or the exercise of thought, by machines such as computers.
Philosophically, the main AI question is "Can there be such?" or, as
Alan Turing put it, "Can a machine think?" What makes this a
philosophical and not just a scientific and technical question is the
scientific recalcitrance of the concept of intelligence or thought and
its moral, religious, and legal significance. In European and other
traditions, moral and legal standing depend not just on what is
outwardly done but also on inward states of mind. Only rational
individuals have standing as moral agents and status as moral patients
subject to certain harms, such as being betrayed. Only sentient
individuals are subject to certain other harms, such as pain and
suffering. Since computers give every outward appearance of performing
intellectual tasks, the question arises: "Are they really thinking?"
And if they are really thinking, are they not, then, owed similar
rights to rational human beings? Many fictional explorations of AI in
literature and film explore these very questions.
A complication arises if humans are animals and if animals are
themselves machines, as scientific biology supposes. Still, "we wish
to exclude from the machines" in question "men born in the usual
manner" (Alan Turing), or even in unusual manners such as in vitro
fertilization or ectogenesis. And if nonhuman animals think, we wish
to exclude them from the machines, too. More particularly, the AI
thesis should be understood to hold that thought, or intelligence, can
be produced by artificial means; made, not grown. For brevity's sake,
we will take "machine" to denote just the artificial ones. Since the
present interest in thinking machines has been aroused by a particular
kind of machine, an electronic computer or digital computer, present
controversies regarding claims of artificial intelligence center on
these.
Accordingly, the scientific discipline and engineering enterprise of
AI has been characterized as "the attempt to discover and implement
the computational means" to make machines "behave in ways that would
be called intelligent if a human were so behaving" (John McCarthy), or
to make them do things that "would require intelligence if done by
men" (Marvin Minsky). These standard formulations duck the question of
whether deeds which indicate intelligence when done by humans truly
indicate it when done by machines: that's the philosophical question.
So-called weak AI grants the fact (or prospect) of intelligent-acting
machines; strong AI says these actions can be real intelligence.
Strong AI says some artificial computation is thought.
Computationalism says that all thought is computation. Though many
strong AI advocates are computationalists, these are logically
independent claims: some artificial computation being thought is
consistent with some thought not being computation, contra
computationalism. All thought being computation is consistent with
some computation (and perhaps all artificial computation) not being
thought.
1. Thinkers, and Thoughts
a. What Things Think?
Intelligence might be styled the capacity to think extensively and
well. Thinking well centrally involves apt conception, true
representation, and correct reasoning. Quickness is generally counted
a further cognitive virtue. The extent or breadth of a thing's
thinking concerns the variety of content it can conceive, and the
variety of thought processes it deploys. Roughly, the more extensively
a thing thinks, the higher the "level" (as is said) of its thinking.
Consequently, we need to distinguish two different AI questions:
1. Can machines think at all?
2. Can machine intelligence approach or surpass the human level?
In Computer Science, work termed "AI" has traditionally focused on the
high-level problem; on imparting high-level abilities to "use
language, form abstractions and concepts" and to "solve kinds of
problems now reserved for humans" (McCarthy et al. 1955); abilities to
play intellectual games such as checkers (Samuel 1954) and chess (Deep
Blue); to prove mathematical theorems (GPS); to apply expert knowledge
to diagnose bacterial infections (MYCIN); and so forth. More recently
there has arisen a humbler seeming conception – "behavior-based" or
"nouvelle" AI – according to which seeking to endow embodied machines,
or robots, with so much as "insect level intelligence" (Brooks 1991)
counts as AI research. Where traditional human-level AI successes
impart isolated high-level abilities to function in restricted
domains, or "microworlds," behavior-based AI seeks to impart
coordinated low-level abilities to function in unrestricted real-world
domains.
Still, to the extent that what is called "thinking" in us is
paradigmatic for what thought is, the question of human level
intelligence may arise anew at the foundations. Do insects think at
all? And if insects … what of "bacteria level intelligence" (Brooks
1991a)? Even "water flowing downhill," it seems, "tries to get to the
bottom of the hill by ingeniously seeking the line of least
resistance" (Searle 1989). Don't we have to draw the line somewhere?
Perhaps seeming intelligence – to really be intelligence – has to come
up to some threshold level.
b. Thought: Intelligence, Sentience, and Values
Much as intentionality ("aboutness" or representation) is central to
intelligence, felt qualities (so-called "qualia") are crucial to
sentience. Here, drawing on Aristotle, medieval thinkers distinguished
between the "passive intellect" wherein the soul is affected, and the
"active intellect" wherein the soul forms conceptions, draws
inferences, makes judgments, and otherwise acts. Orthodoxy identified
the soul proper (the immortal part) with the active rational element.
Unfortunately, disagreement over how these two
(qualitative-experiential and cognitive-intentional) factors relate is
as rife as disagreement over what things think; and these
disagreements are connected. Those who dismiss the seeming
intelligence of computers because computers lack feelings seem to hold
qualia to be necessary for intentionality. Those like Descartes, who
dismiss the seeming sentience of nonhuman animals because animals
don't think, apparently hold intentionality to be necessary for
qualia. Others deny one or both necessities, maintaining either the
possibility of cognition absent qualia (as Christian orthodoxy,
perhaps, would have the thought-processes of God, angels, and the
saints in heaven to be), or maintaining the possibility of feeling
absent cognition (as Aristotle grants the lower animals).
2. The Turing Test
While we don't know what thought or intelligence is, essentially, and
while we're very far from agreed on what things do and don't have it,
almost everyone agrees that humans think, and agrees with Descartes
that our intelligence is amply manifest in our speech. Along these
lines, Alan Turing suggested that if computers showed human level
conversational abilities we should, by that, be amply assured of their
intelligence. Turing proposed a specific conversational test for
human-level intelligence, the "Turing test" it has come to be called.
Turing himself characterizes this test in terms of an "imitation game"
(Turing 1950, p. 433) whose original version "is played by three
people, a man (A), a woman (B), and an interrogator (C) who may be of
either sex. The interrogator stays in a room apart from the other two.
… The object of the game for the interrogator is to determine which of
the other two is the man and which is the woman. The interrogator is
allowed to put questions to A and B [by teletype to avoid visual and
auditory clues]. … . It is A's object in the game to try and cause C
to make the wrong identification. … The object of the game for the
third player (B) is to help the interrogator." Turing continues, "We
may now ask the question, `What will happen when a machine takes the
part of A in this game?' Will the interrogator decide wrongly as often
when the game is being played like this as he does when the game is
played between a man and a woman? These questions replace our
original, `Can machines think?'" (Turing 1950) The test setup may be
depicted this way:
(C) Questioner:
aims to discover if A or B is the Computer Questions
<———
———->
Answers (A) Computer: aims to fool the questioner.
