The Easy Introduction to Machine Learning (Ml) for Nontechnical People--In Business and Beyond Artificial Intelligence for Business is your plain-English guide to Artificial Intelligence (AI) and Machine Learning (ML): how they work, what they can and cannot do, and how to start profiting from them. Writing for nontechnical executives and professionals, Doug Rose demystifies AI/ML technology with intuitive analogies and explanations honed through years of teaching and consulting. Rose explains everything from early "expert systems" to advanced deep learning networks. First, Rose explains how AI and ML emerged, exploring pivotal early ideas that continue to influence the field. Next, he deepens your understanding of key ML concepts, showing how machines can create strategies and learn from mistakes. Then, Rose introduces current powerful neural networks: systems inspired by the structure and function of the human brain. He concludes by introducing leading AI applications, from automated customer interactions to event prediction. Throughout, Rose stays focused on business: applying these technologies to leverage new opportunities and solve real problems. Compare the ways a machine can learn, and explore current leading ML algorithms Start with the right problems, and avoid common AI/ML project mistakes Use neural networks to automate decision-making and identify unexpected patterns Help neural networks learn more quickly and effectively Harness AI chatbots, virtual assistants, virtual agents, and conversational AI applications

There are new and important advancements in todays complexity theories in ICT and requires an extraordinary perspective on the interaction between living systems and information technologies. With human evolution and its continuous link with the development of new tools and environmental changes, technological advancements are paving the way for new evolutionary steps. Complexity Science, Living Systems, and Reflexing Interfaces: New Models and Perspectives is a collection of research provided by academics and scholars aiming to introduce important advancements in areas such as artificial intelligence, evolutionary computation, neural networks, and much more. This scholarly piece will provide contributions that will define the line of development in complexity science.

Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.

Book DescriptionHow will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances? Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community. Martin has wide-ranging conversations with twenty-three of the world's foremost researchers and entrepreneurs working in AI and robotics: Demis Hassabis (DeepMind), Ray Kurzweil (Google), Geoffrey Hinton (Univ. of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel). Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future.

Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine.

The book highlights three types of technologies being developed for autonomous solution of navigation problems. These technologies are based on the polarization structure, ultra-broadband and the fluctuation characteristics (slow and fast) of the radiolocation signals. The book presents the problems of intrinsic thermal radio emission polarization and change in radio waves polarization when they are reflected from objects with non-linear properties. The purpose of this book is to develop the foundations for creating autonomous radionavigation systems to provide aviation with navigation systems that will substantially increase its capabilities, specifically acting where satellite technologies do not work. The book is intended for specialists involved in the development and operation of aviation-technical complexes, as well as for specialists of national aviation regulators and ICAO experts dealing with the problems of improving flight safety.

During the COVID-19 pandemic, computational intelligence and computer-aided diagnosis (CAD) systems have supported the effective treatment of the virus. Artificial intelligence (AI) has been playing a significant role in the rapidly emerging healthcare sector in terms of CAD, software algorithms, hardware implementation, and applications in the medical field. Through this, the constraints of the traditional system must be addressed to innovate and shed light on emerging healthcare technologies. Computational Intelligence and Applications for Pandemics and Healthcare explores the state-of-the-art computational intelligence approaches in medical data and classifies existing computational techniques used in medical areas. It discusses the tactics and methods as well as the limitations and performances of computational intelligence applications for healthcare. The constraints of traditional healthcare systems are addressed by using CAD and computationally-intelligent medical data. Covering topics such as cloud-based monitoring systems, detection and diagnosis, and intelligent medical systems, this book is an excellent resource for computer scientists, government officials, medical students, medical professionals, hospitals, researchers, and academicians.

Multi-Agent Systems for Education and Interactive Entertainment: Design, Use and Experience presents readers with a rich collection of ideas from researchers who are exploring the complex tradeoffs that must be made in designing agent systems for education and interactive entertainment. This book aims to provide a mixture of relevant theoretical and practical understanding of the use of multi-agent systems in educational and entertainment research, together with practical examples of the use of such systems in real application scenarios.

