Synthetic Intelligence: Fundamentals and Applications

By Fouad Sabry

What Is Synthetic Intelligence

An alternate or opposite name for artificial intelligence is synthetic intelligence (SI), which emphasizes that the intelligence of machines does not have to be an imitation or artificial in any sense, but rather, it can be a true type of intelligence. John Haugeland makes a comparison between synthetic diamonds and simulated diamonds, arguing that only the synthetic diamond can be considered a genuine diamond. Synthetic refers to something that is formed by the process of synthesis, which involves joining pieces to make a whole; in common parlance, synthetic refers to a version that was built by humans of something that occurred naturally. Therefore, a "synthetic intelligence" would be human-made or appear to be developed by humans, but it would not be a simulation.

How You Will Benefit

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

Chapter 1: Synthetic Intelligence

Chapter 2: Artificial Intelligence

Chapter 3: Artificial General Intelligence

Chapter 4: Physical Symbol System

Chapter 5: Intelligent Agent

Chapter 6: History of Artificial Intelligence

Chapter 7: Philosophy of Artificial Intelligence

Chapter 8: Outline of Artificial Intelligence

Chapter 9: Turing Test

Chapter 10: GOFAI

(II) Answering the public top questions about synthetic intelligence.

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

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

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

Book preview

Chapter 1: Synthetic intelligence

Instead than highlighting that machine intelligence is a pale replica of human intellect, synthetic intelligence (SI) emphasizes that it may be a true type of intelligence in its own right. Synthetic refers to anything that has been created artificially by mixing different elements, or a synthetic replica of a naturally occurring phenomenon. Accordingly, a synthetic intellect would be human-made or seem to be such, but not be a simulation.

Haugeland coined the phrase in 1986 to characterize the state of artificial intelligence studies at the time, Inquiring, Can Man-Made Aircraft Fly? Airplanes may answer this question positively.

Can artificial organisms swim? The correct response is no, since submarines cannot swim.

To What Extent Can Machines Think? Is this a similar or different query from the first?

For Drew McDermott, thinking is analogous to flying, and he couldn't be more certain about it. To imply that Deep Blue, the world champion chess computer, doesn't actually think about chess is like arguing that an aircraft can't fly because its wings don't flap, he says. One of his main arguments in the Chinese room debate is that a simulated mind is not the same as a genuine one.

According to Daniel Dennett, this argument is mostly about semantics and has little to do with the fundamental issues in the philosophy of artificial intelligence. He explains that any vodka is authentic regardless of who manufactured it, but that even a chemically exact replica of a Chateau Latour is still a phony.

{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 that studies intelligent agents. The term AI impact refers to the process by which activities that were formerly thought to need intelligence but are no longer included in the concept of artificial intelligence as technology advances. AI researchers have adapted and incorporated a broad variety of approaches for addressing issues, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics, in order to tackle these difficulties. Computer science, psychology, linguistics, philosophy, and a great many other academic disciplines all contribute to the development of AI.

The theory that human intellect can be so accurately characterized that a computer may be constructed to imitate it was the guiding principle behind the establishment of this discipline. This sparked philosophical debates concerning the mind and the ethical implications of imbuing artificial organisms with intellect comparable to that of humans; these are topics that have been investigated by myth, literature, and philosophy ever since antiquity.

In ancient times, artificial creatures with artificial intelligence were used in various narrative devices.

and are often seen in works of literature, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R.

The formal design for Turing-complete artificial neurons that McCullouch and Pitts developed in 1943 was the first piece of work that is now widely understood to be an example of artificial intelligence.

Attendees of the conference went on to become pioneers in the field of AI research.

They, together with their pupils, were able to build programs that the press referred to as astonishing. These programs included machines that were able to learn checkers techniques, solving word problems in algebra, demonstrating logical theorems and having good command of the English language.

Around the middle of the decade of the 1960s, research done in the United States

was receiving a significant amount of funding from the Department of Defense, and facilities were being set up all around the globe.

as well as continuous pressure from the Congress of the United States to invest in more fruitful endeavors, The United States of America

both the Canadian and British governments stopped funding exploratory research in artificial intelligence.

The following few years would be referred to in the future as a AI winter.

a time when it was difficult to acquire financing for artificial intelligence initiatives.

a kind of artificial intelligence software that mimicked the knowledge and analytical prowess of human professionals.

