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.

Abstract: In August of 2021, the Santa Fe Institute hosted a workshop on collective intelligence as part of its Foundations of Intelligence project. This project seeks to advance the field of artificial intelligence by promoting interdisciplinary research on the nature of intelligence. The workshop brought together computer scientists, biologists, philosophers, social scientists, and others to share their insights about how intelligence can emerge from interactions among multiple agents--whether those agents be machines, animals, or human beings. In this report, we summarize each of the talks and the subsequent discussions. We also draw out a number of key themes and identify important frontiers for future research. When building intelligent systems, the need to employ complex systems comprising a large number of more basic components seems inescapable. Brains are composed of billions of neurons, and digital computers are composed of billions of transistors. It is the myriad ...

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.

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.

This book will challenge you to learn and understand the most obscure and tricky parts of Javascript.

From commerce, finance and agriculture to self-driving cars, personalised healthcare and social media – advancements in artificial intelligence (AI) unlock countless opportunities. New applications promise to improve the quality of people's lives throughout the world, but at the same time, raise a number of societal questions. A joint panel discussion of the German National Academy of Sciences Leopoldina and the Korean Academy of Science and Technology (KAST) explores AI technologies, their benefits and their challenges for society. Virtual panel discussion of the German National Academy of Sciences Leopoldina and the Korean Academy of Science and Technology „Realizing the Promises of Artificial Intelligence" Thursday, 25 November 2021, 8am to 9am (CET) Online Following opening remarks from the President of the Leopoldina, Prof (ETHZ) Dr Gerald Haug and Prof Min-Koo Han, PhD, President of the KAST, legal scholar Prof Ryan Song, PhD, Kyung Hee University, Seoul/South Korea, will provide an introduction into the topic. Subsequently, computer scientist Prof Alice Oh PhD, KAIST School of Computing, Daejeon/ South Korea, and Member of the Leopoldina Prof Dr Alexander Waibel, Karlsruhe Institute of Technology/Germany and Carnegie Mellon University, Pittsburgh/USA, will provide input statements for further discussion.

Intelligent vehicle (IV) is a comprehensive system that integrates functions such as environment perception, planning, and decision making, and multi-level assisted driving. It concentrates on the technologies of computers, modern sensing, information fusion, communication, artificial intelligence, and automatic control, etc. The improvement of the intelligence level of IV can enhance traffic safety and efficiency effectively. In recent years, with the development of hardware and software, the technology of Intelligent Connected Vehicle (ICV) has achieved rapid progress. However, there are many critical and difficult issues that remain to be addressed.

This Special Issue is devoted to the new trends in optics applied to Information and Communication Technologies (ICT). This issue aims to host original, unpublished, and breakthrough concepts in optics that make use of new tools and mechanisms, such as artificial intelligence, to solve complex problems for applications in ICT. Optical systems use communication and information processing. To name a few large fields, we enumerate telecommunications (fiber optics, etc.), information processing (optical and quantum computing, etc.), sources of light (VCSEL, etc.). Manuscripts should be submitted online at www.mdpi.com