Did you know that AI helped to win the 2023 Rugby World Cup for South Africa? That Africa led the way in small language models? That AI has been supporting farmers in Kenya for the last decade? After reading, you will understand the present and future of AI, and The Hitchhiker’s Guide to AI: The African Edge particularly the distinct advantages presented by and for AI on the African ccontinent.

The book draws on the author’s many years of direct access to global and regional leaders in using AI, from Africa to the Middle East to North America to Europe and Asia, and it provides unique perspectives on generative AI, as well as practical advice for using it.

The Hitchhiker’s Guide to AI is useful for consumers, academics, professionals and anyone in business who wants to get up to speed quickly and practically. It also entertains and inspires anyone who is curious about AI or already engaged in its possibilities.

What can business learn from the use of AI in sports? How can educators embrace AI as a tool rather than a threat? When will AI truly transform health, travel, agriculture, entertainment, shopping and personal services? This book has the answers.

Artificial intelligence (AI) is upending life, work, and play as we know it … and it’s only just getting started. The rise of AI is a milestone on par with the discovery of fire, the invention of the wheel, and the creation of the internet. In short, AI is going to change everything. For some, that’s an exciting prospect. For others, it’s terrifying. However you feel about AI, there’s no escaping it, whether you’re in a global metropolis or a farmer in rural KwaZulu-Natal.

Dr Mark Nasila has been watching AI’s ascent for over a decade, studying its effects on everything from agriculture and aviation to healthcare, education, entertainment, crime prevention, energy management, policy creation, finance, and anything in between, and applying them to his role at one of South Africa’s most successful financial institutions, First National Bank, a division of FirstRand Group.

African Artificial Intelligence is a comprehensive and fascinating journey, tracing the rise of AI and its evolution into the emerging technology underpinning all others – from connected devices and smart chatbots to the metaverse. Mark combines unexpected use cases and tales of cutting-edge innovation with a unique and potent argument: harnessing AI to solve Africa’s problems requires embracing it from an African perspective.

African nations can’t afford to simply import AI solutions from afar. Instead, Mark contends, they need to rework, remix, and refine AI so it’s able to meet uniquely African challenges in uniquely African ways, and to take advantage of the once-in-a-generation opportunity AI represents for every industry, sector, and person, everywhere.

In a world captivated yet bewildered by artificial intelligence, spiritual icon Deepak Chopra explores AI's untapped potential to unlock the mystery of consciousness, positioning AI not as a threat, but as a powerful catalyst for personal and spiritual growth.

Digital Dharma shows how the most popular, freely available chatbots can serve as guides through every level of human potential – survival and safety, emotional connection, self-worth, abundance, creativity, wisdom and the infinite possibilities of cosmic consciousness.

Featuring personal assessments and practical exercises, Deepak Chopra invites you to explore a relationship with AI not merely as a technological tool, but as a partner in shaping a future where human potential solves pressing global issues and empowers individual growth.

Elon Musk calls artificial intelligence a “supersonic tsunami” headed toward humanity. Microsoft AI CEO Mustafa Suleyman warns that in as little as 12 to 18 months, AI could automate nearly every task white collar workers perform. Anthropic CEO Dario Amodei says that within the next 12 months to five years, AI could unleash a jobs bloodbath that disrupts half of all entry-level white-collar jobs.

So, as President Donald Trump leads the nation through AI’s seismic shifts, Americans are left asking the defining question of our time: Is AI hype or hope, promise or peril, existential threat or the next great technological leap toward prosperity and human flourishing?

The answer will affect everything, because AI is not just about jobs. It will shape whether our children are educated or indoctrinated. Whether people form relationships with other human beings or with machines. How and what we worship. And who lives or dies on the battlefield as AI warfare accelerates.

All of it will shock-test our civic order. And whether we are ready or not, you do not get to opt out of the AI revolution. That decision has already been made for you.

Today, 99% of Americans use AI—even if most of us don’t realize it. For conservatives, this is a Code Red moment. Big Tech is quietly hard-coding left-wing ideology into the algorithms that now govern daily life. The Left is already weaponizing AI while conservatives sleepwalk straight into calamity, unless they're ready for what's coming.

In Code Red, Breitbart social media director Wynton Hall exposes where that power hides, how it operates, how conservatives can navigate the AI political battlescape, avert its landmines, and turn peril into promise. AI decides what you see and what gets censored. It’s quietly rewiring our whole way of life. The solution? Americans need a “code”: a set of principles and solutions rooted in the red political worldview to navigate the upheaval and stay ahead. Inside Code Red, you will discover:

Urgent, deeply researched, and written with page-turner elegance, Code Red is the conservative battle plan for the AI era. Either we wake up and fight back, or we lose everything that made America free.

Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation. The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances.

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more.

Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.

Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.
Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions.
Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data miningA valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions

Artificial intelligence (AI) is often discussed as something extraordinary, a dream-or a nightmare-that awakens metaphysical questions on human life. Yet far from a distant technology of the future, the true power of AI lies in its subtle revolution of ordinary life. From voice assistants like Siri to natural language processors, AI technologies use cultural biases and modern psychology to fit specific characteristics of how users perceive and navigate the external world, thereby projecting the illusion of intelligence. Integrating media studies, science and technology studies, and social psychology, Deceitful Media examines the rise of artificial intelligence throughout history and exposes the very human fallacies behind this technology. Focusing specifically on communicative AIs, Natale argues that what we call "AI" is not a form of intelligence but rather a reflection of the human user. Using the term "banal deception," he reveals that deception forms the basis of all human-computer interactions rooted in AI technologies, as technologies like voice assistants utilize the dynamics of projection and stereotyping as a means for aligning with our existing habits and social conventions. By exploiting the human instinct to connect, AI reveals our collective vulnerabilities to deception, showing that what machines are primarily changing is not other technology but ourselves as humans. Deceitful Media illustrates how AI has continued a tradition of technologies that mobilize our liability to deception and shows that only by better understanding our vulnerabilities to deception can we become more sophisticated consumers of interactive media.

This book offers a thorough review of research on intelligent communication systems, focusing on the applications of artificial intelligence to telecommunications that help realize user-friendly interfaces.
Intelligent Communication Systems presents the direct result of more than a decade of the author's experiences, research activity, and education in applying artificial intelligence to telecommunications technology. In this book, several fundamental research areas are covered. Some of the areas covered are human-friendly interfaces for telecommunication services with such concepts as Telesensation and HyperReality, computer vision, and the telecommunication description method based on state space. In artificial intelligence research state space is the set of all attainable states of a problem and the possible alternative courses of action to determine the best solution to the problem.

This book investigates the nature of generalization in language and examines how language is known by adults and acquired by children. It looks at how and why constructions are learned, the relation between their forms and functions, and how cross-linguistic and language-internal
generalizations about them can be explained.
Constructions at Work is divided into three parts: in the first Professor Goldberg provides an overview of constructionist approaches, including the constructionist approach to argument structure, and argues for a usage-based model of grammar. In Part II she addresses issues concerning how
generalizations are constrained and constructional generalizations are learned. In Part III the author shows that a combination of function and processing accounts for a wide range of language-internal and cross-linguistic generalizations. She then considers the degree to which the function of
constructions explains their distribution and examines cross-linguistic tendencies in argument realization. She demonstrates that pragmatic and cognitive processes account for the data without appeal to stipulations that are language-specific.
This book is an important contribution to the study of how language operates in the mind and in the world and how these operations relate. It is of central interest for scholars and graduate-level students in all branches of theoretical linguistics and psycholinguistics. It will also appeal to
cognitive scientists and philosophers concerned with language and its acquisition.

Mumford explains the forces that have shaped technology since prehistoric times and shaped the modern world. He shows how tools developed because of significant parallel inventions in ritual, language, and social organization. "It is a stimulating volume, informed both with an enormous range of knowledge and empathetic spirit" (Eliot Fremont-Smith, New York Times). Index; photographs.

Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up!

In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks.

Build a Large Language Model (from Scratch) teaches you how to:

• Plan and code all the parts of an LLM
• Prepare a dataset suitable for LLM training
• Fine-tune LLMs for text classification and with your own data
• Use human feedback to ensure your LLM follows instructions
• Load pretrained weights into an LLM

Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant.

Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.

Features:

• Understand what AI engineering is and how it differs from traditional machine learning engineering
• Learn the process for developing an AI application, the challenges at each step, and approaches to address them
• Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work
• Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them
• Choose the right model, dataset, evaluation benchmarks, and metrics for your needs

Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.

There is now a plethora of internet of things (IoT) devices on the market that can connect to the internet and the desired environment to produce sufficient and reliable data that is required by the government administration for a variety of purposes. Additionally, the potential benefits of incorporating artificial intelligence (AI) and machine learning into governance are numerous. Governments can use AI and machine learning to enforce the law, detect fraud, and monitor urban areas by identifying problems before they occur. The government can also use AI to easily automate processes and replace mundane and repetitive tasks. AI, IoT, and Blockchain Breakthroughs in E-Governance defines and emphasizes various AI algorithms as well as new internet of things and blockchain breakthroughs in the field of e-governance. Covering key topics such as machine learning, government, and artificial intelligence, this premier reference source is ideal for government officials, policymakers, researchers, academicians, practitioners, scholars, instructors, and students.

Stochastic processes have a wide range of applications ranging from image processing, neuroscience, bioinformatics, financial management, and statistics. Mathematical, physical, and engineering systems use stochastic processes for modeling and reasoning phenomena. While comparing AI-stochastic systems with other counterpart systems, we are able to understand their significance, thereby applying new techniques to obtain new real-time results and solutions. Stochastic Processes and Their Applications in Artificial Intelligence opens doors for artificial intelligence experts to use stochastic processes as an effective tool in real-world problems in computational biology, speech recognition, natural language processing, and reinforcement learning. Covering key topics such as social media, big data, and artificial intelligence models, this reference work is ideal for mathematicians, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

Autonomous Mobile Robots: Planning, Navigation, and Simulation presents detailed coverage of the domain of robotics in motion planning and associated topics in navigation. This book covers numerous base planning methods from diverse schools of learning, including deliberative planning methods, reactive planning methods, task planning methods, fusion of different methods, and cognitive architectures. It is a good resource for doing initial project work in robotics, providing an overview, methods and simulation software in one resource. For more advanced readers, it presents a variety of planning algorithms to choose from, presenting the tradeoffs between the algorithms to ascertain a good choice. Finally, the book presents fusion mechanisms to design hybrid algorithms.

