Artificial Intelligence and Bots for Process Automation
Multi-stage development of cooperation between humans and machines
Artificial intelligence, 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.
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..
Overview
The following overview gives an insight into artificial intelligence and its different fields of application.
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Artificial Intelligence
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.
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Machine Learning
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.
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Deep Learning
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.
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Text
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 content management systems.
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Sound and Language
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.
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Images and Pictures
Images and Pictures
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.
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Video
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).
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