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In what follows, I will present a condensed and non-exclusive list of the five most important problem domains in the development and implementation of Artificial Intelligence (AI), each with practical recommendations.
The first problem domain to be examined is the one which, in my view, is constituted by those issues with the smallest chances of being resolved. It should therefore be approached in a multi-layered process, beginning in the European Union (EU) itself.
"Artificial Intelligence (AI) is the future. [...] Whoever leads in AI will rule the world" (Russia Today, 2018). This was the central message that President Vladimir Putin conveyed to more than one million Russian school students in September 2017. He also promised to ensure that Russian knowledge of AI would benefit the world. However, the competition in this field is already playing itself out globally. Besides Russia, the USA and China are already in the race, with China, for example, having recently published an ambitious AI strategy, namely the "New Generation Artificial Intelligence Development Plan" (Webster et al., 2018). This document predicts China’s world leadership in the AI field as soon as 2030. The EU and several other countries – among them Germany in the autumn of 2018 - have followed suit with their own AI strategies. ...
In IT security today, the usage of AI is already established in multiple domains. SPAM detection is a well-known example where support vector machines try to distinguish wanted from unwanted emails. Author attribution combines natural language forensics and machine learning. Deep learning helps in identifying illicit images and has improved malware detection as well as network intrusion detection. ...
The thought of using Artificial Intelligence (AI) and algorithmic decisionmaking (ADM) processes in our daily lives makes many of us feel insecure. Most consumers see more risks than opportunities, an attitude brought about by the black-box nature of algorithms and AI. When an organisation or public authority makes a decision supported by an algorithm, one can feel that one is at the algorithm’s mercy, finding it incomprehensible. Widespread consumer distrust of AI and ADM processes will make it difficult to improve their societal acceptance and therefore make it challenging to apply them in the business sector and in policy-making. Without trust on the consumer side, there can be no progress.
Artificial Intelligence (AI) will be one of the key technologies driving the future competitiveness of numerous industries. However, the term "AI" is defined in a variety of ways. AI could be understood as an umbrella term for technologies and systems that carry out tasks otherwise only executable with human intelligence. This requires specific skills that fall into the broad categories of "Sense", "Comprehend", "Act" and "Learn". Through machine learning, modern AI systems can be trained to adapt to changes in their environment, self-optimise and hence achieve better results than earlier versions of AI systems that were based on clearly defined, pre-programmed rules. Based on AI methods, rational and autonomous agents can be developed that collect and analyse relevant information from their environments, come to optimal conclusions based on certain performance parameters and eventually perform physical actions (e.g. robotics) or virtual actions (e.g. chat bots). Machine learning algorithms ensure that the information base of the system is continuously updated so that performance of the system is optimised in an iterative process.