Growing Adaptive Machines

The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a reference for experts. Several contributions provide perspectives and future hypotheses on recent highly successful trains of research, including deep learning, the Hyper NEAT model of developmental neural network design, and a simulation of the visual cortex. Other contributions cover recent advances in the design of bio-inspired artificial neural networks, including the creation of machines for classification, the behavioural control of virtual agents, the desi gn of virtual multi-component robots and morphologies and the creation of flexible intelligence. Throughout, the contributors share their vast expertise on the means and benefits of creating brain-like machines. This book is appropriate for advanced students and practitioners of artificial intelligence and machine learning.

This unique book explores fungi as sensors, electronic devices, and potential future computers, offering eco-friendly alternatives to traditional electronics. Fungi are ancient, widely distributed organisms ranging from microscopic single cells to massive mycelium spanning hectares. They possess senses similar to humans, detecting light, chemicals, gases, gravity, and electric fields. It covers fungal electrical activity, sensors, electronics, computing prototypes, and fungal language. Authored by leading experts from diverse fields, the book is accessible to readers of all backgrounds, from high-schoolers to professors. It reveals the remarkable potential of fungal machines while minimizing environmental impact.

This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Mr. Neeraj Sharma, Associate Professor, Department of Electrical Engineering, Vivekananda Global University, Sector 36, Sisyawas, NRI Road, Jagatpura, Jaipur-303012, Rajasthan, India. Mr. Sandeep Kumar Jain, Associate Professor, Department of Electrical Engineering, Vivekananda Global University, Sector 36, Sisyawas, NRI Road, Jagatpura, Jaipur-303012, Rajasthan, India. Mr. Manish Srivastava, Assistant Professor, Department of Electrical Engineering, Vivekananda Global University, Sector 36, Sisyawas, NRI Road, Jagatpura, Jaipur-303012, Rajasthan, India. Mr. Pradeep Kumar Jangid, Assistant Professor, Department of Electrical Engineering, Vivekananda Global University, Sector 36, Sisyawas, NRI Road, Jagatpura, Jaipur-303012, Rajasthan, India. Mr. Ganesh Kumar Kantak, Assistant Professor, Department of Mechanical Engineering, Vivekananda Global University, Sector 36, Sisyawas, NRI Road, Jagatpura, Jaipur-303012, Rajasthan, India.

The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.

This book gathers the proceedings of the 15th IFToMM World Congress, which was held in Krakow, Poland, from June 30 to July 4, 2019. Having been organized every four years since 1965, the Congress represents the world’s largest scientific event on mechanism and machine science (MMS). The contributions cover an extremely diverse range of topics, including biomechanical engineering, computational kinematics, design methodologies, dynamics of machinery, multibody dynamics, gearing and transmissions, history of MMS, linkage and mechanical controls, robotics and mechatronics, micro-mechanisms, reliability of machines and mechanisms, rotor dynamics, standardization of terminology, sustainable energy systems, transportation machinery, tribology and vibration. Selected by means of a rigorous international peer-review process, they highlight numerous exciting advances and ideas that will spur novel research directions and foster new multidisciplinary collaborations.

This two volume set constitutes the refereed proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007. Coverage in the first volume includes evolutionary computation, genetic algorithms, and particle swarm optimization. The second volume covers neural networks, support vector machines, biomedical signal and image processing, biometrics, computer vision.

Dr. M. Kasthuri, Associate Professor, Department of Computer Science, Bishop Heber College (Autonomous), Tiruchirappalli, Tamil Nadu, India. Mrs.M. Kavitha, Assistant Professor, Department of Computer Applications, Bishop Heber College (Autonomous), Tiruchirappalli, Tamil Nadu, India.