AROUND THE WEB - NEWS

• AROUND THE WEB

  • Black Voices

    • Allison Janney, Octavia Spencer Square Off In Hilarious 'Mom' Scene
    • Bill Cosby Takes Over 'Late Night,' Auditions The Roots To Play At His Funeral (VIDEO)
    • Barbara Walters On Her 'The View' Co-Hosts: 'I Don't Like Any Of Them' (VIDEO)
    • 'Dancing With The Stars' Semi-Finals Results: Leah Remini Eliminated In Week 10

  • The Smoking Section

    • The Nightcap: Omni Omar
    • ScHoolBoy Q – “Man Of The Year”
    • Eminem’s Interview With Zane Lowe + “Berzerk” In-Studio Performance
    • Christian Deshun – “Unsung.”

  • Moviefone

    • Zac Efron Accidentally Breaks His Dreamy Jaw
    • Emma Thompson Forgives Helena Bonham Carter for Affair With Kenneth Branagh
    • Alec Baldwin Tears Up During Testimony Against Alleged Stalker
    • George Clooney Has a Very Good Reason for Avoiding Twitter

  • AOL

    • Happy Hour Virus Let's You Leave Work Early
    • 3 Fun Thanksgiving Activities for Kids
    • 3 Ways to Avoid Holiday Stress
    • Terry McMillan On Immigration: 'Marco Rubio Is An Uncle Tom'

  • BlackAmericaWeb

    • Celebrity Men in Fraternities
    • November 17-23: This Week in Black History
    • This Week’s Hottest Instagram Pics [11/8-11/15]
    • 15 Black Movies We’d Like to See Get a Sequel

  • HelloBeautiful

    • Spelman Protesters To Nelly: We Were Hoping You’d Understand The Broader Message
    • How Iyanla Fixed Her Life: Spiritual Coach Shares How She Escaped Domestic Abuse
    • Barneys Suddenly Cancels Jay Z’s Holiday Collaboration Kickoff Party
    • First Lady Michelle Obama Discusses Education On BET’s ’106 & Park!’

  • Global Grind

    • Not Cool: 10 Brands & Companies Accused Of Racial Profiling (LIST)
    • Kick, Push & Declare Your Story With Narrative’s Exclusive Skate Decks (PHOTOS)
    • THE PIED LIFER! 22 R. Kelly Gifs That’ll Give You Life
    • Omarion Says No One Understands Why He Signed With Rick Ross (VIDEO)

  • Elev8

    • RICKI LAKE Wants You to Breastfeed!
    • Deitrick Haddon And Dominique Have The Family Discussion: ‘Stop Judging’ [VIDEO]
    • Joel Osteen: A Night Of Inspiration With Susan Boyle
    • Larry Callahan & Selected Of God Teams With The Clark Sisters For Amazing New Video

  • GIANTlife

    • RES Serves Up A Helping Of “Refried Mac” [VIDEO]
    • Kanye West Explains “The Louis Vuitton Don” Moniker [EXCLUSIVE]
    • Brazilian Beauties Compete For “Miss BumBum Brazil” 2013
    • 12 Digital Toys For Tots

  • BlackDoctor.Org

    • Is There Arsenic In Your Chicken?
    • 1 in 10 People Will Have Diabetes By 2035
    • 5 Strange Health Facts About Love
    • Too Much Sitting Linked To This Dangerous Disease In Men

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America has a new 100 dollar Currency - USD 100 [See photos]

This new 100 dollar bill went into circulation on October 8th this year and features a better portrait of one of the founding fathers of the United States, Benjamin Franklin and other improvements. See the old and new after the cut 

The old dollar bill

The new one

Retailers double down on real-time bidding expenditures

Real-time bidding (RTB) has quickly gained prominence as one of the top methods of selling and buying online display advertisements. These automated systems allow advertisers to bid for units one at a time and display them specifically to customers based on demographics and other such metrics. Not only does RTB enhance the relevance of campaigns, it also makes the whole buying process much easier for the advertiser since it’s done automatically.

Advertisers across industries see the value in RTB

As such, many businesses across a wide swath of industries have begun increasing investments in RTB expenditures. One recent report from Casale Media found that retailers, telecommunication companies, auto businesses and financial services providers were among the most prominent sectors to make heavy use of RTB.

