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Bengio, Hinton, and LeCun will officially receive the 2018 ACM A.M. at the ANNUAL ACM Awards Dinner on June 15, 2019 in San Francisco, California Turing award.

Hinton, LeCun and Bengio, working independently, jointly developed the conceptual basis for the field of deep learning neural networks, demonstrating the advantages of deep neural networks through experiments and practical engineering. In recent years, deep learning methods have been the main reason for surprising breakthroughs in fields like computer vision, speech recognition, natural language processing and robotics.

“Artificial intelligence is now one of the fastest growing areas in all of science and one of the most talked about topics in society,” said ACM President Cherri M. Pancake. “Ai advances and thrives in large part because Bengio, Hinton and LeCun have laid the foundation for the latest advances in deep learning. These technologies are used by billions of people, and anyone with a smartphone can actually experience advances in natural language processing and computer vision. In addition to the products we use every day, new advances in deep learning are giving scientists powerful new tools in medicine, astronomy, and materials science. “

“Deep neural networks have been behind some of the greatest advances in modern computer science, helping to make real progress on long-standing problems in computer vision, speech recognition and natural language understanding,” said Jeff Dean, a senior researcher at Google. “At the heart of this progress are fundamental technologies developed more than 30 years ago by Yoshua Bengio, Geoff Hinton and Yann LeCun, this year’s Turing prize winners. By dramatically improving computers’ ability to understand the world, deep neural networks have transformed not only the field of computing, but virtually every area of science and human endeavor.”

Professor at the University of Montreal, scientific Director of Mila, Quebec Institute for Artificial Intelligence, and co-author of Deep Learning with Ian Goodfellow and Aaron Courville. Bengio’s major contribution was the invention of the Probabilistic models of sequences in the 1990s. He has combined neural networks with probabilistic models, such as hidden Markov models, and is working with AT&T to use new technology to identify handwritten cheques. Speech recognition in modern deep learning techniques is also an extension of these concepts. In addition, Bengio also published an epoch-making paper “A Neural Probabilistic Language Model” in 2000, which used high-dimensional word vectors to represent natural Language. His team also introduced the attention mechanism that made machine translation a breakthrough and became an important technology for deep learning to process sequences.

He is vice President and engineering fellow at Google, chief Scientific Advisor at Vector Institute, and honorary University Professor at the University of Toronto. Hinton’s most important contribution came in his 1986 paper “Learning Internal Representations by Error Propagation,” Boltzmann Machines in 1983, and improvements to convolutional neural networks in 2012. Hinton and his students Alex Krizhevsky and Ilya Sutskever improved convolutional neural networks by correcting Linear Neurons and Dropout Regularization, And scored well in the famous ImageNet review, revolutionizing the field of computer vision.

Professor at New York University and Facebook’s vice president and chief artificial intelligence scientist. One of Yann LeCun’s representative contributions is convolutional neural networks. In the 1980s, LeCun invented convolutional neural network, which has become one of the fundamental technologies in machine learning and makes deep learning more efficient. In the late 1980s, Yan LeCun first applied convolutional neural networks to handwritten number recognition while working at the University of Toronto and Bell LABS. Today, convolutional neural networks have become the industry standard technology, widely used in computer vision, speech recognition, speech synthesis, image synthesis, natural language processing and other academic directions, as well as automatic driving, medical image recognition, voice assistant, information filtering and other industrial applications. LeCun’s second important contribution was to improve the backpropagation algorithm. He proposed an early backpropagation algorithm, Backprop, and gave a concise derivation based on the variational principle. His work has made backpropagation algorithms faster, describing, for example, two simple ways to reduce learning time. LeCun’s third contribution is to extend the range of applications of neural networks. He turned a neural network into a computational model that could perform a multitude of different tasks. Some of the early work he introduced has now become the basic concepts of artificial intelligence. In the field of image recognition, for example, he has studied how neural networks can learn hierarchical features, an approach that is now used in many everyday recognition tasks. They also propose deep learning architectures that can manipulate structured data, such as graph data.

The ACM A.M. Turing Prize, often referred to as the “Nobel Prize in Computing,” is a $1 million prize with financial support from Google. Named after A.M. Turing, a pioneer of computer science and a British scientist and professor at the University of Manchester, it was established in 1966 by the Association for Computing Machinery (ACM) to honor individuals who have made significant contributions to computer science. One of the goals of the prize is to honor the founder of modern computer science. The winner must be a technical contribution that has made a significant and lasting advance in the field of computing. Most of the winners are computer scientists. It is the most prestigious award in the field of computing, known as the “Nobel Prize of computing”.

The Turing Prize is highly demanding, and the selection process is extremely strict. Generally, only one computer scientist is awarded each year, and only a few years have more than two scientists who have made contributions in the same direction.

Reference links:

https://www.eurekalert.org/pub_releases/2019-03/afcm-fod032619.php

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