Attempting to create an intelligent entry about Linux for such a wide ranging audience is a bit like trying to compare a presentation on quantum mechanics for physicists while being readable by somebody with a junior high school education.

If you are a *NIX expert, then I suggest you keep on reading so you know how to explain this world to mere mortals.

I called it *nix for a reason. It represents a family of thinking. It is not unlike the difference between the language C and C++. Are there differences between the two? Certainly. And it is not like the difference between COBOL and C.

Years ago when I ran my own vo-tech school I would teach the students there are only two things in life. MS and *NIX. A friend of mine, a genius developer in her own right was aghast.

Sage wisdom states that there are two sides to every coin. This timeless wisdom will be borne out in spades with Windows 8/RT. Let's get into the dark side first.

If your users are veterans of Windows it is safe bet they are going to take one look at Windows 8 and scream blasphemy. Users whose brains are geared towards visual learning will undoubtedly yell the loudest and longest.

There's a good reason for this. Mick Jagger brought his band to the Redmond campus, performing live "Start Me Up" in the summer of 1995 (it was a great show). This heralded in the abandonment of program icons sitting on the desktop and introduced the now legacy Start button.

Ending the life of the 17-year-old start button is not going to go well with some users.

Static variables in Python are created as part of the class declaration.  By contrast, instance variables are created as part of a regular method and not a classmethod or staticmethod.

class A:
  i=3  # static variable
	
  def dosomethingregularmethod(self):
       self.k=4   # instance variable
	
# to access static variables


A.i

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.”