Eventually the keyboard of a smartphone is able to complete a sentence that we started. Who ever, when starting a browser search, saw the software indicate exactly what they were looking for? This is the concept behind Machine Learning, a branch of artificial intelligence that aims to make systems learn to behave more intelligently based on a large amount of data.
While the idea of AI is to make machines in a certain way “think” like humans, Machine Learning automates processes, creating shortcuts and seeking to predict actions according to user behavior or by analyzing information from a multitude of sources.
As a system that behaves by analyzing data, Machine Learning uses the user’s information to create a line of learning according to the registered behavior. So you may ask yourself, “when did I pass information about my behavior to the machine?”. The answer is: every time you surf the internet, use online services or use a connected device.
Companies like Google, Microsoft and Amazon are responsible for much of the data traffic from various services, such as search engines, e-mail services and e-commerce. These companies have huge computational centers (Big Datas) and receive information about what people are looking for, talking about or even wanting to buy. This happens through algorithms that are able to analyze data from different sources, such as social networks, research histories and the like, and the machine can “understand” the user’s behavior and create different profiles according to location, age group and common interests.
Machine Learning is not simply automation, but understanding routines to establish a working pattern, for example: in a smart home, the owner leaves in the morning and always comes back around 6 pm; when you get home, the lights are turned on automatically and the coffee maker is turned on to make an afternoon coffee. What if the person arrives early so that they don’t have to turn on the lights? And on a hot day, would it not be more interesting to have a drink or water instead of a coffee? This is exactly where Machine Learning can make a difference.
Analyzing the user’s behavior, a machine-learning system is able to only activate the house lights if necessary and can use the room temperature to consider whether it is more interesting to start the coffee maker or send a message to the owner recommending drinking more water in days with low humidity. All of this can be based on searches performed on the smartphone, trends in social networks and mainly cross-data.
In addition to browsers that indicate the best results according to navigation, Machine Learning is used in services such as streaming platforms that indicate content related to what you have recently watched, mobility apps that show the best path according to traffic flow and, of course, operating systems that are capable of creating assistants that behave more and more like real virtual secretaries.
Machine Learning is also very widespread in IT security systems and has more and more solutions that benefit from technological advances to implement machine learning in new areas, such as meteorology and medicine.