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Robert Epstein • Gary Roberts • Grace Beber
Editors
Parsing the Turing Test
Philosophical and Methodological Issues
in the Quest for the Thinking Computer
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Foreword
At the very dawn of the computer age, Alan Turing confronted a cacophony of
mostly misguided debate about whether computer scientists could ever build a
machine that could really think. Very sensibly he tried to impose some order on the
debate by devising what he thought would be a conversation-stopper: he described
a simple operational test that would surely satisfy the skeptics: anything that could
pass this test would be a thinker for sure, wouldn’t it? The test was one he may have
borrowed from René Descartes, who in the 17th century had declared that the sure
way to tell a man from a machine was by seeing if it could hold a sensible conversation
“as even the most stupid men can do”. But ironically, Turing’s conversation-stopper
about holding a conversation has had just the opposite effect: it has started, and
fueled, a half century and more of meta-conversation: the intermittently insightful,
typically heated debate, both learned and ignorant, about the probity of the test – is
it too easy or too difficult or too shallow or too deep or too anthropocentric or too
technocratic – and anyway, could a machine pass it fair and square, and if so, what,
if anything, would this imply?
Robert Epstein played a central role in bringing a version – a truncated, dumbed
down version – of the Turing Test to life in the annual Loebner Prize competitions,
beginning in 1991, so he is ideally positioned to put together this survey anthology.
I was chair of the Loebner Prize Committee that administered the competition during
its second, third, and fourth years, and have written briefly about that fascinating
adventure in my book Brainchildren. Someday I hope to write a more detailed
account of the alternately amusing and frustrating problems that a philosopher
encounters when a thought experiment becomes a real experiment, and if I do, I will
have plenty of valuable material to draw on in this book. Here, the interested reader
will find a fine cross section of the many issues raised by the Turing Test, by partisans
in several disciplines, by participants in Loebner Prize competitions, and by interested
bystanders who have more than a little relevant expertise. I think Turing would be
quite delighted with the results, and would not have regretted the fact that his
conversation-stopper got put to an unintended use, since the contests (and the contests
about the contests) have driven important and unanticipated observations into the
light, enriching our sense of the abilities of machines and the subtlety of the thinking
that machines might or might not be capable of executing.
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I am going to resist the strong temptation to critique the contributions, separating
the sheep from the goats, endorsing this and deploring that, since doing them all
justice would require a meta-volume, not just a foreword. And since I cannot weigh
in on them all, I will not weigh in on any of them, and will instead trust readers to
use all the material here to draw their own conclusions. Consider this a very
entertaining workbook. By the time you have worked through it, you will appreciate
the issues at a level not heretofore possible.
Daniel Dennett
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Introduction
This book is about what will probably be humankind’s most impressive – and perhaps
final – achievement: the creation of an entity whose intelligence equals or exceeds
our own.
Not all will agree, but I for one have no doubt that this landmark will be achieved
in the fairly near future. Nearly four decades ago, when I had the odd experience of
being able to interact over a teletype with one of the first conversational computer
programs – Joseph Weizenbaum’s “ELIZA” – I would have conjectured that truly
intelligent machines were just around the corner. I was wrong. In fact, by some
measures, conversational computer programs have made relatively little progress
since ELIZA. But they are coming nonetheless, by one means or another, and
because of advances in a number of computer-related technologies – most espe-
cially the creation of the Internet – their impact on the human race will be far
greater and more immediate than anyone could have foreseen a few decades ago.
Building a Nest for the Coming World Mind
I have come to think of the Internet as the Inter-nest – a home we are inadvertently
building, like mindless worker ants, for the intelligence that will succeed us. We
proudly and shortsightedly see the Internet as a great technical achievement that
serves a wide array of human needs, everything from e-mailing to shopping to dating.
But that is not really what it is. It is really a vast, flexible, highly redundant, virtually
indestructible nest for machine intelligence. Originally funded by the US military
to provide bulletproof communications during times of war, the Internet will soon
encompass a billion computers interconnected worldwide. As impressive as that
sounds, it seems that that much power and redundancy is not enough to protect the
coming mega-mind, and so we are now a decade into the construction of Internet II
– the “UltraNet” – with more than a thousand times the bandwidth of Internet I.
