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Federated Learning Systems by Muhammad Habib ur Rehman,Mohamed Medhat Gaber Pdf
This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.
AI and Learning Systems by Konstantinos Kyprianidis,Erik Dahlquist Pdf
Over the last few years, interest in the industrial applications of AI and learning systems has surged. This book covers the recent developments and provides a broad perspective of the key challenges that characterize the field of Industry 4.0 with a focus on applications of AI. The target audience for this book includes engineers involved in automation system design, operational planning, and decision support. Computer science practitioners and industrial automation platform developers will also benefit from the timely and accurate information provided in this work. The book is organized into two main sections comprising 12 chapters overall: •Digital Platforms and Learning Systems •Industrial Applications of AI
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
Artificial Intelligence in Education by Vania Dimitrova Pdf
Artificial Intelligence in Education conference 2009 (AIED) is part of a series of biennial international conferences for top quality research in intelligent systems and cognitive science for educational computing applications. This title covers papers presented at the Artificial Intelligence in Education conference 2009 (AIED).
AI and education by Miao, Fengchun,Holmes, Wayne,Ronghuai Huang,Hui Zhang,UNESCO Pdf
Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]
Artificial Intelligence in Education by Ben Du Boulay,R. Mizoguchi Pdf
The theme of this book is Knowledge and Media in Learning Systems, and papers that explore the emerging roles of intelligent multimedia and distributed technologies as well as computer supported collaboration within that theme are included. The spread of topics is very wide encompassing both well- established areas such as student modelling as well as more novel topics such as distributed intelligent tutoring on the World Wide Web. Far from undermining the need to understand how learning and teaching interact, the newer media continue to emphasise the interdependence of these two processes. Collaboration and tools for collaboration are the major topics of interest. Understanding how human learners collaborate, how peer tutoring works and how the computer can play a useful role as either a more able of even a less able learning partner are all explored here.
Intelligent Collaborative e-Learning Systems and Applications by Thanasis Daradoumis,Santi Caballé,Joan Manuel Marquès,Fatos Xhafa Pdf
Intelligent Collaborative e-Learning Systems and Applications is a major research theme in CSCL and CSCW research community. It comprises a variety of research topics that focus on developing systems that are more powerful and flexible and also more adaptable to the learning process and thus provide better answers to the paradigmatic principles of on-line collaborative learning and work. The chapters collected in this book provide new insights, findings and approaches both on the analysis and the development of more powerful e-collaboration settings. Researchers will find in this book the latest trends in these research topics. On the other hand, academics will find practical insights on how to use conceptual and experimental approaches in their daily tasks. Finally, developers from CSCL community can be inspired and put in practice the proposed models and evaluate them for the specific purposes of their own work and context.
Intelligent and Adaptive Educational-Learning Systems by Alejandro Peña-Ayala Pdf
The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form. This book is devoted to the “Intelligent and Adaptive Educational-Learning Systems”. It privileges works that highlight key achievements and outline trends to inspire future research. After a rigorous revision process twenty manuscripts were accepted and organized into four parts: Modeling, Content, Virtuality and Applications. This volume is of interest to researchers, practitioners, professors and postgraduate students aimed to update their knowledge and find out targets for future work in the field of artificial intelligence on education.
In this comprehensive guide, you'll learn the basics about: - The applications we have in AI. - How AI can be a threat and an opportunity. - The historical context of AI. - The effects AI can have on the future. - How AI is applied in marketing. - What big data and neural networks are. - Generation Z and the uses of AI. And a lot more. Don't wait and give yourself this knowledge by getting the book.
AI and Machine Learning for Coders by Laurence Moroney Pdf
If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving
Learning Systems and Intelligent Robots by K. Fu Pdf
This book contains the Proceedings of the S~cond U. S. -Japan Seminar on Learning Control and Intelligent Control. The seminar, held at Gainesville, Florida, from October 22 to 26, 1973, was sponsored by the U. S. -Japan Cooperative Science Program, jointly supported by the National Science Foundation and the Japan Society for the Promotion of Science. The full texts of the twenty-one presented papers are included. The papers cover a variety of topics related to learning control and intelligent control, ranging from pattern recognition to system identification, from learning control to intelligent robots. During the past decade, there has been a considerable increase of interest in problems of machine learning, systems which exhibit learning behavior. In designing a system, if the a priori infor mation required is unknown or incompletely known, one approach is to design a system which is capable of learning the unknown infor mation during its operation. The learned information will then be used to improve the system's performance. This approach has been used in the design of pattern recognition systems, automatic control systems and system identification algorithms. If we naturally extend our goal to the design of systems which will behave more and more intelligently, learning systems research is only a preliminary step towards a general concept of integrated intelligent systems. One example of this class of systems is the intelligent robot, which integrates pattern recognition. learning and problem-solving into one intelligent system.
Artur S. d'Avila Garcez,Krysia B. Broda,Dov M. Gabbay
Author : Artur S. d'Avila Garcez,Krysia B. Broda,Dov M. Gabbay Publisher : Springer Science & Business Media Page : 276 pages File Size : 54,7 Mb Release : 2012-12-06 Category : Computers ISBN : 9781447102113
Neural-Symbolic Learning Systems by Artur S. d'Avila Garcez,Krysia B. Broda,Dov M. Gabbay Pdf
Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
OECD Digital Education Outlook 2021 Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots by OECD Pdf
How might digital technology and notably smart technologies based on artificial intelligence (AI), learning analytics, robotics, and others transform education? This book explores such question. It focuses on how smart technologies currently change education in the classroom and the management of educational organisations and systems.
This book reviews a blend of artificial intelligence (AI) approaches that can take e-learning to the next level by adding value through customization. It investigates three methods: crowdsourcing via social networks; user profiling through machine learning techniques, and personal learning portfolios using learning analytics. Technology and education have drawn closer together over the years as they complement each other within the domain of e-learning, and different generations of online education reflect the evolution of new technologies as researcher and developers continuously seek to optimize the electronic medium to enhance the effectiveness of e-learning. Artificial intelligence (AI) for e-learning promises personalized online education through a combination of different intelligent techniques that are grounded in established learning theories while at the same time addressing a number of common e-learning issues. This book is intended for education technologists and e-learning researchers as well as for a general readership interested in the evolution of online education based on techniques like machine learning, crowdsourcing, and learner profiling that can be merged to characterize the future of personalized e-learning.
Intelligent Open Learning Systems by Przemysław Różewski,Emma Kusztina,Ryszard Tadeusiewicz,Oleg Zaikin Pdf
In presented book the Intelligent Open Learning Systems (IOLS) are proposed, described, discussed, and evaluated. The IOLS is a system in which traditional methods of online teaching are enhanced through the use of artificial intelligence and cognitive science. This is the main topic of the book. It consists of ten chapters and is divided into three parts. The first part concentrates on the Open Learning System (OLS) analysis, in particular: the social and educational meanings of the OLS, the new role of the teacher and the new requirements regarding the structure of didactic material. Moreover, the cybernetic model of student, teacher and computer collaboration is presented, the teaching-learning process content and its main characteristics are discussed, and the system based approach to the OLS design is proposed. The second part is focused on the problem of knowledge modeling in the OLS based on the ontology and the competence approaches and leading to the learning object concept and competence management in open systems. The third part describes applications of the OLS in the virtual laboratory for competence transfer, the community-built system of distance learning network, and the AGH student city – the real-life application of the OLS concept. The authors’ research findings presented in the book should be useful in various applications related to knowledge management, e-learning systems and information systems.