A Survey of Challenges and Potentials for AI-Technologies
Frank Kirchner
Editors: Frank Kirchner, Sirko Straube, Daniel Kuehn, Nina Hoyer
In AI-Technology for Underwater Robots, Springer International Publishing, volume 96, pages 3-17, 2019. ISBN: 978-3-030-30682-3.

Zusammenfassung (Abstract) :

Artificial Intelligence (AI) has received much attention lately for various achievements in areas like face recognition, natural language understanding and production as well as in medical fields like tumor classification, heart failure prediction, and even depression diagnosis, eg. in Chockley and Emanuel (J Am Coll Radiol 13 (12): 1415-1420, 2016, [1]). The fields of application for AI Technologies are currently expanding rapidly into pharmacy, finance, and of course security in all its forms and shapes. What all the above-mentioned areas have in common is the fact that we can apply a specific kind of AI-Technologies that are grounded in statistical analysis of massive amounts of data. These are the so-called data-driven machine learning techniques that increase exponentially in performance with the amount of data that is available for statistical analysis. It is easy to forget that Artificial Intelligence in fact is a much broader field that dates back to the beginning of the last century when scientists from a much broader spectrum of disciplines were focused on the question of modelling intelligent behavior. One key player in this field was Alan Turing himself, who was attracted to the question as a result of his work in the field of computation theory that resulted in the Turing machine as an universal mechanism/theory to efficiently computable functions. It was just consequential that he would start thinking about the set of functions that would not fall into the above-mentioned class and from there it is a small step to discuss intelligence and what mechanisms may be underlying.



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