The Impact of AI Expectations on Technology Perception
The Problem of Expectations
When a person first encounters the topic of artificial intelligence, they don't approach it with a clean slate. They already have certain images formed: robots from sci-fi movies, thinking and feeling computers, or systems that will one day surpass humanity or become a threat to it. These perceptions didn't appear out of thin air – they have been shaped for decades by culture, cinema, journalism, and marketing. And at the center of it all stands one phrase: «artificial intelligence»./p>
The problem isn't that the topic is overhyped or undervalued. The issue is that the term itself creates persistent expectations that do not align with technological reality. Upon hearing the word «intelligence», a person automatically fills in the blanks: understanding, consciousness, intent, and agency. This is precisely why discussions about modern AI systems so often hit a dead end: people aren't discussing actual technologies, but rather what the name suggests to them.
We are beginning this Knowledge Base with this very question because, without the right mindset, all other information will be perceived through a «distorted lens».Our goal isn't to «bust myths» in a chase for sensation, but to learn how to speak about AI more accurately and, as a result, think about it more productively.
History and Origin of the Term Artificial Intelligence
The Origin of the Term
The phrase «artificial intelligence» was first used in 1955. It was proposed by the American scientist John McCarthy while preparing a proposal for a summer research conference at Dartmouth College. This conference, held in 1956, is considered the starting point for AI as a scientific discipline.
McCarthy was a mathematician and computer scientist. He needed a term to designate a new field of research – creating programs capable of solving tasks that, at the time, seemed to require mental effort: playing chess, proving theorems, or pattern recognition. The name had to be punchy enough to unite disparate projects and catchy enough to attract attention and funding.
McCarthy himself later admitted that the term wasn't the most fortunate choice. It was a working title – convenient for grant applications, academic journals, and professional communication. No one back then imagined that this phrase would become a global label for an entire technological era, or that its semantic baggage would define the perception of technology for billions of people seventy years later.
It is important to understand: at the moment of its inception, the term described a research program, not the properties of systems already created. It was the name of an ambition, not a characterization of a result. Over time, this nuance faded, and the name began to take on a life of its own.
Parallel to this, alternative options existed: «machine thinking», «automated reasoning», and «computational intelligence».Some researchers preferred more neutral phrasing, as the word «intelligence» seemed too loaded with unnecessary meanings to them. However, McCarthy's term took root in both academic and public spaces – and this is the legacy we are dealing with to this day.
Semantic Meaning and Human Associations with Intelligence
Associations with the Word «Intelligence»
The word «intelligence» carries a specific semantic weight. It is inextricably linked to humans, thinking, and understanding. It is an attribute of a subject that perceives the world, makes sense of it, and makes decisions based on that experience.
This is exactly what makes the term «artificial intelligence» so deceptive. It literally proclaims: what you see before you is intelligence, albeit artificial. A silicon brain. A mind without a body. Thinking without a human. From this logic, a whole set of questions automatically follows: can AI become self-aware? Does it have feelings? Can it want something contrary to our will? These questions only seem natural because the name itself nudges us toward them.
Meanwhile, modern systems are built on fundamentally different principles. Large language models – the most common type of such systems today – are mathematical functions trained to predict the likely continuation of text based on massive amounts of data. They do not «understand» information the way we do; we discuss in detail why text processing does not generate meaning in the article «AI Is Neither Mind Nor Consciousness». What matters here is something else: the word «intelligence» in the technology's name creates a semantic field that does not apply to it.
This does not diminish the real capabilities of such systems. But it does mean that describing them in the language of «reason» and «understanding» is to speak about the technology inaccurately.
How Media and Marketing Influence AI Perception
The Role of Media and Marketing
If the term «artificial intelligence» had remained strictly academic and used only in scientific papers, its semantic weight wouldn't matter as much. Scientists are used to the conventionality of working titles. Но the term left the laboratories and entered a fundamentally different environment.
Media works with imagery. Complex technical reality requires simplification, and the word «intelligence» is the perfect tool for this. It instantly triggers a visual association, creating an illusion of clarity and significance. A headline like «AI has learned to write poetry» is perceived much more vividly than «a language model generates text with poetic characteristics».The first version grabs attention; the second requires a long explanation.
This isn't due to any's malice on the part of journalists, but rather the logic of media production. Audience attention is a scarce resource, and the anthropomorphization (assigning human qualities) of technology works flawlessly. A reader reacts more actively to a story about a «creature» that learns, makes mistakes, or frightens, than to a dry report on the performance of a statistical model.
Marketing adds its own layer of distortion. Companies selling AI products have a vested interest in making their developments seem ground-breaking and almost magical. The word «intelligence» works for this image: it creates an aura of complexity and the feeling that something living and «understanding» stands behind the code. This increases the perceived value of the product and its appeal to the buyer. As a result, the «AI» acronym is everywhere today: in descriptions of mobile apps and household appliances, and in corporate presentations. Often, simple filtering or sorting algorithms that have nothing to do with neural networks are hidden behind it. We discuss where the line is drawn between an algorithm, machine learning, and what is commonly called AI in the article «Algorithms, Machine Learning, and AI: Where the Lines Are Drawn».
The term has turned into a marketing signal meaning «modern and advanced».This has completely blurred its semantic boundaries.
Consequently, a paradoxical situation has arisen: the term is simultaneously overvalued and undervalued. Overvalued – because it triggers false expectations of consciousness and agency. Undervalued – because the understanding of what these technologies can actually do and how they function is lost behind the advertising noise. Both distortions hinder a sober analysis.
The role of popular culture is also worth noting. Decades of science fiction have formed a steady archetype: artificial reason as a mirror of man, as a threat or an ally, as a being with a rich inner life. HAL 9000, the Terminator, Samantha from the movie «Her», Data from «Star Trek» – all these images are deeply rooted in the collective consciousness. When news reports of a latest breakthrough, the reader involuntarily fits it into this cultural context. The technology is perceived through the prism of narratives that do not apply to it.
Brief Conclusion
The term «artificial intelligence» is a historically established label that is convenient for communication but inaccurate in essence. It emerged as a working title for a scientific program, took root thanks to media and marketing, and today carries a semantic load that does not match the nature of real systems.
This doesn't mean we should abandon the term: it is too deeply entrenched. But it does mean it should be used consciously, with the understanding that behind it lies not a «mind», but specific mathematical methods with their unique capabilities and strict limitations.
A productive dialogue about AI begins with such an understanding. Not with wonder or anxiety, but with a question: what exactly does this system do, how does it do it, and what conclusions follow? This approach allows us to evaluate technology by its real properties, rather than by what its name suggests.
The entire content of this Knowledge Base is built upon this foundation.