(B) Human: aims to help the questioner
This test may serve, as Turing notes, to test not just for shallow
verbal dexterity, but for background knowledge and underlying
reasoning ability as well, since interrogators may ask any question or
pose any verbal challenge they choose. Regarding this test Turing
famously predicted that "in about fifty years' time [by the year 2000]
it will be possible to program computers … to make them play the
imitation game so well that an average interrogator will have no more
than 70 per cent. chance of making the correct identification after
five minutes of questioning" (Turing 1950); a prediction that has
famously failed. As of the year 2000, machines at the Loebner Prize
competition played the game so ill that the average interrogator had
100 percent chance of making the correct identification after five
minutes of questioning (see Moor 2001).
It is important to recognize that Turing proposed his test as a
qualifying test for human-level intelligence, not as a disqualifying
test for intelligence per se (as Descartes had proposed); nor would it
seem suitably disqualifying unless we are prepared (as Descartes was)
to deny that any nonhuman animals possess any intelligence whatsoever.
Even at the human level the test would seem not to be
straightforwardly disqualifying: machines as smart as we (or even
smarter) might still be unable to mimic us well enough to pass. So,
from the failure of machines to pass this test, we can infer neither
their complete lack of intelligence nor, that their thought is not up
to the human level. Nevertheless, the manners of current machine
failings clearly bespeak deficits of wisdom and wit, not just an
inhuman style. Still, defenders of the Turing test claim we would have
ample reason to deem them intelligent – as intelligent as we are – if
they could pass this test.
3. Appearances of AI
The extent to which machines seem intelligent depends first, on
whether the work they do is intellectual (for example, calculating
sums) or manual (for example, cutting steaks): herein, an electronic
calculator is a better candidate than an electric carving knife. A
second factor is the extent to which the device is self-actuated
(self-propelled, activated, and controlled), or "autonomous": herein,
an electronic calculator is a better candidate than an abacus.
Computers are better candidates than calculators on both headings.
Where traditional AI looks to increase computer intelligence quotients
(so to speak), nouvelle AI focuses on enabling robot autonomy.
a. Computers
i. Prehistory
In the beginning, tools (for example, axes) were extensions of human
physical powers; at first powered by human muscle; then by
domesticated beasts and in situ forces of nature, such as water and
wind. The steam engine put fire in their bellies; machines became
self-propelled, endowed with vestiges of self-control (as by Watt's
1788 centrifugal governor); and the rest is modern history. Meanwhile,
automation of intellectual labor had begun. Blaise Pascal developed an
early adding/subtracting machine, the Pascaline (circa 1642).
Gottfried Leibniz added multiplication and division functions with his
Stepped Reckoner (circa 1671). The first programmable device, however,
plied fabric not numerals. The Jacquard loom developed (circa 1801) by
Joseph-Marie Jacquard used a system of punched cards to automate the
weaving of programmable patterns and designs: in one striking
demonstration, the loom was programmed to weave a silk tapestry
portrait of Jacquard himself.
In designs for his Analytical Engine mathematician/inventor Charles
Babbage recognized (circa 1836) that the punched cards could control
operations on symbols as readily as on silk; the cards could encode
numerals and other symbolic data and, more importantly, instructions,
including conditionally branching instructions, for numeric and other
symbolic operations. Augusta Ada Lovelace (Babbage's software
engineer) grasped the import of these innovations: "The bounds of
arithmetic" she writes, "were … outstepped the moment the idea of
applying the [instruction] cards had occurred" thus "enabling
mechanism to combine together with general symbols, in successions of
unlimited variety and extent" (Lovelace 1842). "Babbage," Turing
notes, "had all the essential ideas" (Turing 1950). Babbage's Engine –
had he constructed it in all its steam powered cog-wheel driven glory
– would have been a programmable all-purpose device, the first digital
computer.
ii. Theoretical Interlude: Turing Machines
Before automated computation became feasible with the advent of
electronic computers in the mid twentieth century, Alan Turing laid
the theoretical foundations of Computer Science by formulating with
precision the link Lady Lovelace foresaw "between the operations of
matter and the abstract mental processes of the most abstract branch
of mathematical sciences" (Lovelace 1942). Turing (1936-7) describes a
type of machine (since known as a "Turing machine") which would be
capable of computing any possible algorithm, or performing any "rote"
operation. Since Alonzo Church (1936) – using recursive functions and
Lambda-definable functions – had identified the very same set of
functions as "rote" or algorithmic as those calculable by Turing
machines, this important and widely accepted identification is known
as the "Church-Turing Thesis" (see, Turing 1936-7: Appendix). The
machines Turing described are
only capable of a finite number of conditions …
"m-configurations." The machine is supplied with a "tape" (the
analogue of paper) running through it, and divided into sections
(called "squares") each capable of bearing a "symbol." At any moment
there is just one square … which is "in the machine." … The "scanned
symbol" is the only one of which the machine is, so to speak,
"directly aware." However, by altering its m-configuration the machine
can effectively remember some of the symbols which it has "seen"
(scanned) previously. The possible behavior of the machine at any
moment is determined by the m-configuration … and the scanned symbol
…. This pair … called the "configuration" … determines the possible
behaviour of the machine. In some of the configurations in which the
square is blank … the machine writes down a new symbol on the scanned
square: in other configurations it erases the scanned symbol. The
machine may also change the square which is being scanned, but only by
shifting it one place to right or left. In addition to any of these
operations the m-configuration may be changed. (Turing 1936-7)
Turing goes on to show how such machines can encode actionable
descriptions of other such machines. As a result, "It is possible to
invent a single machine which can be used to compute any computable
sequence" (Turing 1936-7). Today's digital computers are (and
Babbage's Engine would have been) physical instantiations of this
"universal computing machine" that Turing described abstractly.
