Physical Symbol System: Fundamentals and Applications

By Fouad Sabry

What Is Physical Symbol System

In a physical symbol system, physical patterns, also known as symbols, are combined with one another to form structures, also known as expressions, and then those expressions are manipulated to make new expressions.

How You Will Benefit

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

Chapter 1: Physical symbol system

Chapter 2: Artificial intelligence

Chapter 3: Allen Newell

Chapter 4: Symbolic artificial intelligence

Chapter 5: Computing Machinery and Intelligence

Chapter 6: Artificial general intelligence

Chapter 7: Computational cognition

Chapter 8: History of artificial intelligence

Chapter 9: Philosophy of artificial intelligence

Chapter 10: Turing test

(II) Answering the public top questions about physical symbol system.

(III) Real world examples for the usage of physical symbol system in many fields.

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

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 3, 2023

Book preview

Chapter 1: Philosophy of artificial intelligence

The study of artificial intelligence and its implications for our knowledge and understanding of topics such as ethics, consciousness, epistemology, and free will is the focus of the subfield of philosophy known as the philosophy of artificial intelligence, which is a branch of the philosophy of technology.

Is it possible for a machine to behave intelligently? Is there an issue that it cannot tackle that a human would normally solve by thinking?

Is there a difference between human intellect and artificial intelligence? Is the human mind comparable to a computer in certain ways?

Is it possible for a computer to have a mind, as well as mental states and awareness, in the same way that a human person does? Can it perceive the state of things?

These kinds of questions illustrate the distinct interests that AI researchers, cognitive scientists, and philosophers each have in their own fields. The answers to these concerns in the scientific community are dependent on the meanings of the terms intelligence and awareness, as well as the precise machines that are being discussed.

Important ideas in the field of artificial intelligence philosophy include some of the following::

The polite convention proposed by Turing states that if a computer acts as intelligently as a human person, then it must be as intelligent as a human being.

The Dartmouth hypothesis states that any facet of learning or any other trait of intelligence may be so thoroughly characterized that a computer can be constructed to replicate it. [Citation needed]

According to the physical symbol system theory developed by Allen Newell and Herbert A. Simon: A physical symbol system has the necessary and sufficient means of broad intelligent action.

The strong AI hypothesis was developed by John Searle. According to this theory, the suitably designed computer with the right inputs and outputs would therefore have a mind in precisely the same sense as human beings have minds.

The mechanism proposed by Hobbes is as follows: For'reason'... is nothing but'reckoning,' that is adding and subtracting, of the implications of general names agreed upon for the'marking' and'signifying' of our ideas...

Is it conceivable that one day intelligent machines will be able to tackle all of the challenges that now need the application of human intelligence? This issue helps to define the extent of what robots may perform in the future and directs the direction of research in artificial intelligence. To answer this question, it does not matter if a machine is actually thinking (in the same way that a person thinks) or is just acting like it is thinking because it is only concerned with the behavior of machines. This is because it ignores the issues that are of interest to psychologists, cognitive scientists, and philosophers. It is only concerned with the behavior of machines.

This remark, which was included in the proposal for the Dartmouth workshop that took place in 1956, encapsulates the fundamental perspective held by the vast majority of AI researchers:

Every facet of learning, as well as every other characteristic of intelligence, can be fully characterized to the point that a computer can be built to replicate it.

Arguments against the fundamental premise need to demonstrate either that it is impossible to construct an AI system that is functional because there is some practical limit to the abilities of computers or that there is some unique quality of the human mind that is necessary for intelligent behavior but cannot be duplicated by a machine (or by the methods of current AI research). To be persuasive, arguments in support of the fundamental assumption need to demonstrate that such a system is practicable.

It is also feasible to avoid the link that exists between the two components of the proposal that was presented before. For instance, machine learning, which began with Turing's notorious kid machine idea, completely does away with the need for accurate description.

To begin addressing the issue, the first thing we need to do is provide a precise definition of intelligence..

Alan Turing The Turing test is an extension of this courteous standard for use with computers:

If the actions of a machine are as intelligent as those of a person, then the machine is just as intelligent as a human.

