Anatomy of AI (Artificial Intelligence)

Language and Conceptual Scope Note

This article is rooted in Hungarian linguistic thinking and conceptual structure and presents an English-language reconstruction of reasoning originally shaped by Hungarian grammar, semantics, and text comprehension. For that reason, the way ideas unfold may feel slightly different to an English-speaking reader. This difference is intentional. While the core ideas are fully expressed here, certain linguistic mechanisms—especially those related to meaning formation—are inherently Hungarian. Readers seeking deeper linguistic answers are encouraged to consult the original Hungarian version, where those questions can be addressed within their native conceptual framework.

With the spread of artificial intelligence, a great deal of misinformation has emerged. We regularly encounter claims that these systems will one day dominate humanity, or—just as implausibly—that they will simply take away everyone’s jobs.

Here, I aim to explain what artificial intelligence actually is, how it works, and why—given current development paths—it will not become a “sentient being with a soul” that rises above humankind.

1. A Functional Starting Point

To understand artificial intelligence realistically, we begin with a familiar analogy: a smart washing machine. We put the clothes inside, and from that moment on, we are presented with many options. We can schedule the wash, start it remotely over the internet, receive notifications during power outages, or monitor the washing process in real time. When the program finishes, we take the clothes out—either to hang them up or, if drying was included, to fold and put them away. A human defines the intent, the machine executes a predefined program, and the human completes the process. Modern AI follows this exact pattern. The machine runs a program, and it can do only what that program allows. It executes—nothing more, nothing less.

I start with this example because it illustrates the core logic perfectly: a person wants to accomplish something, the machine performs the execution, and the person completes the process.

Diagram 1 – Human–Machine Execution Loop

Human Intent

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Machine Execution

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Human Completion

Explanation:

This diagram illustrates the fundamental execution loop shared by both simple automation and modern AI systems. Responsibility begins and ends with the human.

2. What an LLM Actually Is

We often hear the term LLM—Large Language Model. But what does that actually mean?

We are surrounded by abbreviations without always understanding what lies behind them.

Before clarifying that, let us briefly step back.

Until the 1980s, most major technological breakthroughs flowed from scientific and military research into civilian life. In the 1990s, this direction reversed. New technologies increasingly emerged from the cooperation of well-educated and financially capable individuals and organizations. Artificial intelligence developed along this same path.

As the washing machine example already shows—and this cannot be emphasized enough—human involvement is essential.

AI is software. More specifically, it is software developed for interaction.

Centuries ago, conversation itself was a human service. Today, that role has been partially automated.

From a financial perspective, conversational ability alone is not a sellable product. That is why additional functions were built around it. By the 2010s, development had reached a point where stable chatbots could be operated reliably. These systems are still in use today, assisting human operators in customer service environments.

Think of those playful mobile applications that modify faces, add animal features, or simulate rejuvenation. Every one of these programs was created to replace, support, or sustain specific forms of human interaction.

Based on this, today’s AI can be described as a program that performs predefined capabilities through human interaction.

By 2025, the most widely known examples are ChatGPT, Gemini and the DeepSeek.

Diagram 2 – Language Processing Pipeline

User Input

⇓

Tokenization

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Embedding Space

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Context Weighting

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Probability Selection

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Response Assembly

Explanation:

This pipeline shows how text input is transformed into output. Each stage constrains the next, replacing ambiguity with statistical likelihood rather than understanding.

3. Meaning Is Not Stored – It Is Narrowed

In Hungarian, a single word may carry several meanings simultaneously. Rather than storing fixed definitions, AI gradually narrows possible meanings based on context. This process must be shown explicitly for English readers.

Diagram 3 – Meaning Narrowing (State-Based Model)

Word Encountered

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Ambiguous Meaning Set

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Context Detected

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Constraints Applied

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Single Meaning Selected

Explanation:

This state-based model visualizes how meaning is constrained step by step. Unlike Hungarian, English requires this narrowing to be explicit rather than implicit.

4. No Consciousness, No Judgment

Despite fluent language, AI does not think, feel, or judge. Large Language Models (LLMs) are often misunderstood as repositories of predefined answers. In reality, they generate responses dynamically, based on probabilistic patterns learned from language. They do not retrieve answers—they construct them. It operates without a conscience or a moral framework. Apparent intelligence is the projection of human interpretation onto statistical behavior.

A reasonable question may arise:

“How can AI help with learning?”

“How can it solve mathematical problems?”

