Computers. They’re a part of our everyday lives. Most of us couldn’t imagine living a day without them. We use them for school, work, and fun and use them to stay connected to those we love and care about. Since the invention of the web cam, millions of us use webcams to communicate with loved-ones and business contacts far away.

Web camera use has leveled the playing field for business entrepreneurs and given teenagers a fun way to chat with friends. However, solid citizens aren’t the only ones who make use of this popular modern technology. Recently, there have been reports of criminals using a type of webcam spy hack to insert themselves unseen into the living rooms and bedrooms of millions of unsuspecting users.

The Webcam Spy Hack

The most popular way criminals gain access to your webcam is through innocent-looking emails. You may receive an e-card from someone in your contacts list. When you click on it, you’re directed to another website to view the e-card. While you’re listening to music and watching animated puppies scroll across the screen, a Trojan horse is silently installed into your computer’s hard drive.

A business rule is the basic unit of rule processing in a Business Rule Management System (BRMS) and, as such, requires a fundamental understanding. Rules consist of a set of actions and a set of conditions whereby actions are the consequences of each condition statement being satisfied or true. With rare exception, conditions test the property values of objects taken from an object model which itself is gleaned from a Data Dictionary and UML diagrams. See my article on Data Dictionaries for a better understanding on this subject matter.

A simple rule takes the form:

if condition(s)

then actions.

An alternative form includes an else statement where alternate actions are executed in the event that the conditions in the if statement are not satisfied:

if condition(s)

then actions

else alternate_actions

It is not considered a best prectice to write rules via nested if-then-else statements as they tend to be difficult to understand, hard to maintain and even harder to extend as the depth of these statements increases; in other words, adding if statements within a then clause makes it especially hard to determine which if statement was executed when looking at a bucket of rules. Moreoever, how can we determine whether the if or the else statement was satisfied without having to read the rule itself. Rules such as these are often organized into simple rule statements and provided with a name so that when reviewing rule execution logs one can determine which rule fired and not worry about whether the if or else statement was satisfied. Another limitation of this type of rule processing is that it does not take full advantage of rule inferencing and may have a negative performance impact on the Rete engine execution. Take a class with HSG and find out why.

Rule Conditions

Straight up and full disclosure. I'm prejudiced. As a research assignment, the heading is a joke. I'll give you the answer in two words, and then tell you why.

How does HTML 5 compare with flash? Answer: it doesn't.

Lest you think I dislike Adobe's Flash, let's put the cards on the table. I loved Flash. Long before Adobe was Adobe, they had a competitor called Macromedia. Adobe bought that firm. That made my life simpler. I only had to work with one vendor.

Flash was a pretty compelling solution. I used it to mimic operations in Windows to prepare people for the CompTIA exams. The only bugaboo was that dang right-click stuff. A little bit of code from the Microsoft Visual Studio .Net let me flip the left and right mouse buttons so that the right mouse button instead of controlling the Flash player, emulated doing a right-click in the Windows operating system.

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.”