As a society, we need to talk about how we intend to use artificial intelligence. I think it is important that we research this technology, figure out the opportunities and risks, create an awareness and understanding of it and thus ultimately work out how best to deal with it. To do that, we need a platform that can bring together all the various players and also look at practical applications in view of legal and ethical guidelines.
Self-learning systems are taking the networked economy to a whole new level. They are becoming the driving force for digitalisation and represent a crucial competitive edge. We must make the most of Germany’s technological head start in artificial intelligence to develop successful applications that benefit people in their day-to-day and working lives. To do that, we need more than just new skills, collaborations and innovative prowess – we need a legal framework and an open debate. This is where the experts of Plattform Lernende Systeme are taking the initiative.
AI – an economic factor for Germany
How significant a role will self-learning systems play in the future economy? How might they boost Germany’s comparative advantage? And what is the hallmark of conscientious AI “Made in Germany”? Discover the answers to these questions from acatech President Karl-Heinz Streibich, Co-Chair of Plattform Lernende Systeme (Video in German).
Shaping Artificial Intelligence according to the needs of society
Self-learning systems are the next step in the process of digital transformation. Advances in Artificial Intelligence enable machines, robots and software systems to accomplish increasingly complex tasks. As a result, industry and society will change. In order to design self-learning systems according to the needs of humans and society the Plattform Lernende Systeme brings together leading expertise from science, industry and society and connects discern perspectives and interests. In thematically distinguished interdisciplinary working groups, they discuss technological, economic and social questions on the development and introduction of self-learning systems. From the results they conclude scenarios, recommendations, guidelines or roadmaps. [read more]
