太巧了，前两天转载的本文，昨天就看到了李飞飞原来是大牛啊 并且是机器学习，深度学习领域的大牛，真是太巧了。
另外在推荐几个大牛：行业内人工智能领域的大牛 Andrew Ng ,，这三个人算是学术上的超级大咖，同时又在做落地的产品,计算机视觉领域的大牛Li Feifei..
知乎上论余凯离职，余凯真的为带动了中国的人工智能发展。
虽然你不认识我，但我一直在关注你，余老板
知道余凯离职，真的很惊讶，我是一直崇拜余凯的，他让我记下的第一个印象就是他的微博名字 西二旗农民工，大牛原来都如此低调，百度IDL不是很火呀，怎么离职了呢，很难猜透大牛的内心到底想的啥，离职很是伤感；但让我惊喜的是余凯要创业，并且是机器人行业，做大脑芯片，落地的产品（机器人 大数据 3D打印 这三个终究我都想去从事的行业）以后改叫余老板了 ，求带走啊
另外从这个知乎上也得知阿里，腾讯也办了 人工智能研究院 ，，百度联合组建Distributed Machine Learning Common开源平台；另外 美国那边的谷歌大脑，即google X 实验室；微软的类似的深度学习系统名叫Adam(亚当)；突然感觉人工智能是风口，各互联网公司都想在未来得到一席之地，作为小农的我，也在努力的往前赶，再不赶，，真的拉下来了
阿里巴巴IDST（Institute of Data Science&Technologies）包括了机器学习、大数据挖掘、自然语言处理、移动搜索、多媒体识别等领域；美国西雅图、硅谷、北京、杭州两岸四地；里面的大牛：来自普渡大学计算机系和统计系的终身教授、全球机器学习顶级会议ICML 2014 和 ICML2015 的领域主席。。最近搞各种数据竞赛 “天池”“xxx”，
Li Fei-fei写给她学生的一封信，如何做好研究以及写好English Paper
De-mystifying Good Research and Good Papers
By Fei-Fei Li, 2009.03.01
Please remember this:1000+computer vision papers get published every year!Only 5-10 are worth reading and remembering!Since many of you are writing your papers now, I thought that I’d share these thoughts with you. I probably have said all these at various points during our group and individual meetings. But as I continue my AC reviews these days (that’s 70 papers and 200+ reviews — between me and my AC partner), these following points just keep coming up. Not enough people conduct first class research. And not enough people write good papers.
– Every research project and every paper should be conducted and written with one singular purpose: *to genuinely advance the field of computer vision*. So when you conceptualize and carry out your work, you need to be constantly asking yourself this question in the most critical way you could – “Would my work define or reshape xxx (problem, field, technique) in the future?” This means publishing papers is NOT about "this has not been published or written before, let me do it", nor is it about “let me find an arcane little problem that can get me an easy poster”. It’s about "if I do this, I could offer a better solution to this important problem," or “if I do this, I could add a genuinely new and important piece of knowledge to the field.” You should always conduct research with the goal that it could be directly used by many people (or industry). In other words, your research topic should have many ‘customers’, and your solution would be the one they want to use.
– A good research project is not about the past (i.e. obtaining a higher performance than the previous N papers). It’s about the future (i.e. inspiring N future papers to follow and cite you, N->\inf).
– A CVPR’09 submission with a Caltech101 performance of 95% received 444 (3 weakly rejects) this year, and will be rejected. This is by far the highest performance I’ve seen for Caltech101. So why is this paper rejected? Because it doesn’t teach us anything, and no one will likely be using it for anything. It uses a known technique (at least for many people already) with super tweaked parameters custom-made for the dataset that is no longer a good reflection of real-world image data. It uses a BoW representation without object level understanding. All reviewers (from very different angles) asked the same question "what do we learn from your method?" And the only sensible answer I could come up with is that Caltech101 is no longer a good dataset.
– Einstein used to say: everything should be made as simple as possible, but not simpler. Your method/algorithm should be the most simple, coherent and principled one you could think of for solving this problem. Computer vision research, like many other areas of engineering and science research, is about problems, not equations. No one appreciates a complicated graphical model with super fancy inference techniques that essentially achieves the same result as a simple SVM — unless it offers deeper understanding of your data that no other simpler methods could offer. A method in which you have to manually tune many parameters is not considered principled or coherent.
离开你的那一天开始，左心房渐渐停止跳动…