One of the arguments against the Turing test is that it only evaluates how humanlike the behavior of the computer is, rather than how intelligent the behavior is. The exam does not accurately evaluate intelligence since human behavior and intelligent conduct are not the same thing at all and cannot be directly compared. According to what Stuart J. Russell and Peter Norvig said in their article, aeronautical engineering literature do not characterize the objective of their subject as'making machines that fly so perfectly like pigeons that they may trick other birds.'.

Research in artificial intelligence in the twenty-first century defines intelligence in terms of intelligent agents. Something that both observes and acts in its surroundings is referred to as a agent. What constitutes success for the agent may be defined with the use of a performance measure..

If the behavior of an agent is such that it maximizes the predicted value of a performance measure by drawing on previous experience and knowledge, then the agent may be considered intelligent. or the capacity for receiving an insult. They have the potential flaw of being unable to distinguish between things that think and things that do not, which is a significant limitation. According to this notion, even something as simple as a thermostat has some kind of intelligence.

This line of reasoning asserts that if the neural system obeys the laws of physics and chemistry, as we have every reason to think it does, then.... we... ought to be able to recreate the behavior of the nervous system with some physical apparatus, as described by Hubert Dreyfus. is now connected with the futurist Ray Kurzweil, who predicts that the processing power of computers will be adequate for a full brain simulation by the year 2029. Additionally, simulating one second of brain dynamics on a cluster of 27 processors takes a total of fifty days to complete.

Even the most scathing detractors of artificial intelligence, such as Hubert Dreyfus and John Searle, acknowledge that it is theoretically conceivable to create a brain simulation. However, Searle points out that in theory, anything can be simulated by a computer; consequently, bringing the definition to its breaking point leads to the conclusion that any process at all can technically be considered to be a computation. Searle's argument is that the definition of computation should be rethought. He says, What we sought to discover is what differentiates the mind from thermostats and livers, and this is exactly what we found out.

Allen Newell and Herbert A. Simon came up with the idea that symbol manipulation constituted the core of both human and machine intelligence in 1963. They penned it:

A physical symbol system has the essential and enough means for widespread intelligent action. Hubert Dreyfus, a French philosopher, presented a different interpretation of this stance and referred to it as the psychological presupposition.:

The mind may be seen as a machine that processes pieces of information in accordance with predetermined guidelines.

The symbols that Newell, Simon, and Dreyfus spoke about were high-level and word-like. These symbols precisely match with things that exist in the real world, such as dog and tail. Between the years 1956 and 1990, the majority of artificial intelligence systems were created using this kind of symbol. The contemporary form of artificial intelligence, which is founded on statistics and the mathematical optimization of data, does not make use of the high-level symbol processing that Newell and Simon described.

These arguments demonstrate that human thought does not (exclusively) consist of the manipulation of high-level symbols. They do not prove that artificial intelligence cannot be created; rather, they demonstrate that it requires more than just symbol processing.

In 1931, Kurt Gödel proved with an incompleteness theorem that it is always possible to construct a Gödel statement that a given consistent formal system of logic (such as a high-level symbol manipulation program) could not prove.

Despite the fact that it is a statement of fact, the constructed Gödel statement is unprovable in the given system.

(The truth of the constructed Gödel statement is contingent on the consistency of the given system; When the same method is used to a system that is just somewhat inconsistent, the result will provide the impression of success, but will actually yield a false Gödel statement instead.) More speculatively, Gödel conjectured that the human mind can correctly eventually determine the truth or falsity of any well-grounded mathematical statement (including any possible Gödel statement), and that as a consequence, the power of the human intellect cannot be reduced to a mechanism.

Gödelian anti-mechanist arguments tend to rely on the innocuous-seeming claim that a system of human mathematicians (or some idealization of human mathematicians) is both consistent (completely free of error) and believes fully in its own consistency (and can make all logical inferences that follow from its own consistency, including belief in its Gödel statement).

This is something that can never be accomplished by a Turing computer (see Halting problem); therefore, the Gödelian concludes that human reasoning is too powerful to be captured by a Turing machine, and in logical continuation, any digital mechanical device.

However, The scientific and mathematical community as a whole has come to the conclusion that true human thinking is inconsistent in the present era; that any consistent idealized version H of human reasoning would be logically obliged to adopt a healthy but counterintuitive open-minded skepticism