From Shannon’s prediction game to modern transformers—and what gets lost in meaning

The Mysteries of Words

A few years ago, I sat in a dissertation defense for a student working on language models, and something quietly unsettled me. He was explaining “attention mechanisms,” showing how his system could summarize documents in perfect English. I remember watching the examples scroll by, line after line, unlike earlier machine-generated texts, carrying no seams.

After the defense, there was a small celebration. Over coffee and cake, I asked colleagues where this whole idea had begun. Someone mentioned ELIZA: the first chatbot that simulated conversation. ELIZA’s famous DOCTOR script mimicked a psychotherapist, reflecting users’ words back at them and so creating the illusion of understanding.

It didn’t have a model of meaning; it was based on pattern-matching and substitution. And yet many people responded as if it did. That strange human tendency to attribute feeling to a simple program was later called the ELIZA effect. Curious, I traced the thread further back and realized that ELIZA stood on a foundation laid much earlier by Alan Turing. Turing had in fact written two remarkable papers just after World War II.

In 1948, in Intelligent Machinery, he sketched what he called an “unorganized machine,” a crude model of the infant brain. Its wiring was initially random, but through reward, punishment, and random exploration, it could be “educated” into a universal computer. To prove its worth, he proposed a set of benchmark tasks: games like chess and cryptography as easier challenges, mathematics and translation as harder ones, and at the very top, the learning of natural language. Already, he sensed that teaching a machine language would be the ultimate test.

Two years later, in 1950, in Computing Machinery and Intelligence, he posed his famous provocation: not “Can machines think?” but whether they could succeed in the imitation game, what we now call the Turing Test. Once again, language was the centerpiece. He imagined training a machine as one might teach a child, through interaction, naming, and correction.

In hindsight, ELIZA was almost a toy realization of Turing’s challenge: a machine producing conversation that, for some, passed as human. It was a small step, but a definitive one on the march toward what we now call artificial intelligence. That was the lineage: from Turing, through ELIZA, to the models I had just seen.

The resemblance was striking, but also disorienting. It blurred the boundary between words that merely followed rules and words that carried meaning. The harder task was never finding the words, but shaping the essence they had to carry. Unlike the demo I saw, which simply strung together the most probable words, I faced a different kind of struggle: I knew the words, but the felt essence wasn’t there yet. That quiet, internal wrestling, the pause and reshaping before meaning lands, was absent in the polished texts before me.

This is not merely a technical matter of algorithms. Language is bound up with social practice, with the rituals and repositories by which communities make sense of the world. A sentence does more than map signs to ideas; it declares, commands, consoles, jokes, curses, remembers, and binds people together in common projects. Across cultures and centuries, in oral performance and written text, language accumulates authority and history: it carries law, theology, love letters, treaties, protests, prayers, jokes, and recipes.

I grew up with the Ramayana and the Mahabharata, not as entertainment but as living traditions: vast conversations full of moral dilemmas, strategic debates, and philosophical inquiry. The Bhagavad Gita, nested inside the Mahabharata, is itself a dialogue about duty and liberation that still guides millions. Later, in English, I discovered Shakespeare’s soliloquies and the playful wit of Pygmalion. Across these forms, language is never merely syntax or sequence; it is a vessel through which thought, memory, and moral imagination endure.

But language is not the only such vessel. Music can convey emotion more directly than a sentence. A painting can express what words cannot. Athletes speak through rhythm and gesture; mathematicians through symbols and proof. These are all languages in a broader sense: systems of signs that carry meaning.

What sets natural language apart is its paradoxical combination of simplicity, fragility, and reach. Compared to music, it is a sparse medium. Just strings of symbols. And that simplicity makes it radically fragile: a comma, a tense, or a single word can tilt the whole meaning. Yet because it can describe, question, command, or console, its coverage is unmatched. Perhaps this is why Turing placed language at the pinnacle of machine intelligence: to master something so lean, so delicate, and so general would be to master almost anything.

That ambition, once only theoretical, suddenly felt realized when ChatGPT arrived. What had once been a curiosity in labs and conference rooms suddenly erupted into daily life. A seminar-room marvel became a household word. Classrooms, newsrooms, law firms, and living rooms all found themselves confronted by a machine that could speak in polished paragraphs. Students turned in assignments written by it, professionals drafted memos with it, and friends sent poems and prayers through it. Headlines proclaimed that human thought itself had been hacked.

The effect was electric, unsettling, and irresistible: a machine producing not just a clever reply but a torrent of fluent language on command: text that could mimic styles, explain theories, or console in grief. Where earlier chatbots exposed their seams within a few exchanges, this one could sustain entire conversations, sounding by turns confident, witty, or wise. For many, it felt like crossing a threshold: as if the capture of language, the vessel of our thought, memory, and imagination, had also led to the capture of thought itself.

But had it?

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