|BBVA Frontiers Award Recognizes Advances In Machine Learning|
|Written by Sue Gee|
|Saturday, 29 February 2020|
Isabelle Guyon, Bernhard Schölkopf and Vladimir Vapnik, are the recipients of this year's ICT Frontiers of Knowledge Award from the Spanish bank group BBVA for their 'fundamental contributions to machine learning'.
According to the awarding committee, the methods they developed in artificial intelligence are:
"transforming the everyday world, improving fields as diverse as medical diagnosis, computer vision, natural language processing and the monitoring of climate change."
In this video two of the awardees, Vladamir Vapnik and Bernhard Schölkopf give their reactions to being honored for their work.
Inaugurated in 2008 Frontiers of Knowledge is an international award program which recognizes significant contributions in scientific research and cultural creation, with Information and Communications Technology being one of eight categories. Previous recipients in this category have included Donald Knuth (2010) and Marvin Minsky (2014). As we reported at the time, in 2018, the 10th Edition, it went to Shafi Goldwasser, Silvio Micali, Ronald Rivest and Adi Shamir for their “fundamental contributions to modern cryptology" and last year, the 11th Edition, it was awarded to Ivan Sutherland, widely regarded as the father of computer graphics, for "pioneering the move from text-based to graphical computer displays."
The citation for the 12th edition of the award states:
"Vapnik, Guyon and Schölkopf have collectively performed ground-breaking research that transcends traditional boundaries in computer science, and deservedly rank among the world's leading innovators."
Adding background detail the BBVA's press release explains that the three laureates coincided at America's Bell Laboratories in the early 1990s coming from diverse backgrounds. Vladamir Vapnik was born in 1936 in the former USSR. Having graduated with a degree in mathematics from the Uzbek State University in 1958), he worked until 1990 at the Institute of Control Sciences in Moscow, where he and his then pupil Alexey Chervonenkis - who died in 2014 - laid the mathematical foundations for automated pattern recognition methods.
Vapnik is now widely recognized, according to the awarding, committee remarks, as:
"a living legend in machine learning."
In 1991 Vapnik joined the Adaptive Systems Research Department at AT&T Bell Labs and remained there until 2002, when he was recruited by NEC Laboratories to work in their Machine Learning group, remaining there until 2014. From 1996 to 2014, Vapnik was also Professor of Computer Science and Statistics at Royal Holloway, University of London. Since 2003, he has served as Professor of Computer Science at Columbia University in New York, combining this post with consulting work for Facebook AI Research. He is author of over a hundred publications including The Nature of Statistical Learning Theory, which has been cited more than 85,000 times.
Isabelle Guyon, born in Paris, France in 1961, had joined Bell Labs in 1988 as a postdoctoral researcher, her PhD was in Physical Sciences. Together with Vapnik she created the first proven method enabling optimal classification of data, the support vector machine. In 1994 they were joined in their efforts by one of Vapnik's doctoral students, Bernhard Schölkopf, who expanded the application range of SVMs through the use of kernel methods, which allow for the input of much more specific categories, thereby multiplying applications. These models are described by the committee as representing:
"a major machine learning paradigm in both research and applications."
The awarding committee states:
Thanks to SVM and kernel methods, intelligent machines can now be trained to classify data sets with human precision, or at times even better, enabling them to recognize everything from voices, handwriting or faces to cancer cells or the fraudulent use of credit cards.
SVMs are now being used in genomics, cancer research, neurology, diagnostic imaging, and even in HIV drug cocktail optimization, as well as finding diverse applications in climate research, geophysics and astrophysics.
Looking for examples of the use of SVMs in news items on I Programmer we found AI At The Crossroads - predicting who is going to run a red light and Google Uses AI To Find Good Tables.
Since leaving Bell Labs in 1995 Isabelle Guynon has held teaching posts at ETH Zurich, Aix-Marseille University, New York University, the University of California, Berkeley and Université Paris-Saclay, where she is currently Professor of Big Data in the Laboratoire de Recherche en Informatique (LRI).
Schölkopf is now Director of the Max Planck Institute for Intelligent Systems in Tübingen (Germany), has recently been employing SVMs to analyze data from the NASA satellite Kepler 2. This has helped with the discovery of 21 extrasolar planets, including one with an atmosphere in which signs of water vapor have been detected for the first time.
One of the nominators for this award, Martin Stratmann, President of the Max Planck Society describes machine learning as a core discipline of modern artificial intelligence, consisting of:
“the study of how to extract patterns or regularities from empirical data.”
adding in his letter of recommendation that Vapnik, Guyon and Schölkopf are:
“the three scientists who have jointly shaped the field.”
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|Last Updated ( Saturday, 29 February 2020 )|