by Thomas Fink

Abstract: Creativity has been hailed as a key force behind the economic success of businesses and nations. While many recognise that creativity is a universal human capability, popular views reduce it to the confined domain of a talented few. Yet it remains unclear what drives creativity, or how to promote it. We propose that anonymous collaboration with constrained freedom makes possible emergent creativity: the spontaneous display of creativity amongst non-cooperating individuals, greater than any could achieve alone. We outline a theoretical framework for predicting emergent creativity, and propose platforms for demonstrating it. Emergent creativity may profoundly extend the creative boundaries of mankind.

by François Pachet

Abstract: Style is what makes an author (writer, composer, designer, etc.) recognizable. Can machines understand, model, predict and generate artefacts in a given style? In the Flow-Machines project, we address these questions for music and text. I will show several generative models of style able to capture characteristic traits of musical sequences, as well as polyphonies, both in the symbolic (score) and numeric (audio) domains. These models are based on the combination of combinatorial (so-called Markov constraints) and statistical perspectives on music. I will use various reorchestrations of “Ode to Joy”, the European anthem, to illustrate these various approaches. Along the way these increasingly sophisticated models in turn, raise more fundamental questions about nature of music and the mysterious mechanisms of taste and subjective appreciation.

by Andreas Roepstroff

Abstract: How is it that two persons may solve a task better than each of them may on their own? Using a set of simple experiments, I will examine how people when solving simple tasks in interaction couple to each other in several domains. It seems that the dynamics of these interaction are shaped not just by the task at hand, but very much by the rules of the game. This may have implications for how to design creative settings.

by Jacob Sherson

Abstract: The scienceathome.org project within the interdisciplinary Center for Community Driven Research at Aarhus University works on developing online citizen science games – enabling ordinary users of the internet to contribute to the solution of cutting edge research challenges. The first games within quantum physics and cognitive and social sciences have been played more than 500,000 times and clearly demonstrated the superiority of the human mind over computer algorithms for certain tasks. At the moment we study this individual and collective problem solving and attempt to combine solution strategies from humans and biological systems to make machine learning more intelligent.

by Vito D.P. Servedio

Abstract: Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. I’ll introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling. We focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. I’ll highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.

by Luc Steels 

Abstract: Creativity is inherently related to problem solving – more specifically,  to a process of handling an impasse in a novel way. I will use examples of creativity in language, music,  architecture, and product development to elaborate this point of view and examine its implications for  learning and innovation.

by Francesca Tria

Abstract: Innovation, the emergence and diffusion of something new (new technologies, new genes, new behaviors) drives the evolution of human society as well as of biological systems. A general concept that applies to innovation and the emergence of novelties, is what Kauffman called the expansion of the adjacent possible. By creating fresh opportunities, one novelty can pave the way for others, enlarging the space of possibilities in a self-consistent way.
I will present a recent work aimed at grounding the notion of adjacent possible on real data, by the definition of quantitative measures and the development of a suitable mathematical framework.

Ilan Chabay is Senior Fellow at Institute for Advanced Sustainability Studies in Potsdam Germany, where he co-leads the Sustainable Modes of Arctic Resource-driven Transformations and global interdependencies  (SMART) project and collaborates in the Emerging Technologies and Social Transformations program.
He is honorary member of Swiss Academy of Social Sciences and Humanities, served on Scientific Committee of the International Human Dimensions Programme (IHDP) and Science & Technical Committee of UN International Strategy for Disaster Reduction.
He was Hasselblad Professor in the sociology and applied IT departments at University of Gothenburg and Chalmers University 2006-2011, consulting professor of chemistry at Stanford University 1984-1988. In Silicon Valley he founded and directed The New Curiosity Shop from 1983-2001, which designed and produced hands-on science exhibitions for over 200 science centers worldwide.
His Ph.D. is in chemical physics from University of Chicago.

Mirko Degli Esposti, degree in Physics, PhD in Mathematics, is a full Professor of Mathematical Physics and for the last seven years he has been the chairman of the Department of Mathematics of the University of Bologna.
Initially his researches addressed mathematical questions in: quantum chaos, semiclassical analysis, statistical mechanics, ergodic theory for strongly or weakly chaotic systems.
Few years ago he turned his attention to mathematical techniques for the analysis of literary texts, algorithms for authorship attribution and, more general, to mathematical models for textual data and information dynamics on non structured data. He collaborates with SONY Computer Science Laboratory in Paris, exploring the mathematical aspects of automatic generation of textual and musical contents.

Fink studied physics at Caltech, winning the Fisher Prize for top physicist, and did a PhD at the Cavendish Laboratory, Cambridge with Robin Ball.
He was a Junior Fellow at Caius College, Cambridge, and a postdoc at Ecole Normale Superieure with Bernard Derrida.
He is currently a Charge de Recherche in physics in the French CNRS. Fink has written popular science books with sales of 1/3 million.
Outside of physics, Fink is interested in design, simplicity, adaptability, skiing and shooting.