Why AI Still Misses the Deeper Meanings of Human Language

The Limits of AI Understanding Language

Language is our tool for making sense of experience. When generative AI like ChatGPT emerged, many began to question what “meaning” really entails. A common claim is that AI systems “understand” language. Geoffrey Hinton, a Nobel laureate and AI pioneer, once said neural networks grasp natural language faster and better than expected, even surpassing traditional linguistic theories.

Hinton contrasted AI’s language processing with Noam Chomsky’s linguistic theories, which propose an innate universal grammar in humans enabling language acquisition from birth. Having studied neuroscience and language processing for decades, I must disagree with the idea that AI truly “understands” language.

Generating Text vs. Language

Most often, people confuse written text with natural language. Text is a representation, not language itself. For example, Hindi and Urdu are linguistically similar and mutually intelligible at conversational levels but use completely different scripts. The same applies to Serbian and Croatian. This shows that written symbols do not equal language.

AI systems generate text based on patterns, but natural language communication relies on more than text alone. It involves face-to-face interaction, shared environmental context, tone, pitch, eye contact, and emotional cues.

The Crucial Role of Context

Understanding words requires context. Even infants pick up on contextual clues. Consider the sentence “I’m pregnant.” Its meaning varies dramatically depending on who says it and under what circumstances. A teenager telling her boyfriend, a woman after fertility treatments telling her husband, or a middle-aged person saying it—all evoke different reactions and interpretations.

Research shows that emotional state influences brainwave patterns during language processing. Our brains never process language in isolation from emotion, which current AI cannot replicate. When AI developers mention “neural networks,” they mean algorithms, not the biological neural systems of the human brain. This distinction is critical to avoid misunderstandings—like mixing up “flight” as bird migration versus airline travel.

AI vs. Chomskyan Linguistics

Chomsky’s linguistic framework centers on the idea that all human languages share an underlying grammatical structure and that humans are born with an innate capacity to learn any language. However, his work focuses on language acquisition, not the psychological or neural mechanisms of language processing.

Chomsky was primarily a theoretician, not a neuroscientist, so his theories do not explain how sentences are understood in real-time. The brain’s ability to acquire language at birth is remarkable, with over 7,000 languages worldwide. This readiness to learn language is a neurobiological fact, but the exact neural mechanisms remain unclear.

Confusing AI text generation with human language processing risks misapplying scientific concepts and could lead to serious consequences in how we approach AI and language.

Conclusion

AI systems can generate language-like text, but they lack the biological, emotional, and contextual grounding that human language requires. True language comprehension involves complex brain processes shaped by emotion, context, and innate neural structures—none of which current AI replicates.

For those seeking to deepen their understanding of AI’s capabilities and limits, exploring courses on ChatGPT and large language models may provide practical insights into how these tools function and where they fall short.