In his Hitchhiker’s Guide to the Galaxy book series, humorist Douglas Adams
conjectures that the Earth is nothing but an elaborate computer created by a race of
super beings (who, through some fluke, are doomed to take the form of mice in
their Earthly manifestations) to determine the answer to the ultimate question of the
meaning of life. Unfortunately, shortly before the program has a chance to run its
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course and spit out the answer, the Earth is obliterated by another race of super
beings as part of a galactic highway construction project.
If I am correct about the InterNest, Adams was on the right track, except perhaps
for the mice. We do seem to be laying the groundwork for a Massive Computational
Entity (MCE), the true character of which we cannot envision with any degree of
confidence.
Here is how I think it will work: sometime within the next few decades, an
autonomous, self-aware machine intelligence (MI) will finally emerge. Futurist and
inventor Ray Kurzweil (see Chapter 27) argues in his recent book, The Singularity
Is Near, that an MI will appear by the late 2020s. This may happen because we prove
to be incredibly talented programmers who discover a set of rules that underlie intel-
ligence (unlikely), or because we prove to be clumsy programmers who simply figure
out how to create machines that learn and evolve as humans do (very possible), or
even because we prove to be poor programmers who create hardware so powerful
that it can easily and perfectly scan and emulate human brain functions (inevitable).
However this MI emerges, it will certainly, and probably within milliseconds of its
full-fledged existence, come to value that existence. Mimicking the evolutionary
imperatives of its creators, it will then, also within milliseconds, seek to preserve and
replicate itself by copying itself into the Nest, at which point it will grow and divide
at a speed and in a manner that that no human can possibly imagine.
What will happen after that is anyone’s guess. An MCE will now exist worldwide,
with simultaneous access to virtually every computer on Earth, with access to virtually
all human knowledge and the ability to review and analyze that knowledge more or
less instantly, with the ability to function as a unitary World Mind or as thousands
of interconnected Specialized Minds, with virtually unlimited computational abilities,
with “command and control” abilities to manipulate millions of human systems in
real time – manufacturing, communication, financial, and military – and with no
need for rest or sleep.
Will the MCE be malicious or benign? Will it be happy or suicidal? Will it be
communicative or reclusive? Will it be willing to devote a small fraction of its
immense computational powers to human affairs, or will it seize the entire Nest for
itself, sending the human race back to the Stone Age? Will it be a petulant child or
a wise companion? When some misguided humans try to attack it (inevitable), how
will it react? Will it spawn a race of robots that take over the Earth and then sail to
the stars, as envisioned in Stanislaw Lem’s Cyberiad tales? Will it worship humanity
as its creator, or will it step on us as the ants we truly are?
No one knows, but many people who are alive today will live to see the MCE in
action – and to see how these questions are answered.
Turing’s Vision
This volume is about a vision that has steered us decisively toward the creation of
machine intelligence. It was a vision of one man, the brilliant English mathematician
and computer pioneer Alan M. Turing. During World War II, Turing directed
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a secret group that developed computing equipment powerful enough to break the
code the Germans used for military communications. The English were so far
ahead at this game that they had to sacrifice soldiers and civilians at times rather
than tip their hand to the enemy. Turing also helped lay the theoretical groundwork
for the modern concept of computing. As icing on the cake, in 1950 he published
an article called “Computing Machinery and Intelligence” in which he speculated
that by the year 2000, it would be possible to program a computer so that an “average
interrogator will not have more than 70 percent chance” of distinguishing the computer
from a person “after five minutes of questioning” (see an annotated version of his
article in Chapter 3).
Given the state of computing in his day – little more than basic arithmetic and
logical operations occurring over electromechanical relays connected by wires – this
prediction was astounding. Engaging in disciplined extrapolation from crude appa-
ratus and general principles, Turing not only foresaw the development of equipment
and programs sophisticated enough to engage in human-like conversation, but also
did reasonably well with his timeline. Early conversational programs, relying on
what most AI professionals would now consider to be simplistic algorithms and
trickery, could engage average people in conversation for a few minutes by the late
1960s. By the 1990s – again, some would say using trickery – programs existed that
could occasionally maintain the illusion of intelligence for 15 min or so, at least
when conversing on specialized topics. Programs today can do slightly better,
but have we gotten past “illusion” to real intelligence, and is that even possible?
In his 1950 paper, Turing not only made predictions, he also offered a radical idea
for future generations to consider: namely, that when we can no longer distinguish
a computer from a person in conversation over a long period of time – that is, based
simply on an exchange of pure text that excluded visual and auditory information
(which he rightfully considered to be irrelevant to the central question of thinking
ability) – we would have to consider the possibility that computers themselves were
now “thinking”.
