ACM官網消息：https://awards.acm.org/about/2018-turing
衆所周知的深度學習三巨頭： Yoshua Bengio, Geoffrey Hinton, Yann LeCun 三人共同拿下本年度的圖靈獎，以表彰他們在人工智能領域內所做的貢獻。
圖靈獎是計算機領域內的最高獎項。以著名的計算機科學家艾倫圖靈的名字命名，由ACM設立，每年頒發。
這裏我們還是重點關注三位的selected accomplishment。也即主要的工作成就：
首先是Hinton
Geoffrey Hinton
Backpropagation: In a 1986 paper, “Learning Internal Representations by Error Propagation,” co-authored with David Rumelhart and Ronald Williams, Hinton demonstrated that the backpropagation algorithm allowed neural nets to discover their own internal representations of data, making it possible to use neural nets to solve problems that had previously been thought to be beyond their reach. The backpropagation algorithm is standard in most neural networks today.
Boltzmann Machines: In 1983, with Terrence Sejnowski, Hinton invented Boltzmann Machines, one of the first neural networks capable of learning internal representations in neurons that were not part of the input or output.
Improvements to convolutional neural networks: In 2012, with his students, Alex Krizhevsky and Ilya Sutskever, Hinton improved convolutional neural networks using rectified linear neurons and dropout regularization. In the prominent ImageNet competition, Hinton and his students almost halved the error rate for object recognition and reshaped the computer vision field.
這裏主要提到了Hinton的三個工作：BP，玻爾茲曼機，以及對CNN的改進。
BP的重要性自不必多言，通過BP爲網絡“學習”過程提供了一個通用的方法，在幾乎所有的網絡中都在用。我們知道，對於凸優化問題，我們將其劃分爲各種類型，並且給出求解方式。但是對於數學模型複雜的、不能保證凸性的神經網絡，BP算法是一個應用廣泛且效果良好的方法。有了BP，實現網絡的優化這一問題就不太成爲一個困擾了，人們可以將更多的想法放在模型結構和算法設計上，而對網絡的優化過程則直接利用BP實現。自然，這是一個很有意義的工作。
玻爾茲曼機是最早的神經網絡模型之一，而且現在在有些場景也仍在應用。它的作用是可以學到internal representation，也就是內在結構的一個表示，這是我們最希望的。因爲了解了數據的內部的表徵，用可能不太準確的話來說，就意味着模型一定程度上“理解”了數據，而不是簡單的進行記憶。
最後提到的是CNN的改進。我們知道，CNN最早是Lecunn提出的，LeNet，但是其大放異彩還要歸功於Hinton團隊在ImageNet數據集上用CNN刷榜得到的結果。Hinton和它的學生們用了諸如ReLU和dropout等技巧，增加了網絡的規模，實現了遠超於傳統方法的結果。很多人最早接觸CNN可能就是沿着ImageNet刷榜的那些模型開始學起的。
下面是Bengio
Yoshua Bengio
Probabilistic models of sequences: In the 1990s, Bengio combined neural networks with probabilistic models of sequences, such as hidden Markov models. These ideas were incorporated into a system used by AT&T/NCR for reading handwritten checks, were considered a pinnacle of neural network research in the 1990s, and modern deep learning speech recognition systems are extending these concepts.
High-dimensional word embeddings and attention: In 2000, Bengio authored the landmark paper, “A Neural Probabilistic Language Model,” that introduced high-dimension word embeddings as a representation of word meaning. Bengio’s insights had a huge and lasting impact on natural language processing tasks including language translation, question answering, and visual question answering. His group also introduced a form of attention mechanism which led to breakthroughs in machine translation and form a key component of sequential processing with deep learning.
Generative adversarial networks: Since 2010, Bengio’s papers on generative deep learning, in particular the Generative Adversarial Networks (GANs) developed with Ian Goodfellow, have spawned a revolution in computer vision and computer graphics. In one fascinating application of this work, computers can actually create original images, reminiscent of the creativity that is considered a hallmark of human intelligence.
關於Bengio的工作，有許多也是大家耳熟能詳的，這裏提到的工作有：序列的概率模型，高維詞向量的embedding，以及attenuation機制，都是出自Bengio團隊。另外，還有對抗學習相關的內容，也就是現在的GAN相關的東西，都是發源於他。可能更多的人知道GAN和Ian Goodfellow，其實Bengio是Goodfellow的老師，第一篇介紹GAN原理的文章（題目就叫generative adversarial nets），也是兩人完成的。
然後就是LeCun
Yann LeCun
Convolutional neural networks: In the 1980s, LeCun developed convolutional neural networks, a foundational principle in the field, which, among other advantages, have been essential in making deep learning more efficient. In the late 1980s, while working at the University of Toronto and Bell Labs, LeCun was the first to train a convolutional neural network system on images of handwritten digits. Today, convolutional neural networks are an industry standard in computer vision, as well as in speech recognition, speech synthesis, image synthesis, and natural language processing. They are used in a wide variety of applications, including autonomous driving, medical image analysis, voice-activated assistants, and information filtering.
Improving backpropagation algorithms: LeCun proposed an early version of the backpropagation algorithm (backprop), and gave a clean derivation of it based on variational principles. His work to speed up backpropagation algorithms included describing two simple methods to accelerate learning time.
Broadening the vision of neural networks: LeCun is also credited with developing a broader vision for neural networks as a computational model for a wide range of tasks, introducing in early work a number of concepts now fundamental in AI. For example, in the context of recognizing images, he studied how hierarchical feature representation can be learned in neural networks—a concept that is now routinely used in many recognition tasks. Together with Léon Bottou, he proposed the idea, used in every modern deep learning software, that learning systems can be built as complex networks of modules where backpropagation is performed through automatic differentiation. They also proposed deep learning architectures that can manipulate structured data, such as graphs.
首先自不必說肯定有CNN，另外，Lecun還改進了BP算法，並且對於深度學習CV模型的很多概念也有深入的研究。並且提出了一些可以處理結構化數據，比如graph， 的dl算法。
下面是三位的基本簡歷，主要介紹了他們的工作履歷相關的內容 （orz）：
總之，三位巨巨的獲獎也是實至名歸，也正是以他們三個人爲基礎和代表的早期人工智能科學家在理論和實驗領域的艱辛探索，使得深度學習這一學科和各種工具橫空出世，爲今天很多所謂的AI項目構想的落地提供了可能性。當我們在日常生活中享受着智能化時代帶來的不勝枚舉的各種便利時，我們應當記得，是這些人用思想和智慧開創了這個時代。
2019年03月27日23:23:00
記於 北京 清華園