Why do people go to America

Why do people go to America? Moving involves tremendous emotional, financial and physical expenditure. It is a very different and unique culture full of surprises, your savings will vanish in the expensive dollar economy, and you will have to adjust to a car driven, credit history driven existence.

Why move to America?

The answer lies in the small notion of American dream-

  1. Freedom of speech
  2. Freedom of worship
  3. Freedom from want
  4. Freedom from fear

I can criticize, mock , insult  the American president when in America- a freedom I am denied in most other countries on the planet

I can worship anyone and anything, openly, loudly and not be afraid.

I can get a good living even as a blue collar worker and have unlimited opportunities to start y own business or startup with atmost ease.

But above all,

I am free from fear when I am in America. The FBI needs a warrant and the CIA wont hurt me. That is not true of other countries.

Yet with threats of deportation, there is no more freedom from fear.  Even to children. Hate crimes against minorities is an unprecedented high (by American standards) and even the press is now mocked (reversing traditional American politics or even  politics in other democracies). The Putinization of America is on  way and all we do is twiddle on Twitter.

So why move for the American dream?

As someone said, to dream the American dream, you first need to go to sleep.


No Hacker is a Bandit

When bankers in suits can lose billions, and be bailed out by tax payer money and pay themselves and each other millions- why should the common man , the middle class be left out of the gravy train that economic productivity created by digital revolutions.

A hacker is a highly skilled person, why should he be exploited for visas, billing rates, sub contracting but have no recourse in normal outdated legal processes

If spies can hack ala Stuxnet and get golden medals

If companies can hack and be rewarded by IPOs

Why cant humans hack and be rewarded for it

Hackers and malware creators are highly talented individuals who have been failed with the system, one in which corporations and governments collude for deliberate vulnerabilities on unsuspecting tax paying software buying people

No Linux user is complaining of a virus if you noticed. But you cant sue people for defective software. Yet!

A Healthy Routine for Hackers and Tech Workers

  • Meet people for dinner
  • Sleep
  • Do until


Heuristics and Occam’s razor for Counter Terrorism

When overworked analysts use shortcuts to search huge noisy dirty databases, they create trails which can be mined for actual heuristics

Heuristics –

A heuristic technique (/hjᵿˈrɪstk/; Ancient Greek: εὑρίσκω, “find” or “discover”), often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution



Example- A Police chief in Chicago may adopt different heuristics than in New York than in New Orleans for allocating human resources

Solution- Make a database of heuristics as actually in practice for that particular domain

Additional Solution- Search Companies to partner not just in giving data but also training and in some case search algorithms for database analysis and database design reviews of Homeland Security

Occam’s Razor-

Occam’s razor (also written as Ockham’s razor, and lex parsimoniae in Latin, which means law of parsimony) is a problem-solving principle attributed to William of Ockham (c. 1287–1347), who was an English Franciscan friar, scholastic philosopher and theologian. The principle can be interpreted as stating Among competing hypotheses, the one with the fewest assumptions should be selected.



Related – How to amplify noise in social media using other algorithms







De-anonymizing Social Networks
Arvind Narayanan and Vitaly Shmatikov
The University of Texas at Austin
Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers,
and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social- network graphs. To demonstrate its effectiveness on real-
world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate.





Data Science for Olympics and lack of Reproducible Research

Despite the plethora of data generated in Sports, there is not much open data for Olympics and one wonders why if sharing best practices and data openly on what works and what does not can reduce the level of Russian athletes being banned in a cylical cold war era game.

Some links I found useful


Could data mining techniques accurately predict the medal counts at the Olympics? A predictive model could give us an estimate of the number of medals each nation might win; but how close could we get to the actual outcomes? It was a tantalizing project …

Sochi-Ru By Dan Graettinger with Tim Graettinger

• Which nation will bring home the most medals at the upcoming Winter Olympics in Sochi, Russia?

• Will any nation from Africa, South America, or the Middle East finally break through and win a medal?

• Why do some nations win a bundle of medals while others win only a few?

• Can data mining give us the answers to these questions?


the Graettinger brothers do? They used a seemingly  standard methodology: learn from the past to predict the future.  More precisely, they used past Olympics results to build a predictive model.  Each country is represented by a feature vector, i.e. a set of quantities drawn form several categories:

  • Economic
  • Population
  • Human Development
  • Geography
  • Religion
  • Politics and Freedom

Then they used a standard technique known as linear regression to find which set of features were best for predicting medal count.  I was reading their blog post with great interest until I saw what were the most meaningful features found by the linear regression algorithm:

  • Geographic area
  • GDP per capita
  • Value of Exports
  • Latitude of Nation’s Capital



I was able to find data in many categories:

  • Economic
  • Population
  • Human Development
  • Geography
  • Religion
  • Politics and Freedom

Thankfully, there were some good sources out there[f3], and I collected enough data that I felt I had a good chance to predict some meaningful outcomes.  But would it be enough?  There is more than one way to go about predicting the medal count at the Olympics, and the route before me was the “30,000 feet” approach.

So any takers?

Hackers for Hacking the Olympics 🙂