Download or read book Sodium written by Ron Legarski and published by SolveForce. This book was released on 2024-09-19 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Sodium: From Discovery to Modern Energy Applications" is an in-depth exploration of one of the most versatile and vital elements in the periodic table—sodium. This book traces the journey of sodium from its discovery in the early 1800s to its pivotal role in modern industry and cutting-edge energy technologies, including its revolutionary use in Sodium-Cooled Small Modular Reactors (SMRs). Designed for readers interested in science, engineering, and energy innovation, this book delves into sodium's chemical properties, its interactions with other elements, and its applications across a wide range of fields. From industrial manufacturing and agriculture to its use in advanced nuclear reactors, sodium continues to prove itself as a critical player in both historical and modern contexts. Key topics covered include: The Discovery of Sodium: Explore the early scientific breakthroughs that led to the identification and isolation of sodium, setting the stage for its wide-ranging applications. Sodium in Industry and Chemistry: Understand the diverse uses of sodium in industries such as glass manufacturing, metal alloys, and organic chemistry. Sodium in Agriculture and Environmental Science: Learn about sodium’s role in soil chemistry, crop yield, and its impact on sustainable agricultural practices. Sodium as a Coolant in Nuclear Reactors: Dive into the innovative use of sodium in SMRs, where it acts as an efficient and safe coolant in next-generation nuclear power plants. This includes detailed discussions on its role in sodium-cooled fast reactors, advanced safety systems, and its contribution to carbon reduction and energy security. Environmental Impact and Sustainability: Examine the environmental challenges and sustainability measures associated with sodium production, use, and recycling, including its role in renewable energy systems and climate change mitigation. Written by Ron Legarski, a seasoned expert in telecommunications, technology infrastructure, and energy solutions, this book also integrates unique insights into how digital technologies, connectivity, and smart grids are essential in supporting sodium’s modern applications, especially in energy systems like SMRs. Whether you're a scientist, engineer, student, or simply curious about the evolving role of sodium in today’s world, "Sodium: From Discovery to Modern Energy Applications" offers a comprehensive and engaging look at an element that is driving both technological progress and sustainable energy solutions.

Download or read book Scalable and Distributed Machine Learning and Deep Learning Patterns written by Thomas, J. Joshua and published by IGI Global. This book was released on 2023-08-25 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.

Download or read book Ultimate MLOps for Machine Learning Models written by Saurabh Dorle and published by Orange Education Pvt Ltd. This book was released on 2024-08-30 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: TAGLINE The only MLOps guide you'll ever need KEY FEATURES ● Acquire a comprehensive understanding of the entire MLOps lifecycle, from model development to monitoring and governance. ● Gain expertise in building efficient MLOps pipelines with the help of practical guidance with real-world examples and case studies. ● Develop advanced skills to implement scalable solutions by understanding the latest trends/tools and best practices. DESCRIPTION This book is an essential resource for professionals aiming to streamline and optimize their machine learning operations. This comprehensive guide provides a thorough understanding of the MLOps life cycle, from model development and training to deployment and monitoring. By delving into the intricacies of each phase, the book equips readers with the knowledge and tools needed to create robust, scalable, and efficient machine learning workflows. Key chapters include a deep dive into essential MLOps tools and technologies, effective data pipeline management, and advanced model optimization techniques. The book also addresses critical aspects such as scalability challenges, data and model governance, and security in machine learning operations. Each topic is presented with practical insights and real-world case studies, enabling readers to apply best practices in their job roles. Whether you are a data scientist, ML engineer, or IT professional, this book empowers you to take your machine learning projects from concept to production with confidence. It equips you with the practical skills to ensure your models are reliable, secure, and compliant with regulations. By the end, you will be well-positioned to navigate the ever-evolving landscape of MLOps and unlock the true potential of your machine learning initiatives. WHAT WILL YOU LEARN ● Implement and manage end-to-end machine learning lifecycles. ● Utilize essential tools and technologies for MLOps effectively. ● Design and optimize data pipelines for efficient model training. ● Develop and train machine learning models with best practices. ● Deploy, monitor, and maintain models in production environments. ● Address scalability challenges and solutions in MLOps. ● Implement robust security practices to protect your ML systems. ● Ensure data governance, model compliance, and security in ML operations. ● Understand emerging trends in MLOps and stay ahead of the curve. WHO IS THIS BOOK FOR? This book is for data scientists, machine learning engineers, and data engineers aiming to master MLOps for effective model management in production. It’s also ideal for researchers and stakeholders seeking insights into how MLOps drives business strategy and scalability, as well as anyone with a basic grasp of Python and machine learning looking to enter the field of data science in production. TABLE OF CONTENTS 1. Introduction to MLOps 2. Understanding Machine Learning Lifecycle 3. Essential Tools and Technologies in MLOps 4. Data Pipelines and Management in MLOps 5. Model Development and Training 6. Model Optimization Techniques for Performance 7. Efficient Model Deployment and Monitoring Strategies 8. Scalability Challenges and Solutions in MLOps 9. Data, Model Governance, and Compliance in Production Environments 10. Security in Machine Learning Operations 11. Case Studies and Future Trends in MLOps Index

