Causality Correlation and Artificial Intelligence for Rational Decision Making

Causality  Correlation and Artificial Intelligence for Rational Decision Making
Author: Tshilidzi Marwala
Publsiher: World Scientific
Total Pages: 208
Release: 2015-01-02
ISBN: 9814630888
Category: Computers
Language: EN, FR, DE, ES & NL

Causality Correlation and Artificial Intelligence for Rational Decision Making Book Excerpt:

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

Smart Computing Applications in Crowdfunding

Smart Computing Applications in Crowdfunding
Author: Bo Xing,Tshilidzi Marwala
Publsiher: CRC Press
Total Pages: 512
Release: 2018-12-07
ISBN: 1351265075
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Smart Computing Applications in Crowdfunding Book Excerpt:

The book focuses on smart computing for crowdfunding usage, looking at the crowdfunding landscape, e.g., reward-, donation-, equity-, P2P-based and the crowdfunding ecosystem, e.g., regulator, asker, backer, investor, and operator. The increased complexity of fund raising scenario, driven by the broad economic environment as well as the need for using alternative funding sources, has sparked research in smart computing techniques. Covering a wide range of detailed topics, the authors of this book offer an outstanding overview of the current state of the art; providing deep insights into smart computing methods, tools, and their applications in crowdfunding; exploring the importance of smart analysis, prediction, and decision-making within the fintech industry. This book is intended to be an authoritative and valuable resource for professional practitioners and researchers alike, as well as finance engineering, and computer science students who are interested in crowdfunding and other emerging fintech topics.

Artificial Intelligence and Economic Theory Skynet in the Market

Artificial Intelligence and Economic Theory  Skynet in the Market
Author: Tshilidzi Marwala,Evan Hurwitz
Publsiher: Springer
Total Pages: 204
Release: 2017-09-18
ISBN: 3319661043
Category: Computers
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Economic Theory Skynet in the Market Book Excerpt:

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.

Artificial Intelligence Techniques for Rational Decision Making

Artificial Intelligence Techniques for Rational Decision Making
Author: Tshilidzi Marwala
Publsiher: Springer
Total Pages: 168
Release: 2014-10-20
ISBN: 3319114247
Category: Computers
Language: EN, FR, DE, ES & NL

Artificial Intelligence Techniques for Rational Decision Making Book Excerpt:

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 and Security

Artificial Intelligence and Security
Author: Xingming Sun,Zhaoqing Pan,Elisa Bertino
Publsiher: Springer
Total Pages: 665
Release: 2019-07-18
ISBN: 303024265X
Category: Computers
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Security Book Excerpt:

The 4-volume set LNCS 11632 until LNCS 11635 constitutes the refereed proceedings of the 5th International Conference on Artificial Intelligence and Security, ICAIS 2019, which was held in New York, USA, in July 2019. The conference was formerly called “International Conference on Cloud Computing and Security” with the acronym ICCCS. The total of 230 full papers presented in this 4-volume proceedings was carefully reviewed and selected from 1529 submissions. The papers were organized in topical sections as follows: Part I: cloud computing; Part II: artificial intelligence; big data; and cloud computing and security; Part III: cloud computing and security; information hiding; IoT security; multimedia forensics; and encryption and cybersecurity; Part IV: encryption and cybersecurity.

Rational Machines and Artificial Intelligence

Rational Machines and Artificial Intelligence
Author: Tshilidzi Marwala
Publsiher: Academic Press
Total Pages: 270
Release: 2021-03-31
ISBN: 0128209445
Category: Science
Language: EN, FR, DE, ES & NL

Rational Machines and Artificial Intelligence Book Excerpt:

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

Leveraging Artificial Intelligence in Global Epidemics

Leveraging Artificial Intelligence in Global Epidemics
Author: Le Gruenwald,Sarika Jain,Sven Groppe
Publsiher: Academic Press
Total Pages: 316
Release: 2021-07-28
ISBN: 032390002X
Category: Science
Language: EN, FR, DE, ES & NL

Leveraging Artificial Intelligence in Global Epidemics Book Excerpt:

