Towards the long-standing dream of artificial intelligence, two solution paths have been paved: (i) neuroscience-driven neuromorphic computing; (ii) computer science-driven machine learning. The former targets at harnessing neuroscience to obtain insights for brain-like processing, by studying the detailed implementation of neural dynamics, circuits, coding and learning. Although our understanding of how the brain works is still very limited, this bio-plausible way offers an appealing promise for future general intelligence. In contrast, the latter aims at solving practical tasks typically formulated as a cost function with high accuracy, by eschewing most neuroscience details in favor of brute force optimization and feeding a large volume of data. With the help of big data (e.g. ImageNet), high-performance processors (e.g. GPU, TPU), effective training algorithms (e.g. artificial neural networks with gradient descent training), and easy-to-use design tools (e.g. Pytorch, Tensorflow), machine learning has achieved superior performance in a broad spectrum of scenarios. Although acclaimed for the biological plausibility and the low power advantage (benefit from the spike signals and event-driven processing), there are ongoing debates and skepticisms about neuromorphic computing since it usually performs worse than machine learning in practical tasks especially in terms of the accuracy.

Quality Electronic Design (QED)’s landscape spans a vast region where territories of many participating disciplines and technologies overlap. This book explores the latest trends in several key topics related to quality electronic design, with emphasis on Hardware Security, Cybersecurity, Machine Learning, and application of Artificial Intelligence (AI). The book includes topics in nonvolatile memories (NVM), Internet of Things (IoT), FPGA, and Neural Networks.

"From Schrödinger's Equation to Deep Learning: A Quantum Approach" offers a captivating exploration that bridges the realms of quantum mechanics and deep learning. Tailored for scientists, researchers, and enthusiasts in both quantum physics and artificial intelligence, this book delves into the symbiotic relationship between quantum principles and cutting-edge deep learning techniques. Covering topics such as quantum-inspired algorithms, neural networks, and computational advancements, the book provides a comprehensive overview of how quantum approaches enrich and influence the field of deep learning. With clarity and depth, it serves as an enlightening resource for those intrigued by the dynamic synergy between quantum mechanics and the transformative potential of deep learning.

This book demonstrates several use cases of how artificial intelligence (AI) and machine learning (ML) are revolutionizing problem-solving across various industries. The book presents 18 edited chapters beginning with the latest advancements in human-AI interactions and neuromorphic computing, setting the stage for practical applications. Chapters focus on AI and ML applications such as fingerprint recognition, glaucoma detection, and lung cancer identification using image processing. The book also explores the role of AI in professional operations such as UX design, event detection, and content analysis. Additionally, the book includes content that examines AI's impact on technical operations wireless communication, VLSI systems, and advanced manufacturing processes. Each chapter contains summaries and references for addressing the needs of beginner and advanced readers. This comprehensive guide is an essential resource for anyone seeking to understand AI's transformative role in modern problem-solving in professional industries.

In an age where Artificial Intelligence (AI) evolves at a breakneck pace, the boundaries of machine capabilities are constantly being redefined. Propelled by advancements in deep learning and related technologies, AI is inching ever closer to mimicking human intellect. But can it achieve consciousness? And if so, at what cost to humanity? This book delves deep into the multi-faceted debate surrounding artificially conscious AI. It untangles ethical quandaries, philosophical dilemmas, technological challenges, political considerations, and the regulatory landscape. By drawing connections between AI research, neuroscience, and cognitive science, the narrative provides a comprehensive understanding of what consciousness might mean in the context of AI. As over a thousand AI luminaries globally sound the alarm, urging a pause on certain AI developments, the book underscores the urgency of its message. Recent incidents have spotlighted AI systems with capabilities so advanced that even their creators grapple to fully grasp or control them. It’s imperative, now more than ever, to critically assess the implications of AI consciousness, weighing its potential risks against its benefits. This book offers both a timely warning and a call to informed action.

Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.

This text provides students and researchers with a foundation for examining how brain function gives rise to mental activities such as perception, memory and language. It is grouped into sections that cover attention, vision, auditory and somatosensory systems, memory and higher cortical.