Sign up for these 7 practical applications of machine learning for everyday life that you probably did not know

Sign up for these 7 practical applications of machine learning for everyday life that you probably did not know

The concept of machine learning, a branch of artificial intelligence that provides computers with machine learning without the need to be continuously programmed, has gained greater importance over the past decade. In a few years, algorithms classified as Machine Learning have evolved to handle large volumes of data (Big Data), achieve better results, and solve problems more efficiently.

Its use is more and more varied. Indeed, according to estimates by consulting firm Accenture, its application will increase business productivity by more than 40% by 2035. In addition, currently more than 60% of Spanish CEOs are already using AI in their business processes. automation and 25% of companies are investing up to $ 44 million in modifying and reorienting their business models towards algorithms, according to the latest report from KPNG.

Although it seems that we are talking about the technology of the future, the reality is that its practical application is part of the daily life of the world population. From Ironhack – a leading school in intensive digital talent training – they have compiled 7 examples that show machine learning is part of everyday life:

1. Face detection. Facial recognition is one of the most important revolutions of the decade. It is used to unlock mobile, test Snapchat or Instagram filters, and even to try to predict your age. Although it seems new, it was first used in the late 19th century by French policeman Alphonse Bertillon for the purpose of identifying the faces of criminals and replacing the fingerprint method.

The software identifies faces by means of a group of 68 specific references or points, more or less, whose configuration is different in each person.

2. Voice recognition. The first voice recognition systems were created in 1952 and were based on the power of the speaker’s voice. At present, there are systems such as: “Ok Google” or “Hey Siri”, among others. This is one of the best examples of machine learning. In order to better understand what we need when we ask a question, these virtual assistants end up knowing everything about the user such as: sleep habits, messages, calendar, reminders, emails …

3. Gmail. By marking emails as malware, the system eventually understands and learns how to send these messages directly to the “spam” folder to protect the user from viruses, fraud, or messages that are not of interest to them.

4. Personalized marketing. Based on the user’s performance when using the Internet, their social networks or the way they interact, the ML learns from this behavior to recommend products or services that correspond to him and thus produces marketing personalized based on behavioral patterns. Companies such as Google, Amazon, and Instagram, among others, work with this data because it increases the efficiency and productivity of campaigns. In fact, thanks to AI, companies can know the user’s needs before they know it themselves.

5. Google Maps for traffic. Every day, more than 1,000 million km are traveled around the world thanks to Google Maps. This tool shows the safest and most efficient routes using technologies based on traffic and mobility patterns collected over time and combining them with live traffic conditions. In this way, this is how machine learning is applied to be able to generate predictions based on both sets of data.

6. Autonomous cars. Currently, there are cars capable of driving independently, overtaking, parking or performing any type of maneuver. This type of car offers the possibility of reducing traffic incidents and even the number of accidents, because by removing the human factor from the equation, the margin of error is practically non-existent.

7. Medical diagnostics. The use of intelligent systems in medicine has great potential, as they allow the processing of a large amount of information and generate diagnostics, helping to detect pathologies faster and with a lower margin of error than a To be human.

The main areas in which machine learning is used are: oncology, which has been shown to be 90% effective in detecting breast and prostate cancer; neurology, where great advances have been made in the diagnosis and treatment of stroke, Alzheimer’s disease or senile dementia; gynecology, for detecting malformations or problems during pregnancy; and genetics, with programs capable of detecting more than 8,000 genetic disorders and rare diseases across the face.

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