Not getting any hits on your dating profile? It doesn’t mean you’re a loser. There are literally millions of computer dating profiles out there and dozens of free and paid websites to find a date. A dating profile is kind of like a resume for your personal life. To succeed, you have to learn a few tricks to stand out.

Tips to Improve Your Computer Dating Profile

1.  Make a Good First Impression

Filling out an online dating profile can be a daunting task. When you’re faced with all that blank space to write about yourself, it gets intimidating. The best way to improve your computer dating odds is to make a good first impression. Start with a good opening line such as a favorite quote or a personal motto you can easy build on.

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?

A whole new world was thrown wide open with the advent of the concept of open source. The biggest advantage of open source projects is the easy availability of the source code and also the rights to tweak it or modify the code as we deem fit.

Listed below are some top open source projects that are making waves with their innovative ideas at this moment.

ProjectLibre

Project Libre is the open source replacement of Microsoft Project. It was one among the top 10 open source projects of 2013. With over 6 million downloads; it has most certainly captured the imagination of people around the world. Details of project Libre could be found at http://www.projectlibre.org/ . Project Libre has separate release for mainframes. It also has a web based version which further increases its overall appeal. One of the main advantages is that it has a list of tasks that can be tracked to closure. It can also be used in conjunction with LibreOffice to provide a great set of tools to the team leaders.

Diaspora

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”