Multiple Aspects Of Artificial Intelligence
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Multiple Aspects of Artificial Intelligence
Author: Parul Saxena
language: en
Publisher: Book Saga Publications
Release Date: 2023-07-02
In order to simulate human intellect in robots, a vast and interdisciplinary discipline known as artificial intelligence (AI) integrates computer science, data analysis, and problem-solving strategies. It entails the creation of algorithms and systems that give computers the ability to observe, comprehend, reason, pick up new information, and make decisions. Fundamentally, AI strives to build intelligent computers that can work independently, adapt to new circumstances, and display traits that resemble human intelligence. Machine learning and deep learning are two important subfields and methodologies within the umbrella of artificial intelligence (AI). The main goal of machine learning is to create algorithms that let computers learn from data and get better at what they do without being explicitly taught. Deep learning, a subset of machine learning, processes and analyses complicated data using artificial neural networks modelled after the structure and operation of the human brain. AI's main objective is to create machines that can carry out tasks that have historically required human intelligence, like speech and picture recognition, natural language processing, decision-making, problem-solving, and even creative activities. These systems use enormous databases and computational capacity to do amazing feats as they attempt to comprehend, analyse, and respond to the complexity of the world. AI algorithms can find patterns, connections, and insights by analysing and processing enormous amounts of data that may escape human observers. Significant improvements in a variety of fields, such as business, education, finance, and social media, have been made possible by this skill. By automating procedures, improving judgement, personalising interactions, and gleaning important insights from vast amounts of data, AI is revolutionising a variety of businesses.
Multiple Perspectives on Artificial Intelligence in Healthcare
This book offers a comprehensive yet concise overview of the challenges and opportunities presented by the use of artificial intelligence in healthcare. It does so by approaching the topic from multiple perspectives, e.g. the nursing, consumer, medical practitioner, healthcare manager, and data analyst perspective. It covers human factors research, discusses patient safety issues, and addresses ethical challenges, as well as important policy issues. By reporting on cutting-edge research and hands-on experience, the book offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes. It will also benefit students and researchers whose work involves artificial intelligence-related research issues in healthcare.
Multi-aspect Learning
This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.