Closing the Gap is an accessible overview of the fourth industrial revolution (4IR) and the impact it is set to have on various sectors in South Africa and Africa. It explores the previous industrial revolutions that have led up to this point and outlines South Africa’s position been through each one.

With a focus on artificial intelligence as a core concept in understanding the 4IR, this book uses familiar concepts to explain artificial intelligence, how it works and how it can be used in banking, mining, medicine and many other fields.

Written from an African perspective, Closing the Gap addresses the challenges and fears around the 4IR by pointing to the opportunities presented by new technologies and outlining some of the challenges and successes to date

The world is emerging from the COVID-19 pandemic, more fragmented and further away from the more equal and equitable iteration imagined in 2015 when the Sustainable Development Goals (SDGs) were conceptualised. As we hurtle, at seemingly lightning speed, towards the 2030 deadline to achieve these goals, the urgency is palpable. Although we have certainly strayed further away from the targets, there is still time to act in order to ensure that we inch closer to this vision.

Tshilidzi Marwala paints a stark, and often grim, picture of our current context – one defined by monumental setbacks in the SDGs. Yet, as he carves out each developmental goal and its implications, it is apparent that there are tangible solutions that can be implemented, now. Tshilidzi’s assertion that now is the time to act is backed by intricate and actionable data with a simple mission statement: we must heal the future.

He offers a new narrative that addresses how we can translate the latent potential that exists through technology, innovation and new approaches to leadership and policy-making, to deal with, among others, poverty eradication, joblessness, an education system in crisis, declining economies and food insecurity.

Heal our World is a deep-dive into the Sustainable Development Goals, particularly relating to the African context, and looks toward securing a future in which our divisions are blurred, and our goals almost seem in reach again.

An accessible overview of the fourth industrial revolution (4IR) and the impact it is set to have on various sectors in South Africa and Africa. It explores the previous industrial revolutions that have led up to this point and outlines what South Africa’s position has been through each one.

With a focus on artificial intelligence as a core concept in understanding the 4IR, this book uses familiar concepts to explain artificial intelligence, how it works and how it can be used in banking, mining, medicine and many other fields.

Written from an African perspective, Closing the Gap addresses the challenges and fears around the 4IR by pointing to the opportunities presented by new technologies and outlining some of the challenges and successes to date.

In 2020 the world found itself in a state of flux. A global pandemic disrupted the world order while the digital transformation of the Fourth Industrial Revolution (4IR), with its challenges and huge potential benefits, presented a fundamental paradigm shift. How are Africa’s leaders to respond, today?

In a crisis, decisive leadership is imperative for the public good, but as we move beyond the pandemic and confront the changes of the 4IR, we must determine how we will adapt. What is clear is that leadership will have to be grounded in scientific and mathematical thinking and in good governance. It follows, then, that for South Africa to succeed as a nation in the 21st century we must be able to provide our people with an all-embracing education – not just science and technology but human and social sciences as well.

Leading in the 21st Century presents a comprehensive overview of how the world is changing and lessons we can draw from leaders, particularly in the African context. From Charlotte Maxeke and the Rain Queen Modjadji, to Mangaliso Robert Sobukwe, Eric Molobi and Richard Maponya, there is much to learn from great leaders. The challenges of the 21st century are immense – from climate change to social media and the digital divide that deepens our understanding of inequality, particularly in the ‘new normal’. South Africa faces not only a shifting global context but a fraught local context of stagnant growth, rising unemployment and deep-seated inequality, worsened in 2020 by the national lockdown necessitated by the coronavirus pandemic. The 4IR offers solutions to many of our most pressing problems and we cannot afford to be left behind.

The certainty is that the 4IR has arrived. The debates lie in how we respond to it. Tshilidzi Marwala deciphers it all, while providing a framework for navigating these shifts.

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.

This book explores how AI and mechanism design can provide a new framework for international politics. The international political system is all manners in which countries, governments and people relate. Mechanism design in international politics relates to identifying rules that define relationships between people and countries that achieve a particular outcome, e.g., peace or more trade or democracy or economic development. Artificial intelligence is technique of making machines intelligent. This book explores mechanism design and artificial intelligence in international politics and applies these technologies to politics, economy and society. This book will be of interest to scholars of international relations, politics, sustainable development, and artificial intelligence.

The book explores technological advances in the fourth industrial revolution (4IR), which is based on a variety of technologies such as artificial intelligence, Internet of Things, machine learning, big data, additive printing, cloud computing, and virtual and augmented reality. Critically analyzing the impacts and effects of these disruptive technologies on various areas, including economics, society, business, government, labor, law, and environment, the book also provides a broad overview of 4IR, with a focus on technologies, to allow readers to gain a deeper understanding of the recent advances and future trajectories. It is intended for researchers, practitioners, policy-makers and industry leaders.

This book explains how various forms of artificial intelligence, namely machine learning, natural language processing, and robotic process automation, could provide a source of competitive advantage to firms deploying them compared to those firms that would not have deployed these technologies. The advantages of machine learning, natural language processing, and robotic process automation in strategy formulation and strategy implementation are explored. The book illustrates the potential sources of advantage for the strategy formulation and strategy implementation processes, which can be derived from the deployment of each form of artificial intelligence.

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.

