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    Artificial Intelligence Myths: Fundamentals and Applications
    Artificial Intelligence Myths: Fundamentals and Applications
    Artificial Intelligence Myths: Fundamentals and Applications
    Ebook158 pages1 hourArtificial Intelligence

    Artificial Intelligence Myths: Fundamentals and Applications

    By Fouad Sabry

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    About this ebook

    What Is Artificial Intelligence Myths


    The origins of artificial intelligence (AI) can be traced back to ancient times, when myths, stories, and rumors circulated about artificial beings that were imbued with intelligence or consciousness by skilled artisans. Philosophers who attempted to define the process of human thinking as the mechanical manipulation of symbols were the ones who first laid the seeds for current artificial intelligence. This effort reached its zenith in the 1940s with the invention of the programmable digital computer, a machine that is founded on the fundamental principles of mathematical reasoning in its abstract form. A group of researchers was motivated to begin seriously considering the prospect of developing an electronic brain as a result of this gadget and the theories that underpinned it.


    How You Will Benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: History of artificial intelligence


    Chapter 2: Artificial intelligence


    Chapter 3: Chinese room


    Chapter 4: Marvin Minsky


    Chapter 5: Symbolic artificial intelligence


    Chapter 6: Neats and scruffies


    Chapter 7: Artificial general intelligence


    Chapter 8: Philosophy of artificial intelligence


    Chapter 9: AI winter


    Chapter 10: Outline of artificial intelligence


    (II) Answering the public top questions about artificial intelligence myths.


    (III) Real world examples for the usage of artificial intelligence myths in many fields.


    (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of artificial intelligence myths' technologies.


    Who This Book Is For


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial intelligence myths.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJul 3, 2023
    Artificial Intelligence Myths: Fundamentals and Applications

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      Book preview

      Artificial Intelligence Myths - Fouad Sabry

      Chapter 1: Physical symbol system

      Taking physical patterns (symbols), integrating them into structures (expressions), and then manipulating them (using processes) to generate new expressions is the function of a physical symbol system, which is also known as a formal system.

      Allen Newell and Herbert A. Simon are the ones responsible for developing the physical symbol system hypothesis (PSSH), which is a philosophical perspective in the field of artificial intelligence. They penned it:

      A physical symbol system has the essential in addition to the enough means for broad intelligent action.

      — Allen Newell and Herbert A.

      Simon

      This assertion suggests not only that human thought is a kind of symbol manipulation (because intelligence requires the presence of a symbol system), but also that computers are capable of exhibiting intelligent behavior (because a symbol system is sufficient for intelligence).

      The theory has been subjected to severe scrutiny from a number of different perspectives, despite the fact that it is an essential component of AI research. The notion that the theory appears to be acceptable for higher-level intelligence, such as playing chess, but less fit for ordinary intelligence, such as vision, is one of the more prominent critical perspectives. A line of demarcation is typically drawn between the kinds of high-level symbols that directly correspond with objects in the real world, such as dog and tail, and the more complex symbols that are present in a machine, such as a neural network. This distinction is made because high-level symbols like dog and tail directly correspond with real-world objects.

      The following are some examples of physical sign systems::

      Formal logic: the symbols are words like and, or, not, for all x and so on. These expressions are assertions of formal logic, and like other statements in logic, they may be either true or untrue. The procedures are the guidelines for inference and deduction in logic.

      In algebra, the symbols are the plus sign, ×, x, y, 1, 2, 3, etc.

      Equations may be derived from the expressions.

      The operations are the fundamental laws of algebra, that enable one to modify a mathematical statement while preserving the expression's truth.

      A digital computer, in which the processes are the actions of the central processing unit (CPU) that modify memory and the symbols are the ones and zeros that make up the computer's memory.

      Chess is represented by three types of elements: the pieces, the processes, and the expressions. The pieces represent the symbols, while the processes represent the lawful chess movements.

      According to the physical symbol system theory, both of these may be categorized as exemplifications of physical symbol systems:

      Intelligent human mind is shown by the encoding of the symbols in our brains. The expressions represent different ways of thinking. The processes are the activities of the mind involved in thinking.

      A program for artificial intelligence that is now being executed; the symbols represent data. The phrases constitute additional pieces of data. The processes are the programs that are responsible for the data manipulation.

      The development of artificial intelligence programs and psychological research on human beings provided Allen Newell and Herbert A. Simon with two lines of evidence suggesting that symbol manipulation was at the core of both human and machine intelligence.

      First, in the early decades of artificial intelligence research, there were a number of very successful programs that used high level symbol processing. Some examples of these programs include Newell and Herbert A. Simon's General Problem Solver and Terry Winograd's SHRDLU. Both of these programs were created by Newell and Herbert A. Simon. This heritage is carried on by its offspring, namely logic programming and expert systems. It was indicated by the effectiveness of these programs that symbol processing systems might replicate any intelligent activity.

