Inquire about purchasing the book | Table of Contents | Annotated Bibliography | Class on Nature of Mind
These are excerpts and elaborations from my book "The Nature of Consciousness"
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Artificial Intelligence The term Artificial
Intelligence was coined around 1955 by the US mathematician John McCarthy, but it has never been clarified what it was truly supposed to mean.
The reason is simple: there is no consensus on what makes a machine (or, for
that matter, a human being) intelligent. If opinions vary on whether
Artificial Intelligence is feasible or not, opinions are even more varied on
how Artificial Intelligence should be achieved. At the beginning Artificial
Intelligence was often equated with the quest for the general problem solver,
the program capable of solving all mathematical problems. Because the computer
is a symbolic processor, and proving theorems is about processing symbols, it
was natural to assume that a computer can prove all theorems. However,
scientists soon realized that problem solving is not everything, and in
everyday life we can solve problems that are essential to our survival (such as
deciding when to cross a street) without ever using the Mathematics we studied
in school. Thus intelligence is not
commonly defined by the number of theorems one can prove in a second (otherwise
machines would already be far more intelligent than the most intelligent
humans) but by the ability to move around in the real world and carry on all
the tasks that humans carry out more or less effortlessly during the day. A more realistic view is
that intelligence is the result of reasoning about knowledge. Intelligent
behavior originates from a base of knowledge and from the ability to carry out
inferences on that knowledge base.
Intelligence is essentially knowledge processing. Since a computer is
ultimately a symbol processor, the issue is then how to express knowledge in a
symbolic form. The difference between
knowledge and information is crucial. Information can be found in books,
knowledge comes from experience. Common sense, for example, is a form of
knowledge but not a form of information. Anybody can access the information
stored in a medical encyclopedia, but only physicians have real knowledge about
medicine. The focus of Artificial Intelligence is not in building
encyclopedias, in storing huge amounts of information: it is in cloning
humans who are experts (i.e., have acquired specialized knowledge) in a field
or domain. The difference between information and knowledge is, for example,
the difference between asking who is the president of the United States? and
asking who will be the next president of the United States? The former
question requires only information about who is the current president, the
latter question requires knowledge about the domain of politics. According to John McCarthy("Programs with Common
Sense", 1958), knowledge representation must satisfy three fundamental
requirements: ontological (must allow one to state the relevant facts),
epistemological (allow one to express the relevant knowledge) and heuristic
(allow one to perform the relevant inference). Artificial Intelligence can then
be defined as the discipline that studies what can be represented in a formal
manner (epistemology) and computed in an efficient manner (heuristics). The language of Logic satisfies those
requirements: it allows us to express everything we know and it allows us to
make computations on what is expressed by it. Each set of knowledge is in fact
a mathematical theory. The underlying assumption of
the knowledge-based approach is that symbolic processing per se may lead to
human-like intelligence. Knowledge Representation One of the crucial steps to
build intelligent machines is therefore knowledge representation: first and
foremost, one must encode in a machine the knowledge about the world possessed
by humans. Every science needs to build a mathematical model of its world
before it can perform any inference and draw any conclusions. Physics, for
example, represents natural laws with formulas. Then formulas can be combined
to yield prescriptions about the effects of actions. The world of Artificial
Intelligence is the world of knowledge: what must be represented formally is
knowledge. Knowledge has traditionally
been formalized in three forms: facts, stimulus-response pairs (or
cause-effect, or premise-action, or antecedent-consequent pairs), and relations
between concepts. Facts are easily represented in first-order Predicate Logic in
the form of logical expressions: Piero
is a writer can be represented as writer (Piero), meaning that Piero
satisfies the predicate writer (or that Piero belongs to the set of
individuals that satisfy the predicate writer). For example, if we know that
all writers are creative, then we can apply a simple step of deduction and
derive that Piero is also creative. Production rules are
usually employed to express the causal connection between one fact and another
fact (if something is true, then something else must be true too). For example,
if somebody is a human being, then she is also a mammal. Whenever the
antecedent is true, the consequent is also true. This too can be translated
into Predicate Logic, because the implication is mathematically equivalent to
a logical expression (in Logic, p IMPLIES q is equivalent to NOT p OR q). More
rules can therefore be combined according to Predicate Calculus. Finally, relations between
concepts (i.e., complex concepts) can be represented with systems such as
semantic networks and frames. A semantic network represents concepts as
nodes, links a concept with other concepts, and specifies of what type each
link is. For example, the concept of a human being is linked to the concept of
a mammal by a link of type BELONGS TO. A concept may have many links of many
types to other concepts. Ideally, all human knowledge could be represented by a
gigantic semantic network. A frame can be used to
represent the inner structure of a concept: its attributes, their default
values, the actions associated with the attributes, and, again, the links to
other concepts. A cars attributes include that its function is to move, that
it has four wheels, that it costs so
much, etc. Both semantic networks and frames can also be reduced to expressions
of first-order Predicate Logic. Anything that can be reduced
to Predicate Logic satisfies McCarthys requirements. Back to the beginning of the chapter "Machine Intelligence" | Back to the index of all chapters |
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