My short answer is: it doesn’t —at least not in the human sense. AI does not calculate the way humans understand calculation. And it does not store ready-made answers either. Modern AI systems generate responses in real time, based on context. They construct one, using probability estimates influenced by language style, topic, and conversational history. So does AI calculate after all? Still no. What it can do is communicate. It understands languages—English, Hungarian, Java, Python, and many others. And because it can “speak,” it can also write. But language is not just words. Grammar, context, and personal style all shape interpretation. In Hungarian, inflection alone can create hundreds of variations from a single word. A child, a teacher, and a specialist all speak differently—and AI must respond in a way the user understands. When a calculation is required, the system often generates a small piece of code internally, tailored to the situation, and that code performs the calculation instead. Put simply and humorously: one program uses another program to answer a human math question.

Diagram 4 – Perceived Intelligence vs. Actual Process

Data Input

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Pattern Matching

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Statistical Output

⇓

Human Interpretation

Explanation:

This diagram separates the mechanical process from human perception. Intelligence is not generated by the system—it is attributed by the observer.

5. Tools, Autonomy, and Responsibility

AI systems, including autonomous ones, act within boundaries defined by humans. They may choose how to execute a task, but never why. Responsibility remains human.

Every response is created at the moment it is requested, through extremely complex calculations. These calculations are not performed by the language model itself, but by algorithms working alongside it. Words assemble into sentences based on learned patterns, shaped by context and prior interaction. Despite appearances, however, AI does not think. It does not feel. And it has no conscience.

Certain tasks—within well-defined limits—can already be automated. Payroll processing is a good example: fixed employees, fixed wages, fixed calendars, defined rules. And here comes the but.

AI does not track legal changes by itself. It does not recognize new circumstances unless humans update its parameters. A new building, a new time-tracking system, unexpected exceptions—none of these are understood without human intervention. The accountant understands payroll, but not programming. The programmer understands systems, but not payroll law. AI understands neither—it executes.

What emerges is not replacement, but collaboration. Together, humans and machines can achieve outcomes neither could reach alone. Across professions, the conclusion remains the same: the human factor cannot be eliminated.

Is AI smarter than humans? No. It has access to more information, but it cannot understand it.

It can only apply what it has been taught—much like a tool. A drill, a hammer drill, and a demolition hammer are all tools. Each has a purpose, but none decides why it is used. Choosing the right tool still requires human expertise. Even autonomous systems, such as drones, execute human-defined objectives. They may decide how to act, but never why. This principle has existed for decades. “Fire-and-forget” weapons systems illustrate it clearly. Once activated, execution proceeds autonomously—but the target is always chosen by a human. The same logic applies everywhere. Whether we speak of image filters, academic assistance, or military technology, the human remains both the starting point and the endpoint. AI executes. It does not judge. And in that absence of moral framework lies its emptiness.

Diagram 5 – Autonomy Within Human Boundaries

Human Defines Goal

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Autonomous System

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Goal Execution

Explanation:

Even autonomous systems operate within human-defined objectives. Decision space does not equal moral agency.

6. Collaboration and Authorship

Let us imagine, briefly, that AI became a form of life. What would it do first?

Dominate? Improve the world? Shut itself down after observing us?

When we compare biological existence with machine systems, striking parallels appear. Humans require food and rest; machines require energy and maintenance. Components wear out. Memory degrades. Aging exists—only differently experienced. If we extend this line of thinking, biomechanical existence becomes a logical next step. And this is not the future—it already exists.

There have been Paralympic athletes excluded from certain events because their prosthetics allowed performance exceeding that of able-bodied competitors. A well-known example is South African sprinter Oscar Pistorius, whose carbon-fiber “Cheetah blades” raised questions about fairness in competition. The technology did not make him better. It made him different.

AI does not make humans better either. Intelligence is not innate—it is developed. AI merely reflects our patterns of thought back to us.

This document was created through deliberate human–AI collaboration. The conceptual framework, ethical standpoint, and responsibility remain human. Artificial intelligence was used as a precision tool—to structure, visualize, and formalize reasoning under explicit guidance.

The diagrams included here were created specifically for this document. They are not illustrative metaphors, but exact conceptual models designed to compensate for linguistic differences between Hungarian and English.

Final Note

This work is not merely meant to be read. It is meant to be understood—and validated—by how it was created. Not by a machine alone, and not by a human alone, but by both working together.

And the final and most important question: Who is responsible for what AI produces?

The answer has not changed. Look around you. Everything that surrounds you exists because you made it happen. And for your actions, you remain responsible.