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

With stiff penalties for being caught and the whiff of secretive underground or even nefarious acts, computer hacking can be seen as a somewhat dubious pursuit. Not all hackers operate with the motive of emptying your Paypal account, however; there are many hackers who utilize their skills to aid companies in locating security flaws ("penetration testing") or engage in hacking with the goal of becoming cyber-freedom-fighters that champion simple human freedoms, such as the right to free speech.

Computer hacking is as much an art as it is a skill. At its simplest distillation, hacking is the systematic search for chinks in programming armor. While advanced problem-solving, intuition and sophisticated understanding of programming languages are a distinct advantage, there does exist a number of push-button programs that computing wizards have written allowing those less sophisticated in the art of hacking to break into remote computers in a variety of ways. Because of this new ubiquity, today's hackers no longer need to be a programming Wunderkind; they simply need to know where to download software and be able to turn on a computer. It really is that simple and the implications can be disturbing.

Phishing, Push-Button Programs and Brute Force Tactics

There's no need to crack a company's firewall if you have direct physical access to their computers. One aspect of hacking is the impersonation of an employee or service worker with the goal of gaining access to a company's database, where the hacker can then unleash whatever havoc he or she has planned into the system. Another is to engage in simple phishing techniques, such as impersonating an employee who forgot their password and needs help logging into the system.

Because such impersonations often fail thanks to companies becoming more security-conscious, taking over operations of a computer remotely is often the preferred method of gaining access. Such attempts can be facilitated in a variety of ways. One is the brute-force method, in which a program such as SQLmap, Nmap or Burpsuite is used; running one of these programs is analogous to trying every doorknob in a neighborhood to see which house is unlocked. Using a variety of different parameters, these programs can find access to a vulnerable computer or network in less than a minute.

Hackers can also attempt to gain access with a program like Metasploit. With literally a few clicks of a mouse, access to a remote and vulnerable computer can be achieved by a relative newbie. With a related hacking aid, called Meterpreter, a backdoor is created that allows access into an operating system. It does not install itself onto the remote computer, running instead using the computer's memory; in fact, Meterpreter can hide itself inside the operations of a perfectly valid program, so it cannot be detected even by sophisticated programmers. Once engaged, it allows a remote user carte blanche access to the system in question.

Where to Learn the Art of Hacking

Of course, for those who wish to learn the actual skills rather than download someone else's hack, there are a number of practice sites that pose an increasingly difficult set of challenges intended to train neophytes in the art of hacking. For example, Hack This Site starts beginners with the goal of cracking simple flaws in coding scripts or software such as HTML, Unix, Javascript and Apache. Their structured series of tests increase in complexity, incorporating real-word scenarios and even old-fashioned "phone phreaking" challenges that recall the bygone golden age of hacking skills displayed by Matthew Broderick in "WarGames."

Using just these simple tools and free practice sites, beginners have a powerful array of hacking resources just a simple mouse click away.

In this tutorial we are going to take a look at how you work with strings in Python. Now, any language worth its salt will have a number of options for working with text and Python is probably one of the best to use when it comes to processing text.

If you are new to programming in general you may be wondering what a string is. In terms of programming, a string is classed as any sequence of characters you can type with your keyboard, and let’s face it, if you want your application to be of any use to yourself or other users then you need it to tell you what it’s doing or to prompt you for an action, and that is where strings come into play.

They are your applications way of communicating with the user. Without the ability to enter and display text or software would be pretty useless.

So, how would you create a string in Python? Take a look at the following code: