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57.4 MB
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CA961A12501817F71D0EE1C7C09FAF21FB49F331
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June 24, 2025, 3:01 p.m.
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(Last updated: June 24, 2025, 3:01 p.m.)
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| ['Chiroma H. Emerging Trends in Artificial Intelligence...Theories...Practice 2025.pdf'] | 0 bytes |
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| Uploaded by andryold1 | Size 57.4 MB | Health [ 23 /33 ] | Added 2025-06-24 |
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SOURCE: Chiroma H. Emerging Trends in Artificial Intelligence...Theories...Practice 2025
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MEDIAINFO
Textbook in PDF format Emerging Artificial Intelligence: Integrating theories and practice is specifically tailored for researchers in AI who are eager to delve into the latest developments and explore the integration of theoretical concepts with practical applications. This book is an essential guide for researchers in AI seeking to bridge the gap between theory and practice, embark on a transformative journey through the latest advancements in AI and gain a comprehensive understanding of their real-world applications. After reading the book, the reader will be able to clearly understand the industrial applications of each emerging AI domain and appreciate the operations of AI in industry as well as society in general. LLMs exhibit distinct characteristics in comparison to traditional Machine Learning and Deep Learning approaches. Machine Learning typically operates with smaller datasets, and is often trained using shallow learning algorithms, which excel in performance with limited data. However, as dataset size increases, the performance of traditional Machine Learning tends to degrade. In contrast, Deep learning thrives with larger volumes of data, with performance improving as the dataset grows. Hardware requirements for traditional Machine Learning are generally lower than those for Deep Learning, which demands high-performance computing due to the complexity of its algorithms and the substantial data volumes required for training. Interpretability is another key difference: traditional Machine Learning offers better interpretability as it relies on manual feature extraction, whereas Deep Learning incorporates automatic feature extraction mechanisms, making interpretation more challenging. The LLMs represent an advancement over both traditional Machine Learning and Deep Learning, integrating natural language processing mechanisms. However, they require very high hardware resources and extensive training data more than Deep Learning and Machine Learning. LLMs excel at performing complex tasks across multiple domains, unlike Deep Learning and traditional Machine Learning, which are often specialized to specific domains. Preface Artificial intelligence history from the abacus through science fiction movies and artificial intelligence winter to future expectations Generative artificial intelligence: gateway and recent progress Artificial general intelligence and beyond: large language models tutorial, debates, hypothesis and future outlook AI-DevOps: Proposed artificial intelligence enhanced Development and operations lifecycle Centralized huge graph neural networks advances Brain–machine interface for autonomous robots control with feedback from augmented reality and its relevance to industry 4.0 and 5.0 Recent developments in artificial intelligence and high performance computing Explainable artificial intelligence tailored to emerging concepts linking human acceptability Proposed bibliometric methods for artificial intelligence domain and bibliometric analysis on artificial intelligence for over six decades
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