Is “Artificial Intelligence” a Misnomer?

From Deep Blue an IBM computer defeating the chess superstar, Gary Kasparov in 1997, a modern milestone that marked the arrival of 'intelligence' of machines, to the reigning Grandmaster Magnus Carlson beating ChatGPT in a game of chess a couple of weeks ago proving the supremacy human intelligence, we have come a long way in the ever-evolving prowess of machine intelligence that's producing its fair share of shock and awe. At the same time, questions swirl around how intelligent Artificial Intelligence really is.

The term “Artificial Intelligence” feels both ubiquitous and slippery. It’s on billboards, in coffee shop conversations, and woven into the fabric of our daily lives—Siri scheduling meetings, algorithms curating our feeds, and chatbots like Grok answering our existential queries. But as AI’s presence grows, so does a nagging question: Does the term “Artificial Intelligence” overpromise what these systems deliver? Is it time to call it something else—like “Narrow Intelligence”—or to rethink what “intelligence” means in the first place?

The phrase “Artificial Intelligence,” coined in 1955 by computer scientist John McCarthy, was meant to evoke machines that could mimic human cognitive abilities. It’s a term heavy with ambition, conjuring images of sentient beings like HAL 9000 or the empathetic androids of science fiction. Yet, the reality of AI in 2025 is more mundane, though no less impressive. AI systems excel at specific tasks: recognizing faces, translating languages, recommending movies, or even diagnosing diseases with startling accuracy. But these are not Renaissance minds. A neural network trained to spot cancer in X-rays can’t pivot to writing poetry or navigating a philosophical debate. Unlike humans, who adapt across domains with a general, flexible intelligence, AI is narrowly tailored, its prowess confined to the data and tasks it’s been trained for.

This specificity has led some critics to argue that “Artificial Intelligence” is a misnomer. They propose “Narrow Intelligence” as a more accurate descriptor, emphasizing the specialized nature of these systems. Take, for example, AlphaFold, which solved protein folding—a decades-long scientific puzzle—by predicting molecular structures with unprecedented precision. It’s a triumph, but AlphaFold can’t play chess or analyze a novel. Similarly, large language models like those powering Grok or ChatGPT can generate human-like text, but they don’t “understand” in the way humans do. They’re pattern recognizers, sifting through vast datasets to produce responses that seem intelligent but lack the consciousness, intuition, or adaptability of a human mind.

The anthropomorphic sheen of “intelligence” fuels both fascination and unease. When we call a system “intelligent,” we imbue it with a human-like aura, inviting comparisons to our own cognition. This can mislead. AI doesn’t “think” or “feel”; it processes inputs and outputs based on mathematical models. A 2024 study from MIT showed that even the most advanced language models rely heavily on statistical associations rather than true reasoning, often faltering when faced with novel scenarios outside their training data. Humans, by contrast, can reason abstractly, learn from minimal examples, and navigate ambiguity with a blend of logic and intuition. A toddler, for instance, can learn to avoid a hot stove after one warning; an AI model might need thousands of examples to grasp a similar concept.

Yet, dismissing AI as “just a computer program” risks understating its impact. The sheer scale of data and computational power behind modern AI—trillions of parameters, petabytes of text, and server farms humming across continents—produces results that feel eerily human. When I asked Grok, created by xAI, to explain quantum entanglement, it delivered a lucid response in seconds, complete with metaphors a layperson could grasp. That’s not sentience, but it’s a kind of brilliance. In industries like logistics or finance, AI optimizes systems with a precision no human could match. In creative fields, tools like DALL-E generate art that rivals human output, even if the “artist” lacks a soul.

So, should we ditch “Artificial Intelligence” for “Narrow Intelligence”? The case for it is strong: it’s more precise, tempering expectations and clarifying that these systems are tools, not peers. But there’s a counterargument. The term “AI” has cultural weight, a shorthand that captures the audacity of building machines that emulate human capabilities, however imperfectly. Swapping it for “Narrow Intelligence” might feel like a downgrade, stripping away the aspirational spark that drives innovation. And while today’s AI is narrow, the field is evolving. Researchers at places like DeepMind and xAI are chasing “Artificial General Intelligence” (AGI), a hypothetical system that could match human versatility. If AGI arrives, “Artificial Intelligence” might finally live up to its name.

There’s also the question of whether “intelligence” itself needs redefining. Philosophers and cognitive scientists have long debated what intelligence is—problem-solving, learning, creativity, or something more elusive? If we judge AI by human standards, it falls short. But if we see intelligence as the ability to process information and achieve goals, AI already surpasses humans in certain domains. A chess engine like Stockfish doesn’t “think” like Garry Kasparov, but it consistently beats him. Is that intelligence, or something else entirely?

As I sip coffee in a cafe in downtown Calcutta, which has become a cafe city, I wonder whether we should keep using “Artificial Intelligence” but with a caveat. It’s a term that inspires and misleads in equal measure. We don’t need to anthropomorphize AI to marvel at its capabilities or fear its risks—job displacement, bias amplification, or ethical blind spots. Nor do we need to rebrand it as “Narrow Intelligence” to acknowledge its limits. Instead, we should embrace the tension: AI is a tool of immense power, neither human nor trivial, a mirror reflecting both our ingenuity and our tendency to project humanity onto our creations. For now, let’s call it what it is—a work in progress, intelligent in its alien way, and a reminder that the future is as much about redefining words as it is about redefining machines.