Rational Machines And Artificial Intelligence

Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts. Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective? Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets Discusses the application of Moore’s Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality

Develops insights into solving complex problems in engineering, biomedical sciences, social science and economics based on artificial intelligence. Some of the problems studied are in interstate conflict, credit scoring, breast cancer diagnosis, condition monitoring, wine testing, image processing and optical character recognition. The author discusses and applies the concept of flexibly-bounded rationality which prescribes that the bounds in Nobel Laureate Herbert Simon’s bounded rationality theory are flexible due to advanced signal processing techniques, Moore’s Law and artificial intelligence. Artificial Intelligence Techniques for Rational Decision Making examines and defines the concepts of causal and correlation machines and applies the transmission theory of causality as a defining factor that distinguishes causality from correlation. It develops the theory of rational counterfactuals which are defined as counterfactuals that are intended to maximize the attainment of a particular goal within the context of a bounded rational decision making process. Furthermore, it studies four methods for dealing with irrelevant information in decision making: Theory of the marginalization of irrelevant information Principal component analysis Independent component analysis Automatic relevance determination method In addition it studies the concept of group decision making and various ways of effecting group decision making within the context of artificial intelligence. Rich in methods of artificial intelligence including rough sets, neural networks, support vector machines, genetic algorithms, particle swarm optimization, simulated annealing, incremental learning and fuzzy networks, this book will be welcomed by researchers and students working in these areas.

Artificial intelligence (AI) technologies are one of top investment priorities in these days. They are aimed at finding applications in fields of special value for humans, including education. The fourth industrial revolution will replace not only human hands but also human brains, the time of machines requires new forms of work and new ways of business education, however we must be aware that if there is no control of human-chatbot interaction, there is a risk of losing sight of this interaction’s goal. First, it is important to get people to truly understand AI systems, to intentionally participate in their use, as well as to build their trust, because “the measure of success for AI applications is the value they create for human lives” (Stanford University 2016, 33). Consequently, society needs to adapt to AI applications if it is to extend its benefits and mitigate the inevitable errors and failures. This is why it is highly recommended to create new AI-powered tools for education that are the result of cooperation between AI researchers and humanities’ and social sciences’ researchers, who can identify cognitive processes and human behaviors. This book is authored by a range of international experts with a diversity of backgrounds and perspectives hopefully bringing us closer to the responses for the questions what we should teach (what the ‘right’ set of future skills is), how we should teach (the way in which schools should teach and assess them) and where we should teach (what implications does AI have for today’s education infrastructure). We must remember as we have already noticed before “…education institutions would need to ensure that that they have an appropriate infrastructure, as well as the safety and credibility of AI-based systems. Ultimately, the law and policies need to adjust to the rapid pace of AI development, because the formal responsibility for appropriate learning outcomes will in future be divided between a teacher and a machine. Above all, we should ensure that AI respect human and civil rights (Stachowicz-Stanusch, Amann, 2018)”.

Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.

Causality has been a subject of study for a long time. Often causality is confused with correlation. Human intuition has evolved such that it has learned to identify causality through correlation. In this book, four main themes are considered and these are causality, correlation, artificial intelligence and decision making. A correlation machine is defined and built using multi-layer perceptron network, principal component analysis, Gaussian Mixture models, genetic algorithms, expectation maximization technique, simulated annealing and particle swarm optimization. Furthermore, a causal machine is defined and built using multi-layer perceptron, radial basis function, Bayesian statistics and Hybrid Monte Carlo methods. Both these machines are used to build a Granger non-linear causality model. In addition, the Neyman–Rubin, Pearl and Granger causal models are studied and are unified. The automatic relevance determination is also applied to extend Granger causality framework to the non-linear domain. The concept of rational decision making is studied, and the theory of flexibly-bounded rationality is used to extend the theory of bounded rationality within the principle of the indivisibility of rationality. The theory of the marginalization of irrationality for decision making is also introduced to deal with satisficing within irrational conditions. The methods proposed are applied in biomedical engineering, condition monitoring and for modelling interstate conflict. Contents:Introduction to Artificial Intelligence based Decision MakingWhat is a Correlation Machine?What is a Causal Machine?Correlation Machines Using Optimization MethodsNeural Networks for Modeling Granger CausalityRubin, Pearl and Granger Causality Models: A Unified ViewCausal, Correlation and Automatic Relevance Determination Machines for Granger CausalityFlexibly-bounded RationalityMarginalization of Irrationality in Decision MakingConclusions and Further Work Readership: Graduate students, researchers and professionals in the field of artificial intelligence. Key Features:It proposes fresh definition of causality and proposes two new theories i.e. flexibly bounded rationality and marginalization of irrationality theory for decision makingIt also applies these techniques to a diverse areas in engineering, political science and biomedical engineeringKeywords:Causality;Correlation;Artificial Intelligence;Rational Decision Making

