Artificial Intelligence Is Not Intelligence: Why We Should Call It CLIPs

By Stephen A. Vicinanza
EmbeddedScience.org | October 13, 2026
Opinion and Technical Commentary

Artificial intelligence is one of the most powerful technological developments of our time. It is also one of the most misleadingly named.

We have built computational systems capable of producing remarkably convincing language, generating software, analyzing documents, recognizing patterns, and answering questions across an extraordinary range of subjects. Yet none of these achievements establishes that the systems possess intelligence in the human sense of the word.

I propose that we reconsider the terminology used to describe these technologies.

Instead of calling contemporary generative systems artificial intelligence, I propose a more technically grounded designation: Casual Language Interface Programming Systems, or CLIPs.

The word casual is intentional. It describes the ordinary, conversational language through which people communicate instructions, questions, and requests to a computational system.

CLIPs describes what these systems do without assuming that they possess an independent mind.

The Problem With the Word Intelligence

Intelligence is difficult to define. Human intelligence involves learning, reasoning, adaptation, judgment, memory, and the ability to apply knowledge under changing circumstances. Human experience also includes consciousness, intention, emotion, and biological needs, although scientists disagree about which of these are necessary for intelligence itself.

When we describe a language model as intelligent, we risk combining these distinct concepts into a single, misleading impression.

A computer program that generates an appropriate response does not necessarily understand that response as a person does.

A system that writes an explanation of fear does not establish that it experiences fear. A system that discusses morality does not demonstrate that it possesses moral responsibility. A system that produces a scientific hypothesis has not necessarily acquired an independent scientific purpose.

These distinctions matter because terminology influences public expectations, research priorities, investment decisions, and regulation.

What Contemporary Language Models Actually Do

Modern large language models are trained on enormous collections of text and other data. During training, mathematical parameters are adjusted to improve the system’s ability to predict and generate sequences.

Once trained, the system receives an input, processes it through its computational architecture, and generates an output according to learned representations and statistical relationships.

This is an impressive engineering accomplishment.

It is not evidence, by itself, of consciousness or human-like understanding.

Importantly, language models are not merely lookup tables or simple collections of prerecorded phrases. Their internal representations can support abstraction, problem-solving, and forms of generalization that were not explicitly programmed as individual answers.

That makes the technology significant. It does not settle the philosophical question of whether the resulting behavior constitutes intelligence.

My position is that we should describe the machinery before assigning it a human characteristic.

Why CLIPs Is a More Useful Description

Casual Language Interface Programming Systems emphasizes four essential features.

Casual Language identifies the conversational interaction. Users communicate with the system through ordinary sentences rather than conventional programming syntax.

Interface identifies the system’s role as a point of interaction between human intentions and computational processes.

Programming reflects the structured instructions, algorithms, training procedures, and executable operations that determine the system’s behavior.

Systems recognizes that the resulting technology is rarely a single model operating alone. It may include databases, retrieval mechanisms, software tools, memory functions, safety controls, and external applications.

Under this framework, a CLIPs platform can be extremely sophisticated without being described as an independent thinking entity.

The designation also emphasizes something frequently overlooked: the human being remains responsible for defining objectives, evaluating results, and determining whether the output should be used.

Language Is Not the Same as Understanding

Language can create an extraordinary illusion of personal presence.

When a system responds with confidence, humor, sympathy, or technical precision, people naturally interpret the exchange through their experience of communicating with other human beings.

This tendency is understandable. Language is one of our primary indicators of intelligence.

But convincing language is not conclusive evidence of subjective understanding.

A CLIPs system may explain a medical diagnosis without experiencing illness. It may describe a laboratory experiment without conducting one. It may formulate an ethical argument without possessing an ethical identity.

The practical danger arises when users confuse fluent output with verified knowledge.

A convincing answer can still be wrong. An apparently thoughtful explanation can contain invented information. A persuasive recommendation can fail to account for real-world consequences.

The distinction between generated language and independently verified evidence must remain visible.

The Engineering Achievement Remains Extraordinary

Rejecting the term artificial intelligence as a literal description does not diminish the technology.

Quite the opposite.

CLIPs systems represent a major advancement in human-computer interaction. They make complex computational capabilities accessible to people who do not know programming languages.

A researcher can explore scientific literature through conversation. An engineer can request documentation or examine code. An editor can organize technical information. A small business can obtain assistance with operational tasks that previously required specialized software knowledge.

These capabilities have genuine economic and scientific value.

However, their usefulness should not require us to attribute human qualities to the machinery.

We can recognize powerful computational reasoning without presuming consciousness. We can acknowledge useful generated insights without granting the system independent authority.

Why Terminology Matters for Science

Scientific terminology should clarify mechanisms, limitations, and observable behavior.

When a technology is presented as intelligence, the public may assume it possesses understanding, judgment, or reliability beyond what has actually been demonstrated.

That misunderstanding can influence education, employment, medicine, engineering, and public policy.

CLIPs encourages a more disciplined approach.

Instead of asking whether a machine is intelligent, we can ask measurable questions.

How accurately does it perform a task? How does it handle unfamiliar information? Can its conclusions be verified? What are its failure modes? How dependent is it on training data, external tools, and human oversight?

These questions lead toward engineering evidence rather than anthropomorphic speculation.

They also leave room for future discoveries. If researchers eventually establish stronger evidence of machine consciousness or other forms of intelligence, scientific terminology can evolve accordingly.

Human Responsibility Must Remain Central

The greatest risk is not simply that computational systems will become more capable.

It is that people will surrender judgment to systems whose outputs they mistake for independent authority.

The proper relationship should remain clear.

Human beings establish objectives. Computational systems assist with execution. Humans and accountable institutions evaluate consequences.

CLIPs can help us maintain that distinction.

The designation does not deny that machines may perform forms of reasoning or learning. It challenges the assumption that such capabilities automatically justify describing the system as an intelligent being.

A Proposal for a More Accurate Vocabulary

I propose Casual Language Interface Programming Systems (CLIPs) as an alternative descriptive framework for contemporary conversational generative systems.

The term is intended to encourage technical precision, public understanding, and responsible development.

It is not a claim that every system currently called artificial intelligence operates identically. Robotics, computer vision, reinforcement learning, and other computational disciplines extend beyond conversational interfaces.

Rather, CLIPs identifies a major category of technology whose capabilities are increasingly confused with human intelligence.

We should celebrate the achievement without confusing the instrument with its operator.

A computational system can generate language, solve problems, and assist human reasoning without establishing that it possesses a mind.

That is the distinction I believe our scientific and technological vocabulary must preserve.

The future of computing deserves terminology that explains what we have built—not terminology that encourages us to imagine something we have not yet demonstrated.

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© 2026 Stephen A. Vicinanza. All rights reserved.

CLIPs—Casual Language Interface Programming Systems—is a terminology proposal introduced and advocated by Stephen A. Vicinanza in this article. The views expressed are those of the author.