锐眼视点：CNN 的基本原理是什么？发展历史是怎样的？有哪些应用案例？；ChatBots 的发展如火如荼，面临的挑战有哪些？；共享经济模式下的车辆共享能否替代私家车？。
TD 精选
卷积神经网络的直观解释
原文链接：An Intuitive Explanation of Convolutional Neural Networks
卷积神经网络 (ConvNets or CNNs) 是在像图像识别和分类领域证明了自己的神经网络的一个分类，而 CNN 更是在自然语言处理、机器人智能以及自动驾驶等领域得到广泛使用。
文章作者对 CNN 的基本概念和改进历史作了深入浅出的解释，并且提供了非常多高质量的参考资料，比如在总结 CNN 发展史时，文中提到：
**LeNet (1990s): **Already covered in this article.
1990s to 2012: In the years from late 1990s to early 2010s convolutional neural network were in incubation. As more and more data and computing power became available, tasks that convolutional neural networks could tackle became more and more interesting.
**AlexNet (2012) – **In 2012, Alex Krizhevsky (and others) released AlexNet which was a deeper and much wider version of the LeNet and won by a large margin the difficult ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012. It was a significant breakthrough with respect to the previous approaches and the current widespread application of CNNs can be attributed to this work.
ZF Net (2013) – The ILSVRC 2013 winner was a Convolutional Network from Matthew Zeiler and Rob Fergus. It became known as the ZFNet (short for Zeiler & Fergus Net). It was an improvement on AlexNet by tweaking the architecture hyperparameters.
**GoogLeNet (2014) – **The ILSVRC 2014 winner was a Convolutional Network from Szegedy et al.from Google. Its main contribution was the development of an Inception Module that dramatically reduced the number of parameters in the network (4M, compared to AlexNet with 60M).
VGGNet (2014) – The runner-up in ILSVRC 2014 was the network that became known as theVGGNet. Its main contribution was in showing that the depth of the network (number of layers) is a critical component for good performance.
**ResNets (2015) – **Residual Network developed by Kaiming He (and others) was the winner of ILSVRC 2015. ResNets are currently by far state of the art Convolutional Neural Network models and are the default choice for using ConvNets in practice (as of May 2016).
业界新闻
ChatBots 面临的挑战
原文链接：15% of all Google searches are unique, which is also a problem for chatbots
Facebook 在今年的 F8 大会上宣布 ChatBot 将会是未来的重点，Slack 和 HipChat 上面也涌现出了各种各样的 ChatBot，很多公司都在讲自己的服务 Bot 化到这些平台。应用开发者，特别是 Bots 的创建者，都在积极投入到聊天式接口的崛起中。但是自然语言处理——让用户像和真人聊天一样和 Bots 交互的技术——还没有让客户及其兴奋，因为这并不是一条容易的路。
Talla 在 Slack 平台推出的 Task Assistant 产品拥有超过 700 家公司客户。在经过一段时间的运营以及和客户的沟通后，他们整理了下面的一些经验：
- Lesson 1: Human language is extremely varied
- Lesson 2: You can’t just pass off the unclear use cases to a human
- Lesson 3: Map intent with contextual awareness
- Lesson 4: Sometimes, it’s the human’s ‘NLP’ that’s the problem
智能车共享服务可以取代私家车吗？
原文链接：How A Smart Car-Sharing Service Is Taking Vehicles Off The Road In Cities
加州大学伯克利分校的 Transportation Sustainability Research Center 在一份 研究报告 中发现，一辆属于 car2go ——一家 2009 年成立的提供共享智能车的德国公司——可以替代 7 到 11 辆私家车。
