The innovators in technology have long paved the way for greater social advancement. No one can dispute the fact that the impact of Bill Gates and Microsoft will be far reaching for many years to come. The question is whether or not Microsoft will be able to adapt and thrive in emerging markets. The fact that Microsoft enjoys four decades of establishment also makes it difficult to make major changes without alienating the 1.5 billion Windows users.

This was apparent with the release of Windows 8. Windows users had come to expect a certain amount of consistency from their applications. The Metro tile, touch screen interface left a lot to be desired for enough people that Microsoft eventually more thoroughly implemented an older desktop view minus a traditional Start menu.

The app focused Windows 8 was supposed to be a step towards a greater integration of Cloud technology. In recent years, Microsoft lagged behind its competitors in getting established in new technologies. That includes the billions of dollars the emerging mobile market offered and Cloud computing.

Amazon was the first powerhouse to really establish themselves in the Cloud technology market. Google, Microsoft, and smaller parties are all playing catch up to take a piece of the Cloud pie. More and more businesses are embracing Cloud technology as a way to minimize their equipment and software expenses. While it does take a bit for older businesses to get onboard with such a change, start ups are looking at Cloud computing as an essential part of their business.

But what does that mean for Microsoft? Decisions were made to help update the four decade old Microsoft to the "always on" world we currently live in. Instead of operating in project "silos", different departments were brought together under more generalized headings where they could work closer with one another. Electronic delivery of software, including through Cloud tech, puts Microsoft in the position of needing to meet a pace that is very different from Gates’ early days.

The seriousness of their desire to compete with the likes of Amazon is their pricing matching on Cloud infrastructure services. Microsoft is not a company that has traditionally offered price cuts to compete with others. The fact that they have greatly reduced rates on getting infrastructure set up paves the way for more business users of their Cloud-based apps like Microsoft Office. Inexpensive solutions and free applications open the doors for Microsoft to initiate more sales of other products to their clients.

Former CEO Steve Ballmer recognized there was a need for Microsoft to change directions to remain competitive. In February 2014, he stepped down as CEO stating that the CEO needed to be there through all stages of Microsoft's transition in these more competitive markets. And the former role of his chosen successor, Mr. Satya Nadella? Head of Microsoft's Cloud services division.

Microsoft may not always catch the initial burst of a new development in their space; but they regularly adapt and drive forward. The leadership of Microsoft is clearly thinking forward in what they want to accomplish as sales of PCs have stayed on a continuous decline. It should come as no surprise that Microsoft will embrace this new direction and push towards a greater market share against the likes of Amazon and Google.

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It is rather unfortunate that in the ever changing and rapidly improving world of technology, we hardly remember the geniuses who through their inventions laid the foundation for many of the conveniences and features we now enjoy in our favorite communication devices.

This article is a tribute to the ten people who made these discoveries and an attempt to bring their achievements into the limelight.

1.      Marty Cooper

Did you know that Cooper was the first to file the patent in 1973, when he was already working for Motorola for the “radio telephone system”. The Cooper’s Law is his brainchild and to think that he himself was inspired to come out with the patent was Star Trek and its Captain Kirk is indeed revealing.

2. Mike Lazardidis
 

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.

F# is excellent for specialties such as scientific computing and data analysis. It is an excellent choice for enterprise development as well. There are a few great reasons why you should consider using F# for your next project.

Concise

F# is not cluttered up with coding noise;  no pesky semicolons, curly brackets, and so on. You almost never have to specify the kind of object you're referencing because of its powerful type inference system. It usually takes fewer lines of code to solve the same issue.

Convenient

Common programming tasks are much easier in F#. These include generating and using state machines, comparison and equality, list processing, as well as complex type definitions. It is very easy to generate powerful and reusable code because functions are first class objects. This is done by creating functions that have other functions as parameters or that combine existing functions to generate a new functionality.

Correctness

F# has a strong type system, and, therefore, prevents many common errors such as null reference exceptions. Valuables are immutable by default which, too, prevents a huge class of errors. You can also encode business logic by utilizing the type system. When done correctly, it is impossible to mix up units of measure or to write incorrect code thereby decresing the need of unit tests.

Concurrency

F# has number of built-in libraries. These libraries help when more than one thing at a time is occurring. Parallelism and asynchronous programming are very simple. There is also a built-in actor model as well as excellent support for event handling and functional reactive programming. Sharing state and avoiding locks are much easier because data structures are immutable by default.

Completeness

F# also supports other styles that are not 100 percent pure. This makes it easier to interact with the non-pure world of databases, websites, other applications, and so on. It is actually designed as a hybrid functional/OO language. F# is also part of the .NET ecosystem. This gives you seamless access to all the third party .NET tools and libraries. It operates on most platforms. These platforms include Linux and smartphones via mono. Visual Studio is integrates with F# as well. This means you get many plug-ins for unit tests, a debugger, a IDE with IntelliSense support, other development tasks. You can use MonoDevelop IDE on Linux.

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