The book explores technological advances in the fourth industrial revolution (4IR), which is based on a variety of technologies such as artificial intelligence, Internet of Things, machine learning, big data, additive printing, cloud computing, and virtual and augmented reality. Critically analyzing the impacts and effects of these disruptive technologies on various areas, including economics, society, business, government, labor, law, and environment, the book also provides a broad overview of 4IR, with a focus on technologies, to allow readers to gain a deeper understanding of the recent advances and future trajectories. It is intended for researchers, practitioners, policy-makers and industry leaders.

This self-contained book, written by active researchers, presents up-to-date information on smart maintenance strategies for human-robot interaction (HRI) and the associated applications of novel search algorithms in a single volume, eliminating the need to consult scattered resources. Unlike other books, it addresses maintaining a smart HRI from three dimensions, namely, hardware, cyberware, and hybrid-asset management, covering problems encountered in each through a wide variety of representative examples and elaborated illustrations. Further, the diverse mathematical models and intelligent systems constructions make the book highly practical. It enables readers interested in maintenance, robotics, and intelligent systems but perplexed by myriads of interrelated issues to grasp basic methodologies. At the same time, the referenced literature can be used as a roadmap for conducting deeper researches.

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.

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.

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.

FEM updating allows FEMs to be tuned better to reflect measured data. It can be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. This book applies both strategies to the field of structural mechanics, using vibration data. Computational intelligence techniques including: multi-layer perceptron neural networks; particle swarm and GA-based optimization methods; simulated annealing; response surface methods; and expectation maximization algorithms, are proposed to facilitate the updating process. Based on these methods, the most appropriate updated FEM is selected, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements through the formulations of prior distributions. Case studies, demonstrating the principles test the viability of the approaches, and. by critically analysing the state of the art in FEM updating, this book identifies new research directions.

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.

Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as:

fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines."

Militarized Conflict Modeling Using Computational Intelligence examines the application of computational intelligence methods to model conflict. Traditionally, conflict has been modeled using game theory. The inherent limitation of game theory when dealing with more than three players in a game is the main motivation for the application of computational intelligence in modeling conflict. Militarized interstate disputes (MIDs) are defined as a set of interactions between, or among, states that can result in the display, threat or actual use of military force in an explicit way. These interactions can result in either peace or conflict. This book models the relationship between key variables and the risk of conflict between two countries. The variables include Allies which measures the presence or absence of military alliance, Contiguity which measures whether the countries share a common boundary or not and Major Power which measures whether either or both states are a major power. Militarized Conflict Modeling Using Computational Intelligence implements various multi-layer perception neural networks, Bayesian networks, support vector machines, neuro-fuzzy models, rough sets models, neuro-rough sets models and optimized rough sets models to create models that estimate the risk of conflict given the variables. Secondly, these models are used to study the sensitivity of each variable to conflict. Furthermore, a framework on how these models can be used to control the possibility of peace is proposed. Finally, new and emerging topics on modelling conflict are identified and further work is proposed.

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.

Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as: * fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines.

FEM updating allows FEMs to be tuned better to reflect measured data. It can be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. This book applies both strategies to the field of structural mechanics, using vibration data. Computational intelligence techniques including: multi-layer perceptron neural networks; particle swarm and GA-based optimization methods; simulated annealing; response surface methods; and expectation maximization algorithms, are proposed to facilitate the updating process. Based on these methods, the most appropriate updated FEM is selected, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements through the formulations of prior distributions. Case studies, demonstrating the principles test the viability of the approaches, and. by critically analysing the state of the art in FEM updating, this book identifies new research directions.

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.

Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The book offers a comprehensive introduction to Hamiltonian Monte Carlo methods and provides a cutting-edge exposition of the current pathologies of HMC-based methods in both tuning, scaling and sampling complex real-world posteriors. These are mainly in the scaling of inference (e.g., Deep Neural Networks), tuning of performance-sensitive sampling parameters and high sample autocorrelation. Other sections provide numerous solutions to potential pitfalls, presenting advanced HMC methods with applications in renewable energy, finance and image classification for biomedical applications. Readers will get acquainted with both HMC sampling theory and algorithm implementation.

This book discusses the impact of artificial intelligence (AI) on international relations theories. As a phenomenon, AI is everywhere in the real world and growing. Through its transformative nature, it is simultaneously simplifying and complicating processes. Importantly, it also overlooks and "misunderstands". Globally, leaders, diplomats and policymakers have had to familiarise themselves and grapple with concepts such as algorithms, automation, machine learning, and neural networks. These and other features of modern AI are redefining our world, and with it, the long-held assumptions scholars of IR have relied on for their theoretical accounts of our universe. The book takes a historic, contemporary and long-term approach to explain and anticipate AI's impact on IR - and vice versa - through a systematic treatment of 9 theoretical paradigms and schools of thought including realism, liberalism, feminism, postcolonial theory and green theory. This book draws on original datasets, innovative empirical case studies and in-depth engagement with the core claims of the traditional and critical theoretical lenses to reignite debates on the nature and patterns of power, ethics, conflict, and systems among states and non-state actors.

This book explains how various forms of artificial intelligence, namely machine learning, natural language processing, and robotic process automation, could provide a source of competitive advantage to firms deploying them compared to those firms that would not have deployed these technologies. The advantages of machine learning, natural language processing, and robotic process automation in strategy formulation and strategy implementation are explored. The book illustrates the potential sources of advantage for the strategy formulation and strategy implementation processes, which can be derived from the deployment of each form of artificial intelligence.