Artificial intelligence is a word that carries with it heavy connotations. Although artificial intelligence is nothing more than the capacity for logic and understanding that machines can exhibit, in the minds of most people artificial intelligence is almost a Pandora's box that, when opened, will eventually signal the human race's doom..
The idea that machines pose an existential threat to human beings has been around for at least 60 years. It goes something like this: intelligent machines eventually realize the uselessness of human beings and turn against their creators. Or this: intelligent machines reduce human to cattle or even food after a dramatic war that human beings lose.
Human beings have created countless languages and writing systems that have allowed us to expand collective human knowledge over a period of thousands of years. Much of the knowledge that we utilized today, knowledge about the math, science, and the stars, originates from observations made thousands of years ago but which were recorded by writing systems, allowing this knowledge to be preserved and passed down.
Artificial intelligence has been used for many business, financial, medical, and other applications, and scientists and researchers are actively studying how these applications can be expanded to make human life simpler.
The applications of AI will be explored in this book, both the real applications to business, finance, medicine, and health and the theoretical applications. Even the sensational, perhaps exaggerated applications of AI will be explored in the context of taking a look at how AI may potentially be applied in the future. The purpose of this discussion is for the reader to understand what AI is by understanding how it is used.
Artificial intelligence is certainly a blessing at this point, but the reality that it may become a curse is not lost on some people. Understanding the full implications of AI requires a deep knowledge of what it is and where it came from.
For companies and businesses to take advantage of AI-powered and improved interactions, the conversation has to begin inside the organization. Leaders are supposed to start with the available channels and improve their smartness. From that point, they are supposed to ask key questions about engagements with customers and employees.
Here is a preview of what you will learn...
Brief history of artificial intelligence
The state of art of machine learning
Artificial neural networks applied to machine learning
Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges.
Описание: In the fast-developing world of Industry 4.0, which combines Extended Reality (XR) technologies, such as Virtual Reality (VR) and Augmented Reality (AR), creating location aware applications to interact with smart objects and smart processes via Cloud Computing strategies enabled with Artificial Intelligence (AI) and the Internet of Things (IoT), factories and processes can be automated and machines can be enabled with self-monitoring capabilities. Smart objects are given the ability to analyze and communicate with each other and their human co-workers, delivering the opportunity for much smoother processes, and freeing up workers for other tasks. Industry 4.0 enabled smart objects can be monitored, designed, tested and controlled via their digital twins, and these processes and controls are visualized in VR/AR.
The Industry 4.0 technologies provide powerful, largely unexplored application areas that will revolutionize the way we work, collaborate and live our lives. It is important to understand the opportunities and impact of the new technologies and the effects from a production, safety and societal point of view.
Blockchains are seen as a technology for the future, which reduce the cost of trust and revolutionize transactions between individuals, companies and governments. The sense of using blockchains is to minimize the probability of errors, successful frauds and paper-intensive processes. For these reasons, blockchains already have and will have a significant impact to the society and every day’s life, especially in field of Machine to Machine (M2M) communications, which are one of the basic technologies for Internet of Things (IoT). Therefore, blockchains with their inherent property to provide security, privacy and decentralized operation are engine for todays and future reliable, autonomous and trusted IoT platforms. Specially, a disruptive role of ledger technologies in future smart personal mobility systems, which combine smart car industry, smart energy/smart cities will be explained in the book, considering its importance for development of new industrial and business models.
Описание: This book collects perceptions and needs expectations and experiences concerning the application of Artificial Intelligence (AI) and Machine Learning in the steel sector.
Автор: Vermesan Ovidiu, John Reiner, de Luca Cristina Название: Artificial Intelligence for Digitising Industry: Applications ISBN: 8770226644 ISBN-13(EAN): 9788770226646 Издательство: Taylor&Francis Рейтинг: Цена: 18374.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides in-depth insights into use cases implementing artificial intelligence (AI) applications at the edge. It covers new ideas, concepts, research, and innovation to enable the development and deployment of AI, the industrial internet of things (IIoT), edge computing, and digital twin technologies in industrial environments. The work is based on the research results and activities of the AI4DI (ECSEL JU) project, including an overview of industrial use cases, research, technological innovation, validation, and deployment.
This book's sections build on the research, development, and innovative ideas elaborated for applications in five industries: automotive, semiconductor, industrial machinery, food and beverage, and transportation.
The articles included under each of these five industrial sectors discuss AI-based methods, techniques, models, algorithms, and supporting technologies, such as IIoT, edge computing, digital twins, collaborative robots, silicon-born AI circuit concepts, neuromorphic architectures, and augmented intelligence, that are anticipating the development of Industry 5.0.
Automotive applications cover use cases addressing AI-based solutions for inbound logistics and assembly process optimisation, autonomous reconfigurable battery systems, virtual AI training platforms for robot learning, autonomous mobile robotic agents, and predictive maintenance for machines on the level of a digital twin.
AI-based technologies and applications in the semiconductor manufacturing industry address use cases related to AI-based failure modes and effects analysis assistants, neural networks for predicting critical 3D dimensions in MEMS inertial sensors, machine vision systems developed in the wafer inspection production line, semiconductor wafer fault classifications, automatic inspection of scanning electron microscope cross-section images for technology verification, anomaly detection on wire bond process trace data, and optical inspection.
The use cases presented for machinery and industrial equipment industry applications cover topics related to wood machinery, with the perception of the surrounding environment and intelligent robot applications.
AI, IIoT, and robotics solutions are highlighted for the food and beverage industry, presenting use cases addressing novel AI-based environmental monitoring; autonomous environment-aware, quality control systems for Champagne production; and production process optimisation and predictive maintenance for soybeans manufacturing.
