字幕整理:Fei-Fei Li: How computer understand the world?

整理内容来自:Fei-Fei Li: How computer understand the world?

闲来无事,听了李菲菲博士(女神)在TED上的精彩报告。科普性很好,语言很流畅,可以作为计算机视觉方向英语口语练习的好材料,就把字幕整理了下来,以备学习方便

Let me show you something.

(Video) Girl: Okay, that's a cat sitting in a bed. 
                      The boy is petting the elephant.
                       Those are people that are going on an airplane. That's a big airplane.
Fei-Feo Li: That's a three-year-old child describing what she sees in a series of photos. She might still have a lot to learn about this world, but she's already an expert at one very important task: to make sense of what she sees.
      Our society is more technologically advanced than ever. We send people to the moon, we make phones that talk to us or customize radio stations that can play only music we like. Yet, our most advanced machines and computers still struggle at this task. So I'm here today to give you a progess report on the latest advances in our research in computer vision, one of the most frontier and potentially revolutionary techniques in computer science. 
      Yes, we have prototyped cars that can drive by themselves, but without smart vision, they cannot tell the difference between a crumpled paper bag on the ground, which can be run over, and a rock that size, which should be avoided. We have made fabulous megapixel cameras, but we have not delivered sight to  the blind. Drones  can fly over massive land, but don't have enough vision technology to help us to track the change of the rainforests. Security cameras are everywhere, but they do no alter us when a child is drowing in a swimming pool. Photos and videos are becoming an integral part of global life. They are being generated at a pace that's far beyond what any human or teams of humans, could hope ot view, and you and I are contributing to that at thsi TED. 
      Yet, our most advanced software is still struggling at understanding and managing this enormous content. So in other words, collectively as a society, we're very blind, because our smartest machines are still blind. "Why is this so hard?" you may ask. Camera can take pictures like this one by converting lights into a two-dimensional array of numbers known as pixe
根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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