The BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies goes, in this ninth edition, to Geoffrey Hinton for his pioneering and highly influential work on machine learning.
Hinton is the world’s most important figure in the area of neural networks. His work is inspired by how the human brain functions and how this knowledge can be applied to provide machines with human-like capabilities in performing complex tasks.
Hinton, in collaboration with the late David Rumelhart, developed the backpropagation learning procedure for neural networks. Backpropagation enables such networks to produce their own internal representations and has been used successfully in a wide variety of practical applications. Hinton made many significant contributions to the success of this procedure. He was the first to place emphasis on the notion of a differentiable training criterion for learning in neural networks, and also the first to use backpropagation-through-time for learning sequential structures. He also introduced a variety of new techniques that became prevalent in machine learning.
Hinton is the leading researcher behind deep learning, one of the most exciting developments in modern AI. In 1999, he developed a new algorithm that later became the major building block for deep learning algorithms, which learn high-level abstractions from data, eliminating manual feature engineering and allowing a machine to automatically learn data representations tailored for a task.
His contributions have pushed the boundaries of AI and established the underpinnings of the most successful algorithms in speech recognition, image recognition and natural language processing, changing the way we interact with information.