Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning PDF Download
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Author: Lei Deng
Publisher: Frontiers Media SA
ISBN: 2889667421
Category : Science
Languages : en
Pages : 200
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Book Description
Author: Lei Deng
Publisher: Frontiers Media SA
ISBN: 2889667421
Category : Science
Languages : en
Pages : 200
Get Book Here
Book Description
Author: Huajin Tang
Publisher: Frontiers Media SA
ISBN: 283255363X
Category : Science
Languages : en
Pages : 152
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Book Description
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
ISBN: 3031163443
Category : Technology & Engineering
Languages : en
Pages : 690
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Book Description
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: Manish Soni
Publisher:
ISBN:
Category : Study Aids
Languages : en
Pages : 305
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Book Description
"Deep Learning: A Comprehensive Guide," a book meticulously designed to cater to the needs of learners at various stages of their journey into the fascinating world of deep learning. Whether you are a beginner embarking on your first exploration into artificial intelligence or a seasoned professional looking to deepen your expertise, this book aims to be your trusted companion.
Author: Prof. Rocky Scopelliti
Publisher: Austin Macauley Publishers
ISBN: 1035836300
Category : Education
Languages : en
Pages : 250
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Book Description
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: Uriah Stewart
Publisher: Publifye AS
ISBN: 8233932612
Category : Computers
Languages : en
Pages : 179
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Book Description
""The Brain's Hidden Algorithms"" explores the fascinating intersection of neuroscience and artificial intelligence, unveiling the complex mechanisms of our minds and their implications for AI development. This groundbreaking book delves into three key areas: decoding neural networks, applying brain-inspired algorithms to AI, and examining the ethical consequences of merging human and artificial intelligence. The book's central argument proposes that by unraveling the intricate algorithms underlying human cognition, we can revolutionize AI while gaining unprecedented insights into our own minds. It guides readers through the latest research in mapping neural networks and demonstrates how these discoveries are being applied to create more advanced AI systems. Drawing on cutting-edge studies and interviews with leading experts, the author presents a compelling case for the symbiotic relationship between neuroscience and AI. What sets this book apart is its unique approach to synthesizing complex scientific concepts with philosophical questions about intelligence and consciousness. By balancing academic rigor with accessible language and engaging analogies, it offers valuable insights for both experts and general readers interested in the future of technology and human cognition. The book challenges readers to reconsider their understanding of human intelligence and the potential of AI, paving the way for a new era of discovery at the intersection of mind and machine.
Author: Marcel van Gerven
Publisher: Frontiers Media SA
ISBN: 2889454010
Category :
Languages : en
Pages : 220
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Book Description
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: P. Hitzler
Publisher: IOS Press
ISBN: 1643682458
Category : Computers
Languages : en
Pages : 410
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Book Description
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: Paul R. Prucnal
Publisher: CRC Press
ISBN: 1498725244
Category : Science
Languages : en
Pages : 445
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Book Description
This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics." It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