Download or read book Designing Machine Learning Systems with Python written by David Julian and published by Packt Publishing Ltd. This book was released on 2016-04-06 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design efficient machine learning systems that give you more accurate results About This Book Gain an understanding of the machine learning design process Optimize machine learning systems for improved accuracy Understand common programming tools and techniques for machine learning Develop techniques and strategies for dealing with large amounts of data from a variety of sources Build models to solve unique tasks Who This Book Is For This book is for data scientists, scientists, or just the curious. To get the most out of this book, you will need to know some linear algebra and some Python, and have a basic knowledge of machine learning concepts. What You Will Learn Gain an understanding of the machine learning design process Optimize the error function of your machine learning system Understand the common programming patterns used in machine learning Discover optimizing techniques that will help you get the most from your data Find out how to design models uniquely suited to your task In Detail Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more. Style and approach This easy-to-follow, step-by-step guide covers the most important machine learning models and techniques from a design perspective.

Download or read book Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning written by Lei Deng and published by Frontiers Media SA. This book was released on 2021-05-05 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book MLOps Engineering at Scale written by Carl Osipov and published by Simon and Schuster. This book was released on 2022-03-22 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools! In MLOps Engineering at Scale you will learn: Extracting, transforming, and loading datasets Querying datasets with SQL Understanding automatic differentiation in PyTorch Deploying model training pipelines as a service endpoint Monitoring and managing your pipeline’s life cycle Measuring performance improvements MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities. About the technology A production-ready machine learning system includes efficient data pipelines, integrated monitoring, and means to scale up and down based on demand. Using cloud-based services to implement ML infrastructure reduces development time and lowers hosting costs. Serverless MLOps eliminates the need to build and maintain custom infrastructure, so you can concentrate on your data, models, and algorithms. About the book MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production. What's inside Reduce or eliminate ML infrastructure management Learn state-of-the-art MLOps tools like PyTorch Lightning and MLFlow Deploy training pipelines as a service endpoint Monitor and manage your pipeline’s life cycle Measure performance improvements About the reader Readers need to know Python, SQL, and the basics of machine learning. No cloud experience required. About the author Carl Osipov implemented his first neural net in 2000 and has worked on deep learning and machine learning at Google and IBM. Table of Contents PART 1 - MASTERING THE DATA SET 1 Introduction to serverless machine learning 2 Getting started with the data set 3 Exploring and preparing the data set 4 More exploratory data analysis and data preparation PART 2 - PYTORCH FOR SERVERLESS MACHINE LEARNING 5 Introducing PyTorch: Tensor basics 6 Core PyTorch: Autograd, optimizers, and utilities 7 Serverless machine learning at scale 8 Scaling out with distributed training PART 3 - SERVERLESS MACHINE LEARNING PIPELINE 9 Feature selection 10 Adopting PyTorch Lightning 11 Hyperparameter optimization 12 Machine learning pipeline

