Chapter 11: Artificial Intelligence AI and Automation

Characteristics of Expert Systems

- Highly effective - Understandable - Reliable - Able to process data quickly - Capable of critical decision making

Handle complex situations.

In a business setting, top-level managers and executives must handle a complex market, challenging competitors, intricate government regulations, and a demanding workforce. Even human experts make mistakes in dealing with these matters. Very careful planning and elaborate computer programming are necessary to develop systems that can handle complex situations

AI is a complex, interdisciplinary field

Involves biology, computer science, linguistics, mathematics, neuroscience, philosophy, and psychology

Determine what is important.

Knowing what is truly important is the mark of a good decision maker. Humans can reprogram their thought process and overlook extraneous data to determine what is important.

Natural language processing is a function of machine learning

We use natural language processing every day without realizing we are training a machine to provide better service to the next user

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.

brain computer interface (BCI):

Technology that interacts with a human's neural structure (brain) and translates the information (thoughts) into activity (actions). • Defense Advanced Research Projects Agency (DARPA) • Medical field

Turing Test

The Turing Test attempts to determine whether a computer can successfully impersonate a human • No computer has yet passed the Turing Test

machine learning:

The ability of a computer to learn without having a programmer change the software for every scenario it encounters. • Involves a computer carrying out tasks based on inputs and a set of instructions

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.

vision system:

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

Selective Compliance Assembly Robot Arm (SCARA) robots

• Easier to integrate into complex printing designs • Have both a lateral and rotary movement, • Can move faster than Cartesian models • Often used in the biomedical field due to their faster and wider field of movement An articulated robot is made to function like an arm - May have ten or more rotary joints that can move up and down like an elbow but can also twist - Frequently used in industrial manufacturing settings, such as on automotive lines, as they can move quickly and with precision

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.

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 • Most of these tasks are deemed tedious or dangerous for humans

natural language processing (nLP):

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

artificial intelligence (AI) system:

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). Include people, procedures, hardware, software, data, and knowledge needed to develop computer systems and machines that can simulate human intelligence processes • Learning, reasoning, and self-correction

knowledge user:

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

upskill:

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

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. - type of vision system that is being used widely in the medical field

Some of the specific characteristics of intelligent behavior include the ability to do the following:

- Learn from experience and apply the knowledge acquired from experience. - Handle complex situations. - Solve problems when important information is missing. - Determine what is important. - React quickly and correctly to a new situation. - Understand visual images. - Process and manipulate symbols. - Be creative and imaginative. - Use heuristics.

Participants in Developing and Using Expert Systems

- domain experts - knowledge engineer - knowledge user

knowledge base:

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

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. Programmed to learn from each iteration during the training phase • Process continues even after system is implemented

rule:

A conditional statement that links conditions to actions or outcomes.

cryptocurrency:

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

knowledge engineer:

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

If-tHen statement:

A rule that suggests certain conclusions.

backward chaining:

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

AI and Employment

AI technology is being introduced in new industries and new applications at a rapid pace • Automation has often created a fear of job loss • A report released by the World Economic Forum states that the growth of AI could actually create up to 58 million new jobs by 2022

deep learning:

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

genetic algorithm:

An approach to solving problems based on the theory of evolution; uses the concept of survival of the fittest as a problem- solving strategy.

Solve problems when important information is missing.

An integral part of decision making is dealing with uncertainty. Often, decisions must be made with little or inaccurate information because obtaining complete information is too costly or impossible. Today, AI systems can make important calculations, comparisons, and decisions even when information is missing. However, it must be noted that the decisions made by an AI system are only as good as the data. A decision will be based only on the information available to the system. If vital data is missing, it will have an impact on the quality of the decision. This is much like how humans make decisions:

Why Learn About Artificial Intelligence (AI) and Automation?

Artificial intelligence (AI) has been in development for more than sixty years • As we look to the future, it is important for organizations and managers to understand artificial intelligence and automation and their applications, including how these fields will continue to develop

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.

Artificial intelligence (AI)

Computers with the ability to mimic or duplicate the functions of the human brain Watson is a supercomputer developed by IBM with AI capabilities • Watson defeated two Jeopardy champions using natural language processing

Learn from experience and apply the knowledge acquired from experience.

Learning from past situations and events is a key component of intelligent behavior and is a natural ability of humans, who learn by trial and error. This ability, however, must be carefully programmed into a computer system.

semi-supervised learning:

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

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 onan unlabeled data set. Learning is gained through positive and negative feedback.

Machine Learning Across Industries

Machine learning, as a subset of AI, continues to affect many industries As more functions become automated, companies will need to increasingly rely on machine learning to operate and remain competitive • Data analytics and cybersecurity • Insurance • Logistics and supply chain management • Healthcare

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.

Understand visual images.

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

There are many options for a career in AI

• Data scientist • Machine learning engineer • Software developer • Robotics scientist • Business intelligence developer • AI research scientist

Industrial Robots (1 of 4)

• Designed for speed, accuracy, and safety • The size and look of industrial robots is dependent on the application • Cartesian robots take up a smaller space, called a footprint, and move in straight lines • One of the most common applications is for 3D printing

Components of Expert Systems

• Knowledge base • Development engine • Inference engine • User interface • Explanation facility • Knowledge acquisition facility

Translators

• Online translators must be trained in more than just word-to-word translations • Grammar rules and punctuation can make a difference regarding how a sentence is read and how it is interpreted in another language

React quickly and correctly to a new situation

A small child, for example, can look over an edge and know not to venture too close. The child reacts quickly and correctly to a new situation. On the other hand, without complex programming, computers do not have this ability.

forward chaining:

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

heuristics:

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

Process and manipulate symbols.

People see, manipulate, and process symbols every day. Visual images provide a constant stream of informa- tion to our brains. By contrast, computers cannot intuitively handle sym- bolic processing and reasoning. Although computers excel at numerical calculations, they must have extensive programming to dealing with sym- bols and three-dimensional objects. Recent developments in computer- vision and machine-vision hardware and software, however, allow some computers to process and manipulate certain symbols. Machine-vision uses cameras to view an image, and computer-vision uses programmed algorithms to interpret the images.

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.

expert systems:

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

Domain expert:

person or group with the expertise or knowledge the expert system is trying to capture

Capabilities of expert systems

• Aiding in decision making • Data analysis, interpreting input, justifying conclusions

Industry Applications

• Robotics, along with AI and machine learning, is being applied in many industries • Automotive industry was one of the first industries to embrace robotics • Healthcare is another industry that has seen a rapid rise in the use of robots • Many pharmacies now use robots to prepare medications and IV solutions

What's Next

• Robots are already becoming more commonplace in our everyday lives • Companies are working on new ideas to make our work and home lives easier, with more inventions continually hitting the market • Robotic toys, security robots, robotic window cleaners, autonomous cars, voice-activated devices that turn on our lights, and mobile devices that allow us to ask questions

Natural language processing is widely used in search engines

Each time a search is entered, the engine must interpret what the user is looking for and return the relevant results in a timely manner

development engine:

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