Since its foundation, HSG has been a leader in Business Rule Management Systems Training and Consulting services by way of the Blaze Advisor Rule Engine.  Over the years we have provided such services to many of the worlds largest corporations and government institutions whose respective backgrounds include credit card processing, banking, insurance, health and medicine and more, much more.  Such training and consulting services have included:

Create a wrapper object model in either Java, .NET or XML

Identify and catalog business rules

Develop a rule architecture within Blaze Advisor that isolates rule repositories as they relate to functionality and corporate policies

Configure, develop and implement a variety of interfaces to the rule engine from disparate systems ranging from mainframe applications written in Cobol to UNIX/Windows applications using Enterprise Java Beans, Windows Services, Web Services, Fat Clients, Java Messaging Services and Web Applications.

Review and update code to boost efficiency either by way of

    Removing functions calls within conditional statements

    Ensuring that database calls are essential or can be rearchitected in some other manner

    Employing the rete algorithm where necessary

    Paring down extensively large class models

    Deploying such appliations in multi-threaded systems

·         ...

Call us if you:

    are in need of Blaze Advisor Expertise
    are developing SMEs in Blaze
    want to speak directly with an expert (no placement agencies)
    want an affordable alternative to FICO
    want to work with an industry leader

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.

Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance

There are a lot of articles you will find on the internet that talk about the tenants of having a successful professional career. From soft-skills to job relevant skills, there is an unending list of the do’s and don’ts for establishing a great career. However, a successful career in information technology commands some specific efforts and focus. As a result, it is critical to focus on these 4 key tenants that can help you establish a promising and successful career in Information Technology.

·         Be Multi-lingual– This is the analogy of Steve Job’s famous quote ‘Stay Hungry, Stay Foolish’ as it applies to Information Technology. Gone are the days when you could train yourself on a specific programming language say Java or C++ and code your way to a successful career. The best programmers of today and tomorrow are pushing the limits and becoming experts in one of more languages. Knowing more than one programming language instantly makes you more employable since you can add value to multiple projects that require different languages. If you need proof, IT professionals knowing more than one language can attract a salary premium of £10,000 . Additionally, there is no telling how dynamic technology is and by being open to constantly learning new languages you will position yourself to get technology jobs that did not exist a few years ago

·         Go Beyond the ‘How’, Focus On ‘Why’: A common theme with most information technology professionals is their ability to figure out the HOW or, in other words, applying their technical know-how in achieving the solution to a problem. This is especially true when you are working for a service based IT organization where your key job is to develop a solution for the client’s business problem. Yes, you are and will get paid to be good at the ‘How’ but to advance a career in IT; it will help you immensely to also start focussing on the ‘Why’. This stems from a famous quote by Einsten “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it”. In essence, spend time in understanding ‘Why’ are your trying to solve the problem before you get down with figuring out the ‘How’ part. The reasons for developing this mindset are two-fold. One, you will instantly distinguish yourself from thousands of other IT peers who are content with the ‘How’ part. Two, there is a good chance that you want to get ahead in your career not only as a programmer but as a system architect or a business solution consultant. This is where the habit of asking the right questions pertaining to why a certain IT solution is requires will help you build the right solution.

·         Focus on the impact and results (Financial impact):This may not apply to IT professionals who are early in their careers but is paramount for senior IT professionals. For the most part, IT departments are required to make sure that the systems and the solutions function as desired and help the business run efficiently. In other words, the key metric for success for most IT professionals is being extremely good at technology, languages and Quality Assurance. However, the times are changing! No longer is the Chief Information Officer (CIO) in charge of making IT decisions. With organizations closely guarding the ROI of their investment in technology, CIOs are increasingly required to be cognizant of the financial benefits of technology so that they can justify the spending on IT. No wonder than that CFOs are increasingly pressurizing CIOs to get their act together