EMV in, magnetic strips out


MasterCard reveals roadmap for EMV electronic payments It’s been over fifteen years since MasterCard, Visa and Europay developed EMV technology to make your credit cards more secure, but it has yet to really catch on here in the US. However, MasterCard has created a master plan to help usher in the EMV era and sound the death knell for the magnetic strip. Why? The EMV infrastructure is far more fraud-resistant because each transaction is authenticated dynamically using cryptographic algorithms and a user-specific PIN. That’s why MasterCard plans to help build out the EMV POS infrastructure by April of next year and have its secure e-payment system functioning at ATMs, online and with its myriad mobile payment options as well. For now, the nuts and bolts of how the credit card firm plans to bring its plan to fruition are few, but more details will be forthcoming, and there’s a bit more info at the source and PR below.

Continue reading MasterCard reveals roadmap for our electronic payment future: EMV in, magnetic strips out

! MasterCard reveals roadmap for our electronic payment future: EMV in, magnetic strips out originally appeared on Engadget on Tue, 31 Jan 2012 05:42:00 EDT. Please see our terms for use of feeds.

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Tuesday, January 31st, 2012 news No Comments

Learn Everything You Need to Know About Meat with Meat Master Pat LaFrieda’s Big App for Meat [Video]


Pat LaFrieda, the master butcher and man behind the best burgers in the world, has created an iPad app that’s pretty much the definitive guide to all things meat. Aptly named Pat LaFrieda’s Big App for Meat, you’ll learn about all the cuts and dry aging and grinding techniques with awesome visuals and in-depth videos.

LaFrieda really knows his meat too, he supplies Shake Shack and Minetta Tavern with the most delicious burger patties known to man, so his advice is like canon in the meat world. The app, which is super slick, is deliciously visual, you’ve never seen meat like this before. Each cut of meat (and it details cuts from beef, pork, poultry, veal and lamb) comes with a real life gallery with amazing pictures, a little blurb on the cut, a location of where it can be found on the animal and a 360 degree view.

What’s also great about Pat LaFrieda’s Big App for Meat is how much video content there is. From teaching you Steaks 101 to learning about dry aging to discovering how to grind meat and sharpen knives, LaFrieda himself reveals his secrets. There’s even a fun meat quiz to test yourself on! If you love meat, and I totally expect you to, you’re going to learn everything you need to know. If you’re a vegetarian, I’m sorry. $7 [iTunes]

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Thursday, December 8th, 2011 news No Comments

How Google Crunches All That Data


If data centers are the brains of an information company, then Google is one of the brainiest there is. Though always evolving, it is, fundamentally, in the business of knowing everything. Here are some of the ways it stays sharp.

For tackling massive amounts of data, the main weapon in Google’s arsenal is MapReduce, a system developed by the company itself. Whereas other frameworks require a thoroughly tagged and rigorously organized database, MapReduce breaks the process down into simple steps, allowing it to deal with any type of data, which it distributes across a legion of machines.

Looking at MapReduce in 2008, Wired imagined the task of determining word frequency in Google Books. As its name would suggest, the MapReduce magic comes from two main steps: mapping and reducing.

The first of these, the mapping, is where MapReduce is unique. A master computer evaluates the request and then divvies it up into smaller, more manageable “sub-problems,” which are assigned to other computers. These sub-problems, in turn, may be divided up even further, depending on the complexity of the data set. In our example, the entirety of Google Books would be split, say, by author (but more likely by the order in which they were scanned, or something like that) and distributed to the worker computers.

Then the data is saved. To maximize efficiency, it remains on the worker computers’ local hard drives, as opposed to being sent, the whole petabyte-scale mess of it, back to some central location. Then comes the second central step: reduction. Other worker machines are assigned specifically to the task of grabbing the data from the computers that crunched it and paring it down to a format suitable for solving the problem at hand. In the Google Books example, this second set of machines would reduce and compile the processed data into lists of individual words and the frequency with which they appeared across Google’s digital library.

The finished product of the MapReduce system is, as Wired says, a “data set about your data,” one that has been crafted specifically to answer the initial question. In this case, the new data set would let you query any word and see how often it appeared in Google Books.

MapReduce is one way in which Google manipulates its massive amounts of data, sorting and resorting it into different sets that reveal new meanings and have unique uses. But another Herculean task Google faces is dealing with data that’s not already on its machines. It’s one of the most daunting data sets of all: the internet.

Last month, Wired got a rare look at the “algorithm that rules the web,” and the gist of it is that there is no single, set algorithm. Rather, Google rules the internet by constantly refining its search technologies, charting new territories like social media and refining the ones in which users tread most often with personalized searches.

But of course it’s not just about matching the terms people search for to the web sites that contain them. Amit Singhal, a Google Search guru, explains, “you are not matching words; you are actually trying to match meaning.”

Words are a finite data set. And you don’t need an entire data center to store them—a dictionary does just fine. But meaning is perhaps the most profound data set humanity has ever produced, and it’s one we’re charged with managing every day. Our own mental MapReduce probes for intent and scans for context, informing how we respond to the world around us.

In a sense, Google’s memory may be better than any one individual’s, and complex frameworks like MapReduce ensure that it will only continue to outpace us in that respect. But in terms of the capacity to process meaning, in all of its nuance, any one person could outperform all the machines in the Googleplex. For now, anyway. [Wired, Wikipedia, and Wired]

Image credit CNET

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Wednesday, March 17th, 2010 news No Comments

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