A Brain-Inspired Approach to Natural Language Processing
Springer Nature
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This book brings together key ideas from neuroscience and artificial intelligence to show how they can work together. It helps readers understand how studying the brain can lead to more adaptable and efficient AI systems. Instead of treating the two fields as separate, it highlights how brain-inspired models can help overcome current challenges in AI, improve existing techniques, and spark new and creative solutions. The journey begins with the biological foundations of intelligence, focusing on the brain’s structure, evolution, and functions, particularly the neocortex, which plays a central role in learning and prediction. Building on this foundation, the book surveys both traditional and modern AI methods in an accessible way and offers a critical analysis of their strengths and shortcomings. The discussion then moves from theory to practice, showing how brain-inspired ideas can be applied to real-world Natural Language Processing (NLP) tasks such as spelling correction and Thai word segmentation, where conventional models often struggle with nuance and complexity. In its final sections, the book reflects on the broader significance of integrating neuroscience and AI,…