Provides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring This book is the first to fill the gap between scientific research of computer vision and its practical applications for structural health monitoring (SHM). It provides a complete, state-of-the-art review of the collective experience that the SHM community has gained in recent years. It also extensively explores the potentials of the vision sensor as a fast and cost-effective tool for solving SHM problems based on both time and frequency domain analytics, broadening the application of emerging computer vision sensor technology in not only scientific research but also engineering practice. Computer Vision for Structural Dynamics and Health Monitoring presents fundamental knowledge, important issues, and practical techniques critical to successful development of vision-based sensors in detail, including robustness of template matching techniques for tracking targets; coordinate conversion methods for determining calibration factors to convert image pixel displacements to physical displacements; sensing by tracking artificial targets vs. natural targets; measurements in real time vs. by post-processing; and field measurement error sources and mitigation methods. The book also features a wide range of tests conducted in both controlled laboratory and complex field environments in order to evaluate the sensor accuracy and demonstrate the unique features and merits of computer vision-based structural displacement measurement. Offers comprehensive understanding of the principles and applications of computer vision for structural dynamics and health monitoring Helps broaden the application of the emerging computer vision sensor technology from scientific research to engineering practice such as field condition assessment of civil engineering structures and infrastructure systems Includes a wide range of laboratory and field testing examples, as well as practical techniques for field application Provides MATLAB code for most of the issues discussed including that of image processing, structural dynamics, and SHM applications Computer Vision for Structural Dynamics and Health Monitoring is ideal for graduate students, researchers, and practicing engineers who are interested in learning about this emerging sensor technology and advancing their applications in SHM and other engineering problems. It will also benefit those in civil and aerospace engineering, energy, and computer science.

Diagnostic Biomedical Signal and Image Processing Applications: With Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges, which are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.

Robotics for Cell Manipulation and Characterization provides fundamental principles underpinning robotic cell manipulation and characterization, state-of-the-art technical advances in micro/nano robotics, new discoveries of cell biology enabled by robotic systems, and their applications in clinical diagnosis and treatment. This book covers several areas, including robotics, control, computer vision, biomedical engineering and life sciences using understandable figures and tables to enhance readers' comprehension and pinpoint challenges and opportunities for biological and biomedical research.

Reachable Sets of Dynamic Systems: Uncertainty, Sensitivity, and Complex Dynamics introduces differential inclusions, providing an overview as well as multiple examples of its interdisciplinary applications. The design of dynamic systems of any type is an important issue as is the influence of uncertainty in model parameters and model sensitivity. The possibility of calculating the reachable sets may be a powerful additional tool in such tasks. This book can help graduate students, researchers, and engineers working in the field of computer simulation and model building, in the calculation of reachable sets of dynamic models.

The promises and realities of digital innovation have come to suffuse everything from city regions to astronomy, government to finance, art to medicine, politics to warfare, and from genetics to reality itself. Digital systems augmenting physical space, buildings, and communities occupy a special place in the evolutionary discourse about advanced technology. The two Intelligent Environments books edited by Peter Droege span a quarter of a century across this genre. The second volume, Intelligent Environments: Advanced Systems for a Healthy Planet, asks: how does civilization approach thinking systems, intelligent spatial models, design methods, and support structures designed for sustainability, in ways that could counteract challenges to terrestrial habitability? This book examines a range of baseline and benchmark practices but also unusual and even sublime endeavors across regions, currencies, infrastructure, architecture, transactive electricity, geodesign, net-positive planning, remote work, integrated transport, and artificial intelligence in understanding the most immediate spatial setting: the human body. The result of this quest is both highly informative and useful, but also critical. It opens windows on what must fast become a central and overarching existential focus in the face of anthropogenic planetary heating and other threats-and raises concomitant questions about direction, scope, and speed of that change.

Studies on integer optimization in emergency management have attracted engineers and scientists from various disciplines such as management, mathematics, computer science, and other fields. Although there are a large number of literature reports on integer planning and emergency events, few books systematically explain the combination of the two. Researchers need a clear and thorough presentation of the theory and application of integer programming methods for emergency management. Integer Optimization and its Computation in Emergency Management investigates the computation theory of integer optimization, developing integer programming methods for emergency management and explores related practical applications. Pursuing a holistic approach, this book establishes a fundamental framework for this topic, intended for graduate students who are interested in operations research and optimization, researchers investigating emergency management, and algorithm design engineers working on integer programming or other optimization applications.

Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.