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.

One of the most anticipated features that came on the iPhone 4S was a new thing called: Siri. Zooming out before concentrating on Siri, mobile assistants were the new rage. Beforehand, people were fascinated by the cloud, and how you could store your files in the Internet and retrieve it from anywhere. You could store your file at home, and get it at your workplace to make a presentation. However, next came virtual assistants. When you’re in the car, it’s hard to send text messages. It’s hard to call people. It’s hard to set reminders that just popped into your head onto your phone. Thus, came the virtual assistant: a new way to be able to talk to your phone to be able to do what you want it to do, and in this case, text message, or call people, and many other features. Apple jumped onto the bandwagon with the iPhone 4S and came out with the new feature: Siri, a virtual assistant that is tailored to assist you in your endeavours by your diction.

Getting started with Siri

To get Siri in the first place, you need an iPhone 4S; although you may have the latest updates on your iPhone 4 or earlier, having an iPhone 4S means you have the hardware that is required to run Siri on your phone. Therefore, if you are interested in using Siri, check into getting an iPhone 4S, as they are getting cheaper every single day.

It’s befuddling when you think about how many ways there are to communicate in 2013. I’d say there are too many new ways to communicate – social media, phone, Skype, instant message, text message, email, it goes on and on. But do any of them outweigh the power of a good old-fashioned face-to-face meeting? Most business executives would argue no. Nothing can replace a face-to-face meeting, at least yet.

That said, face-to-face meetings are without question the most expensive kind, given the travel costs required to make them a reality, and companies around the globe have been trying to make them more financially manageable ever since the recession set in. But recession or no, face-to-face meetings are rarely in the budget cards for small businesses. So how can entrepreneurs around the globe get more out of their virtual meetings while ensuring any physical meeting is worth the cost?