AI is everywhere. Whether in smartphones, internet searches, or creative tools – artificial intelligence shapes our everyday lives. At the latest since ChatGPT became publicly accessible in November 2022, it’s no longer just experts talking about machine learning. But how does ChatGPT actually work? In this article, we travel from the theoretical foundations with Alan Turing to the modern GPT-5 architecture – understandable and without technical jargon.
1. The Idea of Thinking Machines: the Turing Test
The British mathematician Alan Turing asked a question in 1950 that continues to shape AI research to this day: “Can machines think?”. He proposed the Turing test to measure machine intelligence. A machine is considered “intelligent” if a person in a conversation cannot tell whether they are talking to a human or a machine. This test was theoretical for a long time – until computing power, data volumes, and algorithms became large enough to approach it realistically.
2. Artificial Intelligence = Pattern Recognition + Probabilities
At its core, AI does something very human: it recognizes patterns and predicts probabilities. In the case of ChatGPT, this means that the model “guesses” the most likely next word, based on all the previous words. The “intelligence” lies in the billions of parameters that have been optimized in a huge training process – much like our brain strengthens or weakens synapses.
3. Deep Learning & Neural Networks: how Machines Learn
Neural networks are inspired by the structure of the human brain. They consist of layers of “neurons” that forward, amplify, or weaken signals. In Deep Learning, there are many of these layers, which enables complex pattern recognition – from images to language. Machines do not learn by memorizing, but by constantly adjusting weights to minimize prediction errors.
4. The Breakthrough: Transformers and “Attention is all You Need”
In 2017, Google researchers published the paper “Attention is all you need”. In it, they described the transformer architecture – the core of today’s language models. Transformers use a so-called self-attention mechanism to understand the context of a word in a sentence. This is how the model knows whether “bank” means a place to sit or a financial institution, depending on the surrounding words.
5. From GPT-3.5 to GPT-5: the Development in Leaps
- GPT-3.5 (2022): Mass-marketable for the first time, trained on approx. 175 billion parameters.
- GPT-4 (2023): Better understanding of context, multimodal inputs (text, images), higher accuracy.
- GPT-5 (2025): Even broader knowledge, longer context windows, more precise answers, stronger multitasking ability and significantly improved controllability.
6. How Does ChatGPT Really Learn?
Training takes place in two main steps:
- Self-Supervised Learning: The model trains by reading large amounts of text and predicting the next word – billions of times.
- Reinforcement Learning from Human Feedback (RLHF): People evaluate model responses to improve quality and safety. The model is rewarded for giving good answers.
7. Is that Really Intelligent – or just Well Copied?
ChatGPT does not understand like a human. It has no consciousness, no emotions, and no real intentions. Nevertheless, it can communicate so convincingly that it sometimes seems “human”. This is no coincidence – the training data comes from billions of real texts. The model combines this information into new answers without copying them verbatim.
8. Conclusion: Understand AI as a Tool – not as a Human
The better we understand how ChatGPT works, the clearer it becomes: It is a tool. An extremely powerful, but still limited tool. Those who use it purposefully – whether for text, research, or creativity – benefit enormously. On the other hand, those who make it “human” run the risk of relying too much on a machine.
