Artificial Intelligence Effect: Fundamentals and Applications

By Fouad Sabry

What Is Artificial Intelligence Effect

The AI effect is the phenomenon that happens when observers rationalize away the actions of an artificial intelligence program by asserting that the program does not possess genuine intelligence.

How You Will Benefit

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

Chapter 1: AI effect

Chapter 2: Artificial intelligence

Chapter 3: History of artificial intelligence

Chapter 4: John McCarthy (computer scientist)

Chapter 5: Symbolic artificial intelligence

Chapter 6: Artificial general intelligence

Chapter 7: Logic in computer science

Chapter 8: Commonsense knowledge (artificial intelligence)

Chapter 9: AI winter

Chapter 10: Turing test

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

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

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

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

Book preview

Chapter 1: AI effect

When people see AI and immediately dismiss its actions because they don't believe AI to be intelligent, this phenomenon is known as the AI effect..

This way of thinking, in which people prefer to say things like AI is anything that has not been done yet, has been dubbed the AI effect. The general public has this false impression that once AI has found a solution to a problem, the means by which the issue was solved no longer fall within the purview of AI. According to Geist, the term AI impact was coined by John McCarthy.

Tesler's Theorem is:

AI is everything that hasn't been done yet. A remark by Larry Tesler

This is a quotation by Douglas Hofstadter.

Researchers in the field of artificial intelligence have created software and algorithms that are currently used in a wide variety of non-AI contexts. This undervaluation is well-known in many other domains, including computer chess, Researchers in the field of artificial intelligence (AI) have discovered that they may get more grants and sell more software if they claim their work has nothing to do with intelligence and instead call it something else. This was notably noticeable during the second AI winter, which occurred in the early 1990s.

Some people think the name robotics is stigmatizing and will damage a company's prospects of being funded, as stated by Patty Tascarella.

'People unconsciously are attempting to maintain for themselves some particular position in the cosmos,' Michael Kearns says. People may keep their sense of individuality and worth by ignoring AI. According to Kearns, the elimination of mystery in the system is to blame for the shift in perspective often referred to as the AI effect. The ability to determine backwards causation is more indicative of automation than intelligence.

When a capability formerly assumed to be distinctively human is found in animals (such as the ability to build tools or pass the mirror test), its overall relevance is downgraded, as has been seen throughout the history of animal cognition and in studies of consciousness.

When asked why artificial intelligence (AI) wasn't getting more attention in the media, Herbert A. Simon said, What set AI apart was the fact that it inspired genuine fear and animosity in certain people. As a result, you are experiencing powerful emotional responses. That's OK, however. That's okay, we can deal.

People said that IBM's chess-playing computer Deep Blue simply utilized brute force tactics and wasn't actual intelligence when it beat Garry Kasparov in 1997. McCarthy expressed regret at the pervasiveness of the AI impact, If and when it's effective, no one calls it AI anymore: 12

but did not think Deep Blue was an adequate illustration.

The impact of AI is unquestionable, according to

{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