The Princess of Asturias Award for Scientific Research honors four experts in systems that mimic the brain

Artificial intelligence, computing designed to perform operations that are considered typical of human intelligence, such as learning, is behind the main technological advances of recent decades in all fields, from robotics to the research of new drugs. The jury of the Princess of Asturias Awards has recognized this year the vital importance of this field by awarding the scientific experts in artificial intelligence Geoffrey Hinton, Yann LeCun, Yoshua Bengio and Demis Hassabis, with the award for Scientific and Technical Research.

Bengio (French, 58 years old), Lecun (also French, 61 years old) and Hinton (British, 75 years old) already had an endorsement for these awards: the Turing Awards, considered the laureate of computing. José Luis Salmerón, professor of Information Systems and Management Computing at the Pablo de Olavide University (Seville), then highlighted that “this field, after a stage of stagnation, has resurfaced again”. Demis Hassabis has a more multifaceted profile: he is, in addition to being an artificial intelligence researcher, a neuroscientist, a computer game designer and a British chess master”.

The priority field of action of the first three are neural networks. According to Salmerón, “they have had an important past and have a promising future.” They are present in many common devices, such as voice assistants or vehicle security systems. These are systems that try to imitate the human brain and have gone from working with simple structures (monolayer) to working with complex systems (deep learning) to identify voices or distinguish images among many other applications.

Its use has become widespread in the work of artificial intelligence to perform sophisticated classifications, predictions and machine learning models. It is about imitating the most complex organ (the human brain), which is why it has been called a “bio-inspired” model or “artificial neurons”.

The resurgence of these systems has a lot to do with the one awarded today with the Princess of Asturias award. Geoffrey Hinton, a professor at the University of Toronto (Canada), developed in 2004 the concepts that have been worked on for half a century and have oriented them towards mechanical learning and recognition of elements as complex as speech or language. image. Hinton created a research community that was joined by Yann LeCun, from New York University, and Yoshua Bengio, from Montreal (Canada).

Neural networks are based on increasingly complex mathematical systems that can learn from the analysis of amounts of information, according to Salmerón. Some of the fields of application are medicine or self-driving cars.

This model tries to imitate the brain and analyzes more conditions than those perceived with the naked eye or those included in current security programs. In this way, for example, the mechanical decision to brake is made not only in the presence of an object, but also in the light of speed, motor power, temperature and humidity data.

The British Demis Hassabis (45 years old), is the co-founder of DeepMind, the artificial intelligence research center that Google bought in 2014. His field is also related to neural networks. “The brain is the only real proof we have in the universe that intelligence is possible,” he said in a communication from his company.

Hassabis, who is also a chess player, developed AlphaGo, a machine that in 2017 beat the world champion in go, a Chinese discipline similar to chess. Since 2013, he collaborates with Google in the development of applications of deep learning as Vice President and is Senior Scientific Advisor at the Vector Institute of Canada.

Geoffrey Hinton, Yann LeCun, and Yoshua Bengio have made advances in fields as diverse as object perception and machine translation using algorithms that convert the biological process of learning into mathematical sequences. It is about the machine learning from its own experience. In 1986, Hinton invented backpropagation algorithms, fundamental for training neural networks. With them, in 2012 he managed to create a convolutional neural network called AlexNet, made up of 650,000 neurons and trained with 1.2 million images, which registered only 26% errors in object recognition and reduced the percentage by half. from previous systems. LeCun added optical character recognition technology.

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