The Frontiers of Knowledge Awards recognize Guyon, Schölkopt and Vapnik for teaching machines how to classify data

The three awardees have developed methods that endow machines with the essential human skill of recognizing patterns in large volumes of data, enabling them to be sorted into categories. This is a process where the machine learns through being presented with numerous examples.

Vladimir Vapnik and Isabelle Guyon created the so-called Support Vector Machines (SVM), while Bernhard Schölkopf extended their range and power through the use of kernel methods, which allow for the input of much more specific categories. According to the jury’s citation, these models represent today "a major machine learning paradigm in both research and applications." Thanks to these creations, 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, credit card fraud or climate, research, geophysics and astrophysics.

Teaching machines

"The main problem in artificial intelligence is how to get the machine to recognize things, how to distinguish, for instance, between men and women or between different medical diagnoses,” explained Vapnik after hearing of the award, This is why they chose this goal for their work: “You cannot give the machine the rule, the machine has to learn the rule. The aim of all these methods of machine learning is simply so the machine can learn from examples."

For the Russian scientist, “machine learning technology is the foundation of almost everything in AI-related business, and will become more and more important in future.”

The latest advances in this matter are aimed at identifying not just statistical correlations in a forest of data but also relations of causality. Guyon and Schölkop are still researching along this path, on what is seen as one of the key problems in the area: Making progress in this terrain would allow determining, for example, whether a genetic mutation is the cause or consequence of a cancerous process.

After learning about the awards, Schölkopf explained how his work on causality aided in the discovery of exoplanets: “We had a causal model to distinguish between the signals coming from the star and planet, and the noise produced by the space telescope itself. With this model we managed to filter out the 'noise' and determine which signals really originated in outer space.”

The future of Artificial Intelligence

“The machine can already do better than humans at recognizing things, for example, in cases of medical diagnosis or facial recognition,” says Vapnik. “But for me that doesn't mean that the machine is intelligent. Intelligence is a whole lot more, and we are only just beginning to understand what it is.”

Schölkopf agrees that "we are extremely far away from a machine being more intelligent than a human being." It is true, he adds, that "in very specific applications, like playing chess or Go, or perhaps in optical recognition scenarios like the diagnosis of skin cancer, machines can do better than humans."

“What is interesting about our intelligence,” Schölkopf continues, "is that we can play Go then get up from the table and make dinner, which a machine cannot do."  The German scientists considers that “today, all machines are still much more stupid than humans.” That said, advances in machine learning are compelling enough, he believes, to cause "legitimate concern that the technology may in future take some kinds of jobs away," and this is something "we should begin to think about as a society."