Jan 11, 2014
parislemon:


tacanderson:

Perspective. 

We’re still the minority.


Wow.

parislemon:

tacanderson:

Perspective. 

We’re still the minority.

Wow.

Jan 8, 2014

Wired.com: How the NSA Almost Killed the Internet

(via shortformblog)

Jan 7, 2014

Wall Street Journal and Reuters confirm China has unblocked sites

poynterinstitute:

Tech In Asia

China has unblocked the Chinese-language sites of both The Wall Street Journal and Reuters, according to a story Monday by Steven Millward for Tech In Asia.

Colleen Schwartz, with corporate communications for the Wall Street Journal, confirmed via e-mail that the Journal’s site had been unblocked.

Heather Carpenter, public relations manager with Reuters, also confirmed via e-mail Monday that Reuters has been unblocked in China.

(story continues)

Kristen Hare writes about China unblocking sites of Wall Street Journal and Reuters. What does this mean going ahead for media in China? 

Jan 6, 2014
Jan 2, 2014
theatlantic:

How Netflix Reverse Engineered Hollywood

If you use Netflix, you’ve probably wondered about the specific genres that it suggests to you. Some of them just seem so specific that it’s absurd. Emotional Fight-the-System Documentaries? Period Pieces About Royalty Based on Real Life? Foreign Satanic Stories from the 1980s?
If Netflix can show such tiny slices of cinema to any given user, and they have 40 million users, how vast did their set of “personalized genres” need to be to describe the entire Hollywood universe?
This idle wonder turned to rabid fascination when I realized that I could capture each and every microgenre that Netflix’s algorithm has ever created. 
Through a combination of elbow grease and spam-level repetition, we discovered that Netflix possesses not several hundred genres, or even several thousand, but 76,897 unique ways to describe types of movies.
There are so many that just loading, copying, and pasting all of them took the little script I wrote more than 20 hours. 
We’ve now spent several weeks understanding, analyzing, and reverse-engineering how Netflix’s vocabulary and grammar work. We’ve broken down its most popular descriptions, and counted its most popular actors and directors. 
To my (and Netflix’s) knowledge, no one outside the company has ever assembled this data before.
What emerged from the work is this conclusion: Netflix has meticulously analyzed and tagged every movie and TV show imaginable. They possess a stockpile of data about Hollywood entertainment that is absolutely unprecedented. The genres that I scraped and that we caricature above are just the surface manifestation of this deeper database.
Read more. [Image: @darth]

theatlantic:

How Netflix Reverse Engineered Hollywood

If you use Netflix, you’ve probably wondered about the specific genres that it suggests to you. Some of them just seem so specific that it’s absurd. Emotional Fight-the-System Documentaries? Period Pieces About Royalty Based on Real Life? Foreign Satanic Stories from the 1980s?

If Netflix can show such tiny slices of cinema to any given user, and they have 40 million users, how vast did their set of “personalized genres” need to be to describe the entire Hollywood universe?

This idle wonder turned to rabid fascination when I realized that I could capture each and every microgenre that Netflix’s algorithm has ever created. 

Through a combination of elbow grease and spam-level repetition, we discovered that Netflix possesses not several hundred genres, or even several thousand, but 76,897 unique ways to describe types of movies.

There are so many that just loading, copying, and pasting all of them took the little script I wrote more than 20 hours. 

We’ve now spent several weeks understanding, analyzing, and reverse-engineering how Netflix’s vocabulary and grammar work. We’ve broken down its most popular descriptions, and counted its most popular actors and directors. 

To my (and Netflix’s) knowledge, no one outside the company has ever assembled this data before.

What emerged from the work is this conclusion: Netflix has meticulously analyzed and tagged every movie and TV show imaginable. They possess a stockpile of data about Hollywood entertainment that is absolutely unprecedented. The genres that I scraped and that we caricature above are just the surface manifestation of this deeper database.

Read more. [Image: @darth]

Dec 19, 2013

flavorpill:

The print industry has a few good respirators. 

10 Magazines That Give Us Hope For Print in 2014

Dec 12, 2013

Nancy Tellem, Ynon Kreiz, Ronnie Screwvalla and Rich Greenfield discuss how data and creativity factor into creating content for future audiences.

Dec 12, 2013

The Next Big Thing in Comedy with SNL’s Paula Pell, Above Average Productions and IFC.

Dec 12, 2013

Eric Schmidt at Paley International Council talking Big Data, Privacy, and the NSA.

Dec 12, 2013

Translation CEO Steve Stoute on how advertisers need to look to global trends along with data and “gut decisions” at International Council 2013. 

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