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?

Structure Rule Language

To aid in the ease of rule authoring, Blaze Software, now Fair Isaac, created the proprietary Structure Rule Language (SRL), an object-oriented programming language designed to enable those with little or no background in software development to pen rules. Although the capabilities of this language are far too extensive to detail in this article, we can examine the basic rule syntax.

Rules in the SRL take the following form:

rule RuleName [at | priority M] is

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.

Viruses, trojans, and other malicious programs are everywhere. There's always a new threat to your computer's security, and many of these threats invade your computer without you even knowing. Most viruses aren't going to loudly announce themselves, so it's important to know the hidden ways in which your computer can become infected.


Infected Files from Other Computers

Whether you're borrowing someone's flash drive or grabbing a file from their computer, your computer can become infected if the file or device you're using already contains a virus, trojan, or other form of malware.

This is a very common issue, and you won't even know there's a problem most of the time. For example, if your computer is connected to other devices on a network, and you decide to pull an important file off of another computer on the network, your computer will become infected if the file you took has a virus attached to it.

Also, if you forgot your flash drive, and you need to use your friend or coworker's device for the day, then even plugging the device into your computer can cause the infection in the flash drive to be transmitted.


Downloading Legitimate Programs

Another way your computer can be secretly infected is when you download a legitimate program and run it. There are numerous legitimate programs on the internet that can help you in many ways. The programs themselves could be infected, though.

Also, one of the most common ways your computer can become infected is when you don't read the fine print before you download a program. Some of them may insist that you install another small program in addition to the one you initially chose. The boxes that you are supposed to click to give your consent may already be clicked.

This small extra program is the one that may carry an infection that will spread to your computer when you run the main program. You may get a lot of good use out of the legitimate program, but the virus attached to the extra hidden program can cause you a lot of trouble.


Using Vulnerable Applications

Security is a serious matter. If even one of the applications you use on your computer is vulnerable to becoming hacked or infected, then your entire computer is at risk and could become secretly infected. Anything from PDF viewing applications to your operating system can become infected if you don't download the latest security patches and keep everything up-to-date.


Not Using Antivirus Software

Antivirus software can protect your computer from a number of viruses, trojans, and other problems. Your computer can become infected in a number of ways, so you need to have good antivirus software to provide strong protection from hidden attacks.


Viruses, trojans, and other malware can infect your computer in a variety of hidden ways. To prevent infection and problems, you need to be careful about what you download, and you should keep your applications secure. Also, find reliable antivirus software to help.

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