From: AAAI Technical Report FS-94-05. Compilation copyright © 1994, AAAI (www.aaai.org). All rights reserved.
Introductory AI for Whom?
Presenting AI to the Non-Scientist
Rebecca E. Skinner,
Visiting Scholar, KnowledgeSystems Laboratory,
Computer Science Department, Stanford University
701 Welch Road, Building C, Palo Alto, CA94304.
415 725 3855. fax 415 725 5850.
skinner@hpp.stanford.edu
Abstract
ArtificialIntelligence,
as a set of toolsfordescribing,
analyzing
andcreatingartifacts, has muchto offer the humanitiesand
sciences.Thesekleinmaybe appliedto far moreinformal
human
artifacts suchas culture,as a means
to better understand
the non-engineered
complex
artifacts whichsurroundus. This
paper describes "AI for Everyone",a coursefor advanced
undergraduates
whichis intendedto introducevariousideas in
knowledge
representation
madAIto the non-scientisL
Theauthor
is currentlyproposing
that this coursebe offeredat Stanford
University during 1994-1995.Thecourse introduces the
conceptsof (a) designof complex
artifacts and(b) internal
representationderivedfromcognitivescience,as seenin the
progress of AI research. The class focuses on symbolic
representation
techniques,
withtwosessionsonreiterationsof
other kindsof intelligence(Nouvelle
AI, Connectionism).
Also
included
aresessionsoncritiquesof symbolic
representation
and
on potential meansto apply KRconceptsto fields outside
computing.
Notoclmicalbackground
in computing
or cognitive
s~enc~
is assumed.
InWoduction
Theproblemas posed assumesthat Introductory AI as a
set of programmingtechniques should be taught to
computerscientists, andonly to computerscientists. The
author questions this premise. She suggeststhat instead
certain of the analytical approachesdevelopedby 33 be
introducedto social science andhumanitiesstudents. The
sciences of the complex,of which33 is the pre-eminent
representativeaddressingcognitivecomplexity,shouldbe
taught to far more people. A well-defined subset of
existing KRconceptsshouldbe incorporatedinto the base
of knowledge
whichdefines a civilized, educatedperson.
The state of knowledgeconcerningthe sciences of the
complexin the educated public and in the fields of
humanities and social sciences is very poor (I). AI,
cognitivescience, and their commercial
applications have
beenpresentedto the public, includingto highlyeducated
non-technicalprofessionals, by the Dreyfusbrothers and
businessjournalistsrespectively.Thelatter present33as
a simplistic, inhumane,and failed attempt to replace
human intelligence with that of machines ("the
Frankensteinmodel").Thefield’s leaders are cognizantof
this unsavorypublic imageof 33 as either "impossibleor
invisible" (see reports on AAAIBoardmeetingsand the
AA33’s1993round of ballots for Councilors). However,
not muchis beingdoneaboutthe situation. Tothose in the
thickof 33research,this institutionalintellectualpassivity
maynot be either obviousor importanLWhyshould the
38
non-specialist knowanything about this field ? AI
softwareimplementations
are typically highlytransparent,
so there is relatively little needfor the averagePCuser to
understand AI. Accordingto this interpretation, the
public’s ignoranceabout 33 is irritating but innocuous.
Theauthorasserts that this is a gravemisreading
of the
situation. ActuallyAI has muchto offer the humanities
andsocial sciences.