      And second, psychological investigations that were being conducted at the same time revealed that individuals employed this kind of symbol processing for complex issues in logic, planning, or any form of puzzle solving. This was discovered at the same time as the first point. Researchers working in the field of artificial intelligence were able to successfully model the process by which individuals solve problems using computer systems. This partnership, along with the questions it posed, would ultimately result in the birth of the academic discipline known as cognitive science. (The researchers gave this kind of investigation the name cognitive simulation.) Based on the findings of this body of research, it was hypothesized that human problem solving essentially consisted of the manipulation of high-level symbols.

      In the arguments of Newell and Simon, the symbols that the hypothesis is referring to are physical objects that represent things in the world, symbols such as dog that have a recognizable meaning or connotation and can be composed with other symbols to create more complex symbols. In other words, symbols are physical objects that represent things in the world.

      On the other hand, one may understand the hypothesis as referring to the basic abstract 0s and 1s that are stored in the memory of a digital computer, or to the stream of 0s and 1s that is processed by a robot's perceptual apparatus. Although it is not always easy to discern precisely what the symbols stand for, they may also be thought of as symbols in some sense. However, it may not always be able to do so. According to David Touretzky and Dean Pomerleau's explanation, this interpretation of the hypothesis does not differentiate between symbols and signals. According to the Church–Turing thesis, every Turing-universal system may mimic any possible process that can be digitized, provided that it is given the time and memory to do so. This theory is generally recognized. Given that every digital computer is a Turing universal, it follows that every digital computer has the potential, at least in principle, to replicate everything that can be digitized to an adequate degree of accuracy. This includes the behavior of sentient beings. Since humans are prepared to accept nearly any signal as a sort of symbol, and since all sentient biological systems include signal pathways, the required condition of the hypothesis of physical symbol systems may also be manipulated in a manner that makes it more plausible.

      Nils Nilsson has outlined four primary themes or lines of criticism that have been leveled against the physical symbol system concept.

      The erroneous argument that the [physical symbol system theory] lacks symbol grounding, which is assumed to be a need for universal intelligent action, is a fallacy.

      The widespread conviction that artificial intelligence must rely on symbolic processing (that which can be supplied by a connectionist architecture for instance).

      The frequent assertion that the brain is in no way a computer and that computation, as it is now understood, does not give an acceptable model for intelligence is a controversial topic.

      And finally, another theory that is held by some people is that the brain is essentially mindless, that the majority of the processes that take place in it are chemical reactions, and that intelligent behavior exhibited by humans is comparable to the intelligent behavior exhibited, for instance, by ant colonies.

      In his critique of the physical symbol system hypothesis's necessary condition, Hubert Dreyfus referred to this assumption as the psychological assumption and defined it as follows::

      One way to think about the mind is as a machine that processes information in accordance with predetermined protocols. has presented actual facts that academics are taking carefully into consideration in contrast to the psychological assumption..

      The Chinese room argument was first presented by John Searle in 1980. Its purpose was to demonstrate that a computer program, or any other physical symbol system, cannot be said to understand the symbols that it employs; that the symbols, in and of themselves, lack any meaning or semantic content; and that, as a result, a machine can never be truly intelligent based solely on its ability to manipulate symbols.

      During the 1960s and 1970s, researchers in a number of labs sought to create robots that could interpret symbols as representations of the real environment and utilize those symbols to plan their movements (such as the Stanford Cart). These initiatives only had moderate levels of success. Rodney Brooks of MIT was able to construct robots in the middle of the 1980s that had superior capacity to move and live without the use of any symbolic thinking at all. These robots were built by Brooks. Brooks (and others, such as Hans Moravec) made the discovery that our most fundamental abilities, such as movement, survival, perception, and balance, did not appear to require the use of high-level symbols at all, and that, in fact, the utilization of high-level symbols was more complicated and resulted in a lower level of success.

      Rodney Brooks, a researcher in robotics, took direct aim at the physical symbol system hypothesis in his paper published in 1990 titled Elephants Don't Play Chess. In this paper, Brooks argued that symbols are not always necessary due to the fact that elephants don't play chess. The world is the best example of itself there is. It is completely up to date at all times. It always includes every piece of information that might possibly be known. The challenge is in sensing it in an acceptable manner and doing it often enough.

      Many people, including George Lakoff, Mark Turner, and others, have proposed that our ability to think abstractly in fields like mathematics, ethics, and philosophy is dependent on skills that arise automatically from the body, and that the ability to consciously manipulate symbols only accounts for a small portion of our overall intelligence.

      {End Chapter 1}

      Chapter 2: Artificial intelligence

      As contrast to the natural intelligence exhibited by animals, including humans, artificial intelligence (AI) refers to the intelligence demonstrated by robots. Research in artificial intelligence (AI) has been described as the area of study of intelligent agents, which refers to any system that senses its surroundings and performs actions that optimize its possibility of attaining its objectives. In other words, AI research is a discipline

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