The word “neural network” sounds from every iron. The neural network draws pictures, writes texts, recognizes faces, makes medical diagnoses, and plays chess better than any person. But what is it really? How does this work?

We explain without formulas, without program code – using simple examples that anyone can understand.

Let’s start with the brain

The name “neural network” is not accidental. It is inspired by the structure of the human brain.

The brain is made up of approximately 86 billion neurons – cells that transmit signals to each other. When you see an apple, a group of neurons fires and transmits a signal to the next group, which sends a signal to the next. As a result, you “understand”: this is an apple, red, edible.

An artificial neural network is designed on a similar principle – only mathematical functions are used instead of biological cells, and numbers are used instead of nerve impulses.

How does a neural network work?

Imagine a conveyor belt in a factory. The input is raw materials (for example, a photograph of a cat). The output is a finished product (answer: “it’s a cat”). Between them there are several processing shops.

In the neural network these “workshops” are calledlayers:

  • Input layer— receives data. For an image, these are the brightness values ​​of each pixel.
  • Hidden layers— process data, look for patterns. There can be from several to hundreds.
  • Output layer– gives the answer. “Cat,” “dog,” or “99% chance it’s a cat.”

Each “neuron” in a layer receives numbers from the previous layer, multiplies them by its coefficients (weights), adds them, and passes the result on. This happens billions of times per second.

The main secret: a neural network is not programmed – it is trained

Here is the key difference from conventional programs. A regular program works according to the rules that the programmer wrote: “if the color is red AND the shape is round, it’s an apple.”

The neural network does not know the rules in advance. Herteach by example.

Imagine teaching your child the difference between cats and dogs:

  • Show thousands of photos of cats and say: “this is a cat”
  • Show thousands of pictures of dogs and say: “this is a dog”
  • The child gradually picks up patterns – the shape of the ears, muzzle, tail

The same thing happens with a neural network – only instead of a child there is an algorithm, and instead of thousands of photographs there are millions.

When a neural network makes a mistake, it receives a “fine” and adjusts its internal coefficients. After millions of iterations, the coefficients are adjusted so that the network begins to give the correct answers.

Why have neural networks become so powerful right now?

The idea of ​​neural networks is not new—the first theoretical works appeared back in the 1940s. But they have become truly powerful only in the last 10–15 years. Why?

Three reasons:

  1. Data.The Internet has generated an unprecedented amount of information – texts, images, videos. Neural networks have a lot to learn from.
  2. Computing power.Modern video cards (GPUs) are ideal for parallel computing that neural networks need. What would previously have been considered years is now hours.
  3. Algorithms.Mathematical methods for training neural networks have improved significantly.

All this came together and there was an explosion of opportunities.

What can neural networks do today?

  • Image recognition— make medical diagnoses based on photographs, identify defects in production, and unlock your phone using your face.
  • Generating Images— Midjourney, DALL-E, Stable Diffusion create photorealistic pictures based on text descriptions.
  • Language processing— ChatGPT, Claude, Gemini understand questions and answer them, write texts, translate, program.
  • Speech synthesis— voice assistants sound almost like real people.
  • Games and strategies— AlphaGo and AlphaZero beat the world’s best players in Go and chess.

What neural networks can’t do

It is important to understand the limitations:

  • The neural network is notunderstands— she finds statistical patterns. ChatGPT doesn’t “think” about the answer, it predicts the next word based on a huge amount of processed text.
  • Neural networksare wrong– and they do it confidently. This is called “hallucinations”.
  • They don’treasonin a human sense – they have no common sense, no life experience, no understanding of context beyond the trained data.

Conclusion

A neural network is a mathematical system that learns to find patterns in data using millions of examples. She doesn’t think or feel, but she does what she can do with incredible accuracy and speed.

It is a powerful tool that is already changing medicine, science, creativity and everyday life. Understanding how it works in general terms means better understanding the world in which we live.