Download or read book Data Management in Machine Learning Systems written by Matthias Boehm and published by Springer Nature. This book was released on 2022-05-31 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many ML systems due to data-driven application characteristics, data-centric workload characteristics, and system architectures inspired by classical data management techniques. In this book, we follow this data-centric view of ML systems and aim to provide a comprehensive overview of data management in ML systems for the end-to-end data science or ML lifecycle. We review multiple interconnected lines of work: (1) ML support in database (DB) systems, (2) DB-inspired ML systems, and (3) ML lifecycle systems. Covered topics include: in-database analytics via query generation and user-defined functions, factorized and statistical-relational learning; optimizing compilers for ML workloads; execution strategies and hardware accelerators; data access methods such as compression, partitioning and indexing; resource elasticity and cloud markets; as well as systems for data preparation for ML, model selection, model management, model debugging, and model serving. Given the rapidly evolving field, we strive for a balance between an up-to-date survey of ML systems, an overview of the underlying concepts and techniques, as well as pointers to open research questions. Hence, this book might serve as a starting point for both systems researchers and developers.

Download or read book Metaheuristic and Machine Learning Optimization Strategies for Complex Systems written by R., Thanigaivelan and published by IGI Global. This book was released on 2024-07-17 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more.

Download or read book Deep Reinforcement Learning written by Robert Johnson and published by HiTeX Press. This book was released on 2024-10-27 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Deep Reinforcement Learning: An Essential Guide" offers a comprehensive introduction to one of the most dynamic and transformative areas of artificial intelligence. This book meticulously unravels the intricate concepts of deep reinforcement learning (DRL), bridging foundational theories with cutting-edge applications. Addressing both newcomers and experienced practitioners, it provides a structured exploration from the basics of neural networks and reinforcement learning to the sophisticated mechanisms that drive core algorithms like DQN, PPO, and policy gradient methods. The book emphasizes real-world applications, showcasing DRL's impact across gaming, finance, healthcare, and autonomous systems, illustrating its vast potential and versatility. By understanding the strategic balance of exploration and exploitation, readers gain insight into designing intelligent agents capable of thriving in complex environments. As DRL continues to evolve, the text also delves into current challenges and future directions, such as ethical considerations, safety, and efficiency, preparing readers to contribute to and innovate within this rapidly advancing field. Comprehensive yet accessible, this guide is an invaluable resource for anyone aspiring to harness the power of deep reinforcement learning.

Download or read book Performance Evaluation and Benchmarking for the Era of Cloud s written by Raghunath Nambiar and published by Springer Nature. This book was released on 2020-07-29 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 11th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2019, held in conjunction with the 45th International Conference on Very Large Databases (VLDB 2019) in August 2019. The 11 papers presented were carefully reviewed and focus on topics such as blockchain; big data and analytics; complex event processing; database Optimizations; data Integration; disaster tolerance and recovery; artificial Intelligence; emerging storage technologies (NVMe, 3D XPoint Memory etc.); hybrid workloads; energy and space efficiency; in-memory databases; internet of things; virtualization; enhancements to TPC workloads; lessons learned in practice using TPC workloads; collection and interpretation of performance data in public cloud environments.

Download or read book Machine Learning Fundamentals Course written by Brian Smith and published by THE PUBLISHER. This book was released on 2024-03-11 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Machine Learning Fundamentals Course provides a comprehensive introduction to the field of machine learning. It covers a wide range of topics, starting with an overview of what machine learning is and its historical development. The course then delves into the basics of machine learning, including data preprocessing, feature engineering, and model evaluation. The course explores both supervised and unsupervised learning techniques, such as linear regression, logistic regression, decision trees, and clustering algorithms. It also covers model optimization and regularization, including cross-validation, hyperparameter tuning, and regularization techniques. One of the highlights of the course is the chapter on neural networks and deep learning, which introduces participants to the fundamentals of neural networks, convolutional neural networks, and recurrent neural networks. The course also covers natural language processing, recommender systems, transfer learning, model deployment, ethical considerations in machine learning, anomaly detection, reinforcement learning, time series analysis, and advanced topics such as ensemble learning and explainable AI. This course provides a solid foundation in machine learning, equipping participants with the necessary knowledge and skills to build and deploy machine learning models in real-world scenarios. Whether you are a beginner or an experienced practitioner, this course offers valuable insights into the fundamental concepts and techniques of machine learning.

