imitating another being, to pleasure—then it will be able to condition itself to pursue activities that are pleasurable. Equally, if actions lead to pain, the MI will avoid thoseactions. On a commercial aside, the process of language understanding requires the construction/generation of a complex of interconnected memory elements—once this structurehasbeenlearnedonce,itmayformthebasisofmanyidenticalunitstofollow.In the same way that cloning copies the raw biological material blueprint, but not the memories of the individual, copying the basic framework for machine intelligence, the languagecentres,doesn’tmeanthatallmachineintelligenceswillbethesame.Therestis experience! C ONCLUSION Therearetwomajorissuesinthecognitivesciencesthatarestilloutstanding.Oneis themanipulationoflanguage,theotheristhemanipulationofourvisualworld.Both currentlyhavecomputationaltheoriesconnectedwiththem.Bothhavethecapabilitytobe handledbyasingletheoryofstoragemanagement.Bothcanutiliseaparallelhardware technologythatissimpleandconstructible. Ourlimitationsintheprogressofintelligentsystemsarereallyoneoftheconstruction ofhardwareandsoftwaretosupportthenewconcepts.Thesenewsystemsrequire developmentwork,clearly,andarenofurtheralongtheevolutionarylinethanthe1950 ENIACtothecurrentDigitalpersonalcomputer. Thebenefitsandimplicationstohumanityarevast–therewillbeimplicationsto psychology,thecognitivesciences,medicineandthetreatmentofthementallyand physicallydisabled. Therehasbeenmorethan2500yearsofphilosophicalprogressformnatural philosophyinGreeceinthetimearound400BCtothepresenttime.Historyhasenabled theconstantbuildingofideasusingthefuzzyandmisleadingconceptssuchasideas, thinking,consciousnessandmind.Itistimetomoveonwiththedevelopmentofsystems thatassisthumans—systemsbasedonmachineintelligence.Galileoupsetthetraditional thoughtofhumanity’spositioninthecosmos.Darwin,followedbyFreudcontinuedthe trendtothepointwherethecommonpersonistodayawareofthedifficultyfacingour modernhumanfamily. ThewayforwardistobuildmachinesusingthenewlinkedsensorymodelthatMIMs holdtogether.Throughthisexplorationandexperimentation,ourappreciationofthe marvelofhumanitywillcontinueforthecenturiesthatfollowaswefocusonthe challengesofthenewage. A PPENDIX A.A DDITION AND M ULTIPLICATION WITHOUT PROCESSING Theideaofmultiplyingandaddingnumberswithouttheneedforcomputationisalien tothestandardtreatmentofintelligence:isn’titprocessingthatdeterminesintelligence? Afterall,acalculatorprocessesnumbersusingacomputationaldesign,whynotan intelligentmachine? Rememberthatthespeedofprocessinginahumanbrainisroughlyamilliontimes slowerthaninatypicalcomputer—atleastintermsofsignaltransmissionandinthe numberofcomputationsacomputercanperforminasinglecycle. Ifthebrain’sspeedisduetoadifferentmechanism,thatcanexplainhowahumancan performlightning-fastcalculations,oncesuitablytrained.Itisinterestingthatfewpeople possessthecapabilitytoaddlargestringsofnumberssuchas3+4+123+67+12+122+… althoughmanycanadd4+6,forexample.Coulditbethatthebrainisusingasystem basedonmemory,inasimilarwaytothatproposedintheMIMmodel? Let’sloadanMIMwithsomepatterns—startwiththefollowing: 1+1=2,1+2=3,1+3=4,1+4=5,1+5=6,etc. Nowlet’squerytheMIM:whatistheanswerto:“1+3=”? TheMIMwillrespond(inonememorysearchperiod)with“4”.Noprocessingis required.Itissimplyapatternmatchingexercise.Processingisaniceideaforsolving mathematicalproblemsusingthetechnologyavailableinthe1950s—itmakesnosense today! TheMIMdesignmaybeusingbrute-forcesearchingandstoragealgorithms,or elegantneuralnetworkstoragemechanismsbasedonhistoricalprobabilities.Thebeauty oftheMIMapproachisthatbydesigningtheMIMasablack-boxproducingaspecific result,themechanismisunimportant. A PPENDIX B.A SSUMPTIONS Thispapermakesuseofthefollowingassumptionsaboutthehumanmindinorderto understandhowtoproduceanintelligentmachine: 1. Thehumanbrainandnervoussystemisaphysicalmachinethatcanbecopiedas thebasisformachineintelligence. 2. Thereisnomind.Thethingcalledthehumanmindmaybeacollectionofmachines operatingindependently.Assumingthatamindexistsprejudicesourmodeltowardan entitycontrolledbyacentralthing. 