1. The IRS is enabling taxpayers to download their tax transcripts over the internet from the Internal Revenue Service. The official secure URL for the transcripts download followed immediately after The White House fact sheet confirmed the decision. According to the Tech Crunch You Can Now Download Your Tax Returns From The IRS  article, there are some minor bug issues when registering. Given that security has long been problematic with the IRS, the best course of action for the public is to take some precautions when downloading personal information on mobile devices, pc’s and laptops. Installing disk encryption software on all your devices will protect your files reduce risk
2. Have an awesome Start-Up with products in ‘stealth or private beta” that you want like to pitch for a chance to get a table at Disrupt NY? TechCrunch is heading to Atlanta and New Orleans February 18^th and 20^th 2014 and will host a beer fest night in exchange for your thoughts and pitches. Tickets are $5, and all entrepreneurs, investors, dreamers or tech enthusiast are welcome.
3. Cloutex, a cloud computing future enabler has just secured their first round of seed funding.  Cloutex is an Estonian tech startup, which connects and syncs various web applications such as e-marketing, CRM and accounting tools under one convenient hub. The United Partners, SmartCap and EstBan business angels seeded the start-up with $6000, 000.
4. Could you get everything important that you need to know about living successfully from a computer program? Ryan Dube seems to think so. In his 6 Life Habits That Programming Could Teach You Today  he speaks in flow charts, loops and program modules, in the same breath as paying off debt or buying a house.  How can your programming skills help in life skills by modeling some of the same principles?
5. 10 Incredibly Simple Things You Can Do To Protect Your PrivacyPassword protect your devices.  Put alerts on your name in Google; simple tasks that can be significant in protecting your privacy.
6. Adobe Brings 3D Printing Support to PhotoshopAmong the latest updates in Adobe, Photoshop users have the ability to design 3D models from scratch as well as being able to toy around with color, shape and angles.

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.”

Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance