Handbook of Research on AI and ML for Intelligent Machines and Systems
Written by Francesco Colace,Brij B. Gupta
530 pages, about 11 hours of reading
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Themes, characters and key ideas in Handbook of Research on AI and ML for Intelligent Machines and Systems, written by Chaptra AI.
- about 50 hours
- advanced
- informative
- academic
- comprehensive
This handbook offers a comprehensive exploration of the critical role played by Artificial Intelligence (AI) and Machine Learning (ML) in the development of intelligent machines and systems. It serves as a vital resource for researchers, students, and professionals, detailing foundational concepts, advanced algorithms like deep learning, and practical applications across various sectors. The book emphasizes the synergistic potential of combining AI and ML, addressing essential security aspects, current trends, and future directions, while offering a flexible, module-based learning path for deep understanding and real-world problem-solving.
“The fusion of AI and ML holds the key to propelling intelligent machines to a new realm of transformation.”
Key themes
- Fusion of AI and ML for Transformation
- This is the central thesis of the book, exploring how combining Artificial Intelligence and Machine Learning paradigms creates more robust, adaptive, and transformative intelligent machines and systems than either technology could achieve alone. It details the synergistic benefits and the underlying principles that enable this fusion, moving beyond siloed understanding.
- Foundational Concepts of AI and ML
- The book provides a thorough grounding in the core principles, algorithms, and methodologies that underpin both Artificial Intelligence and Machine Learning. This includes definitions, historical context, different types of ML (supervised, unsupervised, reinforcement learning), and the basics of neural networks and deep learning, serving as essential building blocks for advanced understanding.
- Applications of Intelligent Machines and Systems
- This theme focuses on the practical deployment of AI and ML technologies in real-world intelligent machines across various sectors. It showcases how theoretical concepts and advanced algorithms are translated into functional systems that address specific industry needs and societal challenges, providing concrete examples of their impact.
Worth discussing
What are the most significant challenges in integrating AI and ML for truly intelligent machines, beyond current capabilities, as discussed in the handbook?
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