This book theoretically and practically updates major economic ideas such as demand and supply, rational choice and expectations, bounded rationality, behavioral economics, information asymmetry, pricing, efficient market hypothesis, game theory, mechanism design, portfolio theory, causality and financial engineering in the age of significant advances in man-machine systems. The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence concepts such as the swarming of birds, the working of the brain and the pathfinding of the ants. Artificial Intelligence and Economic Theory: Skynet in the Market analyses the impact of artificial intelligence on economic theories, a subject that has not been studied. It also introduces new economic theories and these are rational counterfactuals and rational opportunity costs. These ideas are applied to diverse areas such as modelling of the stock market, credit scoring, HIV and interstate conflict. Artificial intelligence ideas used in this book include neural networks, particle swarm optimization, simulated annealing, fuzzy logic and genetic algorithms. It, furthermore, explores ideas in causality including Granger as well as the Pearl causality models.

The world we live in presents plenty of tricky, impactful, and hard-tomake decisions to be taken. Sometimes the available options are ample, at other times they are apparently binary, either way, they often confront us with dilemmas, paradoxes, and even denial of values.In the dawn of the age of intelligence, when robots are gradually taking over most decision-making from humans, this book sheds a bit of light on decision rationale. It delves into the limits of these decision processes (for both humans and machines), and it does so by providing a new perspective that is somehow opposed to orthodox economics. All Economics reflections in this book are underlined and linked to Artificial Intelligence.The authors hope that this comprehensive and modern analysis, firmly grounded in the opinions of various groundbreaking Nobel laureate economists, may be helpful to a broad audience interested in how decisions may lead us all to flourishing societies. That is, societies in which economic blunders (caused by over simplification of problems and super estimation of tools) are reduced substantially.

Apocalyptic AI, the hope that we might one day upload our minds into machines or cyberspace and live forever, is a surprisingly wide-spread and influential idea, affecting everything from the world view of online gamers to government research funding and philosophical thought. In Apocalyptic AI, Robert Geraci offers the first serious account of this "cyber-theology" and the people who promote it. Drawing on interviews with roboticists and AI researchers and with devotees of the online game Second Life, among others, Geraci illuminates the ideas of such advocates of Apocalyptic AI as Hans Moravec and Ray Kurzweil. He reveals that the rhetoric of Apocalyptic AI is strikingly similar to that of the apocalyptic traditions of Judaism and Christianity. In both systems, the believer is trapped in a dualistic universe and expects a resolution in which he or she will be translated to a transcendent new world and live forever in a glorified new body. Equally important, Geraci shows how this worldview shapes our culture. Apocalyptic AI has become a powerful force in modern culture. In this superb volume, he shines a light on this belief system, revealing what it is and how it is changing society.

