1. Contemporary challenges for Complexity Science: understanding complex dynamics
Andrea Rapisarda
Dipartimento di Fisica e Astronomia and INFN
Università di Catania, Italy
CSH Vienna

2. We live in the era of Big Data
…and many are tempted to use them as the only way to solve problems and predict the future by means of a kind of brute force extrapolation
…but in my view this is not enough…
We will always need new models to interpret data
We will always need new ideas to find the meaning of correlations

3. We live in the era of Machine learning and Deep Learning
But is deep learning equivalent to a real understanding ?
Eugene Wigner once said …

4. As scientists, we need to understand…but also to imagine new scenarios and ideas
Imagination is fundamental for problem solving and for finding new interesting directions
Data Mining and deep learning are not enough … one misses modelization, vision and understanding
Scientists will never be substituted by robots or algorithms without imagination and curiosity

5. Creativity is the real engine for a true innovation
We should always pursue curiosity-driven research
in order to find new unexpected solutions and novel applications
something that bots or smart algorithms will probably never be able to do

6. An illuminating deep thought by a famous computer scientist
“ The best way to predict the future is to invent it ! “
Alan Kay
ACM A.M. Turing Award, 2003

7. A very interesting new book
by the founder and first director of the
Institute for Advanced Studies in Princeton
Abraham Flexner
and a preface of the present director
Robbert Dijkgraaf
Princeton University Press 2017