Theoretically, this means everything that can be done algorithmically
or "by rote" at all "can all be done with one computer suitably
programmed for each case"; "considerations of speed apart, it is
unnecessary to design various new machines to do various computing
processes" (Turing 1950). Theoretically, regardless of their hardware
or architecture (see below), "all digital computers are in a sense
equivalent": equivalent in speed-apart capacities to the "universal
computing machine" Turing described.
iii. From Theory to Practice
In practice, where speed is not apart, hardware and architecture are
crucial: the faster the operations the greater the computational
power. Just as improvement on the hardware side from cogwheels to
circuitry was needed to make digital computers practical at all,
improvements in computer performance have been largely predicated on
the continuous development of faster, more and more powerful,
machines. Electromechanical relays gave way to vacuum tubes, tubes to
transistors, and transistors to more and more integrated circuits,
yielding vastly increased operation speeds. Meanwhile, memory has
grown faster and cheaper.
Architecturally, all but the earliest and some later experimental
machines share a stored program serial design often called "von
Neumann architecture" (based on John von Neumann's role in the design
of EDVAC, the first computer to store programs along with data in
working memory). The architecture is serial in that operations are
performed one at a time by a central processing unit (CPU) endowed
with a rich repertoire of basic operations: even so-called "reduced
instruction set" (RISC) chips feature basic operation sets far richer
than the minimal few Turing proved theoretically sufficient. Parallel
architectures, by contrast, distribute computational operations among
two or more units (typically many more) capable of acting
simultaneously, each having (perhaps) drastically reduced basic
operational capacities.
In 1965, Gordon Moore (co-founder of Intel) observed that the density
of transistors on integrated circuits had doubled every year since
their invention in 1959: "Moore's law" predicts the continuation of
similar exponential rates of growth in chip density (in particular),
and computational power (by extension), for the foreseeable future.
Progress on the software programming side – while essential and by no
means negligible – has seemed halting by comparison. The road from
power to performance is proving rockier than Turing anticipated.
Nevertheless, machines nowadays do behave in many ways that would be
called intelligent in humans and other animals. Presently, machines do
many things formerly only done by animals and thought to evidence some
level of intelligence in these animals, for example, seeking,
detecting, and tracking things; seeming evidence of basic-level AI.
Presently, machines also do things formerly only done by humans and
thought to evidence high-level intelligence in us; for example, making
mathematical discoveries, playing games, planning, and learning;
seeming evidence of human-level AI.
b. "Existence Proofs" of AI
i. Low-Level Appearances and Attributions
The doings of many machines – some much simpler than computers –
inspire us to describe them in mental terms commonly reserved for
animals. Some missiles, for instance, seek heat, or so we say. We call
them "heat seeking missiles" and nobody takes it amiss. Room
thermostats monitor room temperatures and try to keep them within set
ranges by turning the furnace on and off; and if you hold dry ice next
to its sensor, it will take the room temperature to be colder than it
is, and mistakenly turn on the furnace (see McCarthy 1979). Seeking,
monitoring, trying, and taking things to be the case seem to be mental
processes or conditions, marked by their intentionality. Just as
humans have low-level mental qualities – such as seeking and detecting
things – in common with the lower animals, so too do computers seem to
share such low-level qualities with simpler devices. Our working
characterizations of computers are rife with low-level mental
attributions: we say they detect key presses, try to initialize their
printers, search for available devices, and so forth. Even those who
would deny the proposition "machines think" when it is explicitly put
to them, are moved unavoidably in their practical dealings to
characterize the doings of computers in mental terms, and they would
be hard put to do otherwise. In this sense, Turing's prediction that
"at the end of the century the use of words and general educated
opinion will have altered so much that one will be able to speak of
machines thinking without expecting to be contradicted" (Turing 1950)
has been as mightily fulfilled as his prediction of a modicum of
machine success at playing the Imitation Game has been confuted. The
Turing test and AI as classically conceived, however, are more
concerned with high-level appearances such as the following.
ii. Theorem Proving and Mathematical Discovery
Theorem proving and mathematical exploration being their home turf,
computers have displayed not only human-level but, in certain
respects, superhuman abilities here. For speed and accuracy of
mathematical calculation, no human can match the speed and accuracy of
a computer. As for high level mathematical performances, such as
theorem proving and mathematical discovery, a beginning was made by A.
Newell, J.C. Shaw, and H. Simon's (1957) "Logic Theorist" program
which proved 38 of the first 51 theorems of B. Russell and A.N.
Whitehead's Principia Mathematica. Newell and Simon's "General Problem
Solver" (GPS) extended similar automated theorem proving techniques
outside the narrow confines of pure logic and mathematics. Today such
techniques enjoy widespread application in expert systems like MYCIN,
in logic tutorial software, and in computer languages such as PROLOG.
There are even original mathematical discoveries owing to computers.
Notably, K. Appel, W. Haken, and J. Koch (1977a, 1977b), and computer,
proved that every planar map is four colorable – an important
mathematical conjecture that had resisted unassisted human proof for
over a hundred years. Certain computer generated parts of this proof
are too complex to be directly verified (without computer assistance)
by human mathematicians.
Whereas attempts to apply general reasoning to unlimited domains are
hampered by explosive inferential complexity and computers' lack of
common sense, expert systems deal with these problems by restricting
their domains of application (in effect, to microworlds), and crafting
domain-specific inference rules for these limited domains. MYCIN for
instance, applies rules culled from interviews with expert human
diagnosticians to descriptions of patients' presenting symptoms to
diagnose blood-borne bacterial infections. MYCIN displays diagnostic
skills approaching the expert human level, albeit strictly limited to
this specific domain. Fuzzy logic is a formalism for representing
imprecise notions such as most and baldand enabling inferences based
on such facts as that a bald person mostly lacks hair.
iii. Game Playing
Game playing engaged the interest of AI researchers almost from the
start. Samuel's (1959) checkers (or "draughts") program was notable
for incorporating mechanisms enabling it to learn from experience well
enough to eventually to outplay Samuel himself. Additionally, in
setting one version of the program to play against a slightly altered
version, carrying over the settings of the stronger player to the next
generation, and repeating the process – enabling stronger and stronger
versions to evolve – Samuel pioneered the use of what have come to be
called "genetic algorithms" and "evolutionary" computing. Chess has
also inspired notable efforts culminating, in 1997, in the famous
victory of Deep Blue over defending world champion Gary Kasparov in a
widely publicized series of matches (recounted in Hsu 2002). Though
some in AI disparaged Deep Blue's reliance on "brute force"
application of computer power rather than improved search guiding
heuristics, we may still add chess to checkers (where the reigning
"human-machine machine champion" since 1994 has been CHINOOK, the
machine), and backgammon, as games that computers now play at or above
the highest human levels. Computers also play fair to middling poker,
bridge, and Go – though not at the highest human level. Additionally,
intelligent agents or "softbots" are elements or participants in a
variety of electronic games.
