Alfio Quarteroni - Algorithms for a New World: When Big Data and Mathematical Models Meet
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- Book:Algorithms for a New World: When Big Data and Mathematical Models Meet
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Translation from the Italian language edition: ALGORITMI PER UN NUOVO MONDO by Alfio Quarteroni, Edizioni Dedalo 2021. Published by EDIZIONI DEDALO. All Rights Reserved.
The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content.
Cover illustration: Alfio Quarteroni
This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Alfio Quarteroni is a mathematician and professor at Politecnico di Milano and the cole Polytechnique Fdrale de Lausanne. Previously, he was a professor at the University of Minnesota in Minneapolis and a scientific director of CRS4. He is the founder and the director of MOX and the co-founder and the president of MOXOFF. He has received countless international prizes and awards, including the NASA Prize for his work in Aerodynamics, the Galileo Galilei International Prize for Science, the Feng Kang prize and the Euler Medal. He holds the Galilean Chair at Scuola Normale Superiore in Pisa and received an honorary degree in Naval Engineering from the University of Trieste. He is a member of the Accademia Nazionale dei Lincei, the European Academy of Science, the Academia Europaea, the Academy of Sciences of Lisbon, and the Istituto Lombardo Accademia di Scienze e Lettere. His work has been applied to medicine, earthquake geophysics, environment and climate change, aeronautics, and the oil industry. He led the mathematical study behind the design of Alinghi, the two-times Americas Cup champion Swiss sailboat.
I wrote this book after a trip to Puglia (Italy) during which Rosella Santoro (the incomparable founder and soul of Il Libro Possibile festival) got me in touch with Claudia Coga of Dedalo. It was only because of Claudias insistence and encouragement that I decided to write Algorithms for a New Worldalthough the fall 2020 lockdown played a role. I would like to thank Claudia (and, indirectly, Rosella) for persuading me to take some time to muse on important topics that, otherwise, my academic commitments would have prevented me from exploring.
How will the increasingly intrusive presence of artificial intelligence and computers capable of autonomous learning define our near future? Using a non-specialist language, this book will take us on a journey through mathematical models based on the knowledge of physical processes, and more recent models based on artificial neural networks. The purpose is to raise our attention to the challenges, opportunities, and dangers of a world regulated by mathematics and its algorithms and to show us how artificial intelligence and humanism can find a synthesis that benefits not only progress but also our well-being.
The sky is clear over the city, the air fresh and the light is bright on this day, the umpteenth, of a strange spring. Silent streets, closed shops and offices, green parks in bloom without the usual chatter of children. And all of us waiting for the usual afternoon bulletin, with its litany of cold numbers that might either give us some hope or unsettle us further. We suddenly realize that we are cursed by averages, curves and ratios that we would have gladly continued to confine to a hidden corner of our memory, the one linked to ouroften difficultrelationship with mathematics.
It is an epidemic whose propagation front would be impossible to trace if we did not have, day after day, the numbers that represent and quantify it. Its the era of Big Data: so much data, heterogeneous in nature. The pandemic, the latest arrival, feeds them copiously. The data, by itself, says little. It must be interpreted, contextualized, made to live dynamically by projecting it into the process we are observing.
This data is collected in a non-homogeneous way. There is a time delay between one city and another, one region and another, because, for example, the processing time of the swabs is not the same, often different criteria, for example with different methods that could indicate positivity in one case or negativity in another. Or it is collected in an incomplete manner, due to a different approach to asymptomatic persons living with infected persons, or an insufficient testing capacity, or because of a precise political will (e.g. that of some countries that in the early stages of the epidemic decided not to communicate the data of patients in retirement homesresidences for the elderly). A similar consideration applies to the case of deaths: with what criteria should the data be attributed to the epidemic in the case of comorbidity?
It is difficult to extract significant information from raw data, like those of the daily bulletin, without a proper interpretation and contextualization, which is only possible through the tools of data analysis.
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