Panel Discussion & Key Notes

Christa Markwalder, Swiss National Councillor, Cyrille Wetter, a Student from the FHNW, André Wolke, CEO & Co-Founder Validity Labs, Sven Mentl from the company Accenture and Prof. Dr. Knut Hinkelmann from the FHNW and Daniel Meier, from the company Huawei, were discussion and exchanging different examples and potential outcomes of the blockchain development and from the artificial intelligence. 

The panel discussion session was opened by the question what the most dynamic impacts are. 

Markwalder was convinced that technology will help improving daily life. What we do not know yet are the consequences of malfunctions or misuses of these technologies. Meier brought an example of a mobile that recognizes a dog on the street while the autonomous car is sidestepping, which demonstrated the powerful potential of A.I. 

Wolke pointed out the importance to have people that are interested in A.I. the question from this area is the legal side. Who is responsible? In case of a car accident with autonomous cars, who takes the blame, the car or the company? Fact is, A.I brings new technology, new business models and new life structures. Mentl mentioned that you always have to prefer material damage over personal damage. There is no allowance for discriminate between age differences for example. 

Hinkelmann talked about the new opportunities and possibilities nowadays. A phone that takes a picture of a Chinese menue cart and translates it in English is a great invention as view years earlier you would have needed to type it into your phone, but how would you type in Chinese signs? So there are many progressions. 

Meier pointed out the challenge, that the question of “what is next?” is always discussed. 

Also the tax issue arose during the discussion. Each token would be needed to be taxed. Not everything is just great. Before investing great amounts of money, it is crucial to understand how the new technology works. 

Mentl underlines the meaning of deep learning, which means a lot of input data is needed. But input data is biased which is a huge challenge. 

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