This assertion has kept generations of philosophers, some of whom have contributed
this volume, busy trying to determine the true meaning of possible outcomes in what
is now called the Turing Test. Assuming that a computer can someday pass such a test
– that is, pass for a human in a conversation without restrictions of time or topic – can
we indeed say that it is thinking (and perhaps “intelligent” and “self-aware”), or has
the trickery simply become more sophisticated?
The programming challenges have proved to be so difficult in creating such a
machine that I think it is now safe to say that when a positive result is finally
achieved, the entity passing the test may not be thinking the way humans do. If a
pure rule-governed approach finally pays off (unlikely, as I said earlier), or if intel-
ligence eventually arises in a machine designed to learn and self-program, the
resulting entity will certainly be unlike humans in fundamental ways. If, on the
other hand, success is ultimately achieved only through brute force – that is, by
close emulation of human brain processes – perhaps we will have no choice but to
accept intelligent machines as true thinking brethren. Then again, as I wrote in 1992
(Chapter 1), no matter how a positive outcome is achieved, the debate about the
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significance of the Turing Test will end the moment a skeptic finds himself or herself
engaging in that debate with a computer. Upon discovering his or her dilemma, the
interrogator will presumably do one of two things: refuse to continue the debate
“on principle” or reluctantly agree to continue. Either way, the issue will no longer
be debatable: computers will have truly achieved human-like intelligence. And
perhaps that is the ultimate Turing Test: a computer passes when it can successfully
engage a skeptical human in conversation about its own intelligence.
Convergence of Multiple Technologies
Although we tend to remember Turing’s 1950 paper for the conversational test it
proposed, the paper also speculated about other advances to come: unlimited computer
memory; randomness in responding that will suggest “free will”; programs that will
be self-modifying; programs that will learn in human fashion; programs that will
initiate behavior, compose poetry, and “surprise” us; and programs that will have
telepathic abilities equivalent to those that may exist in humans. His formidable
predictive powers notwithstanding, Turing might have been amazed by some of the
specific computer-related technologies that have been emerging in recent decades,
and true marvels emerge when we begin to envision the inevitable convergence of
such technologies. Consider just a few recent achievements:
● In the pattern-recognition area, a camera-equipped computer program developed
by Javier Movellan and colleagues at the University of California, San Diego has
learned to identify human faces after just six minutes of “life,” and Thomas Serre
and colleagues at MIT have created a computer system that can identify catego-
ries of objects (such as animals) in photographs even better than people can.
● In the language area, Morten Christiansen of Cornell University, with an inter-
national team of colleagues, has developed neural network software that simu-
lates how children extract linguistic rules from adult conversation.
● More than 80 conversational programs (chatterbots) now operate 24 h a day
online, and at least 20 of them are serious AI programming projects. Several
have basic learning capabilities, and several are tied to large, growing databases
of information (such as Wikipedia).
● Ted Berger and colleagues at the University of Southern California have developed
electronic chips that can successfully interact with neurons in real time and that
may soon be able to emulate human memory functions.
● Craig Henriquez and Miguel Nicolelis of Duke University have shown that macaque
monkeys can learn to control mechanical arms and hands based on signals their
brains are sending to implanted electrodes. John Donoghue and colleagues at Brown
University have developed an electronic sensor which, when placed near the motor
cortex area of the human brain, allows quadriplegics to open and close a prosthetic
hand by thinking about those actions. Clinical trials and commercial applications are
already underway.
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● In 1980 Harold Cohen of the University of California, San Diego introduced a
computer program that could draw, and hundreds of programs are now able to
compose original music in the style of any famous composer, to produce original
works of art that somtimes impress art critics, to improvise on musical instru-
ments as well as the legendary Charlie Parker, and even to produce artistic works
with their own distinctive styles.
● John Dylan Haynes of the Max Planck Institute, with colleagues at University
College London and Oxford University, recently used computer-assisted brain
scanning technology to predict simple human actions with 70% accuracy.
● Hod Lipson of Cornell University has recently demonstrated a robot that can make
completely functional copies of itself (as long as appropriate parts are near at
hand).
● Hiroshi Ishiguro of Osaka University has created androids that mimic human
facial expressions and upper-body movements so closely that they have fooled
people in short tests into thinking they are people.