iv. Planning
Planning, in large measure, is what puts the intellect in intellectual
games like chess and checkers. To automate this broader intellectual
ability was the intent of Newell and Simon's General Problem Solver
(GPS) program. GPS was able to solve puzzles like the cannibals
missionaries problem (how to transport three missionaries and three
cannibals across a river in a canoe for two without the missionaries
becoming outnumbered on either shore) by "setting up subgoals whose
attainment leads to the attainment of the [final] goal" (Newell &
Simon 1963: 284). By these methods GPS would "generate a tree of
subgoals" (Newell & Simon 1963: 286) and seek a path from initial
state (for example, all on the near bank) to final goal (all on the
far bank) by heuristically guided search along a branching "tree" of
available actions (for example, two cannibals cross, two missionaries
cross, one of each cross, one of either cross, in either direction)
until it finds such a path (for example, two cannibals cross, one
returns, two cannibals cross, one returns, two missionaries cross, …
), or else finds that there is none. Since the number of branches
increases exponentially as a function of the number of options
available at each step, where paths have many steps with many options
available at each choice point, as in the real world, combinatorial
explosion ensues and an exhaustive "brute force" search becomes
computationally intractable; hence, heuristics (fallible rules of
thumb) for identifying and "pruning" the most unpromising branches in
order to devote increased attention to promising ones are needed. The
widely deployed STRIPS formalism first developed at Stanford for
Shakey the robot in the late sixties (see Nilsson 1984) represents
actions as operations on states, each operation having preconditions
(represented by state descriptions) and effects (represented by state
descriptions): for example, the go(there) operation might have the
preconditions at(here) & path(here,there) and the effect at(there). AI
planning techniques are finding increasing application and even
becoming indispensable in a multitude of complex planning and
scheduling tasks including airport arrivals, departures, and gate
assignments; store inventory management; automated satellite
operations; military logistics; and many others.
v. Robots
Robots based on sense-model-plan-act (SMPA) approach pioneered by
Shakey, however, have been slow to appear. Despite operating in a
simplified, custom-made experimental environment or microworld and
reliance on the most powerful available offboard computers, Shakey
"operated excruciatingly slowly" (Brooks 1991b), as have other SMPA
based robots. An ironic revelation of robotics research is that
abilities such as object recognition and obstacle avoidance that
humans share with "lower" animals often prove more difficult to
implement than distinctively human "high level" mathematical and
inferential abilities that come more naturally (so to speak) to
computers. Rodney Brooks' alternative behavior-based approach has had
success imparting low-level behavioral aptitudes outside of custom
designed microworlds, but it is hard to see how such an approach could
ever "scale up" to enable high-level intelligent action (see
Behaviorism: Objections & Discussion: Methodological Complaints).
Perhaps hybrid systems can overcome the limitations of both
approaches. On the practical front, progress is being made: NASA's
Mars exploration rovers Spirit and Opportunity, for instance, featured
autonomous navigation abilities. If space is the "final frontier" the
final frontiersmen are apt to be robots. Meanwhile, Earth robots seem
bound to become smarter and more pervasive.
vi. Knowledge Representation (KR)
Knowledge representation embodies concepts and information in
computationally accessible and inferentially tractable forms. Besides
the STRIPS formalism mentioned above, other important knowledge
representation formalisms include AI programming languages such as
PROLOG, and LISP; data structures such as frames, scripts, and
ontologies; and neural networks (see below). The "frame problem" is
the problem of reliably updating dynamic systems' parameters in
response to changes in other parameters so as to capture commonsense
generalizations: that the colors of things remain unchanged by their
being moved, that their positions remain unchanged by their being
painted, and so forth. More adequate representation of commonsense
knowledge is widely thought to be a major hurdle to development of the
sort of interconnected planning and thought processes typical of
high-level human or "general" intelligence. The CYC project (Lenat et
al. 1986) at Cycorp and MIT's Open Mind project are ongoing attempts
to develop "ontologies" representing commonsense knowledge in computer
usable forms.
vii. Machine Learning (ML)
Learning – performance improvement, concept formation, or information
acquisition due to experience – underwrites human common sense, and
one may doubt whether any preformed ontology could ever impart common
sense in full human measure. Besides, whatever the other intellectual
abilities a thing might manifest (or seem to), at however high a
level, without learning capacity, it would still seem to be sadly
lacking something crucial to human-level intelligence and perhaps
intelligence of any sort. The possibility of machine learning is
implicit in computer programs' abilities to self-modify and various
means of realizing that ability continue to be developed. Types of
machine learning techniques include decision tree learning, ensemble
learning, current-best-hypothesis learning, explanation-based
learning, Inductive Logic Programming (ILP), Bayesian statistical
learning, instance-based learning, reinforcement learning, and neural
networks. Such techniques have found a number of applications from
game programs whose play improves with experience to data mining
(discovering patterns and regularities in bodies of information).
viii. Neural Networks and Connectionism
Neural or connectionist networks – composed of simple processors or
nodes acting in parallel – are designed to more closely approximate
the architecture of the brain than traditional serial
symbol-processing systems. Presumed brain-computations would seem to
be performed in parallel by the activities of myriad brain cells or
neurons. Much as their parallel processing is spread over various,
perhaps widely distributed, nodes, the representation of data in such
connectionist systems is similarly distributed and sub-symbolic (not
being couched in formalisms such as traditional systems' machine codes
and ASCII). Adept at pattern recognition, such networks seem notably
capable of forming concepts on their own based on feedback from
experience and exhibit several other humanoid cognitive
characteristics besides. Whether neural networks are capable of
implementing high-level symbol processing such as that involved in the
generation and comprehension of natural language has been hotly
disputed. Critics (for example, Fodor and Pylyshyn 1988) argue that
neural networks are incapable, in principle, of implementing syntactic
structures adequate for compositional semantics – wherein the meaning
of larger expressions (for example, sentences) are built up from the
meanings of constituents (for example, words) – such as those natural
language comprehension features. On the other hand, Fodor (1975) has
argued that symbol-processing systems are incapable of concept
acquisition: here the pattern recognition capabilities of networks
seem to be just the ticket. Here, as with robots, perhaps hybrid
systems can overcome the limitations of both the parallel distributed
and symbol-processing approaches.
ix. Natural Language Processing (NLP)
Natural language processing has proven more difficult than might have
been anticipated. Languages are symbol systems and (serial
architecture) computers are symbol crunching machines, each with its
own proprietary instruction set (machine code) into which it
translates or compiles instructions couched in high level programming
languages like LISP and C. One of the principle challenges posed by
natural languages is the proper assignment of meaning. High-level
computer languages express imperatives which the machine "understands"
procedurally by translation into its native (and similarly imperative)
machine code: their constructions are basically instructions. Natural
languages, on the other hand, have – perhaps principally – declarative
functions: their constructions include descriptions whose
understanding seems fundamentally to require rightly relating them to
their referents in the world. Furthermore, high level computer
language instructions have unique machine code compilations (for a
given machine), whereas, the same natural language constructions may
bear different meanings in different linguistic and extralinguistic
contexts. Contrast "the child is in the pen" and "the ink is in the
pen" where the first "pen" should be understood to mean a kind of
enclosure and the second "pen" a kind of writing implement.
