Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning
| Author | : Lei Deng |
| Publisher | : Frontiers Media SA |
| Total Pages | : 200 |
| Release | : 2021-05-05 |
| Genre | : Science |
| ISBN | : 2889667421 |
Understanding And Bridging The Gap Between Neuromorphic Computing And Machine Learning
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Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning, volume II
| 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.
Frontiers of Quality Electronic Design (QED)
| 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.
A Practitioner's Approach to Problem-Solving using AI
| Author | : Satvik Vats |
| Publisher | : Bentham Science Publishers |
| Total Pages | : 303 |
| Release | : 2024-10-18 |
| Genre | : Computers |
| ISBN | : 9815305379 |
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.
Physical neuromorphic computing and its industrial applications
| Author | : Toshiyuki Yamane |
| Publisher | : Frontiers Media SA |
| Total Pages | : 163 |
| Release | : 2023-08-02 |
| Genre | : Science |
| ISBN | : 2832531288 |
The Conscious Code
| 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.
Artificial Neural Networks as Models of Neural Information Processing
| 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.
Neuro-Symbolic Artificial Intelligence: The State of the Art
| 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.
Frontiers in Cognitive Neuroscience
| Author | : Stephen Michael Kosslyn |
| Publisher | : MIT Press |
| Total Pages | : 744 |
| Release | : 1995 |
| Genre | : Medical |
| 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.
Mathematics for Machine Learning
| Author | : Marc Peter Deisenroth |
| Publisher | : Cambridge University Press |
| Total Pages | : 392 |
| Release | : 2020-04-23 |
| Genre | : Computers |
| ISBN | : 1108569323 |
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.