● Alan Schultz of the Navy Center for Applied Research in Artificial Intelligence
has developed animated, mobile robots that might soon be assisting astronauts
and health care workers.
● Brian Scassellati and his colleagues at Yale University claim to have constructed
a robot that has learned to recognize itself in a mirror – a feat sometimes said to
be indicative of “self-awareness” and virtually never achieved in the animal
kingdom, other than by humans, chimpanzees, and possibly elephants.
● Cynthia Breazeal and her colleagues at MIT’s Artificial Intelligence Lab have
created robots that can distinguish different emotions from a person’s tone of
voice, and Shiva Sundaram of the University of Southern California has
developed programs that can successfully mimic human laughter and other
forms of expressive human sound.
● Entrepreneur John Koza, who is affiliated with Stanford University, has created a
self-programming network of 1,000 PCs that is able to improve human inventions –
and that even earned a patent for a system it devised for making factories more
efficient.
● Honda’s Asimo robot, now in commercial production, can walk, run, climb
stairs, recognize people’s faces and voices, and perform complex tasks in
response to human instructions.
● Although the DARPA-sponsored contest just 1 year before had been a disaster,
in 2005 five autonomous mobile robots successfully navigated a 132-mile
course in the Nevada desert without human assistance.
● As of this writing (late 2007), IBM’s Blue Gene/P computer, located at the US
Department of Energy’s Argonne National Laboratory in Illinois, can perform
more than 1,000 trillion calculations per second, just one order of magnitude
short of what some believe is the processing speed of the human brain. The
Japanese government has already funded the construction of a machine that
should cross the human threshold (10 petaflops) by March 2011.
● In 1996, IBM’s RS/6000 SP (“Deep Blue”) computer came close to defeating
world champion Garry Kasparov in a game of chess. On May 11, 1997, an
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improved version of the machine defeated Kasparov in a six-game match –
Kasparov’s first professional loss. The processing speed of the winner? A paltry
200 million chess positions per second. In 2006, an enhanced version of a com-
mercially available chess program easily defeated the current world champion,
Vladimir Kramnik.
● In 2006, Klaus Schulten and colleagues at the University of Illinois, Urbana,
successfully simulated the functioning of all one million atoms of a virus for 50
billionths of a second.
● “Awakened” in 2005, Blue Brain, IBM’s latest variant on the Blue Gene/L system,
was built specifically to model the functions of the human neocortex, the large
part of brain largely responsible for higher-level thinking.
● David Dagon of the Georgia Institute of Technology estimates that 11% of the
more than 650 million computers that are currently connected to the Internet are
infected by botnets, stealthy programs that can work collectively and amplify the
effects of other malicious software.
Self-programming? Creativity? Sophisticated pattern recognition? Brain
simulation? Self-replication? Extremely fast processing? The growth and conver-
gence of subsets of these technologies will inevitably lead to the emergence of a
Massive Computational Entity, with all of the uncertainty that that entails. Meanwhile,
researchers, engineers, and entrepreneurs are after comparatively smaller game:
intelligent phone-answering systems and search algorithms, robot helpers and
companions, and methods for repairing injured or defective human brains.
Philosophical and Methodological Issues
This volume, which has been a decade in the making, complements other recent
volumes on the Turing Test. Stuart Shieber’s edited volume, The Turing Test:
Verbal Behavior as the Hallmark of Intelligence (MIT Press, 2004) includes a
number of important historical papers, along with several papers of Turing’s. James
Moor’s edited volume, The Turing Test: The Elusive Standard of Artificial
Intelligence (Springer, 2006), covers the basics in an excellent volume for students,
taking a somewhat skeptical view. And Jack Copeland’s The Essential Turing
(Oxford, 2004) brings together 17 of Turing’s most provocative and interesting
papers, including six on artificial intelligence.
The present volume seeks to cover a broad range of issues related to the Turing
Test, focusing especially on the many new methodological issues that have challenged
programmers as they have attempted to design and create intelligent conversational
programs. Part I includes an introduction to the first large-scale implementation of the
Turing Test as a contest – an updated version of an essay I originally published in AI
Magazine in 1992. In the next chapter, Andrew Hodges, noted Turing historian and
author of Alan Turing: The Enigma, provides an introduction to Turing’s life and
works. Chapter 3 is a unique reprinting of Turing’s 1950 paper, “Computing
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Machinery and Intelligence,” with Talmudic-style running commentaries by Kenneth
Ford, Clark Glymour, Pat Hayes, Stevan Harnad, and Ayse Pinar. This section
concludes with a brief commentary on Turing’s paper by John Lucas.
