Conferències Convidades
Challenges of fBIG, small, Rightg Data
NTENT & Northeastern University, USA, and UPF, Catalonia
Abstract. Big data is trendy, but there are any possible interpretations of its real impact, as well as the opportunities, risks and technological challenges. We will start with two key questions: can a company use big data? If so, should it? The opportunities are clear, while the challenges are many, including scalability, bias, and privacy on the problem side, as well as transparency, explainability, and ethics on the machine learning side. So we perform an analysis that includes all the data pipeline process. At the end we will conclude that what is important is the right data, not big data. In fact, the real challenge today, is machine learning for small data.
Mild abstraction in spatial problem solving
University of Bremen
Abstract. Everyday spatial problems such as tying shoelaces, untangling cables, locating objects, opening doors, matching shapes, finding routes, solving puzzles, ... are embedded in 3-dimensional physical space and their solutions need to be manifested in physical space. Such problems can be solved either directly in space by means of perception and manipulation or they can be transformed into abstract representations in order to be solved by reasoning or computation and subsequently transformed back into physical space. In my contribution I will compare the two approaches and I will discuss how they can be combined. I will introduce the notion of mild abstraction as a way of combining the best features of both worlds, present a variety of forms of mild abstraction, demonstrate their uses for spatial problem solving, and propose that mild abstraction can be exploited for non-spatial domains, as well.
What needs to be done to ensure the ethical use of AI?
ICREA / Institut de Biologia Evolutiva (UPF-CSIC)
Abstract. It is now clear to even the greatest doubters that AI is a powerful technology with considerable practical usage. But it has also become clear that this usage can be both positive and negative. AI can improve healthcare, aid environmental monitoring, support community formation, make culture more widely accessible, and do many other good things - always in combination of course with other digital technologies. But AI can also play a role in deceit and manipulation of public opinion, stoking societal divisions, enforcing senseless bureaucracy, cyberattacks, autonomous weapons, job loss, etc. Moreover the application and promises of AI appear to be premature in the face of autonomous cars going through red traffic lights, unacceptable decisions on granting parole, perpetuation of racial and gender bias in recruitment, etc. This discussion paper provides some ideas to foster positive uses of AI and guard us against negative ones, building further on the ’Barcelona Declaration for the Proper Development and Use of AI in Europe’, which you can read and sign here: http://www.iiia.csic.es/barcelonadeclaration/.
Blockchain and Artificial Intelligence: challenges and opportunities
Accenture - Financial Services, Barcelona, Catalonia, Spain
Abstract. Blockchain is a technology to manage data and processes in a decentralized and secured manner. Its characteristics make it an interesting choice for developing disintermediated systems of assets transfers or data sharing. In the last four years, this technology has experienced a big impulse, with thousands of startups being created, open source frameworks developed and significant investments made by big companies.
All the blockchain developments are happening at the same time that artificial intelligence is becoming mainstream in the industry. Despite Blockchain is at a different point in the adoption path than some artificial intelligence applications, it is possible to see confluence points between them from two main perspectives: blockchain can be seen as a multi-agent system in itself and from a different perspective, it can be the necessary infrastructure for other autonomous systems to be audited.
Is Blockchain a hype? Or is it mature? What are the industry expectations about this technology? How AI relates to it? These questions will be addressed in the talk where several real use cases and industry feedback will be shared.
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