Leveraging Artificial Intelligence in Global Epidemics provides readers with a detailed technical description of the role Artificial Intelligence plays in various stages of a disease outbreak, using COVID-19 as a case study. In the fight against epidemics, medical staff are on the front line; but behind the lines the battle is fought by researchers, and data scientists. Artificial Intelligence has been helping researchers with computer modeling and simulation for predictions about disease progression, the overall economic situation, tax incomes and population development. In the same manner, AI can prepare researchers for any emergency situation by backing the medical science. Artificial Intelligence plays a key and cutting-edge role in the preparedness for and dealing with the outbreak of global epidemics. It can help researchers analyze global data about known viruses to predict the patterns of the next pandemic and the impacts it will have. Not only prediction, AI plays an increasingly important role in assessing readiness, early detection, identification of patients, generating recommendations, situation awareness and more. It is up to the right input and the innovative ways by humans to leverage what AI can do. As COVID-19 has grabbed the world and its economy today, an analysis of the COVID-19 outbreak and the global responses and analytics will pay a long way in preparing humanity for such future situations. Provides readers with understanding of how Artificial Intelligence can be applied to the prediction, forecasting, detection, and testing of global epidemics, using COVID-19 and other recent epidemics such as Ebola, Corona viruses, Zika, influenza, Dengue, Chikungaya, and malaria as case studies Includes background material regarding readiness for coping with epidemics, including Machine Learning models for prediction of epidemic outbreaks based on existing data Includes technical coverage of key topics such as generating recommendations to combat outbreaks, genome sequencing, AI-assisted testing, AI-assisted contact tracing, situation awareness and combating disinformation, and the role of Artificial Intelligence and Machine Learning in drug discovery, vaccine development, and drug re-purposing

Economic Modeling Using Artificial Intelligence Methods

Economic Modeling Using Artificial Intelligence Methods
Author: Tshilidzi Marwala
Publsiher: Springer Science & Business Media
Total Pages: 261
Release: 2013-04-02
ISBN: 1447150104
Category: Computers
Language: EN, FR, DE, ES & NL

Economic Modeling Using Artificial Intelligence Methods Book Excerpt:

Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena. The artificial intelligence techniques used to model economic data include: multi-layer perceptron neural networks radial basis functions support vector machines rough sets genetic algorithm particle swarm optimization simulated annealing multi-agent system incremental learning fuzzy networks Signal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace – and vice versa – is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation. Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.

Artificial Intelligence to Solve Pervasive Internet of Things Issues

Artificial Intelligence to Solve Pervasive Internet of Things Issues
Author: Gurjit Kaur,Pradeep Tomar,Marcus Tanque
Publsiher: Academic Press
Total Pages: 430
Release: 2020-11-18
ISBN: 012819698X
Category: Science
Language: EN, FR, DE, ES & NL

Artificial Intelligence to Solve Pervasive Internet of Things Issues Book Excerpt:

Artificial Intelligence to Solve Pervasive Internet of Things Issues discusses standards and technologies and wide-ranging technology areas and their applications and challenges, including discussions on architectures, frameworks, applications, best practices, methods and techniques required for integrating AI to resolve IoT issues. Chapters also provide step-by-step measures, practices and solutions to tackle vital decision-making and practical issues affecting IoT technology, including autonomous devices and computerized systems. Such issues range from adopting, mitigating, maintaining, modernizing and protecting AI and IoT infrastructure components such as scalability, sustainability, latency, system decentralization and maintainability. The book enables readers to explore, discover and implement new solutions for integrating AI to solve IoT issues. Resolving these issues will help readers address many real-world applications in areas such as scientific research, healthcare, defense, aeronautics, engineering, social media, and many others. Discusses intelligent techniques for the implementation of Artificial Intelligence in Internet of Things Prepared for researchers and specialists who are interested in the use and integration of IoT and Artificial Intelligence technologies

Handbook Of Machine Learning Volume 1 Foundation Of Artificial Intelligence

Handbook Of Machine Learning   Volume 1  Foundation Of Artificial Intelligence
Author: Marwala Tshilidzi
Publsiher: World Scientific
Total Pages: 328
Release: 2018-10-22
ISBN: 9813271248
Category: Computers
Language: EN, FR, DE, ES & NL

Handbook Of Machine Learning Volume 1 Foundation Of Artificial Intelligence Book Excerpt:

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.