The field concentrates on the developmentof useful
software (and hardware) tools based on two distinct
exemplarsof intelligence: "33 as empirical inquiry",
entailing the reiteration of logical cognizantintellectual
processes, and the recreation of intelligence fromthe
bottomup (epitomizedby Brooks’as "elephantsdon’t play
chess"). However,both ’AI as complexity’ and ’AI as
simplicity’ orientations overlook the very general
definition of the term "tool": "an implementor machine
usedto do workor performa task". Ideally the pragmatic
softwaretool orientation of the current AI worldcouldbe
complementedby a wider understanding of very basic
conceptsof 33, in a non-technicalform. In a sense such
understanding
is part of oneof the basic imperativesof 33
as empiricalinquiryandtool creation, or the creationof a
Knowledge
Medium(Stefik, 1986). Whatis a Knowledge
Medium
if it is not a meansfor conveyingthought ? The
latter is of course, doneby softwareandmaterial tools,
but it is also donebythinking,andis part of the collective
store of wealthavailableto any givenculture. Thesimple
potential usageof knowledge
representationas a meansto
analyzingproblemsin a moreor less formalwayoffers a
meansto help peoplethink moreclearly. Thedescriptive
andanalytical potencyof the basic conceptsand issues of
AI (in symbolic and connectionist forms alike) hold
regardless of whoseNCAIdemois more impressive. In
this light, the exclusivefocuson softwaretools amidstthe
field’s wealth of ideas about knowledge,intelligence,
information,social interactions(collaboration,interaction)
is curious rather than natural. Theintroversion Of 33
amongacademic disciplines seems incongruent and
inappropriate.
RogerSchankhas defined AI as an algorithmic approach
to various domains: " AI should in principle be a
contribution to a great manyfields of study... 33 is,
potentially, the algorithmicstudy of processesin every
field of inquiry. As such, the future should produce
33/anthropologists, AI/doctors, 33/political scientists
andso on. It is just a matterof timeuntil 33becomes
part
of other fields..." (Schank1991,p7).
Onthe contrary, such individuals havenot flooded AAAI
on their own.It seemsmostunlikelythat they will unless
the field actively undertakesto acquire a wideraudience
in non-CSuniversity circles. Applyingexisting concepts
to newcontexts is essential to intellectual progress
(consider the importanceof analogyin KRandlearning),
but is sufficiently strenuousthat peoplein AI mustwork
to develop analogies betweenAI and semantically and
syntactically distant fields. Shouldsuch an effort be
undertaken,AI could makea significant contribution to
society as an intellectual infrastructure or "publicgood"
for problemanalysis and problem-solving. In a more
abstract sensethan the conceptis usuallyunderstood,the
rudimentsof knowledge
representation could be generally
introducedin highereducation.Theproliferation of AI as
intellectual edifice could and should complementthe
proliferationof AI as softwaretools.
Onecontribution to this effort could be to introduce
courses entitled "AI for Everyone". A version of the
prospective course syllabus follows. The author is
currently trying to arrange to teach this course in a
humanities department at Stanford University. Many
peopleoutsideAI are verycuriousaboutthe field, but see
it as entirely inaccessible. Yet manybasic ideas in AI,
such as internal representation, inferencing, heuristic
vetsas algorithmicsolutions, searchstrategies, flamesand
scripts, schemata, etc., maybe explained without
referenceto FOPC.Boththe student andthe teacher could
benefit fromsuch a course. Figuring out howto explain
the basic concepts of the sciences of the complexin
general and AI in specific without the assumptionsthat
the student knowsFOPCand programminglanguages
would be a fine excercise for AI graduate students.
Perhapsteaching such a course could help to remindAI
practitioners, most of whose work is effectively
engineering,that they are (ostensibly)AI intellectuals
well. Thefragmentationof AI as a field into discrete
subdisciplines whichmakevery different assumptions
aboutthe nature of ~intelligence"or ignorethe latter Big
Question entirely is another salient problem. The
challenge of presenting the field in simple natural
language(that is, English) to a non-technicalaudience
wouldbe a useful ’thoughtexperiment’as well. It might
evenyield AI graduatestudents.
