At the end of the 2019 edition of the Machine Learning Conference of Prague, we spent some time together with one of the event’s speaker, Tomaso Poggio, who is the Eugene McDermott professor in MIT’s Department of Brain and Cognitive Sciences and the director of the NSF Center for Brains, Minds and Machines at MIT. As one of the founders of computational neuroscience, he is a devote supporter of interdisciplinarity as a fundamental instrument for bridging brains and machines to increase our chances to arrive at understanding and developing a ‘real artificial intelligence’.

Nowadays, machine learning has become almost ubiquitous; its applications have demonstrated to be successful in so many different contexts. Professor Poggio compared the results achieved in 1995 for identification of pedestrian with the current solutions developed by Mobileye for driverless cars: 10 false alarms per second then, versus one error every 40,000 km covered by the car today. This means that in the last twenty years, every year we have been able to double the precision of machine learning algorithms, improving their accuracy one million times overall.

“A similar trend cannot continue indefinitely,” adds Tomaso Poggio. “Machine Learning is a mature discipline that now needs to expand its application potential to many different contexts: from images, to voice, text, big data and image generation. I think that more than improving the accuracy of existing algorithms, it is now time to do basic research to develop new ones...”

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