Regulating Artificial Intelligence

Regulating Artificial Intelligence
Author: Thomas Wischmeyer,Timo Rademacher
Publsiher: Springer Nature
Total Pages: 388
Release: 2019-11-29
ISBN: 3030323617
Category: Law
Language: EN, FR, DE, ES & NL

Regulating Artificial Intelligence Book Excerpt:

This book assesses the normative and practical challenges for artificial intelligence (AI) regulation, offers comprehensive information on the laws that currently shape or restrict the design or use of AI, and develops policy recommendations for those areas in which regulation is most urgently needed. By gathering contributions from scholars who are experts in their respective fields of legal research, it demonstrates that AI regulation is not a specialized sub-discipline, but affects the entire legal system and thus concerns all lawyers. Machine learning-based technology, which lies at the heart of what is commonly referred to as AI, is increasingly being employed to make policy and business decisions with broad social impacts, and therefore runs the risk of causing wide-scale damage. At the same time, AI technology is becoming more and more complex and difficult to understand, making it harder to determine whether or not it is being used in accordance with the law. In light of this situation, even tech enthusiasts are calling for stricter regulation of AI. Legislators, too, are stepping in and have begun to pass AI laws, including the prohibition of automated decision-making systems in Article 22 of the General Data Protection Regulation, the New York City AI transparency bill, and the 2017 amendments to the German Cartel Act and German Administrative Procedure Act. While the belief that something needs to be done is widely shared, there is far less clarity about what exactly can or should be done, or what effective regulation might look like. The book is divided into two major parts, the first of which focuses on features common to most AI systems, and explores how they relate to the legal framework for data-driven technologies, which already exists in the form of (national and supra-national) constitutional law, EU data protection and competition law, and anti-discrimination law. In the second part, the book examines in detail a number of relevant sectors in which AI is increasingly shaping decision-making processes, ranging from the notorious social media and the legal, financial and healthcare industries, to fields like law enforcement and tax law, in which we can observe how regulation by AI is becoming a reality.

Artificial Intelligence in Economics and Finance Theories

Artificial Intelligence in Economics and Finance Theories
Author: Tankiso Moloi,Tshilidzi Marwala
Publsiher: Springer Nature
Total Pages: 125
Release: 2020-05-07
ISBN: 3030429628
Category: Computers
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Economics and Finance Theories Book Excerpt:

As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.

Handbook Of Machine Learning Volume 2 Optimization And Decision Making

Handbook Of Machine Learning   Volume 2  Optimization And Decision Making
Author: Marwala Tshilidzi,Leke Collins Achepsah
Publsiher: World Scientific
Total Pages: 320
Release: 2019-11-21
ISBN: 981120568X
Category: Computers
Language: EN, FR, DE, ES & NL

Handbook Of Machine Learning Volume 2 Optimization And Decision Making Book Excerpt:

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.

Artificial Intelligence And Emerging Technologies In International Relations

Artificial Intelligence And Emerging Technologies In International Relations
Author: Bhaso Ndzendze,Tshilidzi Marwala
Publsiher: World Scientific
Total Pages: 192
Release: 2021-06-03
ISBN: 9811234566
Category: Computers
Language: EN, FR, DE, ES & NL

Artificial Intelligence And Emerging Technologies In International Relations Book Excerpt:

Artificial Intelligence and Emerging Technologies in International Relations explores the geopolitics between technology and international relations. Through a focus on war, trade, investment flows, diplomacy, regional integration and development cooperation, this book takes a holistic perspective to examine the origins of technology, analysing its current manifestations in the contemporary world. The authors present the possible future roles of artificial intelligence (AI) and other emerging technologies (including blockchain, 3D printing, 5G connectivity and the Internet of Things) in the context of global arena.This book is essential reading to all who seek to understand the reality of the inequitable distribution of these game-changing technologies that are shaping the world. Research questions as well as some policy options for the developing world are explored and the authors make the case for cooperation by the international community as we enter the fourth industrial revolution.

Knowledge Science Engineering and Management

Knowledge Science  Engineering and Management
Author: Christos Douligeris,Dimitris Karagiannis,Dimitris Apostolou
Publsiher: Springer Nature
Total Pages: 429
Release: 2019-08-21
ISBN: 303029563X
Category: Computers
Language: EN, FR, DE, ES & NL

Knowledge Science Engineering and Management Book Excerpt:

This two-volume set of LNAI 11775 and LNAI 11776 constitutes the refereed proceedings of the 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019, held in Athens, Greece, in August 2019. The 77 revised full papers and 23 short papers presented together with 10 poster papers were carefully reviewed and selected from 240 submissions. The papers of the first volume are organized in the following topical sections: Formal Reasoning and Ontologies; Recommendation Algorithms and Systems; Social Knowledge Analysis and Management ; Data Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge Engineering Applications. The papers of the second volume are organized in the following topical sections: Probabilistic Models and Applications; Text Mining and Document Analysis; Knowledge Theories and Models; and Network Knowledge Representation and Learning.

