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What you need to know about Machine Learning

Updated: Sep 24, 2018

There's huge hype regarding Machine Learning. You can find news like "Machines created a language between them", " Is Skynet real?", and “Machines will betaking everyone's job in the near future".

Some news are accurate, others not so much. Let's go through the basics of Machine Learning in order to have a better understating.

What is Machine Learning?

Machine learning is a technique that uses algorithms that teaches a computer to learn how to have an answer to a question. The question is called an input and the answer an output. The output is created by rules and algorithms which would be considered the program itself.

Think of a machine that can play chess. Do you remember chess champions playing against machines? That was while ago, which tells us something,Machine Learning has been around us for a very long time.

Why now?

Short Answer? Big Data, Computing Power. Long answer: We live in an age where the processing power held in our hands is far more powerful than the one they had when Machine Learning was a new topic of conversation.

Machine learning allows the machine to make a decision based on an algorithm but that's a really short description.

It important to understand how it learns.

Algorithms are run over large number of tests, each test allows the machine to understand what's the output needed for each input. Going back at our chess program this means that hundreds or millions of chess matches were uploaded into the algorithm so the machine can learn faster how to beat the opponent.

The amount of tests that these machines can process nowadays is huge, a software can process thousands of terabytes (Big Data) in a logical timeframe.

10 years ago that was unthinkable, we are living in a time period that even your cellphone can process great amounts of data in very little time.

What are its limitations?

The learning process itself has its limitations. Without diving into too much into detail like something like Solomonoff's Induction Problem or Naturalized Induction.

The main limitation is that the machine can't give an output that defies all the possible known answers (or outputs) to the problem yet.

The machine will always have an output that was learnt and won't be able to create a new output that wasn't in any test or case learnt.

People are working to solve this problem and trying to discover how can we create a creative program that can find answers yet to be learnt.

That's the main worry from well known Tech Guru's such as Elon Musk that says that there should be an organization that controls AI development.

We will be talking about machines taking our jobs, end of the world theories and how we can harness such power to build a better tomorrow in other posts. You are free to comment if you liked the article or you want any more insights in any subjects. I'm far from an expert in any of them but will surely be glad to help you find your path.



Federico Marinic name is an electronic engineer who graduated in Buenos Aires, Argentina from the University of La Marina Mercante and received an award from Argentina's National Engineering Academy. He would describe himself as a curious mind who can never stop learning. He's currently the systems manager at Carfacil. His expertise includes project development and implementation, business intelligence, and data analytics. His interests are mainly in the tech industry (machine learning, virtual reality, and augmented reality) and how to integrate those technologies into daily life (education, finance, politics, etc).

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