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.

When you think about the black market, I’m sure the majority of you will think of prohibition days.  When alcohol was made illegal, it did two things: It made the bad guys more money, and it put the average joe in a dangerous position while trying to acquire it.  Bring in the 21^stcentury. Sure, there still is a black market… but come on, who is afraid of mobsters anymore? Today, we have a gaming black market. It has been around for years, but will it survive? With more and more games moving towards auction houses, could game companies “tame” the gaming black market?

In the old days of gaming on the internet, we spent most of our online time playing hearts, spades… whatever we could do while connected to the internet. As the years went by, better and better games came about. Then, suddenly, interactive multiplayer games came into the picture. These interactive games, mainly MMORPGS, allowed for characters to pick up and keep randomly generated objects known as “loot”. This evolution of gaming created the black market.

In the eyes of the software companies, the game is only being leased/rented by the end user. You don’t actually have any rights to the game. This is where the market becomes black.  The software companies don’t want you making money of “virtual” goods that are housed on the software or servers of the game you are playing on.  The software companies, at this point, started to get smarter.

Where there is a demand…

In the ever changing landscape of software programming, it is not surprising that developers and employees have a different set of preferences for desired skills.  However the number one language that developers want to learn according to a survey of developers by technical recruiter, Hacker Rank is Python. This is not a surprise considering that Python has been in demand for several years and programmers tend to really enjoy this language for clear syntax, good OOP support and great shortcuts. Python, named “the language of the year” in 2007 and 2010 in the TIOBE Index and has climbed to #4 status in May of 2018.

According to the study, employers want developers who:

-  Have problem-solving skills, such as the ability to break down large, complex problems.
- Are proficient in their programming language and debugging.
- Can design systems.
- Can optimize performance.
- Have experience in reviewing and testing code.
- Are proficient in database design

Surprisingly, formal education is not the deciding factor when it comes to what companies care about the most. People with computer degrees or certifications on a resume are not necessarily a first choice for hiring managers. Others that have years of experience even if those individuals are partially self-taught in the field stand to be taken seriously in the field.   For those individuals with a passion to learn and master a skill, there are ample opportunities with smaller to mid-sized companies.

Some interesting FAQ’s from the study:

    On average, developers know 4 languages, and they aspire to learn 4 more.
    Younger developers between 18 and 24 plan to learn 6 languages.
    Folks older than 35 only plan to learn and additional 3 languages.
    The top languages developers said they will learn were, Go, Python, Scala, Kotlin, and Ruby.
    There is a large gap between employers seeking developers that know React than there are folks that can do it.

So, Why Learn Python?
It is now the most popular introductory teaching language in U.S. universities.  Python is easy to use, powerful, and versatile, making it a great choice for beginners and experts alike. It allows you to think like a programmer and not waste time understanding difficult syntax that other programming languages can command. And, because of its rapid growth, many developers contribute to the Python community and share Python libraries making creativity that much more a reality

Over time, companies are migrating from COBOL to the latest standard of C# solutions due to reasons such as cumbersome deployment processes, scarcity of trained developers, platform dependencies, increasing maintenance fees. Whether a company wants to migrate to reporting applications, operational infrastructure, or management support systems, shifting from COBOL to C# solutions can be time-consuming and highly risky, expensive, and complicated. However, the following four techniques can help companies reduce the complexity and risk around their modernization efforts. 

All COBOL to C# Solutions are Equal 

It can be daunting for a company to sift through a set of sophisticated services and tools on the market to boost their modernization efforts. Manual modernization solutions often turn into an endless nightmare while the automated ones are saturated with solutions that generate codes that are impossible to maintain and extend once the migration is over. However, your IT department can still work with tools and services and create code that is easier to manage if it wants to capitalize on technologies such as DevOps. 

Narrow the Focus 

Most legacy systems are incompatible with newer systems. For years now, companies have passed legacy systems to one another without considering functional relationships and proper documentation features. However, a detailed analysis of databases and legacy systems can be useful in decision-making and risk mitigation in any modernization effort. It is fairly common for companies to uncover a lot of unused and dead code when they analyze their legacy inventory carefully. Those discoveries, however can help reduce the cost involved in project implementation and the scope of COBOL to C# modernization. Research has revealed that legacy inventory analysis can result in a 40% reduction of modernization risk. Besides making the modernization effort less complex, trimming unused and dead codes and cost reduction, companies can gain a lot more from analyzing these systems. 

Understand Thyself 

For most companies, the legacy system entails an entanglement of intertwined code developed by former employees who long ago left the organization. The developers could apply any standards and left behind little documentation, and this made it extremely risky for a company to migrate from a COBOL to C# solution. In 2013, CIOs teamed up with other IT stakeholders in the insurance industry in the U.S to conduct a study that found that only 18% of COBOL to C# modernization projects complete within the scheduled period. Further research revealed that poor legacy application understanding was the primary reason projects could not end as expected. 

Furthermore, using the accuracy of the legacy system for planning and poor understanding of the breadth of the influence of the company rules and policies within the legacy system are some of the risks associated with migrating from COBOL to C# solutions. The way an organization understands the source environment could also impact the ability to plan and implement a modernization project successfully. However, accurate, in-depth knowledge about the source environment can help reduce the chances of cost overrun since workers understand the internal operations in the migration project. That way, companies can understand how time and scope impact the efforts required to implement a plan successfully. 

Use of Sequential Files 

Companies often use sequential files as an intermediary when migrating from COBOL to C# solution to save data. Alternatively, sequential files can be used for report generation or communication with other programs. However, software mining doesn’t migrate these files to SQL tables; instead, it maintains them on file systems. Companies can use data generated on the COBOL system to continue to communicate with the rest of the system at no risk. Sequential files also facilitate a secure migration path to advanced standards such as MS Excel. 

Modern systems offer companies a range of portfolio analysis that allows for narrowing down their scope of legacy application migration. Organizations may also capitalize on it to shed light on migration rules hidden in the ancient legacy environment. COBOL to C# modernization solution uses an extensible and fully maintainable code base to develop functional equivalent target application. Migration from COBOL solution to C# applications involves language translation, analysis of all artifacts required for modernization, system acceptance testing, and database and data transfer. While it’s optional, companies could need improvements such as coding improvements, SOA integration, clean up, screen redesign, and cloud deployment.