Download or read book Essential Federated Learning written by Robert Johnson and published by HiTeX Press. This book was released on 2024-10-27 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Essential Federated Learning: AI at the Edge" offers a comprehensive exploration into the transformative domain of federated learning, an innovative approach reshaping the AI landscape by enabling data decentralization. This book demystifies the foundational concepts of federated learning, capturing its potential to increase privacy, enhance data security, and empower industries across sectors such as healthcare, finance, and beyond. By keeping data localized, federated learning minimizes privacy concerns while leveraging the power and capability of edge computing. Each chapter meticulously builds upon the last, guiding readers from basic principles to advanced applications, providing a balanced understanding of technical architectures, algorithms, and real-world implementations. Rich with insights into the ethical and social implications of federated learning, this book addresses the pressing challenges and future directions that are critical for its evolution. Topics such as privacy preservation, bias mitigation, and regulatory compliance are thoroughly examined, offering a holistic view of how federated learning can be applied responsibly and effectively. Whether you're a researcher, practitioner, or policy-maker, "Essential Federated Learning: AI at the Edge" offers the essential knowledge needed to harness the advantages of this cutting-edge technology, ensuring readers are well-equipped to navigate the rapidly expanding landscape of AI and edge computing.

Download or read book Few Shot Machine Learning written by Robert Johnson and published by HiTeX Press. This book was released on 2024-10-27 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Few-Shot Machine Learning: Doing More with Less Data" is an illuminating exploration into the cutting-edge techniques that enable machines to learn efficiently from limited data. This book delves deep into the domain of few-shot learning—a revolutionary approach that challenges the traditional dependency on vast datasets. By uncovering the principles and practices that allow models to generalize from minimal examples, it empowers readers to harness the power of artificial intelligence in resource-constrained environments. Carefully structured to provide both theoretical insights and practical guidance, the book navigates through essential paradigms such as meta-learning, transfer learning, and innovative data augmentation strategies. It emphasizes the building blocks needed to understand and apply few-shot learning across various domains, from healthcare diagnostics to real-time analytics. Through real-world applications and case studies, the text not only illustrates the transformative potential of few-shot learning but also prepares practitioners to address prevalent challenges and seize future opportunities in this dynamic field. "Few-Shot Machine Learning: Doing More with Less Data" serves as an indispensable resource for beginners and experienced professionals alike, offering a comprehensive guide to leveraging advanced machine learning techniques. By presenting complex concepts in an accessible manner, it opens new pathways for creativity and innovation in artificial intelligence, making it an essential companion for anyone interested in the future of machine learning and data science.

Download or read book Kickstart Software Design Architecture written by Dr. Edward D Lavieri Jr. and published by Orange Education Pvt Ltd. This book was released on 2024-09-17 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to design robust software systems using modern architecture principles and practical hands-on experience KEY FEATURES ● Learn about fundamental software architecture concepts, including design patterns, microservices, and cloud computing. ● Bridge theory with practice through real-world examples and case studies. ● Gain expertise through an interactive and engaging learning approach, featuring coding exercises and hands-on opportunities. DESCRIPTION Explore the ever-evolving world of software architecture. Bridge the gap between emerging technologies and foundational principles, with a comprehensive guide tailored for newcomers to the field. The book highlights the significance of software architecture in building scalable, efficient, and robust applications. The book is structured into engaging chapters, each focused on a specific aspect of software architecture. It starts with an introduction to the basics of software design patterns, gaining an understanding of their role in crafting flexible and reusable code. Next, microservices are covered, followed by chapters that focus on cloud computing, containerization, and more. Chapters contain real-world examples, hands-on exercises, and case studies, to help readers gain both foundational knowledge and hands-on experience. By the end of the book, you should have a solid foundation in software architecture and be equipped with the knowledge and skills to confidently address complex software architectural challenges. WHAT WILL YOU LEARN ● Understand the essential principles and concepts of software architecture, including key design considerations and methodologies. ● Explore the principles of design patterns to create flexible, reusable, and maintainable code. ● Learn about the impact of different programming languages on software architecture and development, and how to choose the right language for your projects. ● Gain insight into the microservices architecture, its benefits, challenges, and best practices for implementation. ● Learn the fundamentals of containerization with Docker and streamline development, testing, and deployment processes. ● Get practical knowledge on deploying applications in various cloud environments, focusing on effective strategies and tools for cloud-based deployment. ● Explore essential DevOps practices that enhance collaboration, automation, and continuous delivery in software development. ● Master version control using Git, including branching, merging, and best practices for managing code repositories. ● Learn strategies for designing software systems that scale effectively and operate efficiently, handling increased loads and performance demands. ● Stay ahead of the curve with insights into emerging trends and technologies shaping the future of software architecture and development. WHO IS THIS BOOK FOR? This book is primarily for aspiring software architects and developers who are at the beginning of their careers or those transitioning into software architecture. This includes computer science students, junior software developers, and IT professionals seeking to deepen their understanding of software design principles, design patterns, and modern development practices. The book is also suitable for self-taught programmers and hobbyists who want to gain a structured understanding of software architecture. TABLE OF CONTENTS 1. Introduction to Software Architecture 2. Principles of Design Patterns 3. Role of Programming Languages 4. Introduction to Microservices 5. Building Microservices with Spring Boot 6. Containerization with Docker 7. Fundamentals of Cloud Computing 8. Deploying in the Cloud 9. DevOps Practices 10. Version Control with Git 11. Designing for Scalability and Efficiency 12. Future Trends in Software Architecture Index