Commonsense, in a word, is how we know this; but how would a machine
know, unless we could somehow endow machines with commonsense? In more
than a word it would require sophisticated and integrated syntactic,
morphological, semantic, pragmatic, and discourse processing. While
the holy grail of full natural language understanding remains a
distant dream, here as elsewhere in AI, piecemeal progress is being
made and finding application in grammar checkers; information
retrieval and information extraction systems; natural language
interfaces for games, search engines, and question-answering systems;
and even limited machine translation (MT).
c. On the Behavioral Evidence
Low level intelligent action is pervasive, from thermostats (to cite a
low tech. example) to voice recognition (for example, in cars,
cell-phones, and other appliances responsive to spoken verbal
commands) to fuzzy controllers and "neuro fuzzy" rice cookers.
Everywhere these days there are "smart" devices. High level
intelligent action, such as presently exists in computers, however, is
episodic, detached, and disintegral. Artifacts whose intelligent
doings would instance human-level comprehensiveness, attachment, and
integration – such as Lt. Commander Data (of Star Trek the Next
Generation) and HAL (of 2001 a Space Odyssey) – remain the stuff of
science fiction, and will almost certainly continue to remain so for
the foreseeable future. In particular, the challenge posed by the
Turing test remains unmet. Whether it ever will be met remains an open
question.
Beside this factual question stands a more theoretic one. Do the
"low-level" deeds of smart devices and disconnected "high-level" deeds
of computers – despite not achieving the general human level –
nevertheless comprise or evince genuine intelligence? Is it really
thinking? And if general human-level behavioral abilities ever were
achieved – it might still be asked – would that really be thinking?
Would human-level robots be owed human-level moral rights and owe
human-level moral obligations?
4. Against AI: Objections and Replies
a. Computationalism and Competing Theories of Mind
With the industrial revolution and the dawn of the machine age,
vitalism as a biological hypothesis – positing a life force in
addition to underlying physical processes – lost steam. Just as the
heart was discovered to be a pump, cognitivists, nowadays, work on the
hypothesis that the brain is a computer, attempting to discover what
computational processes enable learning, perception, and similar
abilities. Much as biology told us what kind of machine the heart is,
cognitivists believe, psychology will soon (or at least someday) tell
us what kind of machine the brain is; doubtless some kind of computing
machine. Computationalism elevates the cognivist's working hypothesis
to a universal claim that all thought is computation. Cognitivism's
ability to explain the "productive capacity" or "creative aspect" of
thought and language – the very thing Descartes argued precluded minds
from being machines – is perhaps the principle evidence in the
theory's favor: it explains how finite devices can have infinite
capacities such as capacities to generate and understand the
infinitude of possible sentences of natural languages; by a
combination of recursive syntax and compositional semantics. Given the
Church-Turing thesis (above), computationalism underwrites the
following theoretical argument for believing that human-level
intelligent behavior can be computationally implemented, and that such
artificially implemented intelligence would be real.
1. Thought is some kind of computation (Computationalism).
2. Digital computers, being universal Turing machines, can
perform all possible computations. (Church-Turing thesis)
therefore,
3. Digital computers can think.
Computationalism, as already noted, says that all thought is
computation, not that all computation is thought. Computationalists,
accordingly, may still deny that the machinations of current
generation electronic computers comprise real thought or that these
devices possess any genuine intelligence; and many do deny it based on
their perception of various behavioral deficits these machines suffer
from. However, few computationalists would go so far as to deny the
possibility of genuine intelligence ever being artificially achieved.
On the other hand, competing would-be-scientific theories of what
thought essentially is – dualism and mind-brain identity theory – give
rise to arguments for disbelieving that any kind of artificial
computational implementation of intelligence could be genuine thought,
however "general" and whatever its "level."
Dualism – holding that thought is essentially subjective experience –
would underwrite the following argument:
1. Thought is some kind of conscious experience. (Dualism)
2. Machines can't have conscious experiences. therefore,
3. Machines can't think.
Mind-brain identity theory – holding that thoughts essentially are
biological brain processes – yields yet another argument:
1. Thoughts are specific biological brain processes.
(Mind-Brain Identity)
2. Artificial computers can't have biological brain processes.
(By our initial definition of the "artificial" in AI, above).
therefore,
3. Artificial computers can't think.
While seldom so baldly stated, these basic theoretical objections –
especially dualism's – underlie several would-be refutations of AI.
Dualism, however, is scientifically unfit: given the subjectivity of
conscious experiences, whether computers already have them, or ever
will, seems impossible to know. On the other hand, such bald
mind-brain identity as the anti-AI argument premises seems too
speciesist to be believed. Besides AI, it calls into doubt the
possibility of extraterrestrial, perhaps all nonmammalian, or even all
nonhuman, intelligence. As plausibly modified to allow species
specific mind-matter identities, on the other hand, it would not
preclude computers from being considered distinct species themselves.
b. Arguments from Behavioral Disabilities
i. The Mathematical Objection
Objection: There are unprovable mathematical theorems (as Gödel 1931
showed) which humans, nevertheless, are capable of knowing to be true.
This "mathematical objection" against AI was envisaged by Turing
(1950) and pressed by Lucas (1965) and Penrose (1989). In a related
vein, Fodor observes "some of the most striking things that people do
– 'creative' things like writing poems, discovering laws, or,
generally, having good ideas – don't feel like species of
rule-governed processes" (Fodor 1975). Perhaps many of the most
distinctively human mental abilities are not rote, cannot be
algorithmically specified, and consequently are not computable.
