Artificial Intelligence (AI): The Bumpy Path Throu…

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Author : U. S. Military
Genre : Computers

Summary : The use of artificial intelligence systems is ready to transition from basic science research and a blooming commercial industry to strategic implementation in the Defense Acquisition system. The purpose of this research is to determine the problems awaiting artificial intelligence (AI) systems inherent to defense acquisition. AI is a field of scientific study focused on the construction of systems that can act rationally, behave humanly, and adapt. To achieve AI behavior takes AI essentials, which consider mobility, system perspective, and algorithms. Unfortunately, AI essentials are under addressed in the concept of operations that fuels the Joint Capabilities Integration and Development System. Influences to the concept of operations analyzed in this research include strategic documentation, joint technology demonstrations, and exercises that aim to capture technology-based lessons learned. Failure to address AI essentials causes problems in defense acquisition: system requirements are impossible to define; transition of AI technology fails; testing cannot be evaluated with confidence; and life cycle planning is at best a guess. To address these issues, the Department of Defense needs improved planning, acquisition personnel training, and AI-supported acquisition processes to achieve cost, schedule, and performance goals. Chapter II, "Literature Review" provides a snapshot of AI today. It works to provide a general understanding of the scientific field and technology that is AI, the spectrum of behaviors expected from AI systems, what composition of an AI system, and the identities of AI industry leaders. The reader should be able to understand a working definition of AI systems, a general sense of AI technology readiness, and the emerging industry surrounding AI. Next, Chapter III, "JCIDS," examines the ability for DOD processes to develop requirements for AI applications. Requirements developments starts at a strategic level, directing military resources to achieve present and future military needs. The JCIDS clarifies strategic direction, identifying capability gaps and validating needs (CJCS, 2012, p. 2). This chapter outlines how the JCIDS builds validated requirement documents, then focuses on grading AI elements in the joint CONOPs. The reader should leave this section with an understanding of CONOPs AI maturity and its influence on validated requirements headed for the DAS. Chapter IV, "DAS," focuses on the DAS and the processes that PMs use to manage system acquisition. The DAS is defined by DOD regulation, and gives direction for management of systems engineering, financial management, and contracting efforts (DOD, 2017, p. 51). This chapter analyzes the general process for developing and purchasing defense systems and the seminal areas inside of the DAS where software-intensive systems have struggled. The reader should leave this section understanding the consequences that poorly defined AI requirements would have on program cost, performance, and schedule. Chapter V, "Conclusion," integrates the ideas uncovered from the research in order to answer the secondary research questions and then the primary research question. Next, it makes recommendations based on the research that should help to prepare JCIDS and DAS for success with AI systems. Lastly, Chapter V proposes future areas of research that will generate more comprehensive information about the definition of AI requirements and how to meet cost, schedule, and performance during system fielding.

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Author : Katharina A. Zweig
Genre : Computers

Summary : An expert offers a guide to where we should use artificial intelligence—and where we should not. Before we know it, artificial intelligence (AI) will work its way into every corner of our lives, making decisions about, with, and for us. Is this a good thing? There’s a tendency to think that machines can be more “objective” than humans—can make better decisions about job applicants, for example, or risk assessments. In Awkward Intelligence, AI expert Katharina Zweig offers readers the inside story, explaining how many levers computer and data scientists must pull for AI’s supposedly objective decision making. She presents the good and the bad: AI is good at processing vast quantities of data that humans cannot—but it’s bad at making judgments about people. AI is accurate at sifting through billions of websites to offer up the best results for our search queries and it has beaten reigning champions in games of chess and Go. But, drawing on her own research, Zweig shows how inaccurate AI is, for example, at predicting whether someone with a previous conviction will become a repeat offender. It’s no better than simple guesswork, and yet it’s used to determine people’s futures. Zweig introduces readers to the basics of AI and presents a toolkit for designing AI systems. She explains algorithms, big data, and computer intelligence, and how they relate to one another. Finally, she explores the ethics of AI and how we can shape the process. With Awkward Intelligence. Zweig equips us to confront the biggest question concerning AI: where we should use it—and where we should not.

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Author : Denis Rothman
Genre : Computers

Summary : Be an adaptive thinker that leads the way to Artificial Intelligence Key Features AI-based examples to guide you in designing and implementing machine intelligence Develop your own method for future AI solutions Acquire advanced AI, machine learning, and deep learning design skills Book Description Artificial Intelligence has the potential to replicate humans in every field. This book serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop business vision. What you will learn Use adaptive thinking to solve real-life AI case studies Rise beyond being a modern-day factory code worker Acquire advanced AI, machine learning, and deep learning designing skills Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology Understand future AI solutions and adapt quickly to them Develop out-of-the-box thinking to face any challenge the market presents Who this book is for Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book.