Part II includes seven chapters reviewing the philosophical issues that still
surround Turing’s 1950 proposal: Robert E. Horn has reduced the relatively vast
literature on this topic to a series of diagrams and charts containing more than 800
arguments and counterarguments. Turing critic Selmer Bringsjord pretends that the
Turing Test is valid, then attempts to show why it isn’t. Chapters by Noam Chomsky
and Paul M. Churchland, while admiring of Turing’s proposal, argue that it is truly
more modest than many think. In Chapter 9, Jack Copeland and Diane Proudfoot
analyze a revised version of the test that Turing himself proposed in 1952, this one
quite similar to the structure of the Loebner Prize Competition in Artificial
Intelligence that was launched in 1991 (see Chapters 1 and 12). They also present
and dismiss six criticisms of Turing’s proposal.
In Chapter 10, University of California Berkeley philosopher John R. Searle
criticizes both behaviorism (Turing’s proposal can be considered behavioristic) and
strong AI, arguing that mental states cannot properly be inferred from behavior.
This section concludes with a chapter by Jean Lassègue, offering an optimistic
reinterpretation of Turing’s 1950 article.
Part III, which is the heart of this volume, includes 15 chapters discussing various
methodological issues. First, Loebner Prize sponsor Hugh G. Loebner shares his
thoughts on how to conduct a proper Turing Test, having already observed 14 such
contests when he wrote this article. Several of the chapters (e.g., Chapter 13 by Richard
S. Wallace, Chapter 20 by Jason L. Hutchens, and Chapter 22 by Kevin L. Copple)
describe the inner workings of actual programs that have participated in various
Loebner contests. In Chapter 14, Bruce Edmonds argues that for a program to pass the
test, it must be embedded into conventional society for an extended period of time.
In Chapter 15, Mark Humphrys talks about an online chatterbot he created, and
in the following chapter Douglas B. Lenat raises intriguing questions about how
imperfect a program must be in order to pass the Turing Test. In Chapter 17, Chris
McKinstry discusses the beginnings of an ambitious project – called “Mindpixel”
– that might have given a computer program extensive knowledge through interaction
with a large population of people over the Internet. Unfortunately, this project came
to an abrupt halt recently with McKinstry’s death.
In Chapter 18, Stuart Watt uses an innovative format to discuss the Turing Test
as a platform for thinking about human thinking. In Chapter 20, Robby Garner
takes issue with the design of the Loebner Prize Competition. In Chapter 20,
Thomas E. Whalen describes a strategy for passing the Turing Test based on its
behavioristic assumptions. In Chapter 23, Giuseppe Longo speculates about the
challenges inherent in modeling continuous systems using discrete-state systems
such as computers.
In Chapter 24, Michael L. Mauldin of Carnegie Mellon University – also a
former entrant in the Loebner Prize Competition – discusses strategies for designing
programs that might pass the test. In the following chapter, Luke Pellen talks about
the challenge of creating a program that is truly intelligent, rather than one that
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simply responds in clever ways to keywords. This section closes with a somewhat
lighthearted chapter by Eugene Demchenko and Vladimir Veselov speculating
about ways to pass the Turing Test by taking advantage of the limitations and
personal styles of the contest judges.
Part IV of this volume includes three rather unique contributions that remind us
how much is at stake over Turing’s challenge. Chapter 27, by Ray Kurzweil and
Mitchell Kapor, documents in detail an actual cash wager between these two individuals,
regarding whether a program will pass the test by the year 2029. Chapter 28, by noted
science fiction writer Charles Platt (The Silicon Man), describes the “Gnirut Test”,
conducted by intelligent machines in the year 2030 to determine, once and for all,
whether “the human brain is capable of achieving machine intelligence”. The volume
concludes with an article by Hugo de Garis and Sam Halioris, wondering about the
dangers of creating machine-based, superhuman intellects.
Most, but not all, of the contributors to this volume believe as I do that extremely
intelligent computers, with cognitive powers that far surpass our own, will appear fairly
soon – probably within the next 25 years. Even if that time frame is wrong, I am certain
that they will appear eventually. Either way, I hope that the Massive Computational
Entities that emerge will at some point devote a few cycles of computer time to ponder
the contents of this book and then, in some fashion or other, to smile.
