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12.5 MB
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2E696C02362C285DD870E97D6E723F8B9D00AFED
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Oct. 17, 2025, 10:57 a.m.
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(Last updated: Oct. 17, 2025, 11 a.m.)
| File | Size |
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| Soisoonthorn T. A Brain-Inspired Approach to Natural Language Processing 2026.pdf | 12.5 MB |
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SOURCE: Soisoonthorn T. A Brain-Inspired Approach to Natural Language Processing 2026
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COVER
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MEDIAINFO
Textbook in PDF format This book brings together key ideas from neuroscience and Artificial Intelligence (AI) 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. In its final sections, the book reflects on the broader significance of integrating neuroscience and AI, encouraging continued exploration and innovation at the intersection of these disciplines. Key benefits of this book include - Exploring biologically plausible models of intelligence to open new pathways - Gaining foundational insights into how neuroscience can inform AI design - Presenting practical examples to enhance NLP tasks in complex languages - Offering a testbed for experimentation with brain-inspired computational models - Serving as a valuable resource for advanced students, researchers, and professionals seeking to deepen their understanding of nature-inspired intelligent systems In cases where it is difficult to define clear rules or predict every possible situation, we often rely on data instead. This leads us to machine learning (ML), which allows computers to learn patterns from data and improve over time without being explicitly programmed for each task. Popular ML techniques include ensemble models such as Random Forests and Gradient Boosting, k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Deep Learning (DL), which has driven many recent breakthroughs in AI. However, traditional ML methods are mostly designed for one-time decisions in what are called episodic environments. They tend to struggle with tasks that unfold over time. When the order and timing of information matter, such as in language understanding or behavioral analysis, we need models that can interpret sequences. To address this, researchers introduced time-series methods like ARIMA and neural models such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs). These models enable machines to process and learn from data that changes over time, though they face limitations in handling longrange dependencies and require sequential processing that can be computationally slow. The introduction of the Transformer architecture revolutionized sequence modeling by replacing sequential processing with parallel attention mechanisms. This breakthrough led to the development of Large Language Models (LLMs) like GPT, which have achieved remarkable capabilities through self-attention mechanisms. Even with these advanced approaches, limitations persist. Most ML models learn passively by analyzing large, static datasets. But what happens when data is scarce or when learning must occur through interaction? This is where Reinforcement Learning (RL) becomes essential. RL allows agents to learn through trial and error by actively exploring their environment and discovering which actions lead to better outcomes. This process mirrors how humans and animals learn from experience. While modern techniques like deep learning have achieved remarkable results, yet they also come with significant trade-offs. These models require vast computational resources, consume substantial energy, and often depend on enormous datasets. They function as black boxes, making it difficult to interpret their decision-making processes. Moreover, they are sensitive to small input variations, prone to errors, and lack true reasoning or understanding. Even powerful systems like Large Language Models (LLMs), which are based on the Transformer architecture, face challenges in accuracy, adaptability, and common sense. While they can generate fluent text and understand context, they may hallucinate facts and cannot continuously learn from real-world feedback. While refining existing AI models may lead to meaningful progress, it remains uncertain whether such approaches alone can achieve a deeper form of intelligence. By contrast, drawing inspiration from the structure and function of the human brain may offer a promising direction toward creating systems that are more flexible, adaptive, and capable of exhibiting human-like behavior. Preface The Human Brain The Neocortex The Brain and Methods of ML Spare Distributed Representation (SDRs) A New Brain-Inspired Sequence Learning Memory Spelling Check Problem Thai Word Segmentation Conclusion and Future Work
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