The Gap Between Talking About AI and Understanding It

You use terms like "parameters," "training data," and "AI model" every day. You hear them in meetings, read them in contracts, and negotiate them with suppliers. But if someone asked you to explain the difference between input and training data, could you? When you sign a contract that mentions "70 billion parameters," do you know what you're actually buying?

Why it matters

The technical vocabulary of AI is not just jargon for engineers. It is the language in which regulations are written, licenses are negotiated, and responsibilities are established. The European AI Regulation (AI Act) uses these terms to define who is a provider, who is a deployer, and what obligations each has. If you don't understand the concepts, you risk signing commitments you can't keep or missing out on rights you could have negotiated.

Demystified in 60 seconds

Think of AI as a student who studies and then takes exams. The training data is the books they read in college-the source of their knowledge. The parameters are the neural connections formed after learning—the "crystallized expertise" that remains after the books are closed. When you send a question (input), you activate this knowledge without changing it—just as the exam question does not change what the student knows. And the answer you receive (output) depends on the quality of the "education" received.

But how do they actually learn? This is where nodes and gradients come in. Nodes (neurons) are "brain areas"—computing units that process information, transform it, and pass it on. Their structure is fixed; what changes are the connections between them. Gradients are the teacher's feedback after each wrong test: they tell each parameter exactly how to adjust to reduce the error. Without gradients, AI would learn nothing—it would remain a student repeating the same mistakes over and over again.

What you gain when you understand

When you know that parameters are the result of training, you understand why providers consider them intellectual property—even if the training data belonged to others. When you know that input does not change the model, you can better negotiate confidentiality clauses.

Think legal compliance kills AI innovation? It actually sparks better innovation.

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