Anhistorical approach,whichintroducesthe development
of symbolic representation technology (intellectual
technology,that is) in the formof the Physical Symbol
Systemin mid-century,and follows its elaboration over
the past several decades, could be a reasonableteaching
format. This shouldincorporatea judicious allowancefor
non-symbolic information processing models. Such a
coursecouldintroducethe field in simplecanonicalform,
commencing
with a presentation of the concept of the
sciences of the artificial as stated by Simonin The
Sciencesof the Artificial. This shouldbe followedby a
basic imroductionto knowledgerepresentation concepts,
such as declarative versus procedural decompositionof
problems, state space representations and operators,
heuristic versusalgorithmicsolutions, framesandobjects,
39
and various forms of search. Thehistorical development
fromsyntactic search proceduresto heuristic search and
complementaryknowledgerepresentations should be
illustrated by reference to prominent programs. The
syllabus currently being developedalso includes lessons
on computerlanguages,counectiouist and "Nouvelle"AI
challengesto the ’Classic’ PSStradition, andthe novel
developments of the recent years, including VLKB,
generic task representations, and meta-tools of the
knowledge
sharing variety. Evena simplepresentation of
canonical approachesto symbolicrepresentation and to
non-symbolicinformation processing could provokea
greater degreeof introspection in bright peoplewhohave
beendeprivedof suchtools. This wouldalso help to place
AI in the context of the sciences relating to complex
artifacts. This developmenthas been extraordinarily
fruitful, yet mostnon-scientistsonly see the artifacts of
computingin the opaque form of PCs and operating
software. Computingin general and AI in specific go
much deeper than word-processing
packages,
spreadsheets, and computergraphics, but the outside
world won’t knowit unless people from AI present this
knowledge
to a wideraudience.
Thecreation of a syllabusfor Watching
an Inl~oductionto
AI couldhelp to illuminateArsundeserved
invisibility, as
well as to introducethe field’s analytical wealththrough
culture, rather than solely through software tools. If
technologyis culture, then AI shouldbe high culture.
Notes
(1) This is not an exaggeration.Thewriter beganstudying
commercial AI and KBSunder the auspices of the
economicsof technological change. Whentrying to
explainthe essentialsof AI andreasonfor her interest, she
has been met with commentssuch as: "Whois Noam
Chomsky?"; "I read in the NewRepublic that AI has
failed completely"; and, most commonly:"Hubert
Dreyfussays..." by distinguishedtenured professors in
various social science fields at a major research
university.
Bibliography
D. Bobrow
edited volumeof Artificial Intelligence. 1991.
Haugeland,John. Artificial Intelligence: TheVe~Idea.
1982.
Kuhn,Thomas."A Function for ThoughtExperiments".
The Essential Tension: Selected Studies in Scientific
Changeand Tradition. 1977.
Schank" Whatis AI anyway?". Partridge and Wilks,
eds., The Foundations of Artificial Intelligence: A
Sourcebook. NewYork: CambridgeUniversity Press.
1991:3-13.
Stef’ik, Mark." The KnowledgeMedium".IALMagaz_i~
1986.
ProposedSyllabus
AI for Everyone:AnIntroductionto the Sciences of
the Complexfor Humanities and Social Science
Majors
Brief Description:
The one-semestercourse for juniors and seniors will
providean historical overviewof Artificial Intelligence
with a symbolicrepresentationslant, and the Sciencesof
the Artificial to humanitiesandsocial sciencemajors.
Requirements:
Reading, class attendance twice per week, and two
examinations.
Thereare no prerequisites.
Week1. Inlroduction
This lecture defines the conceptof the sciences of the
complex,artificiality and complexity.It then explains
howa cluster of disciplines has emergedto examine
artifacts, or engineered systemsof both physical and
ephemeralforms (i.e., software). It suggests howthis
study in turn provides novel and insightful meansby
which to consider the humanphenomenatraditionally
studiedin the humanitiesandsocial sciences. Thelecture
suggests reasons why art history and comparative
literature majorsshouldcare about this discipline. Such
rationales call uponpragmatism
(the increasing presence
of computationin daily life in industrial nations);
cynicism(technology
is power);historical interest (this
one of the century’s most significant intellectual
developments);
and philosophicalinterest (the methods
the sciences of the complexmaybe applied outside the
usual doma~).
Termsto be introduced: artifact, sciences of the
complex,technology,
representation;artificial-natural, AI,
cognitivescience, computer,hardware,software.
Reading:(someitems over the
courseof the semester):S andH.
