Yoshua Bengio，蒙特利尔大学教授；
Geoffrey Hinton，多伦多大学教授，Google的VP；
Yann LeCun，纽约大学教授，Facebook的VP；
颁奖词：
for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing
各位老师在概念上和工上的突破，使得深度神经网络称为计算技术的重要一部分
正文：
In recent years, deep learning methods have been responsible for astonishing breakthroughs in computer vision, speech recognition, natural language processing, and robotics—among other applications.
神经网络作为计算机模式识别和AI领域的工具开始于80年代，这三位老师在20世纪始终坚定地为这个领域做贡献；
"Deep neural networks are responsible for some of the greatest advances in modern computer science, helping make substantial 重大的 progress on long-standing 长期存在的 problems in computer vision, speech recognition, and natural language understanding,” said Jeff Dean, Google Senior Fellow and SVP, Google AI. “At the heart of this progress are fundamental techniques developed starting more than 30 years ago by this year's Turing Award winners, Yoshua Bengio, Geoffrey Hinton, and Yann LeCun. By dramatically improving the ability of computers to make sense of the world, deep neural networks are changing not just the field of computing, but nearly every field of science and human endeavor."
In traditional computing, a computer program directs 指挥 the computer with explicit step-by-step instructions. In deep learning, a subfield of AI research, the computer is not explicitly told how to solve a particular task such as object classification. Instead, it uses a learning algorithm to extract patterns in the data that relate the input data, such as the pixels of an image, to the desired output such as the label “cat.” The challenge for researchers has been to develop effective learning algorithms that can modify the weights on the connections in an artificial neural network so that these weights capture the relevant patterns in the data.
Deep Learning和传统的神经网络的区别就在与深度；
三位老师的工作也得益于GPU工具的普及和海量数据的积累；
三位老师工作有合作也有独立的部分，Even while not working together, there is a synergy and interconnectedness in their work, and they have greatly influenced each other.
三位老师还通过Learning in Machines and Brains program 进行机器学习和认知神经科学的交叉研究；
部分技术成绩
Hinton:
方向传播算法；
玻尔兹曼机；
优化了卷积神经网络；
Bengio:
序列数据的概率模型；
高维单词嵌入；
生成对抗网络；
LeCun:
卷积神经网络；
改进方向传播算法；
拓宽了神经网络的视野；
地址：https://awards.acm.org/about/2018-turing