Facebook was originally intended as a way for people to stay in touch with friends and family members by sharing pictures and status updates on their timeline. As the website's popularity has grown, so has criticism that it is becoming one giant, online high school.

Online Bullying

There has been a dramatic increase in recent years in the number of online bullying cases due to the introduction of social media. Bullying isn't just limited to younger Facebook users, either. Many adult users have also resorted to bashing others online through nasty status updates and cruel comments.

Prior to social media, bullying in high school involved "kick me" signs and toilet swirling. Facebook and other social media outlets have allowed users to take bullying to a whole other level. Victims can no longer escape bullying by leaving school or work. The torture continues online, at anytime and anyplace.

Status "Likes"

In high school, everyone wants to be part of the popular crowd; people who are outgoing, beautiful, and seem like they have everything.  Posting a status update is similar to wanting to be popular. Once an update is posted, many users wait with bated breath to see how many friends will "like" their status. They believe that the more "likes" they receive, the more popular they are.

If that isn’t enough, there are many Facebook games that involve "liking" someone's status. Games like "Truth Is", where someone likes a status update and in return the poster writes how they really feel about the friend on their Facebook wall. This can get touchy, especially if the two people aren't friends outside of Facebook. It's similar to high school where someone desperately wants another person to like them, but when they find out how that person really feels they are crushed.

Relationships Are Difficult to Keep Private

When someone signs up for Facebook they’re asked to complete their profile, which includes a relationship section. Users can select from different options including "single", "married", "widowed", and "divorced". Whenever someone changes their relationship status, the update shows up on each of their friend's news feeds.

It's easy to see how this feature correlates with high school where everyone talks about who is dating who or which couple broke up. It used to be that after graduation, people were able to keep their relationships more to themselves. Not so anymore in the age of social media. Now everyone has the ability to state their opinion on a friend's relationship status, either by "liking" their status change or by commenting on it.

Facebook has presented many benefits to its users, including the ability to rekindle old high school friendships. What one must understand when they sign up for the service is that they are opening themselves up to the same criticism and drama that takes place in a high school setting.

Proceed with caution!

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

There has been and continues to be a plethora of observational studies by different researchers in the publishing industry focused on how e-books have affected hard-copy book sales. Evidence from these studies has indicated that there is a significant and monumental shift away from hard-copy books to e-books.[1]These findings precipitate fears that hard-copy books might become more expensive in the near future as they begin to be less available.  This scenario could escalate to the point where only collectors of hard-copy books are willing to pay the high price for ownership.

The founder of Amazon, Jeff Bezos, made a statement in July 2010 that sales of digital books had significantly outstripped U.S. sales of hard-copy. He claimed that Amazon had sold 143 digital books for its e-reader, the Kindle, for every 100 hard-back books over the past three months. The pace of this change was unprecedented;  Amazon said that in the four weeks of June 2010, the rate of sales had reached 180 e-books for every 100 hard-backs sold. Bezos said sales of the Kindle and e-books had reached a "tipping point", with five authors including Steig Larsson, the writer of Girl with a Dragon Tattoo, and Stephenie Meyer, who penned the Twilight series, each selling more than 500,000 digital books.[2] Earlier in July 2010, Hachette said that James Patterson had sold 1.1m e-books to date.

According to a report made by Publishers Weekly, for the first quarter of 2011, e-book sales were up 159.8%; netting sales of $233.1 million. Although adult hard-cover and mass market paperback hard-copies had continued to sell, posting gains in March, all the print segments had declined for the first quarter with the nine mass market houses that report sales. Their findings revealed a 23.4% sales decline, and that children’s paper-back publishers had also declined by 24.1%.[3] E-book sales easily out-distanced mass market paperback sales in the first quarter of 2011 with mass market sales of hard-copy books falling to $123.3 million compared to e-books’ $233.1 million in sales.

According to .net sales report by the March Association of American Publishers (AAP) which collected data and statistics from 1,189 publishers, the adult e-Book sales were $282.3 million in comparison to adult hard-cover book sales which counted $229.6 million during the first quarter of 2012. During the same period in 2011, eBooks revenues were $220.4 million.[4] These reports indicate a disconcerting diminishing demand for hard-copy books.

Social marketing firm Buddy Media is being bought out by Salesforce.com in a $689 million stock and cash deal. The transaction will close Oct. 31 (the end of the third fiscal quarter).

Among its 1,000 customer, Buddy Media includes the companies ofFord, Hewlett-Packard and Mattel. Thanks to its capabilities of sending targeted marketing content through YouTube, LinkedIn and Facebook, Salesforce.com will build on the monitoring technology in social media through its recent Radian6 purchase.

According to Salesforce.com CEO Marc Benioff, the Marketing Cloud leadership will enable the company to take advantage of the massive opportunity within the next five years.

The purchase is arriving on the heels of rival Oracle’s buyout of Virtue, who is the competitor to Buddy Media.