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About half a century ago, artificial intelligence (AI) pioneers like Marvin Minsky embarked on the ambitious project of emulating how the human mind encodes and decodes meaning. While today we have a better understanding of the brain thanks to neuroscience, we are still far from unlocking the secrets of the mind. Especially when it comes to language, the prime example of human intelligence, we face enormous difficulties in replicating how the human mind processes it. “Understanding natural language understanding”, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Large language models (LLMs), such as GPT-4, have astounded us with their ability to generate coherent, contextually relevant text, seemingly bridging the gap between human and machine communication. Yet, despite their impressive capabil- ities, these models operate on statistical patterns rather than true comprehension. This textbook delves into the nuanced differences between these two paradigms and explores the future of AI as we strive to achieve true natural language understand- ing (NLU). LLMs excel at identifying and replicating patterns within vast datasets, producing responses that appear intelligent and meaningful. They can generate text that mimics human writing styles, provide summaries of complex documents, and even engage in extended dialogues with users. However, their limitations become evident when they encounter tasks that require deeper understanding, reasoning, and contextual knowledge. LLMs can produce plausible-sounding but incorrect answers, struggle with ambiguous queries, and often lack the ability to generalize knowledge across different domains effectively. Instead, an NLU system that deconstructs meaning leveraging linguistics and semiotics (on top of statistical analysis) represents a more profound level of language comprehension. It involves understanding context in a manner similar to human cognition, discerning subtle meanings, implications, and nuances that current LLMs might miss or misinterpret. NLU grasps the semantics behind words and sentences, comprehending synonyms, metaphors, idioms, and abstract concepts with precision. This deeper comprehension allows for consistent accuracy and robustness, enabling AI systems to handle ambiguous, incomplete, or novel queries more effectively. One of the key advantages of cognitive-inspired NLU systems is their ability to learn and adapt from interactions meaningfully. Unlike LLMs, which remain static once trained, NLU systems can incorporate new knowledge dynamically, correcting misunderstandings and adapting to new contexts. This adaptability is crucial in fields where information is rapidly evolving, such as technology and medicine. Moreover, NLU systems can generalize knowledge from one domain to another, providing more reliable and versatile applications across interdisciplinary fields. Ethical considerations also play a significant role in the development ofNLUsystems, as these are more transparent and can better recognize and mitigate biases, ensuring fair and ethical responses. They understand the potential impact of their responses, avoiding harmful or inappropriate content more reliably. This understanding of ethical implications and societal norms is crucial for building trustworthy AI systems in sensitive areas like mental health, education, and decision-making. Achieving true NLU involves advanced knowledge representation, such as incor- porating symbolic reasoning and structured knowledge bases. This includes ontolo- gies, semantic networks, and rule-based systems that explicitly encode relationships and rules. Combining symbolic AI with machine learning creates hybrid systems that leverage both structured knowledge and the pattern recognition strengths of LLMs. Neurosymbolic integration, which merges neural networks with symbolic reasoning systems, is a promising approach to understanding and generating more accurate and contextually appropriate responses. Embedding real-world knowledge and com- monsense reasoning into AI systems is also crucial for NLU. Training on diverse data sources and integrating world models that simulate real-world scenarios enable these systems to maintain context over long conversations, understand intents and sentiments, and manage turn-taking effectively. Furthermore, NLU systems should emulate human-like learning and adaptation, continuously learning from human interaction, feedback, and correction. In this textbook, we explore the current state of LLMs, their capabilities, and limitations, and contrast them with the aspirational goals of NLU. We delve into the technical foundations required for achieving true NLU, including advanced knowl- edge representation, hybrid AI systems, and neurosymbolic integration. We also examine some ethical implications and societal impacts of developing AI systems that genuinely understand human language. The textbook features multiple exercises at the end of each chapter, along with a group assignment and a final quiz. It evolved out of the author’s own teaching course SC4021 (NTU CCDS course on information retrieval) but it can be used for many other courses, e.g., natural language processing (NLP), AI, data analytics, data mining, etc., at NTU and universities worldwide. We will explore different ways of encoding and decoding meaning, which can be used for knowledge representation and reasoning in several downstream applications. As we embark on this exploration, we are reminded of the pioneering work of visionaries like Marvin Minsky, whose relentless pursuit of understanding and belief in the potential of the human mind have inspired us to dream of a future where machines not only mimic human language but truly comprehend it. This textbook is dedicated to those who continue to push the boundaries of AI, striving to create systems that enrich our lives with genuine understanding and insight.