Rise of ISIS surprised CIA: Incompetence as a conspiracy

Some liberals think the CIA stands for Christian Intelligence Agency. Some thing it is a Conspiracy Investigation Agency.

But why did CIA get caught by surprise when ISIS rose?

Oh Shit happens!

Who benefits (Quo Bono) when ISIS rose?

Why did the current CIA director get his job when the last Director got caught in a affair (General Petreaus ) who knew the area well.

Is John O. Brennan trying to run for Vice President in 2020 ? George H W Bush did that before

Incompetence in a democracy demands answers! Before you invade the wrong country (it has happened before !)

https://en.wikipedia.org/wiki/John_O._Brennan

Brennan withdrew his name from consideration for Director of the Central Intelligence Agency (CIA) in the first Obama administration over concerns about his support for transferring terror suspects to countries where they may be tortured while serving under President George W. Bush.[3][5] Instead, Brennan was appointed Deputy National Security Advisor, a position which did not require Senate confirmation.[3][5][8]

Brennan’s 25 years with the CIA included work as a Near East and South Asia analyst, as station chief in Saudi Arabia,

After leaving government service in 2005, Brennan became CEO of The Analysis Corporation, a security consulting business, and served as chairman of the Intelligence and National Security Alliance, an association of intelligence professionals

His term as CIA Director coincided with revelations that the U.S. government conducted massive levels of global surveillance, that the CIA had hacked into the computers of U.S. Senate employees, and the release of the U.S. Senate Intelligence Committee report on CIA torture.

Data Science Apps for Plug and Play Data Science

I was reading the 12 factor App and was struck by how much data science practitioners could use these principles too, for example when making a Shiny Dashboard App

Also I hope we can have more plug and play data science for mobile data or data generated by mobile apps (which is increasing)

Screenshot from 2016-05-11 23:11:17

An example is this app here https://gallery.shinyapps.io/CampaignPlanner_v3/ which can possible modified to add integration with Google Web Analytics API (etc).

This approach can make R more enterprise ready for production environments where it currently lags behind Python in terms of both appeal as well as trained people.

http://12factor.net/

The Twelve Factors

I. Codebase

One codebase tracked in revision control, many deploys

II. Dependencies

Explicitly declare and isolate dependencies

III. Config

Store config in the environment

IV. Backing services

Treat backing services as attached resources

V. Build, release, run

Strictly separate build and run stages

VI. Processes

Execute the app as one or more stateless processes

VII. Port binding

Export services via port binding

VIII. Concurrency

Scale out via the process model

IX. Disposability

Maximize robustness with fast startup and graceful shutdown

X. Dev/prod parity

Keep development, staging, and production as similar as possible

XI. Logs

Treat logs as event streams

XII. Admin processes

Run admin/management tasks as one-off processes

Because it is Friday really

Timestamps can be manipulated leading to confusing meta data. cognition can be manipulated using images.

cog·ni·tion
ˌkäɡˈniSH(ə)n/
noun
 
  1. the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.

 

https://www.youtube.com/watch?v=oMX1sc3eOTE

career advice for data science newcomers

Someone I dont know asked this question-

I had a few questions sir. Could I ask them to you? Like mainly based on the direction in which I should work or learn. I don’t mean to bother you. But it’s hard to find the right people who can guide new comers like me.

I first explained why people don’t really give advice for free. I am using principles I learnt from Reader’s Digest about something known as a Fermi Problem. Fermi Problems are common in tech interviews.

  1. there are 3000 newcomers to every such right person (who gives free advice to newcomers he does not know).
  2. out of them only 300 will get the courage to ask the right people.
  3. out of them 250 will write a badly written email.

so by the time the right person has gotten 250 spam emails, he is not responding to the 50 out of the 3000 who

  1. write well, and
  2. are passionate about learning more.

that is an example how you can use mathematical thinking to understand why things work.

Then I gave in and gave her some free advice on what direction a data science newcomer should put efforts in

which direction should you work?-

  1. interest/passion/quality – do something you are good at, because then only it will sustain your interest and you will be put up the 10000 hours to be great at it. and
  2. greed  (higher salary) versus fear (different skills)– it should make you money but you make more money if you create your own niche. so should you be like thousands of analysts in credit card analytics (easy route) or should you do analysis on videos ( tougher).
  3. networking– why dont you atleast go to data science meetups, and try to take part in a few kaggle competitions. also, have you stopped reading r-bloggers.com or kdnuggets.com.Do this for three months and you will find enough opportunities or data. take decisions based on data not from anecdotal advice from experts.

I hope I was able to be useful. What do you think?

Portrai
Adding a 2.4 mb file slows page load on mobile devices, but that is a small cost to learn about this great Italian American – Enrico Fermi. 

Enrico Fermi, Italian-American physicist, received the 1938 Nobel Prize in physics for identifying new elements and discovering nuclear reactions by his method of nuclear irradiation and bombardment. The Fermi technique is named after physicist Enrico Fermi as he was known for his ability to make good approximate calculations with little or no actual data. Fermi problems typically involve making justified guesses about quantities and their variance or lower and upper bounds. Probably you can use it for Big Data Analysis about online chatter when your machine learning is not able to process videos (Youtube) or Images ( Instagram) as efficiently as it analyzes text.