Artemis 0.0 A systemic approach in AI


Hardly any technological development has led to as many discussions as the development of AI systems. Especially since Google has succeeded in bringing a learning system to the level of a human go player. Technological advances have been made in the meantime. The mathematical foundations were created. Not least by the Austrian Sepp Hochreiter in his work on the “Long Term Short Memory”. An algorithm that is built into every “Natural Language Program” today. Language processing and image recognition are the two “domains” that are the most developed. Computers have learned to see and speak. ” Demis Hassabis, the founder of DeepMind on the future of AI: “What I’m really excited to use this kind of AI for is science and advancing that faster.”

All of this trigger’s great economic expectations, but it also leads to great uncertainty. While the US and China are viewed mainly positively, there is great skepticism in Europe. In particular, consumers fear for their privacy. Many EU countries have therefore adopted national laws to protect data. What is good for consumers slows down the development of AI applications in that it provides far too little training data.  Europe is in a dilemma between data protection and innovation. Technical understanding alone does not help in this situation. A more comprehensive approach is needed. The following is a systemics analysis according to the framework by Ken Wilber.

Wilber proposes a model with an internal and external perspective, each divided into collective and individual aspects. This results in the four quadrants: artifacts, consciousness, culture and context. According to these dimensions, the various forms are now being worked out.


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