Artificial intelligence (A.I.), Cognitive Services, Neural Networks, Deep Learning, Machine Learning, …These are all terms that we hear and read more and more frequently nowadays. 

But what does all this mean? And why are these issues coming up right now?

This question is easily answered. In the age of digitization, with the desire for automated processes and innovative business models, what is needed is support that is based on the latest technologies.

In principle, the methods and algorithms of artificial intelligence are "old hat". Artificial intelligence imitates the human ability to see, hear, analyze and understand - in image recognition or the processing of natural language, for example. For a long time, however, the data ("big data") needed for further processing and the computing power available were insufficient to be able to use A.I. realistically in consumer and enterprise markets. 

The wish for automated processes has been granted. Making the most of artificial intelligence today

An outcome of the digital transformation of recent years is that processes are now increasingly digitalized, and thus digitized process data are available in large volumes. Furthermore processing and storage performance can be easily consumed from the cloud. This means that the markets currently find themselves at a time when several success factors are coinciding. The sum of these factors enables practicable and mature A.I. applications. Although artificial intelligence is a generic term here. There are various methods to map artificial intelligence into software:

Digitization initiatives and process automation challenges

In these cases, there are basically two ways to use artifical intelligence. On the one hand, pre-trained A.I. functions can be consumed API-based from the cloud and used, for example, for everyday object recognition. On the other hand, there are also specific cases in which in-house neural networks can be trained.

Artificial Intelligence

Artificial intelligence imitates the human ability to see, hear, analyze and understand - in image recognition or the processing of natural language, for example. For a long time, however, the data ("big data") needed for further processing and the computing power available were insufficient to be able to use A.I. realistically in consumer and enterprise markets. 

Machine learning and deep learning are also sub-categories of artificial intelligence. 

Machine Learning

In the sub-discipline machine learning, IT systems use algorithms to recognize patterns in data sets. The knowledge gained in this way is used in new queries so that the software can learn independently and develop new solutions.

Deep Learning

Deep learning refers to neural networks that imitate human thought processes and can solve classification problems (recognizing and reacting to facts) on text, sound and speech, image or video files by training them in advance.

Text

Predict the likelihood of success of product, service or project descriptions and provide support for processes in which people need to qualify large amounts of text, for example proofreading or editing. In addition, chatbots contribute to the improvement of customer portals and CMS. 

Sound and Language

Predictive maintenance can be used to analyze machine noise and detect anomalies to predict the probability of failure. Speech recognition assistants are also available to complement customer portals and reduce the load on key process resources.

Images

In Content Management, objects and moods in visual material can be recognized and classified appropriately, while in returns management returned articles (without barcodes, e.g. jewelry) can be compared with the product catalog and classified correctly.

Video

As well as supporting video editing, objects and associated scenes can be recognized and classified appropriately in content management systems. In addition, during sports broadcasts the "screen time" of perimeter advertising can be evaluated (and billed accordingly) or video feeds of systems can be examined for anomalies (e.g. for predictive maintenance).

A.I. and bots as part of the overall digital transformation concept

Artificial intelligence alone is not enough

A special mix of skills is needed.

In the area of Artificial Intelligence special cases, where in-house neural networks are e trained, a skill mix of data engineering, data science, industry awareness and cloud native applications is important.

Data Engineering & Data Science

Platform integration, homogenization and utilization of data for business purposes by transforming information into actions and added value through mathematical models. Both are also part of the Big Data & IoT concept.

Industry knowledge

Identify and react to changes in the market early on, see the big picture, have a comprehensive view of all processes. Industry understanding means the ability to understand things and to react appropriately. This concept is immensely important in the field of Artificial Intelligence and bots and goes far beyond the purely technological requirements.

Cloud native applications

Cloud-native applications are characterized by extremely fast time-to-market and continuous value delivery.

Questions?