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Author : Erik J. Larson
Genre : Computers

Summary : “Exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it.” —John Horgan “If you want to know about AI, read this book...It shows how a supposedly futuristic reverence for Artificial Intelligence retards progress when it denigrates our most irreplaceable resource for any future progress: our own human intelligence.” —Peter Thiel Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. A computer scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to reveal why this is a profound mistake. AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don’t correlate data sets. We make conjectures, informed by context and experience. And we haven’t a clue how to program that kind of intuitive reasoning, which lies at the heart of common sense. Futurists insist AI will soon eclipse the capacities of the most gifted mind, but Larson shows how far we are from superintelligence—and what it would take to get there. “Larson worries that we’re making two mistakes at once, defining human intelligence down while overestimating what AI is likely to achieve...Another concern is learned passivity: our tendency to assume that AI will solve problems and our failure, as a result, to cultivate human ingenuity.” —David A. Shaywitz, Wall Street Journal “A convincing case that artificial general intelligence—machine-based intelligence that matches our own—is beyond the capacity of algorithmic machine learning because there is a mismatch between how humans and machines know what they know.” —Sue Halpern, New York Review of Books

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Author : JingTao Yao
Genre : Computers

Summary : This book constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2022, held in Suzhou, China, in November 2022. The 28 full papers included in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: Invited papers, IRSS President Forum; rough set theory and applications; granular computing and applications; classification and deep learning; conceptual knowledge discovery and machine learning based on three-way decisions and granular computing; uncertainty in three-way decisions; granular computing, and data science.

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Author : Radoslaw Katarzyniak
Genre : Mathematics

Summary : To built intelligent systems that can cope with real world problems we need to - velop computational mechanisms able to deal with very large amounts of data, gen- ate complex plans, schedules, and resource allocation strategies, re-plan their actions in real time, provide user friendly communication for human-device interactions, and perform complex optimization problems. In each of these tasks intelligence techno- gies play an important role, providing designers and creators with effective and adequate computational models. The field of intelligence technologies covers a variety of computational approaches that are often suggested and inspired by biological systems, exhibiting functional richness and flexibility of their natural behavior. This class of technologies consists of such important approaches as data mining algorithms, neural networks, genetic al- rithms, fuzzy and multi-valued logics, rough sets, agent-oriented computation, often integrated into complex hybrid solutions. Intelligence technologies are used to built machines that can act and think like living systems, solve problems in an autonomous way, develop rich private knowledge bases and produce results not foreseen and programmed in a direct way by designers and creators.

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Author : Yiyu Yao
Genre : Computers

Summary : This book constitutes the refereed conference proceedings of the 15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2015, held in Tianjin, China in November 2015 as one of the co-located conference of the 2015 Joint Rough Set Symposium, JRS 2015. The 44 papers were carefully reviewed and selected from 97 submissions. The papers in this volume cover topics such as rough sets: the experts speak; generalized rough sets; rough sets and graphs; rough and fuzzy hybridization; granular computing; data mining and machine learning; three-way decisions; IJCRS 2015 data challenge.

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Author : Virginia Dignum
Genre : Computers

Summary : In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens.

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Author : Mikhail Moshkov
Genre : Technology & Engineering

Summary : Decision trees and decision rule systems are widely used in different applications as algorithms for problem solving, as predictors, and as a way for knowledge representation. Reducts play key role in the problem of attribute (feature) selection. The aims of this book are (i) the consideration of the sets of decision trees, rules and reducts; (ii) study of relationships among these objects; (iii) design of algorithms for construction of trees, rules and reducts; and (iv) obtaining bounds on their complexity. Applications for supervised machine learning, discrete optimization, analysis of acyclic programs, fault diagnosis, and pattern recognition are considered also. This is a mixture of research monograph and lecture notes. It contains many unpublished results. However, proofs are carefully selected to be understandable for students. The results considered in this book can be useful for researchers in machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, test theory and logical analysis of data. The book can be used in the creation of courses for graduate students.

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Author : International Monetary Fund. Communications Department
Genre : Business & Economics

Summary : Generative AI has introduced tantalizing new possibilities. Yet the initial excitement surrounding AI has given way to genuine concerns. This issue is an early attempt to understand AI’s implications for growth, jobs, inequality, and finance.