Machine Learning

With Artificial Intelligence, we teach your IT how to learn and think humanely.

Machine learning uses the “artificial” generation of knowledge from experience.
Our algorithms learn to calculate with your data and can thus make preliminary decisions on your projects.

Artificial Neural Networks consist of artificial neurons that, following the biological model of the nerve cell, weight inputs and generate an output via an activation function.

We use deep learning as an approach in machine learning to gain knowledge from experience and to understand the problem to be solved.

Machine Learning at Organon Informationssysteme GmbH

Machine learning uses the “artificial” generation of knowledge from experience.

After the learning phase, our algorithms learn to calculate with your data on the basis of training data and can thus make preliminary decisions on your projects, which you can greatly relieve temporally, personal and financially, with the daily flood of data that has to be dealt with.

How do you teach a machine to learn independently?

The extraordinary abilities and achievements of the human brain are a constant inspiration for the research of artificial intelligence, to understand how the brain works and to map and simulate it in analogy on computers using artificial neural networks.

Since the work on the first model for neurons, the McCulloch-Pitts cell presented by Warren McCulloch and Walter Pitts in 1943, advanced mathematical and programming concepts for artificial neural networks as well as constant increases in the performance of computer hardware have enabled product innovations that were previously considered utopian or impossible.
Despite many demonstrable successes in the development of practical applications for many areas of life, after the initial euphoria, the topic of artificial intelligence and artificial neural networks initially became noticeably quiet, as the initially far too high expectations could not be met.

Deep Convolutional Neural Networks & Machine Learning

In particular, through the introduction of the convolutional neural networks by LeCun et al. enormously increased performance of computers and the use of deep learning in so-called deep convolutional neural networks, outstanding results were achieved in the classification of images. Computers with software applications that use these artificial neural networks are thus able to orient themselves in their environment, analogous to humans, through visual perception. At least since the discussions about automobiles driving autonomously with the help of artificial neural networks in public road traffic, which are already being tested in the implementation, the enormous possibilities offered by the use of artificial neural networks have returned to the public consciousness.