Artificial Intelligence in a Throughput Model: Some Major Algorithms PDF by Waymond Rodgers

Contents

Preface
In 1964, Isaac Asimov envisioned the 2014 World’s Fair for The New York Times. He was correct about the smartphone, self-driving cars and the Keurig machine; however, he was not able to predict advanced battery technology and space colonization. Foresight is challenging, and is only becoming more and more so.

Artificial Intelligence (AI) is the next major disruptor in the way we live, learn, work and adopt to different situations. Disruptors represent a person, place or thing that prevents something, especially a system, process or event, from continuing as usual or as expected. Our houses, our cars, our toasters, all of seem to be teeming, even overflowing with intelligence, like some huge fungus gone amuck. Artificial Intelligence is here to stay, and society needs it, right now! Artificial Intelligence is changing the world each day. At one time, a domain of science fiction, today many of our systems are powered by Artificial Intelligence.

The field of Artificial Intelligence has awed researchers and users alike. Artificial Intelligence is the intelligence of machines and a subset of computer science and cognitive science. Fundamental challenges of Artificial Intelligence embrace such features as reasoning, knowledge, planning, learning, communication, perception and the capability to move and manipulate objects.

This book addresses the promise of Artificial Intelligence that is enhancing our lives. Artificial Intelligence-based systems are now outshining medical specialists in diagnosing certain diseases. The implementation of Artificial Intelligence in the financial system is magnifying access to credit to borrowers whose loan applications were once rejected. In addition, automated hiring systems have the potential to evaluate candidates on the basis of their authentic qualifications as opposed to characteristics such as age or appearance that oftentimes mislead decision makers from making the correct decision.

One of the many advantages of utilizing Artificial Intelligence and machine learning is that it has the ability to ingest huge amounts of data, often in real-time. Further, it can take that data and begin to scrutinize it based upon organizational necessities, conditions and constraints. In addition, it can bring about those necessities, conditions and constraints based on the data an organization owns.

Moreover, many physical and behavioral biometric technologies such as fingerprint, facial recognition, voice identification, etc., have enhanced the level of security substantially. Governments and corporates have embraced these technologies to increase customer satisfaction. Nevertheless, the current state of biometrics still faces challenges to lessen criminal and terrorist activities as well as other digital-based financial frauds. This is especially the case when individuals and organizations are faced with selecting the correct algorithm to a problem. To overcome this state of affairs, the market undertakes a host of research and development programmes to assimilate biometrics with artificial intelligence in decision-making modeling. The advanced software algorithm platform of Artificial Intelligence processes information offered by biometric technology to detect and prevent dubious activities in a bid to confront cyber and physical crimes in the global and local communities. This development has provided an expanded growth opportunity for the biometrics technology, given that the technology is set to increase the security and internal control operations many folds.

This book provides an overview of the various Artificial Intelligence techniques, biometric technologies, decision-making algorithms and the subsequent market expansion opportunities. Further, it proposes a Throughput Model, which draws from computer science, economics and psychology to model perceptual, informational sources, judgmental processes and decision choice algorithms. This approach provides how huge data and biometrics might be implemented to reduce risks to individuals and organizations, especially when dealing with digital-based mediums.

The book also examines the ethics behind Artificial Intelligence. That is, how machine learning, neural networks, and deep learning technology are positioned today for many individual and organizational uses, including self-driving cars, online recommendations, search engines, handwriting recognition, computer vision, online ad serving, pricing, prediction of equipment failure, credit scoring, fraud detection, OCR (optical character recognition), spam filtering, etc. Therefore, this book addresses ethical consideration directed at the growing ubiquity of machine learning, neural networks, and deep learning in organizations. This particular issue is essential in order to understand what and how to mitigate human cognitive biases and heuristics into Artificial Intelligence technology.

Introduction to Artificial Intelligence and Biometrics Applications
Artificial intelligence (AI) is here for stay. For individuals and organizations, Artificial Intelligence is a disruptor in the way we live, learn, work and adopt to different situations. Disruptors represent a person, place or thing that prevents something, especially a system, process, or event, from enduring as customary or as anticipated in the future. We are right in the midst of the information revolution. Although it is an extraordinary time and place to be in, there are caveats that come along with it. Having a machine tell you how long your commute will be, what music you should listen to, and what content you would likely engage with are all relatively innocuous examples.

Artificial Intelligence is presently used as a tool to assist people. It is adopted as point solutions across a wide array of functions such as personal digital assistant, email filtering, search, fraud prevention, engineering, marketing models, digital distribution, video production, news generation, play and game-play analytics, customer service, financial reporting, marketing optimization, energy cost management, pricing, inventory, enterprise applications, etc. Artificial Intelligence is also integrated into biometrics tools such as iris recognition, voice recognition, facial recognition, content classification, gait, and natural language.

In addition, Artificial Intelligence is becoming widespread in most facets of decision-making and will become more so in the near future. Artificial Intelligence has been an aspiration of computer scientists since the 1950s, and has experienced colossal advancement in recent years. Artificial Intelligence implementation is already an integral part of many of our online activities and will become progressively more entrenched in everything we touch, hear, see and do. On a task-by-task basis, Artificial Intelligence systems gradually produce outputs that far exceed the precision and reliability of those produced by individuals. For example, pharmaceuticals and the food industry utilize Artificial Intelligence sensor tools to ensure the optimum temperature for creating drugs or cooking food. Other sensors make certain that products are stored and shipped at safe temperatures.

In agriculture, the implementation of Artificial Intelligence tools has boosted crop yields. Artificial Intelligence has provided for farmers to make better decisions pertaining to crops to sow and how to best manage them. Harvesting everything from grains to root vegetables to soft fruits can now be performed more efficiently and effectively with robots than people. Furthermore, mobile technology has reformed the manner in which field service teams operate. They can get work orders more rapidly, and once the employees are at a particular location, they can access schematics and documents to assist with the repair work. In addition, the Artificial Intelligence system provides for a smoother progression of work orders from fault call to the ordering of parts and the ensuing billing of satisfied customers.

In financial services, PwC has garnered enormous amounts of data from the US Census Bureau, US financial data, and other public licensed sources to create secure, a large-scale model of 320 million US consumers’ financial decisions. The model is intended to assist financial services firms map buyers’ personas, simulate “future selves” and anticipate customer behavior. It has empowered these financial services companies in substantiating real-time business decisions within seconds.

Similar to the financial services sector, Artificial Intelligence has been implemented to develop a model of the automobile ecosystem. Here, you have bots that map the decisions made from automotive players, such as vehicle purchasers, manufacturers, and transportation services providers. This has assisted organizations to predict the adoption of electric and driverless vehicles, and the enactment of non-restrictive pricing schemes that work on their target market. It has also assisted them in providing enhanced advertising decision choices.