Dreyfus, WhatComputersStill
C~’t
Do; Haugeland,
J
Intelli~enoe:TheVeryIdea.
JoJmson-Lmd,
P.
~d~ Mind.
Selectedchapters.
Gardner, H. The Mind’sNewScience: A
Historyof the Counitive
~. McCorduck, P.M~n~
~ Think.Stiilings, N.S. et al.
~0enitiyeScience:AnIntroduction.
Simon,H. TheSciencesof the
~. C2nd ecL).1980. Turing,
A.M." ComputingMachineryand
Intenigence".
(1950).
Week
2. Histori-------------afl
Precedents
Artificial Intelligence and cognitive science emerged
from various methodologicaland substantive theoretical
roots duringthe middledecadesof the Twentiethcentury.
roots includeformallogic in philosophy;operations
research; andcybernetics,or the studyof automata.These
necessary underpinnings entailed (respectively)
improvements in formal logic (Boole, Russell and
Whitehead, Frege, Godel); improvements in the
representation of informationderived from electronics
(Shannon’sconcept of binary-based representation of
switching); and inquiries into the nature of complex
40
intelligent systems(anthropomorphic
or otherwise)under
the auspicesof researchinto intelligent automata(Wiener,
Von Neumann, and Newell Shaw and Simon, and
Turing). Given the prevailing regime of WatsonianSkinnerian behaviorism, the agenda for exploring
intelligencethroughcyberneticswaspractically subrosa.
Termsto be introduced: the Cognitive Revolution,
automata,Behaviorism,cybernetics, operationsresearch,
informationrepresentation, internal representation, Von
Neamannmachine.
Reading:Gardenerchapters.
Heims,S. TheCyberneticsGroup.1991.
Regis, E. WhoGot Einstein’s Office ? Eccentricity_ and
Geniusat the Institute for Advanced
Study.
Week3. The 1950s: Formationof the AI Research
Programs
The formation of agendas for studying intelligent
information processing was slow and collaborative.
Resea~hdirections whichnowseem natural were by no
meansso before 1956. (Alan Newell:"Deepscientific
ideas are exceedinglysimple. Others usually see themas
trivial"). Directlyinspiredby the Cyberneticmodellingof
information processing, several strands of cognitive
science and AI coalesced during this period. These
include the Chomskianlinguistic agenda, the most
directly argumentativewith Behaviorism;Hebb’sprotoconnectionist biological metaphor for modelling
informationprocessing;and the representationofthought
throughcomputationalmanipulationof symbolicsystems.
All three will be sketched, and the latter examinedin
somedetail. Newelland Simon’sGeneralProblemSolver
experimentsat Carnegie-Mellonusheredin the modeling
of intellectual processes through weaksearch. This
yielded various atomistic syntactic problem-solvingand
search methods(the latter issue continued into next
week).
Termsto be introduced: human-computer
interaction,
protocol, biological metaphor, toy problem, neuron,
intelligence, information processing, computational
metaphor, symbolicrepresentation, weakversus strong
methods,deepstructure.
Reading:
Scheerer,
E."Toward
a History
of Cognitive
Science".
International
Social
Science
Journal
40.
Sections
fromGardner,
McCorduck,
Stillings
etal.
Week4. The 1960s: Early Development
of Search and
Problem.Solving
The PSS research program yielded both significant
cognitive science research and powerfulKRtechniques.
Someof these, especially the GPS-basedsemantically
blind search methods,are practically takenfor grantedat
the present, perhapsbecauseof their application to toy
problems.The sessions will introduce the concepts of
internal representation,intelligence as goal-seeking,
and
search as movementthrough a problem space using
operators.
Termsto be introduced: semantics-syntax, problem
space, state space representation, top-downversus
bottom-up, physical symbol system, search, pruning
search spaces, graphs, declarative versus procedural
informmion,
heuristic, algorithm,operator, operand.
Reading: TBA
Reading:Gardener1985and Sailings et al., selections;
Selections from Bobrow,Minsky, Newell and Simon,
Schank, MYCIN
Project.
