Machine Learning

Machine Learning - Definition and application examples

| Editor: Lina Klass

Machine Learning
Machine Learning (Bild: ©Jamrooferpix - stock.adobe.com)

Machine Learning, deep learning, algorithms - you can no longer avoid these buzzwords when it comes to Industry 4.0. Find out what Machine Learning really is and how it is applied in practice.

Machine Learning – Definition

Machine Learning is a sub-area of artificial intelligence, whereby the term refers to the ability of IT systems to independently find solutions to problems by recognizing patterns in databases. In other words: Machine Learning enables IT systems to recognize patterns on the basis of existing algorithms and data sets and to develop adequate solution concepts. Therefore, in Machine Learning, artificial knowledge is generated on the basis of experience.

In order to enable the software to independently generate solutions, the prior action of people is necessary. For example, the required algorithms and data must be fed into the systems in advance and the respective analysis rules for the recognition of patterns in the data stock must be defined. Once these two steps have been completed, the system can perform the following tasks by Machine Learning:

  • Finding, extracting and summarizing relevant data
  • Making predictions based on the analysis data
  • Calculating probabilities for specific results
  • Adapting to certain developments autonomously
  • Optimizing processes based on recognized patterns

Machine Learning: How it works

In a way, Machine Learning works in a similar way to human learning. For example, if a child is shown images with specific objects on them, they can learn to identify and differentiate between them. Machine Learning works in the same way: Through data input and certain commands, the computer is enabled to "learn" to identify certain objects (persons, objects, etc.) and to distinguish between them. For this purpose, the software is supplied with data and trained. For instance, the programmer can tell the system that a particular object is a human being (="human") and another object is not a human being (="no human"). The software receives continuous feedback from the programmer. These feedback signals are used by the algorithm to adapt and optimize the model. With each new data set fed into the system, the model is further optimized so that it can clearly distinguish between "humans" and "non-humans" in the end.

But Machine Learning means much more than just distinguishing between two classes. Using the KUKA table tennis robot as an example, you can see how a machine scans the complex tendencies and the playing style of its opponent, adapts to them and even makes a world champion sweat this way.

Advantages of Machine Learning

Machine Learning undoubtedly helps people to work more creatively and efficiently. Basically, you too can delegate quite complex or monotonous work to the computer through Machine Learning - starting with scanning, saving and filing paper documents such as invoices up to organizing and editing images.

In addition to these rather simple tasks, self-learning machines can also perform complex tasks. These include, for example, the recognition of error patterns. This is a major advantage, especially in areas such as the manufacturing industry: the industry relies on continuous and error-free production. While even experts often cannot be sure where and by which correlation a production error in a plant fleet arises, Machine Learning offers the possibility to identify the error early - this saves downtimes and money.

Self-learning programs are now also used in the medical field. In the future, after "consuming" huge amounts of data (medical publications, studies, etc.), apps will be able to warn a in case his doctor wants to prescribe a drug that he cannot tolerate. This "knowledge" also means that the app can propose alternative options which for example also take into account the genetic requirements of the respective patient.

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