最近常读的社科类书单

繁荣的真谛

大篇幅讨论了裙带资本主义,公司腐败与自由竞争的关系,如果一个公司形成垄断,更容易变成社会主义公司。因为垄断产生了更多的利润,并且公司经理人面临这更小的外部竞争,从而滋生腐败,降低整体公司的运营效率。

购买链接 http://item.jd.com/11745121.html

关于作者

路易吉·津加莱斯(Luigi Zingales),美国芝加哥大学布斯商学院企业家精神和金融学Robert C. McCormack讲席教授。公司金融和公司治理领域最重要的学者之一,欧洲公司治理委员会研究员,美国国民经济研究局、经济政策研究中心研究员。1992年获美国麻省理工学院经济学博士学位。

卓有成效的管理者

德鲁克经典著作,最早是高翔推荐。这本书最重要的观点,贡献通过组织/个体 与外部的交互产生。另外个体也是自身的管理者,每个人都是管理者。

购买链接 http://item.jd.com/10059507.html

彼得原理

劳伦斯J.彼得在层级组织进行多年调查研究之后发现一个颠覆传统思想的“彼得原理”:在层级组织中,员工倾向于晋升到自身不胜任的职位,其结果是,企业中的每个职位终将由不胜任的员工所占据。

同时,针对大型组织的管理,作者提出了蔓藤式晋升与冲击式晋升两个概念。

    冲击式晋升是假晋升。获得晋升的员工并没有比以前担负更重职责,并没有在新职位上完成多于原先的职位的工作量,有时简直是在制造一大堆无意义、无价值的工作机会和垃圾。冲击式晋升这类的假晋升是造成层级组织机构臃肿、冗员众多、人浮于事的原因之一。蔓藤式晋升和冲击式晋升没有什么本质的区别,都是在种种原因不能解雇不胜任员工的前提下,为不胜任员工制造一些可无的、无比有好且员工看起来他们获得了真正的晋升的职位,以欺蒙他人、隔离冗员、改变重要岗位的不胜任状态。

购买链接 http://item.jd.com/11245245.html

Micro web framework for low-resource systems ESP8266

ESP8266 has 64KB DRAM, 96KB SRAM, 32-bit RISC CPU running at 80Mhz and it has to handle also a wireless traffic. Despite this it’s still able to dispatch more than 50 reqs/s on Hello World. And serious optimization hasn’t started yet.

ESP8266

ESP8266

Ref blog : http://www.ureq.solusipse.net/

Ref git :https://github.com/solusipse/ureq

 

 

fixed-gear bikes confuse Google’s self-driving cars

Google self-driving Lexus

Google self-driving Lexus

The cyclist recounted the encounter on an online bike forum:

A  Google self-driving Lexus has been in my neighborhood for the last couple of weeks doing some road testing.
Near the end of my ride today, we both stopped at an intersection with 4-way stop signs.

the car got to the stop line a fraction of a second before I did, so it had the ROW. I did a track-stand and waited for it to continue on through.

it apparently detected my presence (it’s covered in Go-Pros) and stayed stationary for several seconds. it finally began to proceed, but as it did, I rolled forward an inch while still standing. the car immediately stopped…

I continued to stand, it continued to stay stopped. then as it began to move again, I had to rock the bike to maintain balance. it stopped abruptly.

we repeated this little dance for about 2 full minutes and the car never made it past the middle of the intersection. the two guys inside were laughing and punching stuff into a laptop, I guess trying to modify some code to ‘teach’ the car something about how to deal with the situation.

the odd thing is that even tho it was a bit of a CF, I felt safer dealing with a self-driving car than a human-operated one.

This likely wasn’t the first time a Google self-driving vehicle has encountered a cyclist at a four-way stop.

The self-driving cars are notoriously careful, and tend to brake when anyone else is moving forward into the vehicle’s path. In a track stand, a rider on a fixed-gear bike may shift ever so slightly forward and back in an effort to maintain balance

While a human driver can easily see a rider doing a track stand isn’t going anywhere, Google’s self-driving car seems to be still be figuring that out.

So invent self-driving cars can operate in any environment is very difficult. The key lies in the Algorithm of how to understand what’s the car see , and respond appropriately

Google 比利时数据中心遭雷击,导致数据丢失

Google loses data as lightning strikes

Google loses data as lightning strikes

 

 

 

From Thursday 13 August 2015 to Monday 17 August 2015,Google 在比利时的数据中心遭到4次雷击,问题初步原因是雷击破坏了供电系统,导致GCE服务( Google Compute Engine ) 有用户丢失数据。突然断电导致GCE的虚拟机不能及时会写数据。看来这个数据中心的UPS系统完成切换,但是仍旧发生了数据丢失。

受影响的数据存储占总存储空间的0.000001%,但是考虑到Google数据中心的海量数据,总的影响范围会在较高水平。

根据Google公开的报告,在事故发生区间,5%的回写IO至少发生一次读写错误。

Google比利时数据中心2007年开始建造,耗资2.5亿欧元,在2010正式投入使用。数据中心环境友好,能效比高,采用水冷系统,参考(advanced evaporative cooling system )。 这个数据 中心创造了3900个就业岗位,估计带给比利时政府22亿欧元的收入。

参考Google事故声明原文,非常值得国内同行学习。

In an online statement, Google said

Nvidia Deep Learning Courses

The focus of Deep Learning is currently on specific perceptual tasks, and there are many successes.

Course Schedule

All classes start at 9am PT and will be recorded for later viewing.

7/22 Class #1 – Introduction to Deep Learning (30 mins + Q&A) – Recording, Slides, Hands-on lab
7/29 Office Hours for Class #1 (1 hour of Q&A)Recording, Slides, Q&A log
8/5 Class #2 – Getting Started with DIGITS interactive training system for image classification (30 mins + Q&A)Recording, SlidesHands-on lab
8/12 Office Hours for Class #2 (1 hour of Q&A)
8/19 Class #3 – Getting Started with the Caffe Framework
8/26 Office Hours for Class #3
9/2 Class #4 – Getting Started with the Theano Framework
9/9 Office Hours for Class #4
9/16 Class #5 – Getting Started with the Torch Framework
9/23 Office Hours for Class #5

Please send your questions to dl-course@nvidia.com before the Office Hours sessions, so the instructors can prepare helpful answers. You might even get a faster response!

The hands-on lab exercises for each class will be available at nvidia.qwiklab.com for free during the course.

More Deep Learning Courses

  • Andrew Ng’s Coursera course provides a good introduction to deep learning (Coursera, YouTube)
  • Yann LeCun’s NYU Course on Deep Learning, Spring 2014 (TechTalks)
  • Geoffrey Hinton’s “Neural Networks for Machine Learning” course from Oct 2012 (Coursera)
  • Rob Fergus’s “Deep Learning for Computer Vision” tutorial from NIPS 2013 (slides, video)
  • Caltech’s introductory deep learning course taught by Yasser Abu-Mostafa (YouTube)
  • Stanford CS224d: Deep Learning for Natural Language Processing (video, slides, tutorials)