By 1985, Over a billion dollars was now being transacted in the artificial intelligence business.

While this is going on, The United States and the United Kingdom have reestablished support for university research as a direct result of Japan's computer programme for the fifth generation.

However, When the market for lisp machines crashed in 1987, it was the beginning of a downward spiral.

AI once again fallen into disfavor, as well as another, longer-lasting winter started.

Geoffrey Hinton is credited for reviving interest in neural networks and the concept of connectionism.

Around the middle of the 1980s, David Rumelhart and a few others were involved. During the 1980s, many soft computing tools were created.

include things like neural networks, fuzzy systems, Theory of the grey system, the use of evolutionary computing as well as a number of methods derived from statistical or mathematical optimization.

Through the late 1990s and into the early 21st century, AI worked to progressively rehabilitate its image by developing solutions that were tailored to address particular challenges. Because of the tight emphasis, researchers were able to develop conclusions that could be verified, use a greater number of mathematical approaches, and work with experts from other areas (such as statistics, economics and mathematics). In the 1990s, the solutions that were produced by AI researchers were never referred to as artificial intelligence, but by the year 2000, they were being employed extensively all around the world. According to Jack Clark of Bloomberg, the year 2015 was a watershed year for artificial intelligence. This is due to the fact that the number of software projects that employ AI inside Google went from sporadic use in 2012 to more than 2,700 projects in 2015.

The overarching challenge of emulating (or fabricating) intelligence has been segmented into a variety of more specific challenges. These are certain characteristics or skills that researchers anticipate an intelligent system to possess. The greatest emphasis has been paid to the characteristics that are detailed below.

Researchers in the early days of computer science devised algorithms that mirrored the step-by-step reasoning that people use when they solve problems or make logical inferences. Research in artificial intelligence had by the late 1980s and early 1990s established strategies for coping with uncertain or partial information. These approaches used notions from probability and economics. Even among humans, the kind of step-by-step deduction that early studies in artificial intelligence could replicate is uncommon. They are able to address the majority of their issues by making snap decisions based on their intuition.

Information engineering and the representation of that knowledge are what enable artificial intelligence systems to intelligently respond to inquiries and draw conclusions about real-world events.

An ontology is a collection of objects, relations, ideas, and attributes that are formally characterized in order to ensure that software agents are able to comprehend them. An ontology is a description of what exists. Upper ontologies are ontologies that seek to provide a basis for all other information and operate as mediators between domain ontologies, which cover specialized knowledge about a particular knowledge domain. Upper ontologies are the most broad ontologies, and they are also termed ontologies (field of interest or area of concern). A software that is genuinely intelligent would also require access to commonsense knowledge, which is the collection of facts that the typical human is aware of. In most cases, the description logic of an ontology, such as the Web Ontology Language, is used to express the semantics of an ontology. In addition to other domains, situations, events, states, and times; causes and effects; knowledge about knowledge (what we know about what other people know); default reasoning (things that humans assume are true until they are told differently and will continue to be true even when other facts are changing); and knowledge about knowledge about knowledge are all examples of domains. The breadth of commonsense information (the number of atomic facts that the typical human is aware of is immense) and the sub-symbolic nature of the majority of commonsense knowledge are two of the most challenging challenges in artificial intelligence (much of what people know is not represented as facts or statements that they could express verbally). Image interpretation, therapeutic decision assistance, knowledge discovery (the extraction of interesting and actionable insights from big datasets), and other disciplines are all areas that might benefit from artificial intelligence.

An intelligent agent that is capable of planning creates a representation of the current state of the world, makes predictions about how their actions will affect the environment, and makes decisions that maximize the utility (or value) of the available options. In traditional problems of planning, the agent may make the assumption that it is the only system working in the world. This enables the agent to be assured of the results that will come from the actions that it takes. However, if the agent is not the sole player, it is necessary for the agent to reason under ambiguity, continually reevaluate its surroundings, and adapt to new circumstances.

The study of computer systems that can improve themselves automatically via the accumulation of experience is referred to as machine learning (ML), and it has been an essential part of AI research ever since the start of the subject. In the learning method known as reinforcement, the agent is rewarded for appropriate replies and disciplined for inappropriate ones. The agent organizes its replies into categories in order to formulate a strategy for navigating the issue area it faces.

The term natural language processing (NLP)