What is artificial intelligence?
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It focuses on the simulation of human intelligence processes by machines, especially computer systems. These processes include the acquisition of information and rules for using the information, the use of rules to draw approximate or final conclusions and self-correction. In general, the term artificial intelligence refers to the imitation of human decision-making behavior by simple algorithms.

In theory, we talk about artificial intelligence when a computer solves challenging problems in a simple way that actually requires the intelligence of a human being. A distinction is made between weak and strong AI. Weak AI is a system that has been developed and trained for a specific task. Virtual personal assistants such as Apple's Siri are a form of weak AI. Strong AI, also known as artificial general intelligence, has generalized human cognitive abilities. It is designed to mechanize human behaviour. It can find a solution to unfamiliar tasks without the need for human intervention. It is designed to support humans in the thought process.

When is a machine intelligent?
Whether a machine is capable of thinking like a human can be determined using the Turing test as an accepted measuring tool. The test goes back to the English mathematician Alan Turing, who was a pioneer in the field of artificial intelligence in the 1940s and 1950s and invented this test. According to this test, a computer is certified as having artificial intelligence if, under certain conditions, it can imitate human responses in such a way that a human being cannot accurately determine whether the answers to the questions posed were given by a computer or another human being.

How does artificial intelligence work in practice?
The fields of application for artificial intelligence are very diverse. AI is used to fend off cyber attacks, as an assistant in medical diagnostics and to realize the idea of autonomous driving. AI is already being used successfully in medicine in particular. Intelligent machines are already performing numerous surgical procedures more precisely than a human surgeon. AI-based systems convert computer tomography scans into three-dimensional images, enabling doctors to obtain a specific image of each part of the body. More and more expert systems used in specialized fields of application are relying on artificial intelligence.

Chatbots function as text-based dialog systems, especially in customer service, using AI-based speech recognition technologies. Digital assistants such as Google Assistant are getting better and better because they learn automatically with every new request. Large, complex or poorly structured mass data can hardly be used productively without the use of AI.

Intelligent algorithms help to recognize patterns in the immense amounts of data and divide them into clear categories. AI enables automation in customer service and commercial processes. Thanks to their cognitive capabilities, the systems learn with every customer contact and every business transaction, enabling them to respond ever more precisely to requirements. Computers with artificial intelligence have the potential to make predictions about the future based on their wealth of experience. Intelligent algorithms can use previous purchasing behavior to predict when a customer will want to buy a certain product.

Different types of technology
Automation:For example, Robotic Process Automation (RPA) can be used to automatically perform repetitive, high-volume tasks that are normally carried out by humans. RPA differs from IT automation in that it can adapt to changing circumstances.

Machine learning: Machine learning is considered the core technology of artificial intelligence. In simple terms, it is the automation of predictive analytics. The more sample or training data the learning process receives, the more it can improve its model.

Learning algorithms extract statistical regularities from the data provided and use them to develop models that can react to new, previously unseen data by categorizing it, generating predictions or suggestions.

There are three types of machine learning algorithms:

Supervised learning: Data records are labelled so that patterns can be recognized and used to label new data records.

Unsupervised learning: Data sets are not labeled and are sorted by similarities or differences.

Reinforcement learning: Data sets are not labeled, but after performing one or more actions, the AI system receives feedback.

Machine vision: This technology captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. Machine vision can be programmed to see through walls, for example. The fields of application range from signature identification and the classification of product parts to medical image analysis.

Natural Language Processing (NLP): NLP involves the processing of human language by a computer program. One of the best-known application examples is spam detection, where the subject line and text of an email are checked and a decision is made as to whether it is junk. NLP is mainly used for text translation, sentiment analysis and speech recognition.

Robotics: It deals with the design and manufacture of robots. They are not only used in production or by NASA to move large objects in space. With the help of machine learning, robots can also interact in social environments.

Self-driving cars: By combining computer vision and image recognition, vehicles can drive automatically, without the influence of a human driver, keep in a lane, avoid obstacles and park.

AI is penetrating our everyday lives at an unprecedented speed in the form of digital assistants, cooperative robots, autonomous vehicles and drones. Big data and American internet companies are driving the development of artificial intelligence, supported by increasingly powerful hardware and software platforms. They are the tools that machine learning needs to process large amounts of data, recognize complex correlations and learn from them without explicit programming. It won't be long before the first smart, predictive systems monitor themselves, provide forecasts and independently suggest or implement measures. Research is still in its infancy, so technological optimization is currently associated with enormous added value for users and companies. as