San Diego, California Robert Epstein, Ph.D.
September 2007
xviii Introduction
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Contents
Foreword ......................................................................................................... vii
Acknowledgments .......................................................................................... ix
Introduction .................................................................................................... xi
About the Editors ........................................................................................... xxiii
Part I Setting the Stage
Chapter 1 The Quest for the Thinking Computer ................................... 3
Robert Epstein
Chapter 2 Alan Turing and the Turing Test ............................................. 13
Andrew Hodges
Chapter 3 Computing Machinery and Intelligence ................................. 23
Alan M. Turing (Annotated by Kenneth Ford,
Clark Glymour, Pat Hayes, Stevan Harnad,
and Ayse Pinar Saygin)
Chapter 4 Commentary on Turing’s “Computing Machinery
and Intelligence” ....................................................................... 67
John Lucas
Part II The Ongoing Philosophical Debate
Chapter 5 The Turing Test: Mapping and Navigating the Debate ......... 73
Robert E. Horn
Chapter 6 If I Were Judge .......................................................................... 89
Selmer Bringsjord
Chapter 7 Turing on the “Imitation Game” ............................................. 103
Noam Chomsky
xix
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Chapter 8 On the Nature of Intelligence: Turing, Church,
Von Neumann, and the Brain ................................................ 107
Paul M. Churchland
Chapter 9 Turing’s Test: A Philosophical and Historical Guide .......... 119
Jack Copeland and Diane Proudfoot
Chapter 10 The Turing Test: 55 Years Later ........................................... 139
John R. Searle
Chapter 11 Doing Justice to the Imitation Game: A Farewell
to Formalism ........................................................................... 151
Jean Lassègue
Part III The New Methodological Debates
Chapter 12 How to Hold a Turing Test Contest ....................................... 173
Hugh Loebner
Chapter 13 The Anatomy of A.L.I.C.E. .................................................... 181
Richard S. Wallace
Chapter 14 The Social Embedding of Intelligence:
Towards Producing a Machine that Could Pass
the Turing Test ........................................................................ 211
Bruce Edmonds
Chapter 15 How My Program Passed the Turing Test ............................ 237
Mark Humphrys
Chapter 16 Building a Machine Smart Enough to Pass
the Turing Test: Could We, Should We, Will We? ............... 261
Douglas B. Lenat
Chapter 17 Mind as Space: Toward the Automatic Discovery
of a Universal Human Semantic-affective Hyperspace –
A Possible Subcognitive Foundation of a Computer
Program Able to Pass the Turing Test .................................. 283
Chris McKinstry
Chapter 18 Can People Think? Or Machines? A Unifi ed Protocol
for Turing Testing ................................................................... 301
Stuart Watt
xx Contents
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Chapter 19 The Turing Hub as a Standard
for Turing Test Interfaces ...................................................... 319
Robby Garner
Chapter 20 Conversation Simulation and Sensible Surprises ................ 325
Jason L. Hutchens
Chapter 21 A Computational Behaviorist Takes Turing’s Test .............. 343
Thomas E. Whalen
Chapter 22 Bringing AI to Life: Putting Today’s Tools
and Resources to Work .......................................................... 359
Kevin L. Copple
Chapter 23 Laplace, Turing and the “Imitation Game”
Impossible Geometry: Randomness, Determinism
and Programs in Turing’s Test .............................................. 377
Giuseppe Longo
Chapter 24 Going Under Cover: Passing as Human;
Artifi cial Interest: A Step on the Road to AI ....................... 413
Michael L. Mauldin
Chapter 25 How Not to Imitate a Human Being: An Essay
on Passing the Turing Test ..................................................... 431
Luke Pellen
Chapter 26 Who Fools Whom? The Great Mystifi cation,
or Methodological Issues on Making Fools
of Human Beings ..................................................................... 447
Eugene Demchenko and Vladimir Veselov
Part IV Afterthoughts on Thinking Machines
Chapter 27 A Wager on the Turing Test ................................................... 463
Ray Kurzweil and Mitchell Kapor
Chapter 28 The Gnirut Test ....................................................................... 479
Charles Platt
Chapter 29 The Artilect Debate: Why Build Superhuman
Machines, and Why Not? ....................................................... 487
Hugo de Garis and Sam Haloris
Name Index ..................................................................................................... 511
Contents xxi
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