Retailers were the biggest RTB spenders during the first half of 2013, with several retail sub categories (such as apparel and house and home) boasting expenditures that were higher than within other standalone sectors. Auto manufacturers, financial services providers and the food and drink industry were some of the sectors that spent significantly more on RTB during the second quarter of 2013 when compared to the first.

Overall, the number of brands investing in RTB has skyrocketed significantly over the past year, up 69 percent between the fourth quarter of 2012 and the second quarter of 2013. According to a separate report from MAGNA GLOBAL, the United States is currently the biggest market for programmatic buying, with approximately $3.9 billion worth of inventory sold via RTB.

“The value of programmatic transactions will continue to grow by double-digit growth rates to nearly $17 billion by 2017,” the MAGNA GLOBAL report asserted. “The share of programmatic spend in U.S. digital media (display-related) transactions, when looking at both RTB and [non-real-time bidding], will expand from 38 percent in 2012 to 53 percent this year and to 83 percent by 2017.”

Fortunately, RTB has become nearly ubiquitous over the past year. There are now RTB platforms available for every major media channel, including Web, social, mobile, video and even email advertising. This will allow companies to carefully pick and choose which channels they want to use to engage their customers, enabling them to maximize their relevance to particular target audiences.

Deus Ex Machina - Machine Learning is your friend

Why? Well, in a nutshell, machine learning is the use of computers to refine patterns at a level beyond human ability. It’s kind of like if our brains were supercharged and able to store thousands of years of data, keep it all in memory, isolate the most important information, and then use all that information to predict results in real-time.

Let’s look at an example inspired by an earlier piece written by Quora:

Say you’re looking to buy the sweetest oranges. You ask your mother, who always has the ones you like, and she tells you that the biggest oranges from the local market are the sweetest.

So, following mom’s directions, you go to the local market and you buy ten of the biggest oranges you can find. Sadly, only about half of your oranges are sweet. Further investigation reveals that the oranges that happen to be the sweetest, also have bright spots.

You come to the conclusion that the sweetest oranges are the ones with the bright spots… and that your mother is a liar.

Armed with this knowledge, the next time you go to the store you only get oranges with bright spots. Once again, only half of your oranges are sweet, but this time, the differentiating quality is that the biggest are the sweetest.

You realize that the oranges that are both big and have bright spots are sweetest.

Now you can go to store and always pick the sweetest oranges…after a quick apology to your mother. 

Next time you go to the local market, however, your roommate asks your to pick up the juiciest oranges. But you don’t have any information on how to buy the juiciest oranges, so it’s back to the drawing board. It would take multiple trips back and forth from the market, trying different combinations of oranges, and maybe even stores, before you would ever find the right mix of attributes that equaled a “juicy orange”.

That means a lot of wasted time and money… unless, of course, you have a machine with a good algorithm to predict results based on a sampling of millions of different oranges across thousands of different stores. Then it would be able to use this information to find the desirable oranges (whether sweet, juicy, or sour) by generalizing and isolating all the combinations of attributes (in the case of oranges, might be big, small, smooth, lumpy, spotty, soft, hard, pale, etc.) that are of statistical importance among the infinite sets of possible variations. By paying attention to only the attributes that are known and show statistical significance towards a specific outcome, the algorithm would be able to pinpoint what matters and calculate results in a highly efficiently manner, providing results before even leaving for the store.. and without having to eat a ton of ”bad” oranges!

But this isn’t just about oranges. Let’s look at how this process may effect an advertiser:

The graph below shows the daily spend of a fashion brand that ran on our network. Follow the paths of daily spend.  Over the first few days of the campaign we’re spending more with MSIE, and less with Safari. The CPA on MSIE was high and burning most of the budget too quickly, so the system stopped buying it as much. In this case, the machine was able to anticipate the outcome by recognizing trends in the data.  Eventually, we were essentially spending nothing against MSIE.  

The best part? The client asked us to stop spending against MSIE on August 22, but looking at the graphs you can see that we were automatically decreasing spending against it.  

The machines were a step ahead, and this gave the client great confidence that our system – machine learning, works.