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| Author | : Lei Deng |
| Publisher | : Frontiers Media SA |
| Total Pages | : 200 |
| Release | : 2021-05-05 |
| Genre | : Science |
| ISBN | : 2889667421 |
| Author | : Huajin Tang |
| Publisher | : Frontiers Media SA |
| Total Pages | : 152 |
| Release | : 2024-08-26 |
| Genre | : Science |
| ISBN | : 283255363X |
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.
| Author | : Ali Iranmanesh |
| Publisher | : Springer Nature |
| Total Pages | : 690 |
| Release | : 2023-01-11 |
| Genre | : Technology & Engineering |
| ISBN | : 3031163443 |
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.
| Author | : Toshiyuki Yamane |
| Publisher | : Frontiers Media SA |
| Total Pages | : 163 |
| Release | : 2023-08-02 |
| Genre | : Science |
| ISBN | : 2832531288 |
| Author | : Prof. Rocky Scopelliti |
| Publisher | : Austin Macauley Publishers |
| Total Pages | : 250 |
| Release | : 2023-12-08 |
| Genre | : Education |
| ISBN | : 1035836300 |
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.
| Author | : Jewel YunHsuan Lee |
| Publisher | : |
| Total Pages | : 51 |
| Release | : 2018 |
| Genre | : Machine learning |
| ISBN | : |
Experimental investigation of the collective dynamics in large networks of neurons is a fundamental step towards understanding the mechanisms behind signal and information processing in the brain. In the last decade, the emergence of high performance computing technology has allowed long-duration numerical simulations to model large-scale neural networks. These simulated networks exhibit behaviors (ranging from stochastic spiking to synchronized bursting) that are observed in living preparations. These simulations' high spatiotemporal resolution and long duration produce data that, in terms of both quantity and complexity, challenge our interpretative abilities. This thesis presents an application of machine learning techniques to bridge the gap between microscopic and macroscopic behaviors and identify the small-scale activity that leads to large-scale behavior, reducing data complexity to a level that can be amenable to further analysis.
| Author | : Marcel van Gerven |
| Publisher | : Frontiers Media SA |
| Total Pages | : 220 |
| Release | : 2018-02-01 |
| Genre | : |
| ISBN | : 2889454010 |
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.
| Author | : Stephen Michael Kosslyn |
| Publisher | : MIT Press |
| Total Pages | : 744 |
| Release | : 1995 |
| Genre | : Cognitive Neuroscience |
| ISBN | : 9780262611107 |
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.
| Author | : P. Hitzler |
| Publisher | : IOS Press |
| Total Pages | : 410 |
| Release | : 2022-01-19 |
| Genre | : Computers |
| ISBN | : 1643682458 |
Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two hitherto distinct approaches. ”Neuro” refers to the artificial neural networks prominent in machine learning, ”symbolic” refers to algorithmic processing on the level of meaningful symbols, prominent in knowledge representation. In the past, these two fields of AI have been largely separate, with very little crossover, but the so-called “third wave” of AI is now bringing them together. This book, Neuro-Symbolic Artificial Intelligence: The State of the Art, provides an overview of this development in AI. The two approaches differ significantly in terms of their strengths and weaknesses and, from a cognitive-science perspective, there is a question as to how a neural system can perform symbol manipulation, and how the representational differences between these two approaches can be bridged. The book presents 17 overview papers, all by authors who have made significant contributions in the past few years and starting with a historic overview first seen in 2016. With just seven months elapsed from invitation to authors to final copy, the book is as up-to-date as a published overview of this subject can be. Based on the editors’ own desire to understand the current state of the art, this book reflects the breadth and depth of the latest developments in neuro-symbolic AI, and will be of interest to students, researchers, and all those working in the field of Artificial Intelligence.
| Author | : Qing Wan |
| Publisher | : John Wiley & Sons |
| Total Pages | : 258 |
| Release | : 2022-05-16 |
| Genre | : Technology & Engineering |
| ISBN | : 3527349790 |
Explore the cutting-edge of neuromorphic technologies with applications in Artificial Intelligence In Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics, a team of expert engineers delivers a comprehensive discussion of all aspects of neuromorphic electronics designed to assist researchers and professionals to understand and apply all manner of brain-inspired computing and perception technologies. The book covers both memristic and neuromorphic devices, including spintronic, multi-terminal, and neuromorphic perceptual applications. Summarizing recent progress made in five distinct configurations of brain-inspired computing, the authors explore this promising technology’s potential applications in two specific areas: neuromorphic computing systems and neuromorphic perceptual systems. The book also includes: A thorough introduction to two-terminal neuromorphic memristors, including memristive devices and resistive switching mechanisms Comprehensive explorations of spintronic neuromorphic devices and multi-terminal neuromorphic devices with cognitive behaviors Practical discussions of neuromorphic devices based on chalcogenide and organic materials In-depth examinations of neuromorphic computing and perceptual systems with emerging devices Perfect for materials scientists, biochemists, and electronics engineers, Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics will also earn a place in the libraries of neurochemists, neurobiologists, and neurophysiologists.
