Language: The Original Artificial Intelligence?

Language is a powerful force that shapes how we think, feel, and communicate. For centuries, philosophers and scientists have debated the nature of language—its role in shaping reality and whether it is a mere tool for communication or something deeper. 

But there is something that most every human being realizes at some point in our lives. It is that language was created to explain and describe thoughts, feelings, and emotions. But they are NOT those things themselves, and many time “words can’t express” or “there are no words..”.

With the rise of advanced AI systems like ChatGPT and Google’s LaMDA, it’s worth revisiting a fundamental question: Can language itself be considered a form of artificial intelligence? 

Language as a Human-Made Construct

Language is a product of human ingenuity. It didn't emerge naturally like a tree or a mountain—it was developed as a structured system to express thoughts, emotions, and abstract ideas. Think about a simple sentence like, “I feel sad.” This phrase is a symbolic representation of a complex emotional state. But words like “sad” or “happy” don’t directly convey the raw experience—they attempt to bridge the gap between inner feelings and external expression.

This is where the analogy to AI becomes interesting. Just as AI models, such as those based on the transformer architecture, use symbols (words) and rules to generate sentences, human language functions similarly. It’s a system of symbols, bound by syntax and grammar, to convey meaning. And just like an AI model, language can be precise yet limited. The words themselves are not the thoughts or emotions; they are stand-ins, a kind of code that we use to try to transmit our internal experiences to others.

The Limits of Language

To understand why language is often seen as insufficient to fully capture human experience, we can look to the philosophy of Ludwig Wittgenstein. Wittgenstein famously said, “The limits of my language mean the limits of my world.” He argued that language is inherently restricted in its ability to describe the vastness of human experience. It gives us a framework, but within that framework, much of what we feel or perceive remains unspoken and, sometimes, unspeakable.

For example, try explaining the feeling of nostalgia. You might say it’s a “bittersweet longing for the past,” but that hardly captures the layered sensations—memories tinged with warmth and a touch of melancholy. Language, as a symbolic system, simplifies complex emotions, just as an AI model simplifies patterns in data. This limitation also becomes evident in how AI-generated texts, though coherent, often miss the nuance of human experience. They can reproduce patterns of speech but often fall short when it comes to truly understanding the depth of what those words mean.

How AI Reflects Our Linguistic Framework

The emergence of large language models (LLMs) like GPT-4 has reignited discussions about the nature of thought and language. These models are trained on vast amounts of text, learning to predict what word comes next in a sentence based on patterns they’ve identified in their training data. The results are impressive—GPT-4 can generate human-like text, write essays, even provide emotional responses to questions.

However, it’s crucial to understand that LLMs, despite their linguistic prowess, lack the conscious experience behind their words. They manipulate symbols (words) without any direct access to sensory or emotional input. This is why AI can generate text that sounds empathetic or insightful without having any true understanding of what it means to feel empathy or insight. Their responses, much like language itself, are based on patterns rather than lived experiences.

This distinction highlights a key point: Language, whether human or machine-generated, serves as a representation rather than a reality. It constructs a version of the world that we can share with others, but it does not encompass the full scope of our internal life.

Language, AI, and the Art of Misdirection

The debate over whether AI systems like LaMDA could ever be considered sentient, as claimed by a Google engineer, reveals a broader societal confusion about language and intelligence. Many people reacted with skepticism or outright ridicule to the idea that a chatbot could possess personhood, while others wondered whether a more advanced version of AI might one day achieve something like consciousness. But this debate, in many ways, misses the mark. It conflates the ability to generate human-like language with the ability to have human-like thoughts.

This misunderstanding is akin to the way humans are often tricked by language itself. Just as we might attribute human-like qualities to an AI that can carry on a sophisticated conversation, we sometimes mistake the words we use for the full reality they represent. 

For example, we might say someone is “brokenhearted” after a breakup, but we know there is no literal damage to their heart. The phrase is a metaphor—a linguistic shortcut that evokes a shared understanding of emotional pain. Similarly, when AI generates text that sounds insightful, it’s drawing on learned patterns, not a genuine experience of insight.

The Power and Peril of Symbolic Systems

Understanding language as a form of artificial intelligence forces us to confront the limits of both. Language allows us to encode and communicate knowledge, but it can never fully embody the richness of human experience. AI models, trained to predict and replicate human language, similarly fall short of true understanding. They excel at manipulating symbols but lack the embodied context that gives those symbols their full meaning.

This raises important questions for the future of AI. As AI systems become more integrated into our daily lives, from customer service bots to writing aids, how do we manage our expectations? Should we view them as tools that mimic aspects of human thought, or do they represent something fundamentally new—a different kind of intelligence that challenges our traditional understanding of what it means to think?

Moving Beyond Language-Based AI

One possible direction is to develop AI that goes beyond linguistic capabilities, incorporating sensory and embodied experiences. Just as humans understand the world not only through words but through touch, sight, and intuition, future AI could integrate more than just text-based data. This could bring AI a step closer to understanding the world in a way that mirrors human cognition, though the question of consciousness would still remain.

So even as we push the boundaries of AI, the analogy between language and artificial intelligence remains valuable. It reminds us that both systems, whether human or machine, operate within constraints. And while those constraints can be stretched, they can never fully encompass the richness of lived experience.

Language, AI, and the Human Condition

In the end, treating language as a form of artificial intelligence offers a way to understand both the promise and the limitations of AI. It highlights how our reliance on language to represent reality is mirrored in the way AI uses text to approximate understanding. 

But perhaps more importantly, it invites us to reflect on our own human experience—on the things that language can never fully capture, and the gaps that remain when we try to translate our inner lives into words. In this way, the conversation about AI becomes a conversation about ourselves, about the boundaries of human expression, and about the enduring mystery of consciousness, and the very mystery of what it means to be human.

As we continue to develop technologies that push the limits of what we can do with language, we must should be mindful of what they can and cannot teach us about the nature of thought, emotion, and understanding. The true challenge lies not in creating machines that speak like us but in recognizing the unique, irreplaceable qualities of what it means to be human. And THAT, we may never be able to fully achieve. Because that is the mystery that remains.