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.

Another blanket article about the pros and cons of Direct to Consumer (D2C) isn’t needed, I know. By now, we all know the rules for how this model enters a market: its disruption fights any given sector’s established sales model, a fuzzy compromise is temporarily met, and the lean innovator always wins out in the end.

That’s exactly how it played out in the music industry when Apple and record companies created a digital storefront in iTunes to usher music sales into the online era. What now appears to have been a stopgap compromise, iTunes was the standard model for 5-6 years until consumers realized there was no point in purchasing and owning digital media when internet speeds increased and they could listen to it for free through a music streaming service.  In 2013, streaming models are the new music consumption standard. Netflix is nearly parallel in the film and TV world, though they’ve done a better job keeping it all under one roof. Apple mastered retail sales so well that the majority of Apple products, when bought in-person, are bought at an Apple store. That’s even more impressive when you consider how few Apple stores there are in the U.S. (253) compared to big box electronics stores that sell Apple products like Best Buy (1,100) Yet while some industries have implemented a D2C approach to great success, others haven’t even dipped a toe in the D2C pool, most notably the auto industry.

What got me thinking about this topic is the recent flurry of attention Tesla Motors has received for its D2C model. It all came to a head at the beginning of July when a petition on whitehouse.gov to allow Tesla to sell directly to consumers in all 50 states reached the 100,000 signatures required for administration comment. As you might imagine, many powerful car dealership owners armed with lobbyists have made a big stink about Elon Musk, Tesla’s CEO and Product Architect, choosing to sidestep the traditional supply chain and instead opting to sell directly to their customers through their website. These dealership owners say that they’re against the idea because they want to protect consumers, but the real motive is that they want to defend their right to exist (and who wouldn’t?). They essentially have a monopoly at their position in the sales process, and they want to keep it that way. More frightening for the dealerships is the possibility that once Tesla starts selling directly to consumers, so will the big three automakers, and they fear that would be the end of the road for their business. Interestingly enough, the big three flirted with the idea of D2C in the early 90’s before they were met with fierce backlash from dealerships. I’m sure the dealership community has no interest in mounting a fight like that again. 

To say that the laws preventing Tesla from selling online are peripherally relevant would be a compliment. By and large, the laws the dealerships point to fall under the umbrella of “Franchise Laws” that were put in place at the dawn of car sales to protect franchisees against manufacturers opening their own stores and undercutting the franchise that had invested so much to sell the manufacturer’s cars.  There’s certainly a need for those laws to exist, because no owner of a dealership selling Jeeps wants Chrysler to open their own dealership next door and sell them for substantially less. However, because Tesla is independently owned and isn’t currently selling their cars through any third party dealership, this law doesn’t really apply to them. Until their cars are sold through independent dealerships, they’re incapable of undercutting anyone by implementing D2C structure.

Back in the late 90's, there were a number of computer scienctists claiming to know java in hopes of landing a job for $80k+/year.  In fact, I know a woman you did just that:  land a project management position with a large telecom and have no experience whatsoever.  I guess the company figured that some talent was better than no talent and that, with some time and training, she would be productive.  Like all gravey train stories, that one, too, had an end.  After only a year, she was given a pink slip.

Not only are those days over, job prospects for the IT professional have become considerably more demanding.  Saying you know java today is like saying you know that you have expertise with the computer mouse; that's nice, but what else can you do.   This demand can be attributed to an increase in global competition along with the introduction of a number of varied technologies.   Take .NET, Python, Ruby, Spring, Hibernate ... as an example;  most of them, along with many others, are the backbone of the IT infrastructure of most mid-to-large scale US corporations.  Imagine the difficulty in finding the right mix of experience, knowledge and talent to support, maintain and devlop with such desparate technologies.

Well imagine no more.  According to the IT Hiring Index and Skills Report, seventy percent of CIO’s said it's challenging to find skilled professionals today.  If we add the rapid rate of technological innovation into the mix of factors affecting more businesses now than ever before, it’s understandable that the skill gap is widening.  Consider this as well:  the economic downturn has forced many potential retires to remain in the workforce.  This is detailed in MetLife's annual Study of Employee Benefits which states that“more than one-third of surveyed Baby Boomers (35%) say that as a result of economic conditions they plan to postpone their retirement.”  How then does the corporation hire new, more informed/better educated talent?    Indeed, the IT skills gap is ever widening.

In order to compensate for these skill discrepencies, many firms have resorted to hire the ideal candidates by demanding they possess a christmas wish list of expertise in a variety of different IT disciplines.  It would not be uncommon that such individuals have a strong programming background and are brilliant DBA's.  What about training?  That is certainly a way to diminish the skills gap.

Like me, I believe most people go about their business never to give a serious thought about their assumed private correspondence when using Gmail to email friends, colleagues and business associates.  As it turns out, your daily banter may not be so private after all.  A recent article in Fortune Magazine, “Judge Rejects Google Deal Over Email Scanning” caught my attention and an immediate thought dominated my curiosity…Google email and scanning scam.  

In essence, the article describes Googles’ agreement to change the way it scans incoming messages so that it no longer reads emails while they are in transit, but only when they are in someone's inbox! So, what exactly does that mean? Judge Koh, a San Francisco federal judge, said she's not so sure about that. Her ruling claims the settlement does not provide an adequate technical explanation of Google's workaround, which involves scanning in-transit emails for security purposes, and then later parsing them for advertising data. The judge also proposed a legal settlement to pay $2.2 million to lawyers, but nothing to consumers.

My interest in this story is not so much about the proposed settlements or the specific details about how Google or any of the web giants settle claims based on vague legal language. It is however, more about the naiveté of myself and perhaps many others that never question how the email scanning process really works. I wonder, do most of us really care that Gmail uses contents of our mail to display targeted ads?