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Autonomous Vehicles
This edited book aims to address challenges facing the deployment of autonomous vehicles. Autonomous vehicles were predicted to hit the road by 2017. Even though a high degree of automation may have been achieved, vehicles that can drive autonomously under all circumstances are not yet commercially available, and the predictions have been adjusted. Now, experts even say that we are still decades away from fully autonomous vehicles. In this volume, the authors form a multidisciplinary team of experts to discuss some of the reasons behind this delay. The focus is on three areas: business, technology, and law. The authors discuss how the traditional car manufacturers have to devote numerous resources to the development of a new business model, in which the sole manufacturing of vehicles may no longer be sufficient. In addition, the book seeks to introduce how technological challenges are creating a shift toward connected autonomous vehicles. Further, it provides insight into how regulators are responding to the insufficiently tested technology and how lawyers try to answer the liability question for accidents with these autonomous vehicles.
The AI Conundrum
A timely, practical guide to AI—its strengths, weaknesses, and real-world applications—for business professionals and policymakers. Artificial intelligence, or AI, can recognize a pattern from any set of data it is given, which is what makes it such an extraordinarily powerful tool. But because not all patterns are authentic or reliable, AI’s pattern-finding superpower can lead to spurious patterns—and to disastrous results for business and government entities that rely on them. Hence the conundrum at the heart of AI: its greatest strength can also be its greatest weakness. Targeting the businessperson who needs to know how to use AI profitably and responsibly, Caleb Briggs and Rex B...
Dynamic System Modelling and Analysis with MATLAB and Python
Dynamic System Modeling & Analysis with MATLAB & Python A robust introduction to the advanced programming techniques and skills needed for control engineering In Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers, accomplished control engineer Dr. Jongrae Kim delivers an insightful and concise introduction to the advanced programming skills required by control engineers. The book discusses dynamic systems used by satellites, aircraft, autonomous robots, and biomolecular networks. Throughout the text, MATLAB and Python are used to consider various dynamic modeling theories and examples. The author covers a range of control topics, including attitude dynamics, attit...
Designing Machine Learning Systems
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objec...
Legal Tech and the New Sharing Economy
The exponential growth of disruptive technology is changing our world. The development of cloud computing, big data, the internet of things, artificial intelligence, machine learning, deep learning, and other related autonomous systems, such as self-driving vehicles, have triggered the emergence of new products and services. These significant technological breakthroughs have opened the door to new economic models such as the sharing and platform-based economy. As a result, companies are becoming increasingly data- and algorithm-driven, coming to be more like “decentralized platforms”. New transaction or payment methods such as Bitcoin and Ethereum, based on trust-building systems using B...
Interpretable Machine Learning
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Research Handbook on Big Data Law
This state-of-the-art Research Handbook provides an overview of research into, and the scope of current thinking in, the field of big data analytics and the law. It contains a wealth of information to survey the issues surrounding big data analytics in legal settings, as well as legal issues concerning the application of big data techniques in different domains.
Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development
This book focuses on different algorithms and models related to AI, big data and IoT used for various domains. It enables the reader to have a broader and deeper understanding of several perspectives regarding the dynamics, challenges, and opportunities for sustainable development using artificial intelligence, big data and IoT. Applications of Artificial Intelligence, Big Data and Internet of Things (IoT) in Sustainable Development focuses on IT-based advancements in multidisciplinary fields such as healthcare, finance, bioinformatics, industrial automation, and environmental science. The authors discuss the key issues of security, management, and the realization of possible solutions to hu...
Malware Analysis Using Artificial Intelligence and Deep Learning
This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
