Practical examples of Machine Learning
In 1950 the mathematician Alan Turing first posed a question: can machines learn to think? Thus began the history of Machine Learning . In 1952, Marvin Minsky created SNARC, the first artificial self-learning machine that learned to solve a task without being explicitly programmed to do so.
However, at that time data availability and computing power were scarce, so the world entered the first winter of Artificial Intelligence . At the end of the 20th century, the arrival of the internet, the increase in the computing power of computers and the large amounts of information available gave a boost to this technology.
In 1997 the history of Machine Learning took a turn with the IBM Deep Blue system , which managed to defeat the world chess champion, Garri Kasparov. This technology has continued to advance and we can already find multiple examples of Machine Learning in different sectors.
WHAT IS MACHINE LEARNING?
Machine Learning is a form of artificial intelligence that allows a system to learn from data, instead of learning through explicit programming. As the algorithm processes the training data, it makes inferences from new data sets for which it had not been previously trained, so that it creates more accurate models, predicts future scenarios, and / or automatically takes action under certain conditions.
This technology allows automating different operations to reduce human intervention, which represents a great advantage in terms of time and resources. It can analyze a huge amount of data in a few minutes that would take a team of people months or even years.
In addition, Machine Learning can detect complex patterns that escape statistics by examining large unstructured data sets more efficiently. That is why more and more companies use it to analyze their data, find meaning, detect new business opportunities and make more strategic decisions.
EXAMPLES AND APPLICATIONS OF MACHINE LEARNING
We are surrounded by practical examples of Machine Learning. The recommendation engines like those used Netflix, Amazon or Spotify are one of them. This technology not only takes into account our consumption habits and preferences but also those of millions of users with a profile similar to ours and new trends to recommend products that fit our tastes, interests and needs.
The attendees voice , like Siri and Alexa, are another example of Machine Learning in everyday life. This technology is capable of cleaning ambient noise, capturing silences between words and understanding the language to interpret our orders. If they make a mistake when they respond to our request, they use that data to improve next time and also take note of whether they got it right.
Some social networks are also applying Machine Learning. Twitter, for example, has the BotMaker system to fight spam and Facebook is testing this technology to detect fake news.
Machine Learning also has a great future in the Health sector . A practical example of Machine Learning is Watson from IBM. Through this technology, health centers, such as Akershus University Hospital , are optimizing the use of diagnostic tests and treatments to improve patient care. Watson Imaging Clinical Review, for example, is a retrospective imaging test review tool that helps the physician make a diagnosis and make better clinical decisions.
On the other hand, its Sugar.IQ application is a personal assistant that continuously analyzes everything that affects the glucose levels of the diabetic person and understands their daily patterns to predict changes up to three hours before they occur. This helps to keep glucose levels within the normal range.
The financial institutions also use Machine Learning to analyze transactions and detect anomalous patterns to help them combat fraud. It allows them to notify their clients of abnormal activity on their credit cards or bank account, detect signs of possible defaults or reduce risk when granting credits.
However, if there is a sector where Machine Learning will be essential, it is autonomous driving . It will not only be used to make the car move the wheel but to analyze the images, detect other vehicles on the road or even predict how they will move to avoid accidents, as well as to choose alternative routes according to your traffic predictions.
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