Does my Chatbot Really Understand Me? The Illusion of Meaning in AI

Last year, diving deep into AI philosophy, I kept circling back to this core question: are these language models really understanding, or just putting on a dazzling show? I thought it was a great subject that gets to the heart of understanding much of the subtext (or hype) around whether large language models have contextual understanding of the question being asked.

My paper addressed the question (which, actually, has been debated for decades now) of whether machines can truly understand what they process.  John Searle’s Chinese Room thought experiment (in Minds, Brains, and Programs, 1980), laid the foundation for this discussion by asserting that symbol manipulation does not entail understanding. Searle's Chinese Room illustrates that a computer program, even one that mimics Chinese understanding, lacks genuine comprehension. By picturing a person mechanically manipulating Chinese symbols without knowing their meaning, he concludes that mere symbol manipulation does not equate to understanding. 

It’s reasonable to suggest that his argument (which certainly has its critics) remains as relevant today as it was over forty years ago, especially in light of recent advancements in large language models (LLMs) from OpenAI, Anthropic, and DeepSeek. These models, despite their seemingly impressive linguistic capabilities, still suffer from the core limitation that Searle highlighted: they manipulate symbols syntactically (syntax is rules that govern the structure of language) but do not grasp meaning semantically (semantics is the link between language and the real world). 

Understanding vs. Symbol Manipulation

Understanding is intertwined with intentionality and semantics; understanding is not just knowing facts or recalling information; it’s about grasping meaning and recognising context, and then applying knowledge in a way that reflects genuine comprehension. Philosophers including Pritchard (2009) emphasise that understanding requires more than memorisation or rule-following; it necessitates intellectual engagement and experience. Searle’s Chinese Room experiment illustrates this principle by showing that an individual following syntactic rules to process Chinese characters does not understand Chinese, even if they produce responses indistinguishable from those of a fluent speaker.

Large language models could be said to function in a similar way. They generate text based on probabilistic patterns derived from vast datasets, but lack genuine understanding of the material they produce. OpenAI’s GPT-4, Anthropic’s Claude, and DeepSpace’s latest models excel at predicting the most plausible next word in a sentence based on their training data. However, their responses, while coherent and contextually relevant, are generated without comprehension or intentionality. These models do not “know” what they are saying; they merely reflect statistical relationships within their training corpus.

The Illusion of Understanding in AI and the Role of Intentionality

My view is that Searle’s critique of artificial intelligence understanding extends to the present-day capabilities of LLMs, which, despite their sophistication, do not possess genuine cognition. Their responses are based on pattern recognition rather than semantic depth. The term stochastic parrots, used by Bender, Gebru et al in their excellent paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” (2021), aptly describes this phenomenon: AI models mimic human language convincingly but without any true grasp of the underlying meaning. This aligns with Searle’s argument that computation alone does not generate intentionality; true understanding requires more than syntactic manipulation.

Intentionality, the capacity for thoughts to be directed toward something with purpose, is a critical aspect of understanding. Unlike AI systems, human cognition is inherently intentional.  We interpret words based on context, emotions, and prior knowledge. When a person reads a novel, they do not merely recognise words; they infer motives, appreciate subtext, and engage in reflective thought. That is, if I am reading about a “cold, dark and gloomy day”, I can visualise it, feel it, understand what it means and how it would feel if I ventured outside. AI lacks this ability because it does not possess intrinsic goals or self-generated intentional states. AI does not feel or visualise anything - it merely processes text statistically.

Critics of Searle’s position have argued that AI systems might achieve understanding through embodied cognition; the idea that true intelligence emerges when AI interacts with the world in a meaningful way. The ‘robot reply’ (formulated by various philosophers), is a common counterargument to the Chinese Room experiment that suggests a system equipped with sensory input and motor functions could develop genuine comprehension. Searle rebuffed this idea, asserting that mere interaction with the environment does not bestow understanding. 

The consequences for self-driving cars, chatbots, etc. 

Even if an AI system such as a self-driving car processes visual data and reacts to obstacles, it does so based on preprogrammed rules rather than experiential awareness. This distinction has real-world consequences. AI-powered recruitment tools, for instance, may scan resumes and rank candidates based on learned statistical patterns, but they do not 'understand' qualifications the way a human recruiter does by interpreting career trajectories, considering cultural fit, or reading between the lines of a cover letter. Similarly, chatbots designed for customer support can provide helpful responses but fail when asked ambiguous or context-heavy questions that require genuine comprehension.

In medical diagnostics, AI tools can identify patterns in radiology images with high accuracy, yet they lack the human ability to integrate subtle contextual factors, such as a patient’s medical history or rare case nuances. These real-world examples underscore the fundamental limitation: AI can process and respond to input but without intentionality, it cannot genuinely understand the content it analyses. The car does not “understand” traffic laws or road conditions; it merely follows its programming to optimise performance.

Large Language Models and the Future of AI

Despite their limitations, modern LLMs continue to advance.  OpenAI’s latest iterations demonstrate improved contextual awareness and coherence, while Anthropic’s models emphasise ethical AI considerations. DeepSeek is exploring hybrid models that integrate symbolic reasoning with deep learning techniques. Yet these efforts still fall short of achieving genuine understanding.  They remain tethered to the fundamental limitation identified by Searle: they manipulate symbols without grasping their meaning.

One might argue that as AI continues to evolve, it could eventually achieve true understanding. Advancements in artificial general intelligence (AGI) are often cited as potential pathways toward machines that do more than simulate comprehension. However, as Searle pointed out, the mere execution of more complex programs does not bridge the gap between syntax and semantics. Unless AI systems develop a form of intentionality, they will remain sophisticated pattern-matching machines rather than entities capable of true understanding.

Conclusion: AI’s Limitations and the Road Ahead

Perhaps for now, Searle’s Chinese Room argument remains a crucial lens through which to evaluate modern AI. While performance can be measured in terms of accuracy, coherence, and responsiveness, true comprehension involves deeper cognitive functions such as abstraction, reasoning, and contextual understanding. Whether such a breakthrough is possible remains an open question.

References:

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 49-57). Association for Computing Machinery. https://dl.acm.org/doi/10.1145/3442188.3445922

Searle, J. (1980). Minds, brains and programs. Behavioral and Brain Sciences, 3(3), 417-457. 

Searle, J. R. (1993). The problem of consciousness. Social Research, 60(1), 3-16. http://www.jstor.org/stable/40970726

Pritchard, D. (2009). Knowledge, understanding, and epistemic value. Royal Institute of Philosophy Supplement, 64, 19-43. https://doi.org/10.1017/S1358246109000046