Easy and simple: machine learning

The self-driving car, an accurate translation program, or artificial intelligence (AI) that detects diseases: these are all examples of machines that have learned a lot and quickly – from patterns in huge amounts of data.

Easy and simple: Natural Language Understanding

Fish patterns out of the data soup at lightning speed: Today, machines can do this better than humans.

With Big Data and the appropriate analysis rules, humans provide the material from which artificial intelligence dreams are made: IT systems use it to learn quickly and impressively. They find patterns in large databases faster than the human brain. Such machine learning is regarded as a sub-discipline of AI.

The applications appear unlimited. Thanks to machine learning, systems predict when, for example, production lines will have to be serviced ("predictive maintenance"), and even the probability of a device failing and when. Face and image recognition are also part of this.

Machine learning was born from pattern recognition and the theory that computers can learn to perform certain tasks independently. Researchers developed models that learn from previous calculations and arrive at new reliable decisions and results on this basis.

Algorithms for machine learning have existed for a very long time. But the possibility of automatically applying complex mathematical calculations to enormous amounts of data – again and again and faster – is new. And it also owes its success to new and, above all, more powerful computer technology.