How People Created a Neural Network That Can Learn on Its Own

A neural network is not a brain and not intelligence. It is a mathematical model that guesses the next word, pixel, or sound based on what came before.

For example, if you say “tea with lemon and…”, the model might continue with: “honey,” “ginger,” “sugar.” It chooses the most likely option based on the examples it was trained on.

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Who teaches a neural network?

Neural networks are taught by people and machines. At the first stage, the model processes billions of texts: books, articles, dialogues. Then people fine-tune it — they rate which answer is better. This is called reinforcement learning from human feedback (RLHF).

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What about “learning on its own”?

After training, the model does not keep learning — it simply uses what it has already learned.

But new versions are trained on user feedback, statistics, and errors. In other words, training happens in batches, not in real time.

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Are there Chinese people sitting there answering manually?

No. People are involved in the process — but they do not manually suggest answers for every request. They help during the training stage and sometimes filter errors, especially in moderation.

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Why does it seem like a neural network is smart?

Because it copies people. It does not understand, but it simulates understanding. And if you ask a question for which similar answers have already existed before, it produces a very convincing result.

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✅ Conclusion

A neural network does not learn on its own in the literal sense. People train it, and then it works based on patterns and probabilities. It is a powerful imitation of thinking, but not intelligence. And there are no “Chinese people with prompts” sitting inside.