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Chapter Ten
Artificial Intelligence I: Definitional
Perspective
Historical Perspective

Artificial Intelligence (AI) has been a driving force
behind our quest to create a machine in our own
image.

Automata begin to appear for general public
consumption during the late 19th century.

The advent of electronic computation has led to an
information revolution in the 21st century
characterized by machines that supply expert
advice, control the environment, and emulate
human thinking.
Philosophical Issues-Man as a
Machine

Descartes (1596-1650) raises the mind-body question. In
particular, “What is the difference between a person and a
machine?” “Treatise on Man” includes a comparison
between a human being and a hypothetical “statue or
machine” that operates like a clock or hydraulic fountain.
Descartes proposes that humans possess a “rational soul”
whereas animals are not capable of reasoning (“I think,
therefore I am.”)

Humans have had difficulty accepting two important
theories: the earth is not the center of the universe and
evolution. Is there a third theory that we need to accept—
man is a complex machine?
Evaluating Descartes’ Approach
Debate over Descartes is religious in nature. The
atheist philosophers of the 18th century took issue
with the idea that a “soul” separates us from the
machine-like model for other animals.
• Descartes' ideas came into direct conflict with the
teachings of the religious community of his time.
• Descartes tries to avoid the conflict; his “machine”
is not a man, simply a “statue” that God forms with
the explicit intention of making it as much as
possible like us.
•
Mechanical Computation

One aspect of an intelligent agent rests on its
ability to perform calculations
 Mechanical calculators represent automata
capable of “imitating” human intelligence on a
“primitive” level.
 The abacus is developed in 2600 B.C.
 In the 19th century, Charles Babbage develops
mechanical machines that anticipate the modern
electronic digital computer. They are a forerunner
of an ultimate “intelligent agent.”
Defining AI
There may not be a single definition. Alternatives
serve to support the authors' special interests.
• The cognitive scientific goal of AI is to codify
knowledge (and meta-knowledge or “knowledge
about knowledge itself”) in order to assemble
systems to explain intelligence and consciousness
itself.
• The engineering goal of AI is to assemble systems
using a computer’s inventory of knowledge and
facilities in order to solve real-world problems.
•
Evaluating the Concept of AI
Strong AI: advocates seek to build machines
whose total abilities (including intellectual) cannot
be distinguished from those of a human being.
The optimism of these advocates has gradually
given way as they appreciate the extreme
challenges to be surmounted. They continue to
dream of this ultimate goal.
• Applied AI: employs advanced information
processing and has enjoyed the most success
regarding “intelligent” machines. The focus of its
advocates is to produce commercially sustainable
“smart machines.”
•
Can a Machine Think and
Understand?
•
This question may be pointless. Noam Chomsky
suggests it is a question of decision, not fact. We must
agree on what defines intelligence, thinking,
understanding (and ultimately consciousness).
•
If a computer passes an “intelligence” test, would we
impart intelligence to it? McCarthy and Shannon note
that we could “stack the deck”—design a program with
preprogrammed responses.
•
Minsky: “Intelligence” is our name for whichever
problem-solving mental processes we do not yet
understand.
Learning as an Element of
Intelligence
Behaviorists identify several forms of learning
that should form part of an intelligent agent’s
repertoire of capabilities.
• Trial-and-error: try various responses to a
stimulus and remember the one that solves the
problem.
• Rote: direct association between a stimulus and
a response (memorizing).
• Operant conditioning: Using a system of positive
and negative reinforcements, gradually develop
a response that is appropriate to the stimuli.
•
Reasoning as an Aspect of
Intelligence
• The ability to draw inferences appropriate to the facts
and situation.
• Problem-solving: devise a strategy to complete a task.
• (Machine) Perception: achieved when the environment
is scanned and converted to a set of abstractions
(objects, features, relationships).
• Language understanding may comprise the highest
form of intelligence. This remains a daunting achievement
for machines because it must involve “linguistic
understanding.”
The Computer as the Tool of AI
Research
External environments
The Central
Processing Unit
(CPU)
Memory
Communication Channel or “Bus”
CPU: Executes instructions, controls sequence
Memory: Stores instructions, answers
I/O: Interacts with external world (e.g., a user)
Bus: Communication between parts of the system
Input/Output
(I/O)
Evaluation of the Computer as a
Model of Brain Organization
• The functioning of the body is too complicated
to be supported by a single computer (“brain”).
• The analogy of the brain as the repository of a
“CPU” cannot support the processing necessary
to maintain homeostasis.
• The Network Approach considers processing
and knowledge representation to be distributed.
Programming and Its Limitations
• Program: a series of instructions that a user
devises for the computer to execute.
• High Level Language programming:
Programs built to understand “English-like”
commands.
• The limitations of HLL programs have led to
the development of “user-friendly”
applications focused on solving problems in
AI.
The Turing Test
Interrogator
Machine intelligence
Human intelligence
If an interrogator cannot distinguish between machine intelligence and human
intelligence then the machine passes the Turing Test (TT) for intelligence.
Evaluation of the TT
•
•
•
•
•
•
Theological: Thinking is a function of man’s God-given
immortal soul. No animal or machine can think.
“Head-in-the-sand” argument: Consequences of thinking
machines are too dreadful to contemplate.
Mathematical: Some theorems can neither be proved nor
disproved.
Consciousness: No machine can write a sonnet.
Disabilities: Machines can follow instructions but you
cannot instruct them to have a sense of humor.
Lady Lovelace argument: A machine can never do anything
really new.
Overall Evaluation of the AI
Concept
•
•
•
Turing’s predictions have failed to
materialize.
Cognitive scientists have an abiding belief
that humans cannot, and should not, be
viewed merely as machines.
The modern computer has the capacity to
re-create “human-like” behavior for limitedenvironment circumstances.