Week5a. The Tropismto Domainsin the Late 1960s
The syntactic approachto blind search in toy spaces
shifted pivotally to search of moreambitious domains
(areas of expertise) duringthe late 1960s. This session
will describe two of the strategies of this period which
movedtoward systematic representation of difficult
(rather than toy) problems, such as the modeling
scientific reasoningin the Dendralproject at Stanford,and
the introductionof productionsystemsat CMU
in the late
1960s.
Termsto be introduceA:domainrepresentation, control
slzncture, generateand test; productionsystems,control
structure;
Reading:Chapter from Newell’s Unitary Theories of
~; other
TBA.
Week7. RepresentingOther Formsof Intelfigence
The cognitive metaphoras presented in this course
certainly cannot presumeto embracethe entirety of
humanexperience and reaction to the environment.Much
of the latter includes emotion,physical reaction, and
instant reactionsto the environment.
Thelatter shouldbe
thoughtof as different sorts of intelligence. "Nouvelle
AI"
replicates simple, rapid reactions to the physical
environmentusing robotic techniques. The biological
metaphor, which tries to reiterate the rudimentary
intelligence captured in the neural system of living
creatures (not necessarily humans),is a useful if very
distinct meansto understandintelligence. Still another
approach,closer to the Classical symbolicgenre, is the
examinationof intelligence in a modularperspective.
There are various componentsin conscious cogitation,
including memory, analogical reasoning, spatial
reasoning,etc.
Is it true, as MarvinMinskyis purportedto havesaid,
that "Consciousnessis overrated"? If consciousnessis
manythings, howcan each of these be translated into
usefulartifacts ?
Termsto be introduced:massiveparallelism ’wetware’,
’meat machines’, emergent states, perceptrons,
Connectionism, robot, knowbot, neural network,
modularityof mind,faculties theory;
Reading:M. Minsky and S. Papert, Percevtrons. H.
Moravec,MindChildren; D. Hillis article in Wired
October 1993. Minsky, The Society of Mind. Daedalus
1988Special Issue on AI article on Connectionism.
Week5b. ComputerLanguages
A languageis a protocol for communication,between
people, agents, or other systems. Computerlanguages
descn~oeentities andactivities, just as natural languages
do, but in a far moreformalandrigidly definedfashion.
Natural languageand computerlanguageshoweverdiffer
in formality,lexicai andreferential ambiguity,syntactic
idiosyncrasies,andnuancesof semantics.This lecture will
explain the need for formal languages to control
engineeredartifacts, and the distinctions betweenlowlevel andhigher-levelcomputational
languages.
Terms to be introduced: computer versus natural
language;syntax, semantics,lexical ambiguity,protocol;
functional versus procedural language, object,
encapsulation, global versus local variable; high-level
language,platform, environment,application, operating
system,binary, Boolean;
Reading:(selected chapters): Brookshear,J.G.
Science: AnOverview.Baron, N. ComputerLant, uages:
AGuidefor the Perplexed.1986.
Week 6. Changing the Scope of Knowledge
Representationin the 1970S
Despite the reach of GPSand similar programs,muchof
its initial technical obstacles hadinvolvedvery minute
manipulationof particles of thought. In the early 1970s,
the technologyfor representing small-scale information
manipulationhad advancedsufficiently to allow several
large-scale conceptsof AI representationof intelligence.
Theseinclude production systemsand rules (Newelland
Simon, Buchananand Shortliffe), frames (Minsky),
schemata(Bobrowand Collins), and scripts (Schank).
Thesewaysof representingintelligence (usually basedon
cognitive science research on people) werealso waysof
creating artifacts that had certain forms of knowledge.
Thus the concepts embodied both scientific and
technological knowledge.
Termsto be introduced:rules, objects, frames,domains,
script, expert systems, knowledge base systems;
conceptmlprimitive, blackboard;
41
Week8, a. Critiques of AI, KRand Technologyin
General:
Week 8, b. AI Views of Emotions, Music, and
Creativity:
a) Critiques of AI and cognitive science have been
virulent, althoughmostpractitioners havepaid no notice.