Download or read book Machine Learning Using R written by Karthik Ramasubramanian and published by Apress. This book was released on 2016-12-22 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. For every machine learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All the images are available in color and hi-res as part of the code download. This new paradigm of teaching machine learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in this book makes it easy for someone to connect the dots.. What You'll Learn Use the model building process flow Apply theoretical aspects of machine learning Review industry-based cae studies Understand ML algorithms using R Build machine learning models using Apache Hadoop and Spark Who This Book is For Data scientists, data science professionals and researchers in academia who want to understand the nuances of machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.

Download or read book Emerging Trends in Cloud Computing Analytics Scalability and Service Models written by Darwish, Dina and published by IGI Global. This book was released on 2024-01-25 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Academic scholars and industry professionals alike face the formidable challenge of staying informed about emerging trends and innovations in cloud computing. The expansive realm of cloud technology has been the catalyst for several transformative changes across industries, offering unparalleled opportunities for optimization and innovation. However, even seasoned experts may find themselves daunted by the intricate web of new technologies, including green cloud computing, edge computing, cryptography in the cloud, load balancing strategies, and cloud analytics insights. Emerging Trends in Cloud Computing: Analytics, Scalability, and Service Models provides academic scholars and industry professionals with a comprehensive exploration of these critical cloud computing topics and more. This invaluable resource provides clarity and insight, serving as a guiding beacon in the ever-evolving world of cloud technology. Whether you're seeking to understand the intricacies of cloud security solutions, the nuances of scalability in cloud computing, or the various service models in the cloud, this book empowers you to navigate this dynamic field with confidence and expertise.

Download or read book New Trends in Disruptive Technologies Tech Ethics and Artificial Intelligence written by Daniel H. de la Iglesia and published by Springer Nature. This book was released on 2023-07-21 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers the evidence-based insights into the ethical considerations surrounding disruptive technologies. In the rapidly evolving landscape of technology, where breakthroughs in artificial intelligence, big data, the Internet of Things, and bioinformatics have revolutionized our world, a critical need arises to reassess our ethical frameworks. This need has given birth to the thriving field of technology ethics, or tech ethics, which has grown exponentially in recent years. Once a niche area of research, it now encompasses a multitude of technology experts dedicated to understanding the societal impact of these advancements and striving for the development of more ethically grounded technology. At the forefront of this movement stands the International Conference on Disruptive Technologies, Tech Ethics, and Artificial Intelligence (DITTET 2023). Serving as a paramount platform for scholars, professionals, and experts, this conference presents an unparalleled opportunity to explore the latest scientific and technical progress and its profound ethical implications. DITTET facilitates the exchange of cutting-edge research on disruptive technologies, fostering knowledge transfer and collaboration among interdisciplinary fields. DITTET 2023 aspires to bring together a diverse range of industry leaders, humanists, and academics, providing a comprehensive overview of the scientific advancements and applications of artificial intelligence while examining their ethical dimensions in areas such as climate change, politics, economy, and security. By delving into these crucial topics, the conference aims to unravel the intricate relationship between technology and ethics, paving the way for responsible and conscientious innovation in today's world.