‘Professor Marwala has sought to understand what good leadership should mean by drawing on the collective experience of authors who have written on many topics.’ – Former President of South Africa, THABO MBEKI We cannot underestimate how critical strong leadership is in all aspects of our lives. It enables us to run our lives, homes, communities, workplaces and nations. Given its importance, it is pertinent to ask: What is the source of good leadership? Albert Einstein once said, ‘The only source of knowledge is experience.’ Many philosophers have observed this and, if we accept experience as the only source of knowledge, can we extend this conclusion to leadership? Or is the basis of good leadership intuition or instinct? Or is it perhaps a combination of these? In Leadership Lessons From Books I Have Read, Tshilidzi Marwala adopts the thesis that the source of good leadership is knowledge, and the source of knowledge is experience, which can take many forms: reading widely, listening, and engaging in discussion and debate with other knowledge seekers. If leadership is derived from knowledge and knowledge is derived from experience, the ‘experience’ in this book is from 50 books that Tshilidzi has read, and so the source of knowledge informing leadership is the collective experience of the more than 50 accomplished authors who wrote those books including, among others, Chinua Achebe, Thomas Sankara, NoViolet Bulawayo, Nelson Mandela, Mandla Mathebula, Eugène Marais, Chimamanda Ngozi Adichie, Jean-Jacques Rousseau, Daniel Kahneman, Karl Marx, Ngũgĩ wa Thiong’o, Nassim Taleb and Aristotle. Divided into four sections, Tshilidzi shares his leadership lessons in the areas of Africa and the diaspora, the search for the ideal polity, science, technology and society, and the leadership of nations. ‘Those who do not read, should not lead.’ – THILIDZI MARWALA

To the age-old debate over what it means to be human, the relatively new fields of sociobiology and artificial intelligence bring new, if not necessarily compatible, insights. What have these two fields in common? Have they affected the way we define humanity? These and other timely questions are addressed with colorful individuality by the authors of The Boundaries of Humanity. Leading researchers in both sociobiology and artificial intelligence combine their reflections with those of philosophers, historians, and social scientists, while the editors explore the historical and contemporary contexts of the debate in their introductions. The implications of their individual arguments, and the often heated controversies generated by biological determinism or by mechanical models of mind, go to the heart of contemporary scientific, philosophical, and humanistic studies. Contributors: Arnold I. Davidson, John Dupré, Roger Hahn, Stuart Hampshire, Evelyn Fox Keller, Melvin Konner, Alan Newell, Harriet Ritvo, James J. Sheehan, Morton Sosna, Sherry Turkle, Bernard Williams, Terry Winograd This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1991.

This volume presents new research in artificial intelligence (AI) and Law with special reference to criminal justice. It brings together leading international experts including computer scientists, lawyers, judges and cyber-psychologists. The book examines some of the core problems that technology raises for criminal law ranging from privacy and data protection, to cyber-warfare, through to the theft of virtual property. Focusing on the West and China, the work considers the issue of AI and the Law in a comparative context presenting the research from a cross-jurisdictional and cross-disciplinary approach. As China becomes a global leader in AI and technology, the book provides an essential in-depth understanding of domestic laws in both Western jurisdictions and China on criminal liability for cybercrime. As such, it will be a valuable resource for academics and researchers working in the areas of AI, technology and criminal justice.

Jason Gregory reveals the psychological and spiritual damage digital technology and social media are having on the human mind, and gifts you a digital free lifestyle to reclaim your health and sanity for a better world. Spiritual Freedom in the Digital Age demonstrates that even though we are more connected than ever before, we are subtly going insane and also diminishing our health. Even though we are more connected; the constant conflict in the digital world proves that we are more divided than ever. Spiritual Freedom in the Digital Age is an antidote to the digital problem.

As machines are trained to “think,” many tasks that previously required human intelligence are becoming automated through artificial intelligence. However, it is more difficult to automate emotional intelligence, and this is where the human worker’s competitive advantage over machines currently lies. This book explores the impact of AI on everyday life, looking into workers’ adaptation to these changes, the ways in which managers can change the nature of jobs in light of AI developments, and the potential for humans and AI to continue working together. The book argues that AI is rapidly assuming a larger share of thinking tasks, leaving human intelligence to focus on feeling. The result is the “Feeling Economy,” in which both employees and consumers emphasize feeling to an unprecedented extent, with thinking tasks largely delegated to AI. The book shows both theoretical and empirical evidence that this shift is well underway. Further, it explores the effect of the Feeling Economy on our everyday lives in the areas such as shopping, politics, and education. Specifically, it argues that in this new economy, through empathy and people skills, women may gain an unprecedented degree of power and influence. This book will appeal to readers across disciplines interested in understanding the impact of AI on business and our daily lives. It represents a bold, potentially controversial attempt to gauge the direction in which society is heading.

