Artificial Intelligence
Transcending Traditional Paradigms
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Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 3 January 2026
- Number of Volumes 1 pieces, Book
- ISBN 9783031910838
- Binding Hardback
- No. of pages568 pages
- Size 235x155 mm
- Language English
- Illustrations XXIV, 568 p. 52 illus., 24 illus. in color. With online files/update. Illustrations, black & white 684
Categories
Long description:
"
Artificial Intelligence: Transcending Traditional Paradigms is a significant new introductory text for undergraduate and graduate course use. It introduces students and professionals to the state-of-the-art development of Artificial Intelligence techniques to develop smart real-world solutions. As an active researcher, the author presents the material authoritatively and with insights that reflect a modern, firsthand understanding of the field.
Artificial intelligence is a science still in its infancy, and that makes it special. It’s not like more established fields, and the author has tried in writing this textbook to honour this characteristic and the ways it makes AI unique.
" MoreTable of Contents:
Part 1: Foundations of artificial intelligence.- The genesis of artificial intelligence: Origins and evaluation.- The dawn of AI: Early innovations and discoveries.- Part 2: The learning revolution: Data-driven intelligence.- Soft computing paradigms: Bridging precision and flexibility.- Machine learning: Building intelligence from patterns.- Deep learning: The fabric of representation.- Reinforcement learning: Shaping behaviour through rewards.- Part 3: AI Odyssey: Exploring language and intelligent agents.- Journey through language: Models and prompt engineering.- Autonomous decision maker: AI agents.- Part 4: Expanding the frontiers of AI.- The best of both Worlds: Neuro-symbolic AI.- Mimicking the mind: Evolution of cognitive AI.- Robots with a sense of self: Exploring embodied intelligence.- Part 5: Building advanced and responsible AI.- Blueprint for intelligence: Building robust AI systems.- Never stop learning: The principles of continual learning.- Opening the black box: The role of explainability in AI.- Part 6: Conclusion and future outlook.- Epilogue: AI at the threshold.
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