AI is a very general concept; in fact, it can be a very simple program. Any system that has defined behaviour that varies depending on certain stimuli it receives is usually classified as AI. In Pac-Man arcade machines, for example, one of the ghosts moved randomly, but when it sensed Pac-Man nearby, it began to follow him. This is an example of basic artificial intelligence, although it does not resemble what we usually imagine today. Within artificial intelligence, there are systems that learn by themselves: this is where machine learning comes in. These AIs receive a large amount of sample data and based on this, refine the parameters that describe their actions without being explicitly programmed for each step. Finally, within machine learning, there are neural networks. These are programs structured by layers of mathematical “neurons,” arranged in different ways depending on the objective. These neurons have weights and parameters that, when adjusted using data, can mimic extremely complex behaviours.
Artificial intelligence is the big picture – just as "history" is an entire subject. Among them, there are various methods, and one of them is machine learning. Here, systems learn from examples, similar to how students better understand tasks through practice. Instead of prescribing fixed rules, the machine is allowed to recognize patterns. Neural networks, in turn, are a special technique within this machine learning. They are roughly based on the human brain, consist of many small computing units and are particularly suitable for complex tasks such as language processing or image recognition. For me, the key point is: AI is the goal (intelligent behavior), machine learning is the way (learning from data), and neural networks are one of the most powerful tools for this. In class, I would perhaps compare it to a pyramid: the goal at the top, the methods below and the concrete tools at the bottom.
You can think of the three terms as a small "family" that belong together: Artificial intelligence (AI) is the generic term. That's all that makes computers do tasks that normally require human thinking – for example, understanding language, recognizing images or playing chess. Machine learning (ML) is part of that. Here, computers are not simply programmed ("if X, then Y"), but they learn from examples. For example: You show a program many cat pictures, and it learns for itself what a cat is. Neural networks, on the other hand, are a special method within machine learning. They are roughly inspired by the brain and consist of many "artificial neurons" that work together to recognize patterns – such as faces in photos or spoken words.
AI is the broader terminology for making machines (mostly computers) perform tasks that are usually performed by humans. Machine learning is just one way one can develop an AI system or model, and neural networks are a technique that can be used for machine learning.
We must distinguish between two completely different areas: 1) using AI tools in daily life and business (consuming AI) 2) creating AI models and training AI (creating AI) Using AI is part of everyday life for any competent modern person. Creating AI models, neural networks, and ML projects is a specialized task for computer science experts, and only a small number of people with deep scientific interests should be involved in it.