3. Thereisnoconsciousness.Althoughthemachinemustbeawareofitsenvironment andhavethecapabilityoffocussedattentiontoasubsetoftheexternalworld,thereis no single entity that is the single controlling feature. The term consciousness again suggestsasinglecontrollingentitythatmaybesimplyaby-productofourlanguage use—humans think they are a single entity based on their language which uses singulartermsforthe“self”(suchas:I,me,myself,mysubconsciousmind).Thefact thattheinternalquestion:“WhatamI?”presupposesthat“I”isasingleitem—thiscan bemisleading. 4. Thereisnosubconsciousorunconsciousmind.Freud,Jungandmanypsychologists usethemodelthatstatesthatthereisapartofourmindwhichholdsinformationnot availabletoourconsciousmind.Theterm‘unconscious’suggeststhatthereisasingle entity that is in our brain that enables or forces us to behave as we do. This information is often used in our law courts to explain why individuals are not responsiblefortheiractions.Afterall,wearenotresponsibleforthat“otherself”,the subconscious.Whatifthesubconsciousistemporarilybroken?Themachinewebuild will not act on all stored information at the same time—some available information maybeusedtomakeadecision,althoughitwillnotbespecifiedintheanswer.This does not mean that the machine has a subconscious mind, only that its actions are based on information not made public. There is more information stored than is communicated. 5. Dreamsdonothavemeaning.Theintelligentmachinewillreduceitsstorageusage bycombiningpiecesofinformationinitsdatabaseofknowledgethroughaprocessof consolidation.Thisprocessrequiresthesystemtobeshutdowntonewinformationin ordertoavoidinterferingwiththenormaloperationofthememorysystem.Shouldthe system be observed during this time of consolidation, it may appear that there are images,feelings,andothersensesactivethatnormallyhasmeaning.Meaningcanbe foundinalmostanythingthatisprovidedtothemachine.Thisisduetothedesignof themachine(thecombinationofinformationusingtheprocessofassociation).Aheart and a box of chocolates has meaning to most of us, and a dream about these two objectshasnomeaningotherthanthesystemispossiblyreducingtheredundancyof theassociationsbetweenheartsandchocolates. 6. Psychoanalysis is a method of understanding learning, not to understand the unconscious.Humanpsychoanalysisaimstoexplaintheunconscious.Intermsofan intelligent machine, psychoanalytic procedures such as free association enable the tracing of patterns of association that makes up intelligence. These patterns are the cornerstone of the new intelligent machine and represent valuable pieces of knowledge about why the machine does what it does. Remember that there is no intelligence about what is stored. It is simply a process of pattern matching and linkagethroughassociation.Ifassociationstoresincorrectinformation,thereneedsto beamechanismtocorrectit. 7. Humanthoughtisanoutcomeproducedbythebrain—itisnotasingleprocess. The process of thinking is a term for labelling the passing of time during which the brain produces an outcome. The outcome is usually based on some of the initial conditions of a situation. Thought is definitely not: (a) a centralised function controlled by a “self”, or by (b) the subconscious, or by (c) any other term representingadescriptionofanobjectnotdemonstratedtoexist. 8. Thoughtisnotacomputationalprocess.Thereisnoreasontoassumethatthought exists as a computational process. Thinking appears to combine information that requires inferences and deductions to be made. This process may be the result of memory storage and recall not based on processing at all. The example of parallel additionshownlaterdemonstratesthis(seetheexampletoadd“1+4”inAppendix A). 9. Computermemorydoesnotworklikehumanmemory.Computermemoryisreally simply a store (a place to store information)—memory (and in particular human memory)isandmustbeanactivecomponent. F URTHER R EADING “EncyclopaediaBritannicaCD97”,1997. “TheBrain:AUser’sManual”,BerkleyPublishingCorporation,London,1982. “TheMacquarieDictionary”,HerronPublications,SouthAustralia,1987. 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