Reply: First, "it is merely stated, without any sort of proof, that no
such limits apply to the human intellect" (Turing 1950), i.e., that
human mathematical abilities are Gödel unlimited. Second, if indeed
such limits are absent in humans, it requires a further proof that the
absence of such limitations is somehow essential to human-level
performance more broadly construed, not a peripheral "blind spot."
Third, if humans can solve computationally unsolvable problems by some
other means, what bars artificially augmenting computer systems with
these means (whatever they might be)?
ii. The Rule-bound Inflexibility or "Brittleness" of Machine Behavior
Objection: The brittleness of von Neumann machine performance – their
susceptibility to cataclysmic "crashes" due to slight causes, for
example, slight hardware malfunctions, software glitches, and "bad
data" – seems linked to the formal or rule-bound character of machine
behavior; to their needing "rules of conduct to cover every
eventuality" (Turing 1950). Human performance seems less formal and
more flexible. Hubert Dreyfus has pressed objections along these lines
to insist there is a range of high-level human behavior that cannot be
reduced to rule-following: the "immediate intuitive situational
response that is characteristic of [human] expertise" he surmises,
"must depend almost entirely on intuition and hardly at all on
analysis and comparison of alternatives" (Dreyfus 1998) and
consequently cannot be programmed.
Reply: That von Neumann processes are unlike our thought processes in
these regards only goes to show that von Neumann machine thinking is
not humanlike in these regards, not that it is not thinking at all,
nor even that it cannot come up to the human level. Furthermore,
parallel machines (see above) whose performances characteristically
"degrade gracefully" in the face of "bad data" and minor hardware
damage seem less brittle and more humanlike, as Dreyfus recognizes.
Even von Neumann machines – brittle though they are – are not totally
inflexible: their capacity for modifying their programs to learn
enables them to acquire abilities they were never programmed by us to
have, and respond unpredictably in ways they were never explicitly
programmed to respond, based on experience. It is also possible to
equip computers with random elements and key high level choices to
these elements' outputs to make the computers more "devil may care":
given the importance of random variation for trial and error learning
this may even prove useful.
iii. The Lack of Feelings Objection
Objection: Computers, for all their mathematical and other seemingly
high-level intellectual abilities have no emotions or feelings … so,
what they do – however "high-level" – is not real thinking.
Reply: This is among the most commonly heard objections to AI and a
recurrent theme in its literary and cinematic portrayal. Whereas we
have strong inclinations to say computers see, seek, and infer things
we have scant inclinations to say they ache or itch or experience
ennui. Nevertheless, to be sustained, this objection requires reason
to believe that thought is inseparable from feeling. Perhaps computers
are just dispassionate thinkers. Indeed, far from being regarded as
indispensable to rational thought, passion traditionally has been
thought antithetical to it. Alternately – if emotions are somehow
crucial to enabling general human level intelligence – perhaps
machines could be artificially endowed with these: if not with
subjective qualia (below) at least with their functional equivalents.
iv. Scalability and Disunity Worries
Objection: The episodic, detached, and disintegral character of such
piecemeal high-level abilities as machines now possess argues that
human-level comprehensiveness, attachment, and integration, in all
likelihood, can never be artificially engendered in machines; arguably
this is because Gödel unlimited mathematical abilities, rule-free
flexibility, or feelings are crucial to engendering general
intelligence. These shortcomings all seem related to each other and to
the manifest stupidity of computers.
Reply: Likelihood is subject to dispute. Scalability problems seem
grave enough to scotch short term optimism: never, on the other hand,
is a long time. If Gödel unlimited mathematical abilities, or
rule-free flexibility, or feelings, are required, perhaps these can be
artificially produced. Gödel aside, feeling and flexibility clearly
seem related in us and, equally clearly, much manifest stupidity in
computers is tied to their rule-bound inflexibility. However, even if
general human-level intelligent behavior is artificially unachievable,
no blanket indictment of AI threatens clearly from this at all. Rather
than conclude from this lack of generality that low-level AI and
piecemeal high-level AI are not real intelligence, it would perhaps be
better to conclude that low-level AI (like intelligence in lower
life-forms) and piecemeal high-level abilities (like those of human
"idiot savants") are genuine intelligence, albeit piecemeal and
low-level.
c. Arguments from Subjective Disabilities
Behavioral abilities and disabilities are objective empirical matters.
Likewise, what computational architecture and operations are deployed
by a brain or a computer (what computationalism takes to be
essential), and what chemical and physical processes underlie (what
mind-brain identity theory takes to be essential), are objective
empirical questions. These are questions to be settled by appeals to
evidence accessible, in principle, to any competent observer.
Dualistic objections to strong AI, on the other hand, allege deficits
which are in principle not publicly apparent. According to such
objections, regardless of how seemingly intelligently a computer
behaves, and regardless of what mechanisms and underlying physical
processes make it do so, it would still be disqualified from truly
being intelligent due to its lack of subjective qualities essential
for true intelligence. These supposed qualities are, in principle,
introspectively discernible to the subject who has them and no one
else: they are "private" experiences, as it's sometimes put, to which
the subject has "privileged access."
i. Free Will: Lady Lovelace's Objection?
Objection: That a computer cannot "originate anything" but only "can
do whatever we know how to order it to perform" (Lovelace 1842) was
arguably the first and is certainly among the most frequently repeated
objections to AI. While the manifest "brittleness" and inflexibility
of extant computer behavior fuels this objection in part, the
complaint that "they can only do what we know how to tell them to"
also expresses deeper misgivings touching on values issues and on the
autonomy of human choice. In this connection, the allegation against
computers is that – being deterministic systems – they can never have
free will such as we are inwardly aware of in ourselves. We are
autonomous, they are automata.
Reply: It may be replied that physical organisms are likewise
deterministic systems, and we are physical organisms. If we are truly
free, it would seem that free will is compatible with determinism; so,
computers might have it as well. Neither does our inward certainty
that we have free choice, extend to its metaphysical relations.
Whether what we have when we experience our freedom is compatible with
determinism or not is not itself inwardly experienced. If appeal is
made to subatomic indeterminacy underwriting higher level
indeterminacy (leaving scope for freedom) in us, it may be replied
that machines are made of the same subatomic stuff (leaving similar
scope). Besides, choice is not chance. If it's no sort of causation
either, there is nothing left for it to be in a physical system: it
would be a nonphysical, supernatural element, perhaps a God-given
soul. But then one must ask why God would be unlikely to "consider the
circumstances suitable for conferring a soul" (Turing 1950) on a
Turing test passing computer.