This book highlights numerical models as powerful tools for the optimal design of Micro-Electro-Mechanical Systems (MEMS). Most MEMS experts have a background in electronics, where circuit models or behavioral models (i.e. lumped-parameter models) of devices are preferred to field models. This is certainly convenient in terms of preliminary design, e.g. in the prototyping stage. However, design optimization should also take into account fine-sizing effects on device behavior and therefore be based on distributed-parameter models, such as finite-element models. The book shows how the combination of automated optimal design and field-based models can produce powerful design toolboxes for MEMS. It especially focuses on illustrating theoretical concepts with practical examples, fostering comprehension through a problem-solving approach. By comparing the results obtained using different methods, readers will learn to identify their respective strengths and weaknesses. In addition, special emphasis is given to evolutionary computing and nature-inspired optimization strategies, the effectiveness of which has already been amply demonstrated. Given its scope, the book provides PhD students, researchers and professionals in the area of computer-aided analysis with a comprehensive, yet concise and practice-oriented guide to MEMS design and optimization. To benefit most from the book, readers should have a basic grasp of electromagnetism, vector analysis and numerical methods.

This volume provides an extensive overview of the Ethics of Artificial Intelligence for the Sustainable Development Goals. The authors are experts contributing with perspectives from different fields. The comprehensive collection of chapters illustrates the pressing governance problems related to using AI for the SDGs, and case studies describing how AI is advancing and can advance the achievement of the Goals. Students, scholars, and practitioners working on AI for SDGs, the ethical governance of AI, sustainability, and the fourth revolution can find this book a helpful reference.

This book has a focus on the development and deployment of the Industrial Internet of Things (IIoT) paradigm, discussing frameworks, methodologies, benefits and limitations, as well as providing case studies of employing the IoT vision in the industrial domain. IIoT is becoming an attractive business reality for many organisations such as manufacturing, logistics, oil and gas, energy and other utilities, mining, aviation, and many more. The opportunities for this paradigm are huge, and according to one report, the IIoT market is predicted to reach $125 billion by 2021. The driving philosophy behind the IIoT is that smart machines are better than humans at accurately capturing, analysing and communicating real-time data. The underlying technologies include distributed computing, machine learning, artificial intelligence, and machine-to-machine communication, with a typical IIoT system consisting of intelligent systems (applications, controllers, sensors, and security mechanisms), data communication infrastructure (cloud computing, edge computing, etc.), data analytics (to support business intelligence and corporate decision making), and most importantly the human element. The promised benefits of the IIoT include enhanced safety, better reliability, smart metering, inventory management, equipment tracking, and facilities management. There are, however, numerous issues that are also becoming the focus of active research, such as concerns regarding service availability, data security, and device communication. Lack of ubiquitous interoperability between heterogeneous devices is also a major concern. This book intends to fill a gap in the IIoT literature by providing the scientific contributions and latest developments from researchers and practitioners of international repute, focusing on frameworks, methodologies, benefits, and inherent issues/barriers to connected environments, especially in industrial settings. The intended audience includes network specialists, hardware engineers, and security experts who wish to adopt newer approaches for device connectivity, IoT security, and sensor-based devices design. University level students, researchers and practitioners will also find the latest innovation in technology and newer approaches relevant to the IIoT from a distributed computing perspective.

The digital age is ripe with emerging advances and applications in technological innovations. Mimicking the structure of complex systems in nature can provide new ideas on how to organize mechanical and personal systems. The Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms is an essential scholarly resource on current algorithms that have been inspired by the natural world. Featuring coverage on diverse topics such as cellular automata, simulated annealing, genetic programming, and differential evolution, this reference publication is ideal for scientists, biological engineers, academics, students, and researchers that are interested in discovering what models from nature influence the current technology-centric world.

This edited volume explores the intersection between philosophy and computing. It features work presented at the 2016 annual meeting of the International Association for Computing and Philosophy. The 23 contributions to this volume neatly represent a cross section of 40 papers, four keynote addresses, and eight symposia as they cut across six distinct research agendas. The volume begins with foundational studies in computation and information, epistemology and philosophy of science, and logic. The contributions next examine research into computational aspects of cognition and philosophy of mind. This leads to a look at moral dimensions of man-machine interaction as well as issues of trust, privacy, and justice. This multi-disciplinary or, better yet, a-disciplinary investigation reveals the fruitfulness of erasing distinctions among and boundaries between established academic disciplines. This should come as no surprise. The computational turn itself is a-disciplinary and no former discipline, whether scientific, artistic, or humanistic, has remained unchanged. Rigorous reflection on the nature of these changes opens the door to inquiry into the nature of the world, what constitutes our knowledge of it, and our understanding of our place in it. These investigations are only just beginning. The contributions to this volume make this clear: many encourage further research and end with open questions.

