The Zen of Python, by Tim Peters has been adopted by many as a model summary manual of python's philosophy.  Though these statements should be considered more as guideline and not mandatory rules, developers worldwide find the poem to be on a solid guiding ground.

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

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.”

Not too long ago, Apple added something phenomenal to the iPhone OS: a dashboard screen. If you have a Macintosh computer, you may be familiar with the dashboard that is available (regularly) by pressing F4. Otherwise, you can draw similarities to your Windows 7 Dashboard on the right hand side of your desktop, that shows you updates on your applications and widgets you add to it. Finding your dashboard on your iPhone is just as easy: just put your finger on the top of your iPhone screen, and drag down.

Here, in your dashboard, you will see all of the updates that has been pushed into such by your applications that desire to send you messages: things like new text messages, new updates to your subscribed magazines, your messages on payment applications. If you have reviewed a message set by an application by tapping on it, that message will automatically become deleted. However, if you don’t desire to go into the application to delete it, simply tap in the top right on the bar that categorizes that particular application, and tap again to clear all of the messages set by that application, and clear up your dashboard.

But, your dashboard isn’t all about your application. You not only get your messages, but you get important information set by default applications, such as the weather. If you don’t feel like scouting out your weather application amidst all your applications you have downloaded, simply go into your dashboard, and find out the forecast for the whole week, just by a simple swipe. Not only that, tickers for your stocks are displayed near the bottom of the dashboard.

I remember the day like it was yesterday. Pac Man had finally arrived on the Atari 2600.  It was a clear and sunny day, but it was slightly brisk. My dad drove us down to the video store about three miles from our Michigan house. If I remember correctly, the price for the game was $24.99.  It was quite expensive for the day, probably equaling a $70 game in today’s market, but it was mine. There *was* no question about it. If you purchase a game, it’s your game… right?

You couldn’t be more wrong.  With all the licensing agreements in games today, you only purchase the right to play it. You don’t actually “own” the game. 

Today, game designers want total control over the money that comes in for a game. They add in clauses that keep the game from being resold, rented, borrowed, copied, etc. All of the content in the game, including the items you find that are specifically for you, are owned by the software developer. Why, you ask, do they do this? It’s all about the money.

This need for greed started years ago, when people started modifying current games on the market. One of the first games like this was Doom. There were so many third part mods made, but because of licensing agreement, none of these versions were available for resale. The end user, or you, had to purchase Doom before they could even install the mod.  None of these “modders” were allowed to make any money off their creation.