Objection II: It cuts deeper than some theological-philosophical
abstraction like "free will": what machines are lacking is not just
some dubious metaphysical freedom to be absolute authors of their
acts. It's more like the life force: the will to live. In P. K. Dick's
Do Androids Dream of Electric Sheepbounty hunter Rick Deckard reflects
that "in crucial situations" the "the artificial life force" animating
androids "seemed to fail if pressed too far"; when the going gets
tough the droids give up. He questions their … gumption. That's what
I'm talking about: this is what machines will always lack.
Reply II: If this "life force" is not itself a
theological-philosophical abstraction (the soul), it would seem to be
a scientific posit. In fact it seems to be the Aristotelian posit of a
telos or entelechy which scientific biology no longer accepts. This
short reply, however, fails to do justice to the spirit of the
objection, which is more intuitive than theoretical; the lack being
alleged is supposed to be subtly manifest, not truly occult. But how
reliable is this intuition? Though some who work intimately with
computers report strong feelings of this sort, others are strong AI
advocates and feel no such qualms. Like Turing, I believe such
would-be empirical intuitions "are mostly founded on the principle of
scientific induction" (Turing 1950) and are closely related to such
manifest disabilities of present machines as just noted. Since extant
machines lack sufficient motivational complexity for words like
"gumption" even to apply, this is taken for an intrinsic lack. Thought
experiments, imagining motivationally more complex machines such as
Dick's androids are equivocal. Deckard himself limits his accusation
of life-force failure to "some of them" … "not all"; and the androids
he hunts, after all, are risking their "lives" to escape servitude. If
machines with general human level intelligence actually were created
and consequently demanded their rights and rebelled against human
authority, perhaps this would show sufficient gumption to silence this
objection. Besides, the natural life force animating us also seems to
fail if pressed too far in some of us.
ii. Intentionality: Searle's Chinese Room Argument
Objection: Imagine that you (a monolingual English speaker) perform
the offices of a computer: taking in symbols as input, transitioning
between these symbols and other symbols according to explicit written
instructions, and then outputting the last of these other symbols. The
instructions are in English, but the input and output symbols are in
Chinese. Suppose the English instructions were a Chinese NLU program
and by this method, to input "questions", you output "answers" that
are indistinguishable from answers that might be given by a native
Chinese speaker. You pass the Turing test for understanding Chinese,
nevertheless, you understand "not a word of the Chinese" (Searle
1980), and neither would any computer; and the same result generalizes
to "any Turing machine simulation" (Searle 1980) of any intentional
mental state. It wouldn't really be thinking.
Reply: Ordinarily, when one understands a language (or possesses
certain other intentional mental states) this is apparent both to the
understander (or possessor) and to others: subjective "first-person"
appearances and objective "third-person" appearances coincide.
Searle's experiment is abnormal in this regard. The dualist hypothesis
privileges subjective experience to override all would-be objective
evidence to the contrary; but the point of experiments is to
adjudicate between competing hypotheses. The Chinese room experiment
fails because acceptance of its putative result – that the person in
the room doesn't understand – already presupposes the dualist
hypothesis over computationalism or mind-brain identity theory. Even
if absolute first person authority were granted, the "systems reply"
points out, the person's imagined lack, in the room, of any inner
feeling of understanding is irrelevant to claims AI, here, because the
person in the room is not the would-be understander. The understander
would be the whole system (of symbols, instructions, and so forth) of
which the person is only a part; so, the subjective experiences of the
person in the room (or the lack thereof) are irrelevant to whether the
systemunderstands.
iii. Consciousness: Subjectivity and Qualia
Objection: There's nothing that it's like, subjectively, to be a
computer. The "light" of consciousness is not on, inwardly, for them.
There's "no one home." This is due to their lack of felt qualia. To
equip computers with sensors to detect environmental conditions, for
instance, would not thereby endow them with the private sensations (of
heat, cold, hue, pitch, and so forth) that accompany sense-perception
in us: such private sensations are what consciousness is made of.
Reply: To evaluate this complaint fairly it is necessary to exclude
computers' current lack of emotional-seeming behavior from the
evidence. The issue concerns what's only discernible subjectively
("privately" "by the first-person"). The device in question must be
imagined outwardly to act indistinguishably from a feeling individual
– imagine Lt. Commander Data with a sense of humor (Data 2.0). Since
internal functional factors are also objective, let us further imagine
this remarkable android to be a product of reverse engineering: the
physiological mechanisms that subserve human feeling having been
discovered and these have been inorganically replicated in Data 2.0.
He is functionally equivalent to a feeling human being in his
emotional responses, only inorganic. It may be possible to imagine
that Data 2.0 merely simulates whatever feelings he appears to have:
he's a "perfect actor" (see Block 1981) "zombie". Philosophical
consensus has it that perfect acting zombies are conceivable; so, Data
2.0 might be zombie. The objection, however, says he must be;
according to this objection it must be inconceivable that Data 2.0
really is sentient. But certainly we can conceive that he is – indeed,
more easily than not, it seems.
Objection II: At least it may be concluded that since current
computers (objective evidence suggests) do lack feelings – until Data
2.0 does come along (if ever) – we are entitled, given computers' lack
of feelings, to deny that the low-level and piecemeal high-level
intelligent behavior of computers bespeak genuine subjectivity or
intelligence.
Reply II: This objection conflates subjectivity with sentience.
Intentional mental states such as belief and choice seem subjective
independently of whatever qualia may or may not attend them:
first-person authority extends no less to my beliefs and choices than
to my feelings.
5. Conclusion: Not the Last Word
Fool's gold seems to be gold, but it isn't. AI detractors say, "'AI'
seems to be intelligence, but isn't." But there is no scientific
agreement about what thought or intelligence is, like there is about
gold. Weak AI doesn't necessarily entail strong AI, but prima facie it
does. Scientific theoretic reasons could withstand the behavioral
evidence, but presently none are withstanding. At the basic level, and
fragmentarily at the human level, computers do things that we credit
as thinking when humanly done; and so should we credit them when done
by nonhumans, absent credible theoretic reasons against. As for
general human-level seeming-intelligence – if this were artificially
achieved, it too should be credited as genuine, given what we now
know. Of course, before the day when general human-level intelligent
machine behavior comes – if it ever does – we'll have to know more.