Artificial Intelligence and Causal Inference

Artificial Intelligence and Causal Inference
Author: Momiao Xiong
Publsiher: CRC Press
Total Pages: 424
Release: 2022-01-20
ISBN: 1000531759
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Causal Inference Book Excerpt:

Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine. Key Features: Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin’s Maximum Principle for network training. Deep learning for nonlinear mediation and instrumental variable causal analysis. Construction of causal networks is formulated as a continuous optimization problem. Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks. Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes. AI-based methods for estimation of individualized treatment effect in the presence of network interference.

Artificial Intelligence Applications and Innovations

Artificial Intelligence Applications and Innovations
Author: Lazaros Iliadis,Ilias Maglogiannis,Grigorios Tsoumakas,Ioannis Vlahavas,Max Bramer
Publsiher: Springer Science & Business Media
Total Pages: 535
Release: 2009-04-17
ISBN: 1441902201
Category: Computers
Language: EN, FR, DE, ES & NL

Artificial Intelligence Applications and Innovations Book Excerpt:

The ever expanding abundance of information and computing power enables - searchers and users to tackle highly interesting issues, such as applications prov- ing personalized access and interactivity to multimodal information based on user preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The general focus of the AIAI conf- ence is to provide insights on how AI can be implemented in real world applications. This volume contains papers selected for presentation at the 5th IFIP Conf- ence on Artificial Intelligence Applications & Innovations (AIAI 2009) being held from 23rd till 25th of April, in Thessaloniki, Greece. The IFIP AIAI 2009 conf- ence is co-organized by the Aristotle University of Thessaloniki, by the University of Macedonia Thessaloniki and by the Democritus University of Thrace. AIAI 2009 is the official conference of the WG12.5 "Artificial Intelligence Appli- tions" working group of IFIP TC12 the International Federation for Information Processing Technical Committee on Artificial Intelligence (AI). It is a conference growing and maintaining high standards of quality. The p- pose of the 5th IFIP AIAI Conference is to bring together researchers, engineers and practitioners interested in the technical advances and business / industrial - plications of intelligent systems. AIAI 2009 is not only focused in providing - sights on how AI can be implemented in real world applications, but it also covers innovative methods, tools and ideas of AI on architectural and algorithmic level.

A Guided Tour of Artificial Intelligence Research

A Guided Tour of Artificial Intelligence Research
Author: Pierre Marquis,Odile Papini,Henri Prade
Publsiher: Springer Nature
Total Pages: 803
Release: 2020-05-08
ISBN: 3030061647
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

A Guided Tour of Artificial Intelligence Research Book Excerpt:

The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Then the volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, reasoning based on similarity (case-based, interpolative, or analogical), as well as reasoning about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statistical computational learning and reinforcement learning (other machine learning topics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.

Leadership Lessons from Books I Have Read

Leadership Lessons from Books I Have Read
Author: Tshilidzi Marwala
Publsiher: Jonathan Ball Publishers
Total Pages: 296
Release: 2021-06-18
ISBN: 1776260937
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Leadership Lessons from Books I Have Read Book Excerpt:

‘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

Resisting AI

Resisting AI
Author: McQuillan, Dan
Publsiher: Policy Press
Total Pages: 160
Release: 2022-07-15
ISBN: 1529213517
Category: Social Science
Language: EN, FR, DE, ES & NL

Resisting AI Book Excerpt:

Artificial Intelligence (AI) is everywhere, yet it causes damage to society in ways that can’t be fixed. Instead of helping to address our current crises, AI causes divisions that limit people’s life chances, and even suggests fascistic solutions to social problems. This book provides an analysis of AI’s deep learning technology and its political effects and traces the ways that it resonates with contemporary political and social currents, from global austerity to the rise of the far right. Dan McQuillan calls for us to resist AI as we know it and restructure it by prioritising the common good over algorithmic optimisation. He sets out an anti-fascist approach to AI that replaces exclusions with caring, proposes people’s councils as a way to restructure AI through mutual aid and outlines new mechanisms that would adapt to changing times by supporting collective freedom. Academically rigorous, yet accessible to a socially engaged readership, this unique book will be of interest to all who wish to challenge the social logic of AI by reasserting the importance of the common good.