How does artificial intelligence work?

To answer the question "How does artificial intelligence work?", we need to start with the basic principle of AI that gives systems their ability to learn and make decisions: machine learning (ML).

While classical algorithms work deterministically (input A always leads to output B, based on manually written code), AI learns probabilistically. In other words, it recognizes statistical probabilities in training data. AI functionality is therefore based on deriving rules from data itself, rather than having them dictated.

Depending on the task and objective, we generally distinguish between three types of machine learning:

Certain prerequisites must be met for an AI system to function at all. Artificial intelligence requires a total of three basic building blocks in order to deliver the desired results:

Algorithms determine the rules and processes for how data is processed. They range from simple decision trees to complex deep learning architectures. The model is the result of the training process—essentially the "learned knowledge" that is applied to new data.

Here's how these algorithms affect your everyday life:

• Spam filter (classification): The algorithm checks characteristics (subject, sender) and calculates the probability of spam.
• Streaming & E-commerce (recommendation systems): Netflix or Amazon analyze your past behavior to identify patterns and generate suitable suggestions.
• Medicine (pattern recognition): In radiology, algorithms analyze X-ray images for abnormalities (e.g., tumors), often more accurately than the human eye.

The functioning of artificial intelligence depends not only on the three basic components mentioned above, but also on the approach chosen. Depending on the area of application, there are various approaches to artificial intelligence to choose from, each of which is characterized by different modes of operation:

Deep learning is a special method of machine learning that uses deep neural networks, i.e., networks with many hidden layers, as a basis. This enables feature extraction without human intervention. It is the standard for complex tasks such as autonomous driving or generative AI (e.g., ChatGPT).

Here is an example:

• Using neural networks, an AI system can recognize whether, for example, an image shows a dog or a cat.
• With deep learning, the AI system can solve more complex tasks—for example, recognizing different dog breeds.

As data passes through the individual layers, each layer learns something new. For example, an early layer recognizes simple edges in images, while later layers recognize shapes or entire objects. The system automatically corrects errors and becomes more accurate with each pass.

Deep learning is most commonly used for these areas of application:

• Autonomous vehicles: Self-driving cars use deep learning to analyze road images and make decisions in real time.
• Fraud detection: Financial institutions detect fraudulent patterns in transaction data.
• Generative AI: Systems such as text or image generators (e.g., ChatGPT) use deep learning to generate creative content.

Heuristic methods are AI approaches that use simplified solution strategies to solve problems quickly and efficiently. They are based on rules or empirical values – so-called rules of thumb. Instead of performing computationally intensive searches for the perfect solution, the system settles for a sufficiently good solution. This AI approach is particularly useful when time and resources are limited.

Heuristic methods are used in the following areas of application:

• Chess programs: Chess AIs evaluate potential moves based on heuristics, e.g., "protect the queen" or "capture the center of the board."
• Navigation systems: Apps such as Google Maps use heuristics to calculate the best route, e.g., "Avoid traffic jams" or "Take the shortest route."
• Product recommendations: E-commerce platforms such as Amazon suggest products based on simple rules such as "Customers who bought this also bought ...".