For the transportation sector, the use cases presented cover the mobility-as-a-service development of AI-based fleet management for supporting multimodal transport.
This book highlights the significant technological challenges that AI application developments in industrial sectors are facing, presenting several research challenges and open issues that should guide future development for evolution towards an environment-friendly Industry 5.0.
The challenges presented for AI-based applications in industrial environments include issues related to complexity, multidisciplinary and heterogeneity, convergence of AI with other technologies, energy consumption and efficiency, knowledge acquisition, reasoning with limited data, fusion of heterogeneous data, availability of reliable data sets, verification, validation, and testing for decision-making processes.
Put the power of AWS Cloud machine learning services to work in your business and commercial applications
Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services.
Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems.
- Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building
- Discover common neural network frameworks with Amazon SageMaker
- Solve computer vision problems with Amazon Rekognition
- Benefit from illustrations, source code examples, and sidebars in each chapter
The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.
Автор: Moises Rivas-Lopez, Oleg Sergiyenko, Wendy Flores-Fuentes, Julio Cesar Rodriguez-Quinonez Название: Optoelectronics in Machine Vision-Based Theories and Applications ISBN: 1522557512 ISBN-13(EAN): 9781522557517 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28215.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Sensor technologies play a large part in modern life, as they are present in things like security systems, digital cameras, smartphones, and motion sensors. While these devices are always evolving, research is being done to further develop this technology to help detect and analyze threats, perform in-depth inspections, and perform tracking services.Optoelectronics in Machine Vision-Based Theories and Applications provides innovative insights on theories and applications of optoelectronics in machine vision-based systems. It also covers topics such as applications of unmanned aerial vehicle, autonomous and mobile robots, medical scanning, industrial applications, agriculture, and structural health monitoring. This publication is a vital reference source for engineers, technology developers, academicians, researchers, and advanced-level students seeking emerging research on sensor technologies and machine vision.
Описание: This book is to presents and evaluates a way of modelling and optimizing nonlinear RFID Network Planning (RNP) problems using artificial intelligence techniques. It uses Artificial Neural Network models (ANN) to bind together the computational artificial intelligence algorithm with knowledge representation an efficient artificial intelligence paradigm to model and optimize RFID networks.This effort leads to proposing a novel artificial intelligence algorithm which has been named hybrid artificial intelligence optimization technique to perform optimization of RNP as a hard learning problem. This hybrid optimization technique consists of two different optimization phases. First phase is optimizing RNP by Redundant Antenna Elimination (RAE) algorithm and the second phase which completes RNP optimization process is Ring Probabilistic Logic Neural Networks (RPLNN).The hybrid paradigm is explored using a flexible manufacturing system (FMS) and the results are compared with well-known evolutionary optimization technique namely Genetic Algorithm (GA) to demonstrate the feasibility of the proposed architecture successfully.
Описание: Deep learning, as a recent AI technique, has proven itself efficient in solving many real-world problems. Deep learning algorithms are efficient, high performing, and an effective standard for solving these problems. In addition, with IoT, deep learning is in many emerging and developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an efficient solution to several image processing-related problems that have long remained unresolved or moderately solved. Various significant IoT technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving and the supported interaction between human beings and their surroundings.
The Handbook of Research on the Impact of Deep Learning and IoT on Multi-Industry Applications provides insights on how deep learning, together with IoT, impacts various sectors such as healthcare, agriculture, cyber security, and social media analysis applications. The chapters present solutions to various real-world problems using these methods from various researchers' points of view. While highlighting topics such as medical diagnosis, power consumption, livestock management, security, and social media analysis, this book is ideal for IT specialists, technologists, security analysts, medical practitioners, imaging specialists, diagnosticians, academicians, researchers, industrial experts, scientists, and undergraduate and postgraduate students who are working in the field of computer engineering, electronics, and electrical engineering.
Описание: Deep learning, as a recent AI technique, has proven itself efficient in solving many real-world problems. Deep learning algorithms are efficient, high performing, and an effective standard for solving these problems. In addition, with IoT, deep learning is in many emerging and developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an efficient solution to several image processing-related problems that have long remained unresolved or moderately solved. Various significant IoT technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving and the supported interaction between human beings and their surroundings.
Examining the Impact of Deep Learning and IoT on Multi-Industry Applications provides insights on how deep learning, together with IoT, impacts various sectors such as healthcare, agriculture, cyber security, and social media analysis applications. The chapters present solutions to various real-world problems using these methods from various researchers' points of view. While highlighting topics such as medical diagnosis, power consumption, livestock management, security, and social media analysis, this book is ideal for IT specialists, technologists, security analysts, medical practitioners, imaging specialists, diagnosticians, academicians, researchers, industrial experts, scientists, and undergraduate and postgraduate students who are working in the field of computer engineering, electronics, and electrical engineering.
Автор: Elspeth McKay, Mahmoud Numan Bakkar Название: Advanced Research and Real-World Applications of Industry 5.0 ISBN: 1799888053 ISBN-13(EAN): 9781799888055 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 39085.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Industry 5.0 is a growing field that has many potential future directions and opportunities for businesses and companies. To ensure society is prepared for this evolving technological world, further study is required on the potential challenges and pitfalls. Advanced Research and Real-World Applications of Industry 5.0 presents an overview of Industry 5.0 and related advanced research and real-world applications. The book also discusses several real-time issues, problems, and applications with corresponding solutions and suggestions. Covering critical topics such as optimization models, cybersecurity threats, and sustainability, this reference work is ideal for business owners, computer scientists, industry professionals, managers, researchers, scholars, academicians, practitioners, instructors, and students.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru