Although human intelligence is deeply investigated by neuroscientists, psychologists, philosophers, and AI researchers, we still lack of a widely accepted definition of what it is. If we exploit the emergence theory from Complexity Science to give a definition, we might state that human intelligence is the emergent property of the human nervous system. Such fascinating emergent property allows us to handle both accurate and vague information by computing with numbers and words. Moreover, it allows us to reason, speak and take rational decisions in an environment of uncertainty, partiality and relativity of truth, when the “Incompatibility Principle” holds: “As the complexity of a system increases, accuracy and significance become almost mutually exclusive characteristics of our statements”. Finally, our intelligence allows us recognizing quite easily variable patterns. Therefore, it is worthwhile investigating human intelligence and trying to mimic it by developing Artificial Intelligence. Nowadays, Artificial Intelligence is in vogue: it is applied in both basic and applied science. Traditionally, there are two strategies to develop Artificial Intelligence. A strategy consists in writing human-like intelligent software running in von Neumann computers or special-purpose hardware. The other strategy consists in neuromorphic engineering. Neuromorphic engineering implements surrogates of neurons through non-biological systems, either for neuro-prosthesis or to design brain-like computing machines. A third strategy is now blooming and it consists in using molecular, supramolecular, materials, and systems chemistry to mimic some basic functions of human intelligence such as Boolean, multi-valued logic gates, and Fuzzy logic. This third strategy is originating Chemical Artificial Intelligence (CAI). A relevant purpose of CAI is to design modules for Chemical Robots. A Chemical Robot is thought of as a molecular assembly that reacts autonomously to its environment by probing it with molecular sensors, making decisions by its intrinsic Artificial Neural Networks or logic gates, and performing actions upon its environment through molecular effectors. The intelligent activities of any Chemical Robot should be sustained energetically by a metabolic unit. Chemical Robots should be easily miniaturized and implanted in living beings to interplay with cells or organelles for biomedical applications. They should become auxiliary elements of the natural immune system.

Frontiers in Neuroscience, Neuromorphic Engineering is delighted to present the ‘Reviews and Perspectives in’ series of article collections. Reviews and Perspectives in Neuromorphic Engineering: Novel Neuromorphic Computing Approaches Research Topic will publish high-quality scholarly reviews and perspective papers on key topics in Neuromorphic Computing. It aims to highlight recent advances in neuromorphic computing in software, hardware, and wetware whilst emphasizing important directions, novel and unconventional approaches, and new possibilities for future inquiries. The research presented will promote discussion in the neuromorphic computing community that will translate to best practice applications. We welcome Review, Mini Review, Opinion, General Commentary, and Perspective articles on themes including, but not limited to: • Innovative architectures and models in neuromorphic computing • Oscillatory Neural Networks computing • Reservoir computing • Chemical computing • Protein computing • Synthetic cells • Analog computing • Bayesian inference and fuzzy logic • Linking neuromorphic and quantum computing • Novel materials for neuromorphic computing • Unconventional neuromorphic approaches • Photonic computing • 3D integrated Neural Network • Physical Chemistry of materials and systems for neuromorphic computing.

Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. - Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms - Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis - Gives direction to future development trends of AI technologies in chemical and process engineering

Combining the knowledge involved in process engineering and process modeling, this is the first book to cover all modeling methods applicable to process intensification. Both the editors and authors are renowned experts from industry and academia in the various fields of process modeling and integrated chemical processes. Following an introduction to the topic, the book goes on to look at equipment and operational methods, monolithic catalysis, HEX, micro- and reverse flow reactors, catalytic and reactive distillation, the simulated-moving bed and vibration bubble column as well as ultrasound and ultrasonic reactors. A final chapter is devoted to processes under supercritical conditions. In its treatment of hot topics of multidisciplinary interest, this book is of great value to researchers and engineers alike.

"Did you know that computation can be implemented with cytoskeleton networks, chemical reactions, liquid marbles, plants, polymers and dozens of other living and inanimate substrates? Do you know what is reversible computing or a DNA microscopy? Are you aware that randomness aids computation? Would you like to make logical circuits from enzymatic reactions? Have you ever tried to implement digital logic with Minecraft? Do you know that eroding sandstones can compute too? This volume will review most of the key attempts in coming up with an alternative way of computation. In doing so, the authors show that we do not need computers to compute and we do not need computation to infer. It invites readers to rethink the computer and computing, and appeals to computer scientists, mathematicians, physicists and philosophers. The topics are presented in a lively and easily accessible manner and make for ideal supplementary reading across a broad range of subjects"--

Presenting the theoretical and practical developments of an autonomous decision-making methodology, this work describes the strides made by intelligent systems and soft computing for the control of industrial systems. It uses practical examples of qualitative control techniques tested in industry and provides suitable intelligent computational algorithms and interfaces for industrial applications.

Based on recent theoretical developments in the enactive approach to life and mind, this book elaborates a series of contributions to a non-representational theory of action and perception. These enactive ideas are applied and extended to provide a theory rich, naturalistic account of the sensorimotor meaning and agency.

This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.

M. C. Roco and W.S. Bainbridge In the early decades of the 21st century, concentrated efforts can unify science based on the unity of nature, thereby advancing the combination of nanotechnology, biotechnology, information technology, and new technologies based in cognitive science. With proper attention to ethical issues and societal needs, converging in human abilities, societal technologies could achieve a tremendous improvement outcomes, the nation's productivity, and the quality of life. This is a broad, cross cutting, emerging and timely opportunity of interest to individuals, society and humanity in the long term. The phrase "convergent technologies" refers to the synergistic combination of four major "NBIC" (nano-bio-info-cogno) provinces of science and technology, each of which is currently progressing at a rapid rate: (a) nanoscience and nanotechnology; (b) biotechnology and biomedicine, including genetic engineering; (c) information technology, including advanced computing and communications; (d) cognitive science, including cognitive neuroscience. Timely and Broad Opportunity. Convergence of diverse technologies is based on material unity at the nanoscale and on technology integration from that scale.

The authors develop methods for assessing the impact of deploying artificial intelligence (AI) systems to support intelligence missions.