Hybrid learning is now the single-greatest trend in education today due to the numerous educational advantages when both traditional classroom learning and e-learning are implemented collectively. The Handbook of Research on Hybrid Learning Models: Advanced Tools, Technologies, and Applications collects emerging research and pedagogies related to the convergence of teaching and learning methods. This significant Handbook of Research provides researchers, scholars, students, and professionals access to the latest knowledge related to hybrid learning, discovered and written by an international gathering of e-learning experts.

In today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method. Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.

In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.

Over the last few years, the area of Natural Language Processing has drastically grown in recognition, not only within the research and development community, but also with industry professionals. As NLP continues to be discussed and researched, certain areas continue to grow and mature. As a result, the need for advanced research and information is in high demand. Emerging Applications of Natural Language Processing: Concepts and New Research provides pertinent and vital information that researchers, postgraduate, doctoral students, and practitioners are seeking. This book compiles the latest discoveries and advances in NLP methodologies and applications while expanding upon various topics regarding the future of NLP.

This book systematically presents the operating principles and technical characteristics of the main radio navigating systems (RNSs) that make it possible to adequately evaluate the corresponding scratch indexes and levels of air safety for air vehicles, the chief concern of the International Civil Aviation Organization (ICAO). The book discusses how RNS systems substantially determine navigation accuracy and reliability, and therefore air safety; in addition, it presents practical solutions to problems arising in the operation and development of RNS systems.

The emergence of artificial intelligence has created a vast amount of advancements within various professional sectors and has transformed the way organizations conduct themselves. The implementation of intelligent systems has assisted with developing traditional processes including decision making, risk management, and security. An area that requires significant attention and research is how these companies are becoming accustomed to computer intelligence and applying this technology to their everyday practices. Transdisciplinary Perspectives on Risk Management and Cyber Intelligence is a pivotal reference source that provides vital research on the application of intelligent systems within various professional sectors as well as the exploration of theories and empirical findings. While highlighting topics such as decision making, cognitive science, and knowledge management, this publication explores the management of risk and uncertainty using training exercises, as well as the development of managerial intelligence competency. This book is ideally designed for practitioners, educators, researchers, policymakers, managers, developers, analysts, politicians, and students seeking current research on modern approaches to the analysis and performance of cyber intelligence.

This book discusses the computational geometry, topology and physics of digital images and video frame sequences. This trio of computational approaches encompasses the study of shape complexes, optical vortex nerves and proximities embedded in triangulated video frames and single images, while computational geometry focuses on the geometric structures that infuse triangulated visual scenes. The book first addresses the topology of cellular complexes to provide a basis for an introductory study of the computational topology of visual scenes, exploring the fabric, shapes and structures typically found in visual scenes. The book then examines the inherent geometry and topology of visual scenes, and the fine structure of light and light caustics of visual scenes, which bring into play catastrophe theory and the appearance of light caustic folds and cusps. Following on from this, the book introduces optical vortex nerves in triangulated digital images. In this context, computational physics is synonymous with the study of the fine structure of light choreographed in video frames. This choreography appears as a sequence of snapshots of light reflected and refracted from surface shapes, providing a solid foundation for detecting, analyzing and classifying visual scene shapes.

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.

Without mathematics no science would survive. This especially applies to the engineering sciences which highly depend on the applications of mathematics and mathematical tools such as optimization techniques, finite element methods, differential equations, fluid dynamics, mathematical modelling, and simulation. Neither optimization in engineering, nor the performance of safety-critical system and system security; nor high assurance software architecture and design would be possible without the development of mathematical applications. De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences (AMEIS) focusses on the latest applications of engineering and information technology that are possible only with the use of mathematical methods. By identifying the gaps in knowledge of engineering applications the AMEIS series fosters the international interchange between the sciences and keeps the reader informed about the latest developments.

This book presents a variety of perspectives on vision-based applications. These contributions are focused on optoelectronic sensors, 3D & 2D machine vision technologies, robot navigation, control schemes, motion controllers, intelligent algorithms and vision systems. The authors focus on applications of unmanned aerial vehicles, autonomous and mobile robots, industrial inspection applications and structural health monitoring. Recent advanced research in measurement and others areas where 3D & 2D machine vision and machine control play an important role, as well as surveys and reviews about vision-based applications. These topics are of interest to readers from diverse areas, including electrical, electronics and computer engineering, technologists, students and non-specialist readers. * Presents current research in image and signal sensors, methods, and 3D & 2D technologies in vision-based theories and applications; * Discusses applications such as daily use devices including robotics, detection, tracking and stereoscopic vision systems, pose estimation, avoidance of objects, control and data exchange for navigation, and aerial imagery processing; * Includes research contributions in scientific, industrial, and civil applications.