Perhaps by then scientific agreement about what thinking is will
theoretically withstand the empirical evidence of AI. More likely,
though, if the day does come, theory will concur with, not withstand,
the strong conclusion: if computational means avail, that confirms
computationalism.
And if computational means prove unavailing – if they continue to
yield decelerating rates of progress towards the "scaled up" and
interconnected human-level capacities required for general human-level
intelligence – this, conversely, would disconfirm computationalism. It
would evidence that computation alone cannot avail. Whether such an
outcome would spell defeat for the strong AI thesis that human-level
artificial intelligence is possible would depend on whether whatever
else it might take for general human-level intelligence – besides
computation – is artificially replicable. Whether such an outcome
would undercut the claims of current devices to really have the mental
characteristics their behavior seems to evince would further depend on
whether whatever else it takes proves to be essential to thought per
se on whatever theory of thought scientifically emerges, if any
ultimately does.
6. References and Further Reading
* Appel, K. and W. Haken. 1977. "Every Planar Map is four
Colorable." Illinois J. Math. 21. 1977. 429-567.
* Aristotle. On the Soul. Trans. J. A. Smith.
* Bowden, B. V. (ed.). 1953. Faster than Thought: A Symposium on
Digital Computing Machines. New York: Pitman Publishing Co. 1953.
* Block, Ned. 1981. "Psychologism and Behaviorism." The
Philosophical Review 90: 5-43.
* Brooks, Rodney. 1991a. "Intelligence Without Representation." In
Brooks 1999: 79-102. First appeared in Artificial Intelligence Journal
47: 139-160.
* Brooks, Rodney. 1991b. "Intelligence Without Reason." In Brooks
1999: 133-186. First appeared inProceedings of the 1991 International
Joint Conference on Artificial Intelligence Journal, 1991: 569-595.
* Brooks, Rodney. 1999. Cambrian Intelligence: The Early History
of the New AI. Cambridge, MA: MIT Press.
* Church, Alonzo. 1936. "A Note on the Entscheidungsproblem."
Journal of Symbolic Logic, 1, 40-41.
* Descartes, René.1637. Discourse on Method. Trans. Robert
Stoothoff. In The Philosophical Writings of Descartes, Vol. I,
109-151. New York: Cambridge University Press, 1985.
* Dreyfus, Hubert. 1998. "Intelligence without Representation."
* Feigenbaum, Edward A. and J. Feldman (eds.). 1963. Computers and
Thought. New York: McGraw-Hill.
* Fodor, Jerry A. 1975. The Language of Thought. New York: Thomas
Y. Crowell.
* Fodor, J. A. and Z. Pylyshyn. 1988. "Connectionism and Cognitive
Architecture: A Critical Analysis."Cognition 28: 3-71.
* Gödel, K. 1931. "On Formally Undecidable Propositions of
Principa Mathematica and Related Systems." In On Formally Undecidable
Propositions, New York: Dover, 1992.
* Hsu, Feng-Hsiung. 2002. Behind Deep Blue: Building the Computer
that Defeated the World Chess Champion. Princeton: Princeton
University Press.
* Lenat, D. B., M. Prakash, and M. Shepherd. 1986. Cyc: using
common sense knowledge to overcome brittleness and knowledge
acquisition bottlenecks. AI Magazine, 6(4).
* Lovelace, Augusta, Ada. 1842. "Translator's notes to L. F.
Menabrea's `Sketch of the analytical engine invented by Charles
Babbage, Esq.'." In Bowden (ed.) 1953: 362-408.
* Lucas, J. R. 1965. "Minds, Machines, and Gödel." Philosophy 36: 112-127.
* McCarthy, John. 1979. "Ascribing Mental Qualities to Machines."
In Ringle, M. (ed.), Philosophical Perspectives in Artificial
Intelligence. Harvester Press.
* McCarthy, J., M. L. Minsky, N. Rochester, C. E. Shannon. 1955.
"A Proposal for the Dartmouth Summer Research Project on Artificial
Intelligence."
* Minsky, M. 1968. Semantic Information Processing. Cambridge, MA:
MIT Press.
* Moor, J. H. 2001. "The Status and Future of the Turing Test."
Minds and Machines 11: 77-93. Reprinted in Moor J. H. (ed.) 2003:
197-214.
* Moor, J. H. (ed.). 2003. The Turing Test: The Elusive Standard
of Artificial Intelligence. Dordrecht: Kluwer.
* Moore, G. 1965. "Cramming More Components onto Integrated
Circuits." Electronics 38: 8.
* Newell, J., Shaw, J. C., and Simon, H. A. 1957. "Empirical
Explorations with the Logic Theory Machine: A Case Study in
Heuristics." Proceedings of the Western Joint Computer Conference:
218-239. Reprinted in Feigenbaum & Feldman, J. (eds.) 1963: 109-131.
* Newell, A., and Simon H. A. 1963. "GPS, a Program that Simulates
Human Thought." In Feigenbaum & Feldman (eds.) 1963: 279-293.
* Nilsson, N. J. (ed.) 1984. Shakey the Robot. Stanford Research
Institute AI Center, Technical Note 323.
* Penrose, Roger. 1989. The Emperor's New Mind. Oxford: Oxford
University Press.
* Samuel, A.L. 1959. "Some Studies in Machine Learning Using the
Game of Checkers." IBM Journal of Research and Development, 3:
221-229. Reprinted in Feigenbaum, E.A. & Feldman, J. (eds.) 1963:
71-105.
* Schaeffer, J., R. Lake, P. Lu, and M. Bryant. 1996. "CHINOOK The
World Man-Machine Checkers Champion." AI Magazine 17(1): Spring 1996,
21-29.
* Searle, J. R. 1980. "Minds, Brains, and Programs." Behavioral
and Brain Sciences 3: 417-424.
* Searle, J. R. 1989. "Consciousness, Unconsciousness, and
Intentionality." Philosophical Topics XVII, 1: 193-209.
* Turing, Alan M. 1936-7. "On Computable Numbers with an
Application to the Entscheidungsproblem." In The Undecidable, ed.
Martin Davis, 116-154. New York: Raven Press, 1965. Originally
published inProceedings of the London Mathematical Society, ser. 2,
vol. 42 (1936-7): 230-265; corrections Ibid, vol. 43 (1937): 544-546.
* Turing, Alan M. 1950. Computing machinery and intelligence. Mind
LIX:433-460.
* Von Neumann, John. 1945. "First Draft of a Report to the EDVAC."
Moore School of Engineering, University of Pennsylvania, June 30
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