MARK COECKELBERGH University of Vienna, Austria
Artificial Intelligence: Challenges for Ethics and Policy
Artificial intelligence invites fear and wild speculations about the technological future. This talk turns away from science fiction and focuses on the real and urgent challenges for society, policy, and business in the near future. Drawing on expertise in ethics of technology and experience with policy advice for the European Commission and the Austrian government, this talk outlines the ethical problems raised by AI and discusses the challenges for policy and innovation. How can we ensure responsibility for AI and promote democratic decision making about the future of human lives and society in an environment pervaded by AI? What are the barriers that need to be overcome? And what does AI ethics mean in a global context?

LEO MRŠIĆ Algebra University College, Zagreb, Croatia
Artificial Intelligence: Walking the line between creativity, business value and chaos
Delivering on its strategy on artificial intelligence adopted in April 2018 today the European Commission presented a coordinated plan prepared with Member States to foster the development and use of artificial intelligence in Europe. Commission in four key areas: increasing investment, making more data available, fostering talent and ensuring trust.  Complementing national investments, the Commission will invest €1.5 billion by 2020, 70% more than in compared to 2014-2017. For the next long-term EU budget (2021-2027) the EU has proposed to invest at least €7 billion supported by various project lines. A recent poll completed by Tata Consultancy Services focused on the current and future impact of AI, shows that 84% of companies see the use of AI as “essential” to competitiveness, with a further 50% seeing the technology as “transformative.” Almost all of them believe the greatest disruption by 2020 will be the business implications of AI in every division: whether that’s in marketing, customer service or human resources. AI technologies have emerged as transformative tools, spreading across almost all areas of business therefore it’s essential to become more familiar with existing and emerging technologies to prepare for the inevitable implications for leadership and management. Lecture will discuss how your company can benefit from AI and how to avoid most common pitfalls learned from real life project and education experience in past years.

MARTIN MIHAJLOV Ss. Cyril & Methodius University, Skopje, Republic of North Macedonia and Jožef Stefan Institute, Ljubljana, Slovenia
From Worthless to Valuable: A Brief Introduction in Blockchain Economics
Blockchain is just one of the many distributed-ledger technologies that first became popular due to the emergence of the Bitcoin cryptocurrency. This has initiated a tidal wave of change in the worlds of both Information Technology and Economic Science, requiring a realignment of the concept of value. This is necessary to determine how investors can place value on computer code, with no central entity or physical asset as a backing support. Drawing insight from the realms of  behavioral economics this talk will provide a clear understanding of how a blockchain-based technology can create (and dismantle) value out of “thin air”, just like our entire economic system.

VOJISLAV KECMAN Virginia Commonwealth University, USA (video lecture)
Machine Learning, Artificial Intelligence, New/Old Algorithms
The seminar has presentation at two levels but before entering the main topics it touches some hot events around the recently very popular area of Artificial Intelligence (AI) first. Next, it presents some commonalities shared by the various well known machine learning tools today which belong to the additive models. This will lead us to discussing similarities and differences between Neural Networks (NN) and Support Vector Machines (SVMs). However, this can’t be done without pointing at the different norms used in designing the two models. Why composite norms of the two parts (loss and regularizer) are needed will be explained on a very simple example. Finally, we’ll point at basic ideas of Deep Learning Algorithms.