This book examines the role of computer-assisted techniques for discovering, designing, optimizing and manufacturing new, effective, and safe pharmaceutical formulations and drug delivery systems. The book discusses computational approaches, statistical modeling and molecular modeling for the development and safe delivery of drugs in humans. The application of concepts of QbD (Quality by Design), DoE (Design of Experiments), artificial intelligence and in silico pharmacokinetic assessment/simulation have been made a lot easier with the help of commercial software and expert systems. This title provides in-depth knowledge of such useful software with illustrations from the latest researches. The book also fills in the gap between pharmaceutics and molecular modeling at micro, meso and maro scale by covering topics such as advancements in computer-aided Drug Design (CADD), drug-polymer interactions in drug delivery systems, molecular modeling of nanoparticles and pharmaceutics/bioinformatics. This book provides abundant applications of computers in formulation designing and characterization are provided as examples, case studies and illustrations. Short reviews of software, databases and expert systems have also been added to culminate the interest of readers for novel applications in formulation development and drug delivery. Computer-aided pharmaceutics and drug delivery is an authoritative reference source for all the latest scholarly update on emerging developments in computed assisted techniques for drug designing and development. The book is ideally designed for pharmacists, medical practitioners, students and researchers.

KI 2008 was the 31st Annual German Conference on Arti?cial Intelligence held September 23–26 at the University of Kaiserslautern and the German Research Center for Arti?cial Intelligence DFKI GmbH in Kaiserslautern, Germany. The conference series started in 1975 with the German Workshop on AI (GWAI), which took place in Bonn, and represents the ?rst forum of its type for the German AI Community. Over the years AI has become a major ?eld in c- puter scienceinGermanyinvolvinga numberof successfulprojects thatreceived much international attention. Today KI conferences are international forums where participants from academia and industry from all over the world meet to exchange their recent research results and to discuss trends in the ?eld. Since 1993 the meeting has been called the “Annual German Conference on Arti?cial Intelligence,” designated by the German acronym KI. This volume contains the papers selected out of 77 submissions, including a number of submissions from outside German-speaking countries. In total, 15 submissions (19%) were accepted for oral and 30 (39%) for poster presentation. Oralpresentationsattheconferenceweresingletrack. Becauseofthis,thechoice of presentation form (oral, poster) was based on how well reviews indicated that the paper would ?t into one or the other format. The proceedings allocate the same space to both types of papers. In addition, we selected six papers that show high application potential - scribing systems or prototypical implementations of innovative AI technologies. They are also included in this volume as two-page extended abstracts.

This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence.

Introduces artificial intelligence, what it can do, myths about it, and ways it may expand in the future.

This important book presents new and original work at the frontiers of economics, namely the interface between artificial intelligence (AI) and neoclassical economics. Artificial Intelligence and Economic Analysis focuses on three quite distinct lines of AI orientated research in economics: applications intended to extend neoclassical theory, applications intended to undermine neoclassical theory and applications which ignore neoclassical theory in the quest for new modelling techniques and fields of analysis. The contributors - all of whom are well established in the field - do not simply report established results but seek to identify those areas where the science of artificial intelligence could enrich standard economic analysis. It includes material from mainstream economists who are willing to express their own views about the limits of mainstream economic modelling and AI based economic modelling. The book makes an important contribution to a new and exciting area of economics which holds much hope for the future.

SOFT COMPUTING book was written by Dr.Dinesh G, Dr.Pilli Lalitha Kumari, Dr.Mahesh Lokhande, Dr.Syed Azahad