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Strong AI Model, Knowledge Representation Language

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Knowledge representation language is based on the strong AI model, which completely complies with the requirements of a strong AI originally formulated by Weizenbaum in 1976. – PowerPoint PPT presentation

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Title: Strong AI Model, Knowledge Representation Language


1
Theory of Meaningful Information
  • Introduction

We live in the age of applied sciences. Consider
the role of applied biology responsible for the
unbelievable progress in medicine or applied
physics and applied chemistry, forming the basis
of modern industry and modern infrastructure in
general. It is nearly impossible to imagine our
society without the applications of social
sciences like economics, sociology, social
psychology, political science, criminology and so
on. Frequently ignored, however, is the fact that
the boom of applied sciences was only possible
because of the fundamental sciences, without
which the development of the last 150 years would
never have occurred. The only major type of
activities remaining without adequate theoretical
conceptualization is that which is concerned with
meaningful information. While technical aspects
of information transmission are effectively
covered by the information theory of Claude
Shannon, the theory of meaningful information
never possessed any relevant theoretical support.
Despite more than a hundred years of research
devoted to the formalization of meaning, no
research dedicated to the subject was ever able
to produce an applicable theory having any impact
on the practical information-related activities
2
Theory of Meaningful Information
  • Problem Statement

The word information is derived from the Latin
word informatio meaning to form, to put in
shape, which designated physical forming as well
as the forming of the mind, i.e. education and
the accumulation of knowledge. This relatively
broad meaning was narrowed down in the middle
Ages when information equated into education. It
had been continually refined up until the first
half of the twentieth century when it was equated
with transmitted message. Today, various kinds
of information have been intensively studied by
numerous information-related sciences like
communication media, information management,
computing, cognitive sciences, physics,
electrical engineering, linguistics, psychology,
sociology, epistemology and medicine. While every
discipline concentrates on specific features of
information and ignores others, they produce
multiple incompatible approaches making
generalizations of the studied subject virtually
impossible. This is even more complicated by the
widespread practice of defining Meaningful
information with the help of other concepts like
knowledge and meaning, while simultaneously
ignoring related concepts like data, signs,
signals. Taking into account that the absolute
majority of informational studies are trivial
research without any pretension for broad
generalizations, it is no wonder that the
Information Age ? as often referred to as
nowadays ? has failed until now to produce a
general information theory despite several
ambitious attempts in developing universal
approaches.
3
Theory of Meaningful Information
  • Information Theory of Claude Shannon

The theory was very efficient in solving
practical problems of electrical engineering.
Though the designation Information Theory,
under which Shannons work is commonly known, is
mainly a misnomer spontaneously produced by the
scientific community on the surge of popularity
of his approach. Shannon himself never referred
to his work in such a manner. The name Shannon
gave his theory in the first draft in 1940 was
Mathematical theory of communication and he
retained the same name in a publication in 1963
(Claude Elwood Shannon and Weaver 1963). He also
deliberately evaded consideration of the semantic
characteristics of information stressing the
aforementioned publication Frequently the
messages have meaning that is they refer to or
are correlated according to some system with
certain physical or conceptual entities. These
semantic aspects of communication are irrelevant
to the engineering problem (Claude Elwood
Shannon and Weaver 1963). The citation of Weaver
from the same publication The word information,
in this theory, is used in the special sense that
must not be confused with its ordinary usage. In
particular, information must not be confused with
meaning.... These citations actually define the
limits of Shannons theory concerned with the
processes of information transmission. This
theory however showed no interest in studying the
main characteristic of information, which is the
ability to designate meaning.
4
Theory of Meaningful Information
  • The Intelligence Theory

According to the definition in Wikipedia
(Artificial Intelligence 2018) Artificial
intelligence (AI, also machine intelligence, MI)
is intelligence displayed by machines, in
contrast with the natural intelligence (NI)
displayed by humans and other animals. In
computer science, AI research is defined as the
study of "intelligent agents" any device that
perceives its environment and takes actions that
maximize its chance of success at some goal.
Colloquially, the term "artificial intelligence"
is applied when a machine mimics "cognitive"
functions that humans associate with other human
minds, such as "learning" and "problem
solving". Though normally this online
encyclopedia is not considered as scientifically
reliable it is exactly the right choice, in this
particular case, because one can await that the
site getting circa 10000 clicks everyday probably
contains the pretty adequate definition of its
subject. The definition made on basis of (Poole,
Mackworth, and Goebel 1998, 17 Russell and
Norvig 2010 Nilsson 1998 Legg and Hutter 2007),
demonstrates two key specifics of the modern AI
research first, AI is understood as the antipode
of NI, which is the only form of the true
intelligence known at this time second, the task
of explaining the phenomenon of intelligence is
completely ignored because the focus of the
research is concentrated on the study of the
artificial intellectual entities no matter how
intelligent they in reality are. The Achilles'
heel of this approach is that the feature of
being intelligent is not a primary characteristic
of someone (something) but rather an individual
assessment of some observer judging the behavior
of a watched thing. Attributing a certain thing
with intelligence is exactly the same as
attributing it with lightness, softness, bigness
or goodness. In its genuine form, it is just an
individual opinion assessing some particular
feature, like lightness - a thing which light
for one person can be very heavy to another
5
Theory of Meaningful Information
  • The Universal Representation Language

The fundamental problem hindering understanding
of languages is the incompatible approaches to
the different language types. Even though
easy-to-use-natural-language-like-notation was
one of the original reasons for developing the
first high level programming languages, the
theory and practice of programming never
intersected with the linguistics research. The
highly mathematized theory of formal language
achieved outstanding results in formalizing
miscellaneous syntax structures, but was never
successful in explaining the nature of the
languages semantics nor the links existing
between the syntax and the meaning. Finally, it
failed due to its almost total inability to
understand the real features of real programming
languages and the inability to answer also the
most obvious questions like why programmers
prefer to compose code in such languages as C
and Pascal and not in Lisp and Prolog. In lack
of a working theory, developers created new
formal languages on the basis of miscellaneous
practical and theoretical considerations, which
however always concentrated on programming and
never on language. The only reference to natural
languages was restricted to the periodical
prophecies about a supposed next programming
language generation, which could do everything
better by enabling the code to be composed in a
natural language (so-called natural language
programming). These declarations however were
never pursued even by a single attempt to
understand why these two kinds of languages are
or appear to be that
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