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

In the fourth industrial revolution, or Industry 4.0, a key objective is to enhance equipment's ability to perceive its own health state and predict future behavior. The development of artificial intelligence, especially the progress made in deep learning, in the recent decade provides a promising tool in bolstering this enhancement. Such a tool can be a complement or alternative to conventional physics-based and signal-processing-based techniques in fault detection, diagnosis and prognosis applications. Researchers have started to build data-driven or hybrid models to further boost their prediction accuracy in the above applications, yet there are still some untouched or underexplored territories, such as causal inference, demystifying the black-box modelling, domain adaptation, automatic feature learning, etc. This special issue is to present current innovations and engineering achievements of scientists and industrial practitioners in the area of adopting artificial intelligence techniques in fault detection, diagnosis and prognosis.

Artificial Intelligence (AI) is entering all domains, with innovative solutions and results that would have been both unpredictable and unachievable just a few years ago. In a few words, AI (including machine and deep learning methods) is the ability of computers to perform a task that typically requires some level of human intelligence. AI is bringing advantages in many fields, and everybody is talking about the adoption of AI methods to solve problems and process data. Geospatial AI, i.e., the use of AI for the processing and understanding of large geospatial data, is also growing. Geospatial data include images and point clouds captured and generated from spaceborne and airborne sensors or mobile mapping platforms.

The multi-task learning (MTL) paradigm can be traced back to an early paper of Caruana (1997) in which it was argued that data from multiple tasks can be used with the aim to obtain a better performance over learning each task independently. A solution of MTL with conflicting objectives requires modelling the trade-off among them which is generally beyond what a straight linear combination can achieve. A theoretically principled and computationally effective strategy is finding solutions which are not dominated by others as it is addressed in the Pareto analysis. Multi-objective optimization problems arising in the multi-task learning context have specific features and require adhoc methods. The analysis of these features and the proposal of a new computational approach represent the focus of this work. Multi-objective evolutionary algorithms (MOEAs) can easily include the concept of dominance and therefore the Pareto analysis. The major drawback of MOEAs is a low sample efficiency with respect to function evaluations. The key reason for this drawback is that most of the evolutionary approaches do not use models for approximating the objective function. Bayesian Optimization takes a radically different approach based on a surrogate model, such as a Gaussian Process. In this thesis the solutions in the Input Space are represented as probability distributions encapsulating the knowledge contained in the function evaluations. In this space of probability distributions, endowed with the metric given by the Wasserstein distance, a new algorithm MOEA/WST can be designed in which the model is not directly on the objective function but in an intermediate Information Space where the objects from the input space are mapped into histograms. Computational results show that the sample efficiency and the quality of the Pareto set provided by MOEA/WST are significantly better than in the standard MOEA.

AI is a broad and rapidly developing field of technology. Our goal is to make sure all young people have the skills, knowledge, and confidence to use and create AI systems. So what should AI education in schools look like? To hear a range of insights into this, we organised a panel discussion as part of our seminar series on AI and data science education, which we co-host with The Alan Turing Institute. You can also watch the recording below.

Artificial intelligence (AI) and its applications are now the hottest research areas. In recent years, there have been more and more AI applications in the medical field. AI technology is promoting the development of the medical and health industries. In the medical domain, AI techniques can be used to develop clinical decision support systems to help with medical diagnostics. AI technologies can be also deployed in various medical devices, trackers, and information systems.

Artificial intelligence (AI) is paving the way towards the fourth industrial revolution with the fire domain (Fire 4.0). As a matter of fact, the next few years will be elemental to how this technology will shape our academia, practice, and entrepreneurship. Despite the growing interest between fire research groups, AI remains absent of our curriculum, and we continue to lack a methodical framework to adopt, apply and create AI solutions suitable for our problems. The above is also true for parallel engineering domains (i.e., civil/mechanical engineering), and in order to negate the notion of history repeats itself (e.g., look at the continued debate with regard to modernizing standardized fire testing, etc.), it is the motivation behind this letter to the Editor to demystify some of the big ideas behind AI to jump-start prolific and strategic discussions on the front of AI & Fire. In addition, this letter intends to explain some of the most fundamental concepts and clear common misconceptions specific to the adoption of AI in fire engineering. This short letter is a companion to the Smart Systems in Fire Engineering special issue sponsored by Fire Technology. An in-depth review of AI algorithms [1] and success stories to the proper implementations of such algorithms can be found in the aforenoted special issue and collection of papers. This letter comprises two sections. The first section outlines big ideas pertaining to AI, and answers some of the burning questions with regard to the merit of adopting AI in our domain. The second section presents a set of rules or technical recommendations an AI user may deem helpful to practice whenever AI is used as an investigation methodology. The presented set of rules are complementary to the big ideas.