Tshilidzi Marwala

UPDATED EDITION ‘A holistic take on AI from an African perspective, Closing the Gap joins the dots on deploying AI efficiently into everyday business and life.’ – RENUKA METHIL, editor of Forbes Africa ‘This book simplifies complex concepts through relatable stories and awakens fellow Africans to the opportunities ushered in by the 4IR. Closing the Gapmust occupy our waking times.’ – MTETO NYATI, chief executive of Altron 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 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.

The Fourth Industrial Revolution in Africa

Closing the Gap

Bhaso Ndzendze

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.

Artificial Intelligence and International Relations Theories

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.

Artificial Intelligence And Emerging Technologies In International Relations

Collins Achepsah Leke

Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation.

Deep Learning and Missing Data in Engineering Systems

‘It became apparent to me that, due to the complexity of problems that face humanity today, those who do not know should not lead.’ – Professor Tshilidzi Marwala 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. A leading academic of international standing, and Deputy Chair of South Africa’s Presidential 4IR Commission, Tshilidzi Marwala provides valuable insights into how leadership should be responding to the digital challenges of the 21st century.

The Call for a New Type of African Leader

Leading in the 21st Century

Wesley Doorsamy

Babu Sena Paul

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.

Technology, Society and Beyond

The Disruptive Fourth Industrial Revolution

Fernando Buarque

Daniel Muller

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.

On Rationality, Artificial Intelligence And Economics

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

Causality, Correlation and Artificial Intelligence for Rational Decision Making

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 Techniques for Rational Decision Making

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

Rational Machines and Artificial Intelligence

‘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

The collective wisdom, knowledge and experience from the pages of fifty books

Leadership Lessons from Books I Have Read

Evan Hurwitz

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.

Tshilidzi Marwala

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