- Written by Carlos Alvarez
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Put simply, machine learning is a field of study in which we endeavor to teach computers cognition–the ability to reason, understand, and adapt beyond their explicit programming.
The concept was conceived about 50 years ago with the idea of making computers learn as humans do and it is closely related to that of artificial intelligence, where high-level mathematics simulate the patterns of thinking by making decisions based on gathered information rather than preordained choices. The high-level math in machine learning takes the form of algorithms, which are complex instructions written as formulas that the software uses to make predictive decisions.
The reason machine learning is only now topping the list of tech buzzwords is that nowadays we’ve achieved computational power enough to process big data: huge and unstructured data sets with possibly thousands of variables.
In traditional computer science, an algorithm inputs data and outputs data. machine learning algorithms are different: they input data and output other algorithms. In other words, machine learning differs from simple processing by absorbing data sets to form models, which allows the computer’s brain to make dynamic choices that evolve with additional information rather than making the same static choices no matter the input. Sounds like sci-fi? Maybe, but it is not. The way to address this is to apply an algorithm which would differ from the diligent but narrow “if-then” programs that we’re used to dealing with. Thus, machine learning isn’t limited to narrow-task execution.
Now well, what is the use of machine learning in software development? Neural Networks, data mining, natural language processing... There are many ways in which machine learning algorithms are used in software development, anyway a most interesting questión would be: what is the practical application of all of this in the real word?
Today we are surrounded by machine learning classifiers. They are there when Netflix suggests you a TV serie, or Amazon a new product (in fact, this determines the success of books and products much more than the quality of the book and products themselves). They are there when Google translates a website for you, when you ask something to Siri or when Google Assist recommends you to come out with an umbrella.
Let's deepen and see some practical applications that while may seem simple on the surface, actually are applying simulated reasoning that is the direct result of machine learning:
What began as a relatively straightforward indexing engine has grown into a search platform that has a mind of its own. We’ve seen how Google “remembers” previous searches and uses them to provide suggestions based on our interest, but it’s also applying weighted values based on a constantly evolving set of parameters. Behind the scenes, complex machine learning algorithms that are fed information daily from hundreds of millions of test cases. In this, Google Search is an example of an enormous neural network. Currently, any popular web search engines also use natural language processing as a means to understand spoken query and match it to tokens from an enormous repository of matching symbols. The combined efforts of neural networks and NLP are what makes modern search engines so dynamic.
BA are most effective when they have data mining capabilities backed by machine learning algorithms that are capable of recognizing patterns. Since business is a constantly evolving entity, static programming doesn’t have the agility to keep up with such a vast number of sources, and here is where machine learning comes into play.
As previously described, e-Commerce use machine learning to provide better service to consumers. Where we see these sites offering additional results under such banners as “people also search for”, the presented items are constantly changing based on the underlying algorithms which simulate the machine learning our shopping tendencies.
Everything you do, everything you use, everything you like, machine learning is there and certainly it is shaping a different world. Therefore, to those involved in software development, machine learning should be high on the list of skills to master. Meaning, it may not be long before some of those science fiction concepts become reality.
About Carlos Alvarez
Carlos is a Software Engineer with more than 8 years of experience developing Web and Mobile applications for some of the most important Fortune 500 companies
Nowadays Carlos works at the Engineering department of TISA, looking for implementing the latest technologies and frameworks to be used in future projects.
Beyond his technical knowledge and passion for the technology Carlos enjoys motorbikes and mixed martial arts.