The first book of its kind, Unity in Embedded System Design and Robotics provides a step-by-step guide to Unity for embedded system design and robotics. It is an open gateway for anyone who wants to learn Unity through real projects and examples as well as a particularly useful aid for both professionals and students in the fields of embedded system design and robotics. Each chapter contains a unique project. The user is guided through the different windows and sections of Unity every step of the way. The book also includes projects that connect Unity to Arduino and Raspberry Pi, which will help readers better understand various Unity applications in the real world.
The first book of its kind, Unity in Embedded System Design and Robotics provides a step-by-step guide to Unity for embedded system design and robotics. It is an open gateway for anyone who wants to learn Unity through real projects and examples as well as a particularly useful aid for both professionals and students in the fields of embedded system design and robotics. Each chapter contains a unique project. The user is guided through the different windows and sections of Unity every step of the way. The book also includes projects that connect Unity to Arduino and Raspberry Pi, which will help readers better understand various Unity applications in the real world.
By proposing and forming a mobile manipulator for modern multi-floor buildings, A Robotic Framework for the Mobile Manipulator: Theory and Application helps readers visualize an end-to-end workflow for making a robot system work in a targeted environment. From a product-oriented viewpoint, this book is considered as a bridge from theories to real products, in which robotic software modules and the robotic system integration are mainly concerned. In the end, readers will have an overview of how to build and integrate various single robotic modules to execute a list of designed tasks in the real world, as well as how to make a robot system work independently, without human interventions. With references and execution guidelines provided at the end of each chapter, the book will be a useful tool for developers and researchers looking to expand their knowledge about the robotics and the robotic software.
Soon, robots will leave the factories and make their way into living rooms, supermarkets, and care facilities. They will cooperate with humans in everyday life, taking on more than just practical tasks. How should they communicate with us? Do they need eyes, a screen, or arms? Should they resemble humans? Or may they enrich social situations precisely because they act so differently from humans? Meaningful Futures with Robots: Designing a New Coexistence provides insight into the opportunities and risks that arise from living with robots in the future, anchored in current research projects on everyday robotics. As well as generating ideas for robot developers and designers, it also critically discusses existing theories and methods for social robotics from different perspectives - ethical, design, artistical and technological – and presents new approaches to meaningful human-robot interaction design. Key Features: Provides insights into current research on robots from different disciplinary angles with a particular focus on a value-driven design. Includes contributions from designers, psychologists, engineers, philosophers, artists, and legal scholars, among others. Licence line: Chapters 1, 3, 12, and 15 of this book are available for free in PDF format as Open Access from the individual product page at www.crcpress.com. They have been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems. This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.
As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold. In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and – to the best of our knowledge – in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet. This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.
An Introduction to Universal Artificial Intelligence provides the formal underpinning of what it means for an agent to act intelligently in an unknown environment. First presented in Universal Algorithmic Intelligence (Hutter, 2000), UAI offers a framework in which virtually all AI problems can be formulated, and a theory of how to solve them. UAI unifies ideas from sequential decision theory, Bayesian inference, and algorithmic information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI is the theoretical gold standard for intelligent behavior. The book covers both the theoretical and practical aspects of UAI. Bayesian updating can be done efficiently with context tree weighting, and planning can be approximated by sampling with Monte Carlo tree search. It provides algorithms for the reader to implement, and experimental results to compare against. These algorithms are used to approximate AIXI. The book ends with a philosophical discussion of Artificial General Intelligence: Can super-intelligent agents even be constructed? Is it inevitable that they will be constructed, and what are the potential consequences? This text is suitable for late undergraduate students. It provides an extensive chapter to fill in the required mathematics, probability, information, and computability theory background.
AI in and for Africa: A Humanistic Perspective explores the convoluted intersection of artificial intelligence (AI) with Africa’s unique socio-economic realities. This book is the first of its kind to provide a comprehensive overview of how AI is currently being deployed on the African continent. Given the existence of significant disparities in Africa related to gender, race, labour, and power, the book argues that the continent requires different AI solutions to its problems, ones that are not founded on technological determinism or exclusively on the adoption of Eurocentric or Western-centric worldviews. It embraces a decolonial approach to exploring and addressing issues such as AI’s diversity crisis, the absence of ethical policies around AI that are tailor-made for Africa, the ever-widening digital divide, and the ongoing practice of dismissing African knowledge systems in the contexts of AI research and education. Although the book suggests a number of humanistic strategies with the goal of ensuring that Africa does not appropriate AI in a manner that is skewed in favour of a privileged few, it does not support the notion that the continent should simply opt for a "one-size-fits-all" solution either. Rather, in light of Africa’s rich diversity, the book embraces the need for plurality within different regions’ AI ecosystems. The book advocates that Africa-inclusive AI policies incorporate a relational ethics of care which explicitly addresses how Africa’s unique landscape is entwined in an AI ecosystem. The book also works to provide actionable AI tenets that can be incorporated into policy documents that suit Africa’s needs. This book will be of great interest to researchers, students, and readers who wish to critically appraise the different facets of AI in the context of Africa, across many areas that run the gamut from education, gender studies, and linguistics to agriculture, data science, and economics. This book is of special appeal to scholars in disciplines including anthropology, computer science, philosophy, and sociology, to name a few.
AI's impact on human societies is and will be drastic in so many ways. AI is being adopted and implemented around the world, and government and universities are investing in AI studies, research, and development. However, very little research exists about the impact of AI on our lives. This book will address this gap; it will gather reflections from around the world to assess the impact of AI on different aspects of society as well as propose ways in which we can address this impact and the research agendas needed.
The leveraging of artificial intelligence (AI) for model discovery in dynamical systems is cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavor, taking model discovery to the next level. Dealing with artificial intelligence, this book delineates AI’s role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multiscale hierarchies hitherto considered off limits for data science. Key Features: Introduces new and advanced methods of model discovery for time series data using artificial intelligence Implements topological approaches to distill "machine-intuitive" models from complex dynamics data Introduces a new paradigm for a parsimonious model of a dynamical system without resorting to differential equations Heralds a new era in data-driven science and engineering based on the operational concept of "computational intuition" Intended for graduate students, researchers, and practitioners interested in dynamical systems empowered by AI or machine learning and in their biological, engineering, and biomedical applications, this book will represent a significant educational resource for people engaged in AI-related cross-disciplinary projects.
