Artificial Intelligence And Machine Learning

Machine learning methods allow large amounts of data to be analyzed and processed quickly and precisely. Therefore, artificial intelligence and machine learning show great potential in areas of application such as autonomous driving, Industry 4.0, or medical technology. Still, they also entail new challenges for safe and reliable use.

What is artificial intelligence? What is machine learning?

Artificial intelligence (AI) is a branch of computer science. It mimics human cognitive abilities by recognizing and sorting information from input data. This intelligence can be based on programmed processes or generated by machine learning.

In recent years, great progress has been made, especially in machine learning. This is mainly due to the increasing availability of large amounts of data and high computing power, which are basic requirements for the complex calculations of machine learning.

With machine learning methods, an algorithm learns to perform a task independently through repetition. The machine orientates itself on a specified quality criterion and the information content of the data. Unlike conventional algorithms, no solution path is modeled.

The computer independently learns to recognize the structure of the data. For example, robots can learn how to grab certain objects to transport them from A to B. They are only told from where and to where they should transport the objects. How exactly the robot grips, it learns through repeated trial and error and feedback from successful attempts.

Neural Networks and Deep Learning

A subfield of artificial intelligence and machine learning is neural networks. Nerve cell connections inspire these learning algorithms in the human brain. The brain processes information through neurons and synapses. Analogously, artificial neural networks consist of several rows of data nodes networked with each other with weighted connections.

The neural network is trained by being presented with data repeatedly. The neural network learns to classify the data more precisely each time through this repetition. This works by constantly adjusting the weighting for the individual connections between the neuron layers.

The model generated in the learning runs can also be applied to data the artificial intelligence has yet to become acquainted with in training.

If neural networks have hidden neuron layers not directly coupled to the input or output layer, they are called deep neural networks. Deep neural networks can have hundreds of thousands or millions of layers of neurons. Increasingly complex problems can thus be solved in so-called deep learning.

Diverse application areas of machine learning methods and AI algorithms

AI training is used in very different areas:

Image recognition :

Machine vision algorithms can be used to recognize and categorize images. In this way, much data can be processed at lightning speed. Machine vision is used, among other things, in medical diagnostics or face recognition, but it can also be used to translate handwritten characters into block letters. Image recognition is also crucial for autonomous driving.

Speech recognition :

Recognizing and interpreting verbal language can also be learned using machine learning methods. These algorithms are used in language assistance systems, for example.

Semantic language recognition :

Written text can be interpreted semantically via machine learning. This allows context-related translation applications or chatbots that independently generate meaningful solutions.

Pattern recognition:

Machine learning methods can also recognize patterns in event sequences that are not recognizable to humans due to the large amounts of data points, variables, and dependencies.

For example, an AI can learn error patterns in vehicle electronics from data and compare these anomalies with behavior during operation. Anomalies are thus recognized more quickly, so countermeasures can be taken early on, for example, replacing a component before it causes an error.

Process optimization :

The recognized patterns can also be used as an information basis for optimization processes. Machine learning methods are machine-generated process models that enable optimized process control.