The argumentsinclude a) ad hominencritiques of the
social skills of hackers (Turkle, Weizenbaum);
assertions of a simplistically mechanistic worldview
entailed in symbolicabstractions (Merchant);c) a more
open phenomenological
attack on the ’atomism’ingrained
in the PSSmodel(Searle and Dreyfusbrothers). However,
harsh machofunctionalismand ’atomism’are out of style
in AI itself, suggestingfor a start that the critiques are
dated. Giventhe prominenceof distributed and situated
knowledgeand beterogenous representation amongthe
’newsensitive AI researchers’of both the NouvelleAI and
the KRcommunities,
are these critiques still valid ?
b) Recent work in AI instead suggests the potential
contribution of KR to art, entertainment, and
developmental
andclinical psychology.
Terms to be introduced: Luddites; phenomenology
versusfunctionalism;intentionality, fringe consciousness;
Reading(a): Selections: D. Harraway;C. Merchant,The
l~,~k.o.f~]alll~;S. Turkle,Ihf,.i,~f.011d.~g~;
J. Searle; J.
Weizenbaum,ComputerPower and HumanReason.
Reading(b): Articles on AI and
Music, ~ 1991; work by
J. Bates; D. Gele~nter. ~e Muse
ia the Machine:Computerizing
the
Poetry of Human
Tilought. 1994.
Schank,R." Creativityas a
MechanicalProcess". In R.
Sternberg, ed. Tbe N~of
.C, IgiliX~.1988:220-238.
Week9. Current Workin KR:
a. CurrentWorkin Artificial Intelligence:
Criticism of the first generationof KBsystemson the
groundsof their "orittle andisolated’ quality havebeen
taken very seriously in the KRcommunity.Current
research in this communityemphasizesthe creation of
shared knowledgerepresentations. Ongoingresearch in
this effort focuseson the technical andrepresentational
challengesof buildingintermediate"interlingua", a kind
of Esperanto between disparate existing computer
languages and knowledge representations in those
languages.This research challenge,intensified by the end
of the Cold War, forces the PSS/KRcommunityto
providepractical softwarein a shorter time framethan it
is used to. The concept of an "intelligent agent"
(undefined)is replacingthe knowledge
base systemas the
predominantartifact and vehicle for AI platforms. These
are less directly anthropomorphic and possibly
complementary to more numerous if less lofty
applications than KBSseemsto havebeen.
Termsto be introduced: CYC;very large knowledge
base; device modeling; agents; knowledgesharing;
KnowledgeMedium;
b. In-class field trip to aa AALab, TBA:HowThings
Workproject at the Heuristic ProgrammingProject 7
Tools for MedicalInformatics KRat KSL-CAMIS
.9
Reading TBA.
Week10. Potential Contributions of Mand Cognitive
Scienceto other fields;
The sciences of the complex consist of ways to
understand humanintelligence and meansby whichto
create newartifacts. This has not beenusedto analyzethe
artifacts- physicalor intellectual- of the past. Apartfrom
their intrinsic interest, these conceptscouldbe usedin the
humanities and social sciences in various ways. The
descriptive terminology concerning various forms of
internal representationsand cognitiveemulationcouldbe
used for the discipline of history. This wouldpertain
specificallyto intellectualhistory(of givencultures,eras,
etc.), as well as to the discrete studies of women
and
different ethnic and economicgroups which are now
prevalent. Thecontributionsof these fields to economics
is cmTentlybeing explored,andcognitiveanthropologyis
an established field of study. The breadth of domain,
extensibility of existing concepts,andcharacteristics of
search or problem-solving which demarcate a given
cultural artifact
may be described by KR’s
interdisciplinav/terminology
in a waynot nowpossible.
Week11. Final Examination
Acknowledgements:Thanks to Lee Brownston of the
KnowledgeSystems Lab at Stanford for insightful
comments.