Welcome to the second Communications Regional Special Section spotlighting European countries and Israel. On a relatively small portion of the Earth, this region includes almost 50 countries with enormous cultural and socioeconomic diversity that is also reflected in the richness of its business structures and computer science research. The first Hot Topic article in this section illustrates the high overall share of European public research on a global scale, and further highlights significant differences within the region. We are happy to report the authors in this special section represent 15 countries throughout Europe plus Israel. An important goal emphasized by the European Union (E.U.) and many individual countries is to attain digital sovereignty of the private and public sectors, while further developing areas of traditional industrial and design strengths into the future.

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases."

A panel discussion on leveraging data, artificial intelligence and machine learning for business transformation had executives from companies …

Machine learning (ML) technology allows machines to learn, think, and make intelligent decisions autonomously. The fundamental approach of ML is building efficient algorithms that are capable of predicting the future learned through experience. Blockchain, on the other hand, is distributed ledger technology that is immutable, decentralized, and provides secure storage of data without the need for a trusted third party. The convergence of ML and blockchain will complement each other to produce a greater impact and availability of different services, including healthcare, supply chain, transportation, and power sectors. These services include a large number of network elements and edge devices that generate a huge amount of data that raise potential security concerns and data optimization issues.

This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.

A Special Issue on the topic "Recent advances of Deep learning and Machine learning in Bioinformatics" is being prepared for the journal Life. In recent years, machine learning is one of the most exciting tools that have entered the bioinformatics toolbox. The statistical method has already proved to be capable of considerably speeding up both fundamental and applied research in the field. At present, we are witnessing an explosion of works that develop and apply machine learning and deep learning to bioinformatics and computational biology. We begin a Special Issue which accepts the manuscript of the most recent research on this topic.

This open access book proposes a novel approach to Artificial Intelligence (AI) ethics. AI offers many advantages: better and faster medical diagnoses, improved business processes and efficiency, and the automation of boring work. But undesirable and ethically problematic consequences are possible too: biases and discrimination, breaches of privacy and security, and societal distortions such as unemployment, economic exploitation and weakened democratic processes. There is even a prospect, ultimately, of super-intelligent machines replacing humans. The key question, then, is: how can we benefit from AI while addressing its ethical problems?This book presents an innovative answer to the question by presenting a different perspective on AI and its ethical consequences. Instead of looking at individual AI techniques, applications or ethical issues, we can understand AI as a system of ecosystems, consisting of numerous interdependent technologies, applications and stakeholders. Developing this idea, the book explores how AI ecosystems can be shaped to foster human flourishing. Drawing on rich empirical insights and detailed conceptual analysis, it suggests practical measures to ensure that AI is used to make the world a better place.

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

This year the 34th edition of the IEA/AIE (International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems), abbreviated as IEA/AIE 2021, was held in Kula Lumpur, https://ieaaie2021.wordpress.com/ The IEA/AIE conference is a longstanding conference, held every year since 1988, which focuses on artificial intelligence and its applications. Over many years, the IEA/AIE conference has been held worldwide in more than twenty different countries. The IEA/AIE 2021 conference is sponsored by the International Society of Applied Intelligence (ISAI) in cooperation with Springer, University Teknologi Malaysia, the i-SOMET incorporated Association, Association for the Advancement of Artificial Intelligence (AAAI) / Assoc. This year, 145 papers were submitted to the conference.

"This is an ideal introduction for people who have limited time but still want to go beyond trivial, hand-waving explanations about the core concepts in deep learning. The book's focus is illustrations with a minimal amount of text. The illustrations are clear, crisp, and accurate. Moreover, they perfectly balance the text. Many books are too verbose. Some are too terse. Here, Meor strikes the perfect balance -- enough text to explain the little the illustrations don't. The book is like a CEO summary of deep learning and serves as a good starting point for people who want an overview before diving in or who simply want an overview to see what the fuss is all about."— Ronald T. Kneusel, Ph.D. (author of Practical Deep Learning: A Python-Based Introduction and Math for Deep Learning)"I am always on the lookout for effective ways to summarize concepts visually. This book takes an impressive no frills approach for people who want to learn about the underpinnings of neural networks in the most time-effective way possible."— Sebastian Raschka, Ph.D. (author of Python Machine Learning)Deep learning is the algorithm powering the current renaissance of Artificial Intelligence (AI). And the progress is not showing signs of slowing down. A McKinsey report estimates that by 2030, AI will potentially deliver $13 trillion to the global economy, or 16% of the world's current GDP. This opens up exciting career opportunities in the coming decade.But deep learning can be quite daunting to learn. With the abundance of learning resources in recent years has emerged another problem—information overload.This book aims to compress this knowledge and make the subject approachable.By the end of this book, you will be able to build a visual intuition about deep learning and neural networks. Who is this for:If you are just beginning your journey in deep learning, or machine learning in general.If you have already got started with deep learning but want to gain further intuition.If you are a leader looking to understand deep learning and AI from first principles. The book's contents are designed to help you navigate the various concepts with as little friction as possible:Each of the 235 pages is visual-led and supported by concise text.The math is kept to a minimum.The same dataset is used in all chapters so you have the same, consistent reference.The dataset is small and simple so you can 'touch and feel' it and grasp the dynamics more easily. What the Book CoversThe motivation behind deep learning and machine learning in general.Deep dive into a feedforward neural network via four tasks - linear regression, nonlinear regression, binary classification, multiclass classification. These will be demonstrated using tabular data.A quick tour of the different variants of a neural network - convolutional, recurrent and generative - and the different types of data - images, text, etc.Content Overview Table of Contents What the Book Doesn't CoverMathematical derivationsCode examplesFurther topics such as optimizers, regularization, embeddings, etc. DetailsLength: 235 pagesAuthor: Meor Amer About the AuthorMy journey into AI began in 2010 after my son was born with a limb difference. I became interested in machine learning in prosthetics and did an MSc at Imperial College London majoring in neurotechnology.I have also worked in the telecoms data analytics space, where I did solution engineering for clients in over 15 countries.Above all, I am passionate about education and how we learn. I am currently working on projects that explore ways to create alternative learning experiences using visuals, storytelling, and games.Connect with me on LinkedIn Refund PolicyThere is a 30-day refund policy. And to compensate for your time, you get to keep the book even after the refund. For any queries, send your email to contact@kdimensions.com.Reader ReviewsOne of our most advanced senses is sight. Our eyes alert us to danger, lead us to sustenance, and allow us to enjoy stories. Meor Amer is a master storyteller. In A Visual Introduction to Deep Learning, Meor is our tour guide for a journey of discovery in this amazing field of Artificial Intelligence. His hand-crafted minimalist graphics are accompanied by succinct descriptions where he illuminates the subtle hints in each picture. I enthusiastically recommend this learning resource for AI enthusiasts. — Jack CrawfordThis is an amazing visual illustration book on deep learning. It bridges the gap between textual reading and contextual thinking. You can see what you learn. It's like "things coming to life!".— Raj ArunYou have made it really simple.— Sanjay MahanaYou really did a great job in explaining the concepts and reflecting them visually.— Alia HamwiVery clear non-technical explanations of deep learning. As AI becomes more prevalent in many businesses, it’s important that leaders understand the first principles.— Emily Ryder MartinsYou can’t miss anymore the basics of this. Love this book. The visuals help a lot. Meor Amer has produced, for me, the unique foundation overview. — Francisco TosteAre you a visual learner and want to build an intuition about deep learning? Here is a good, very easy-to-read book.— Andrew YaroshevskyI have been looking for this type of formatted approach. A no-risk investment with huge rewards!— Louis Girardin