Abstract
AI was first coined by John McCarthy in 1956. Vannevar Bush penned an article, “As We Make Think,” that was first published in The Atlantic, and five years later, Alan Turning wrote a paper on the notion of machines being able to simulate human beings. AI had a number of significant contributors, which this chapter chronicles along with the definitions and their achievements. This chapter will provide an introduction, history, and overview of AI. It will also provide examples of the four waves of AI and the current applications and future applications of AI.
TopIntroduction
I thought about the title for my talk about A.I. quite a bit and felt asking the question, “Is A.I. in your future?” would help position this talk on a more strategic basis. I say strategic because implementing A.I. requires vision, leadership and significant investment.
In this chapter, we will discuss the following topics:
My hypothesis is that A.I. should be in your plans to create new products and services to improve the scholarly research eco-system. It will provide many opportunities to improve the efficiencies of scholarly publishing and data analytic tools. Let’s look at the various areas where A.I. can be of service to Scholarly Publishers.
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Peer Review
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Search and Discovery Platforms
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Hypothesis Generation – Determining where research is going
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Selecting employees for specific positions
TopHistory Of A.I.
The term artificial intelligence was first coined by John McCarthy in 1956 when he held the first academic conference on the subject (Peart, 2020). But the journey to understand if machines can honestly think began much before that. In Vannevar Bush’s seminal work “As We May Think” he proposed a system that amplifies people’s own knowledge and understanding (Bush, 1945).
“As We May Think” is a 1945 essay by Vannevar Bush described as visionary and influential, anticipating many aspects of the information society. It was first published in The Atlantic in July 1945 and republished in an abridged version in September 1945—before and after the atomic bombings of Hiroshima and Nagasaki. Bush expresses his concern for the direction of scientific efforts toward destruction, rather than understanding, and illustrates a desire for a sort of collective memory machine with his concept of the memex that would make knowledge more accessible, believing that it would help fix these problems. Through this machine, Bush hoped to transform an information explosion into a knowledge explosion.
Five years later, Alan Turing wrote a paper on the notion of machines being able to simulate human beings and the ability to do intelligent things, such as play Chess (Stezano, 2018).
Key Terms in this Chapter
Generative Adversarial Networks: A generative adversarial network is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Two neural networks contest with each other in a game. Given a training set, this technique learns to generate new data with the same statistics as the training set.
Turing Test: The Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.
Heuristics: A heuristic technique, or a heuristic, is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, short-term goal, or approximation.
Natural Language Processing: Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.
Neural Network: Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.
Expert System: In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.
Strong AI: Strong artificial intelligence (AI) is a theoretical form of machine intelligence that is equal to human intelligence. Key characteristics of Strong AI include the ability to reason, solve puzzles, make judgments, plan, learn, and communicate. It should also have consciousness, objective thoughts, self-awareness, sentience, and sapience.
Artificial Intelligence: 1) A branch of computer science dealing with the simulation of intelligent behavior in computers; 2) the capability of a machine to imitate intelligent human behavior.
Artificial Neural Network: Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.
Backpropagation: In machine learning, backpropagation is a widely used algorithm for training feedforward neural networks. Generalizations of backpropagation exist for other artificial neural networks, and for functions generally. These classes of algorithms are all referred to generically as “backpropagation.”
Artificial General Intelligence: Artificial general intelligence is the hypothetical ability of an intelligent agent to understand or learn any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futures studies.
Forward Chaining: Forward chaining is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, business, and production rule systems.
Deep Learning: Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
Convolutional Neural Network: In deep learning, a convolutional neural network is a class of deep neural network, most commonly applied to analyze visual imagery.
Machine Learning: Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.
Reinforcement Learning: Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning, and unsupervised learning.
A.I. Algorithm: A procedure for solving a mathematical problem (as of finding the greatest common divisor) in a finite number of steps that frequently involves repetition of an operation broadly: a step-by-step procedure for solving a problem or accomplishing some end.
Artificial Narrow Intelligence: Artificial narrow intelligence (ANI or narrow AI) refers to a computer's ability to perform a single task extremely well, such as crawling a webpage or playing chess. Artificial general intelligence (AGI) is when a computer program can perform any intellectual task that a human could.