If you’re interested in building modern websites or applications which use ASP, XML, or mobile technology, you’ve heard of Visual Studio .NET.  It is one of the more popular suites of development tools available to aspiring programmers, as it consolidates several different tools and languages into the same development environment, which helps in turn to integrate this code across development languages.  Here are three important benefits to using the visual studio suite:

·         Use of Visual J# - This development tool is specifically oriented towards people who already are familiar with basic Java syntax, and is designed for use by those people to build apps or services which will then run on the Microsoft .NET Framework.  This is useful because it fully supports Microsoft Extensions, among other reasons.  Visual J# was developed completely independently by Microsoft.

·         Utility for Smart Devices – Another huge benefit of using visual studio .NET is the ability to immediately integrate your programming efforts with deployment across a variety of smart devices.  PDAs, smartphones, Pocket PCs, and any device which has a limited amount of resources all require a compact framework for the programming of applications it is designed to run.

·         XML Web Usage and Support – Because XML services aren’t married to any particular technology or programming language, they can be accessed by any system, and this broad-based utility has made the services increasingly popular.  Visual Studio .NET takes advantage of this by fully integrating with XML services, including the ability to create and edit them from scratch.

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 recent additions to the iPhone is the Photo Editor, directly in the iPhone. Added in the update that came from Apple over the summer, this new photo editor brings efficiency, and simplicity to photo editing, right in your phone. If you have a photo that you just took a moment ago of you with your friends, and you want to edit some features before posting it on a social networking site, it becomes simpler with this new addition, right in the Photos Application.

Open up the Photos application, and tap on a picture you would like to edit. Once your picture comes up, tap in the top right on the button named “Edit.” A user interface that deals with editing will show up, and you are ready to rock and roll. First off, many times we take pictures at weird angles, we take them sideways, upside down, to the right, to the left, and our phone doesn’t recognize them. In the bottom left, you will see an arrow that is pointing counter clockwise; this is the button that you want to press if you want to flip your picture around to the correct orientation. Keep in mind that this flips counter clockwise, and it doesn’t matter if you pass the orientation that you wanted. Just keep flipping!

Next up is the simple enhance tool. Sometimes colors get drowned out if we don’t have the right lighting in our pictures, and makes the photo look dull, and dreary. You don’t want your colors to look dull and dreary while you are celebrating your trip to New York and seeing Times Square! Tapping on the button that looks similar to a magic wand, your picture will begin to look brighter and fuller. With the tap of a button, the iPhone detects what points in the picture is, as we said earlier, “dull, and dreary” and enhances those colors to their predicted colors, if the light was in the correct intensity. However, if you are dissatisfied with the outcome of the enhance tool, if your picture is not handled well by the phone, you are able to tap on the wand again, and remove your auto enhance.

In the rare case of red eye in your picture, the new photo editor has a solution. Moreover, a one-tap solution. With a simple tap on the red eye correction tool, between the crop tool, and the auto-enhance tool, you bring up a screen where you are now able to tap anywhere on your photo where red eye exists, and remove it. As simple as that. Remember when you had to do crazy dragging, selection, and odd stunts to remove red eye? Not any more.

I suspect that many of you are familiar with the term "hard coding a value" whereby the age of an individual or their location is written into the condition (or action) of a business rule (in this case) as shown below:

if customer.age > 21 and customer.city == 'denver'

then ...

Such coding practices are perfectly expectable provided that the conditional values, age and city, never change. They become entirely unacceptable if a need for different values could be anticipated. A classic example of where this practice occurred that caused considerable heartache in the IT industry was the Y2K issue where dates were updated using only the last 2 digits of a four digit number because the first 2 digits were hard-coded to 19 i.e. 1998, 1999. All was well provided that the date did not advance to a time beyond the 1900’s since no one could be certain of what would happen when the millennia arrived (2000). A considerably amount of work (albeit boring) and money, approximately $200 billion, went into revising systems by way of software rewrites and computer chip replacements in order to thwart any detrimental outcomes. It is obvious how a simple change or an assumption can have sweeping consequences.

You may wonder what Y2K has to do with Business Rule Management Systems (BRMS). Well, what if we considered rules themselves to be hard-coded. If we were to write 100s of rules in Java, .NET or whatever language that only worked for a given scenario or assumption, would that not constitute hard-coded logic? By hard-coded, we obviously mean compiled. For example, if a credit card company has a variety of bonus campaigns, each with their own unique list of rules that may change within a week’s time, what would be the most effective way of writing software to deal with these responsibilities?