How many times have we heard about Machine Learning and Artificial Intelligence lately? Many, right? But do we really know what they mean or how they work? Probably not. 

 

Adding to the confusion, the terms Machine Learning and Artificial Intelligence (AI) are often used as equivalents, although technically they are not the same thing. 

 

To better understand the relationship between the concepts, it might be helpful to think of something completely different. Let's imagine a matrioska, that Russian doll (whose nationality is a matter of debate, but for this analogy, it's as much Russian as Japanese) that contains a set of similar dolls, but of varying sizes, inside. 

 

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Can you visualize? Very good. The oldest and largest doll is Artificial Intelligence (born in the 50s of the 20th century), the middle one is Machine Learning (a few years younger than the first one), and the last and most recent one is called Deep Learning (which fits inside the other two). 

 

In most cases, when industry and media talk about AI, they are referring more specifically to Machine Learning.

 

What is Machine Learning then made of and why is it so important?

 

While Artificial Intelligence is the most comprehensive area of ​​science that deals with mimicking human capabilities, Machine Learning is a specific branch of AI that trains systems to learn. Deep Learning, on the other hand, is a form of Machine Learning that uses a specific class of algorithms — called neural networks (maybe we'll look more closely at this in another opportunity). 

 

Machine Learning models look for patterns in large amounts of data, learn from that data, and then try to draw conclusions or make predictions about something. Thus, instead of a programmer writing code with specific instructions on how the software should perform a certain task, the system is able to learn by itself through examples. 

 

Essentially, Machine Learning is what allows systems to learn, without having to be programmed.

 

Let's think that this particular thing is the image of a cat. For us humans, who have millions of years of evolution in our favor, looking at the picture of the pussy and inferring that what we are seeing is an animal, more precisely from the Felid family, is a relatively simple task. Even so, and without realizing it, our brain had to do (in a fraction of a second) a series of calculations to reach what seems to us an obvious and natural conclusion: it is a cat. 

 

Now imagine the same task performed by a computer. What is the best approach to instructing the system what a cat is? Write countless lines of code that define the animal's characteristics? Or show the system several (millions to be exact) examples of cats and other animals and let it figure out which is what? 

 

That's why Machine Learning is so important, because it allows you to automate tasks that until now required a human at the controls. 

 

The renewed interest in Machine Learning in recent years, and in AI in general, has mainly to do with three things: the amount and variety of data we now have at our disposal, an increasingly powerful computational capacity, and storage more secure and affordable. 

 

Together, these factors make it possible to create robust models capable of analyzing larger and more complex volumes of data, in order to return increasingly faster and more accurate results. Which means companies can now take advantage of these models to identify more profitable business opportunities as well as avoid unnecessary risks.

 

Source: Property News

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