Chapter 11 Ai

artificial intelligence (AI)

artificial intelligence (AI)

The ability to mimic or duplicate the functions of the human brain.

Watson

Supercomputer

Artificial intelligence (AI) systems

The people, procedures, hardware, software, data, and knowledge needed to develop computer systems and machines that can simulate human intelligence processes, including learning (the acquisition of information and rules for using the information), reasoning (using rules to reach conclusions), and self-correction (using the outcome from one scenario to improve its performance on future scenarios).

intelligent behavior

The ability to learn from experiences and apply knowledge acquired from those experiences; to handle complex situations; to solve problems when important information is missing; to determine what is important and to react quickly and correctly to a new situation; to understand visual images, process and manipulate symbols, and be creative and imaginative; and to use heuristics.

perceptive system

A system that approximates the way a person sees, hears, and feels objects.

heuristics

A trial-and-error method of problem solving used when an algorithmic or mathematical approach is not practical.

Expert systems

The decision-making computer systems in AI, designed to be the most advanced and most reliable in solving complex problems.

knowledge base

A component of an expert system that stores all relevant information, data, rules, cases and relationships used by the expert system.

rule

A conditional statement that links conditions to actions or outcomes.

IF-THEN statements

A rule that suggests certain conclusions.

development engine

Engine that builds the sets of rules and processes used by AI systems.

inference engine

Part of the expert system that seeks information and relationships from the knowledge base and provides answers, predictions, and suggestions, often taking the place of the human experts.

Forward chaining

A strategy used by the inference engine to process data using a set of known facts to make decisions.

Backward chaining

A strategy used by the inference engine to determine how a decision was made.

explanation facility

Component of an expert system that allows a user or decision maker to understand how the expert system arrived at certain conclusions or results.

knowledge acquisition facility

Part of the expert system that provides a convenient and efficient means of capturing and storing all the components of the knowledge base.

domain expert

The person or group with the expertise or knowledge the expert system is trying to capture (domain).

knowledge engineer

A person who has training or experience in the design, development, implementation, and maintenance of an expert system.

knowledge user

The person or group who uses and benefits from the expert system

vision systems

The hardware and software that permit computers to capture, store, and manipulate visual images.

Augmented reality (AR)

Vision system software that takes computer-generated images and superimposes them on a user’s view of the world through the use of specialized glasses or goggles.

intelligent agent

Programs and a knowledge base used to perform a specific task for a person, a process, or another program; also called an intelligent robot or bot.

artificial neural network

A computer system that can recognize and act on patterns or trends that it detects in large sets of data; developed to operate like the human brain.

upskill

The practice of training a workforce to perform higher-skilled roles to ensure they meet their full potential.

machine learning

The ability of a computer to learn without having a programmer change the software for every scenario it encounters.

Supervised learning

Machine learning using a labeled data set and examples to produce output that is compared to a predefined correct output.

Unsupervised learning

Machine learning using an unlabeled data set and no examples. The data is labeled through observations, and learning is through observation.

Reinforced learning

Machine learning using trial and error on an unlabeled data set. Learning is gained through positive and negative feedback.

Semi-supervised learning

Machine learning using a combination of supervised and unsupervised learning techniques.

Cryptocurrency

A digital currency, such as Bitcoin, used for financial transactions.

optical character recognition (OCR)

Technology that distinguishes printed or handwritten text in a digital image, such as a scanned document, that is converted into a computer-generated document, such as a PDF.

Natural language processing (NLP)

The part of machine language that allows computers to understand, analyze, manipulate, and generate natural language for processing.

deep learning

Allows programs to grow and learn from examples provided users, either typed or spoken.

brain computer interface (BCI)

Technology that interacts with a human’s neural structure (brain) and translates the information (thoughts) into activity (actions).

Robotics

Technology using a combination of mechanical engineering, computer science, and machine learning to create a device that can perform tasks with a high degree of precision.