Some technologies become so ingrained in our daily lives that we rarely notice them. Machine learning, for example, is a notion connected with artificial intelligence, which is why the media is increasingly emphasizing it. Despite this, few individuals grasp the concept.
If that’s the case, don’t worry: in the following lines, you’ll learn what machine learning is and about various applications that already use this technology.
What is machine learning?
Machine learning is a term that can be defined in a variety of ways. Here’s one that’s easy to grasp: machine learning is a system that can change its behavior on its own, based on its own experience — the training we talked about before. There is very little human meddling here.
Such behavioral modification entails developing logical principles, so to speak, with the goal of improving work performance or, depending on the application, making the best option for the situation. These rules are created by recognizing patterns in the data that has been analyzed.
Consider someone who enters the term brave into a search engine. The service must examine a number of factors to determine whether it provides results that are furious or brave, two different meanings. The user’s search history is one of the many parameters available: if he had searched for courage minutes before, for example, the second meaning is the most likely.
This is a pretty basic example, but it highlights several key principles of machine learning. To begin, it is critical that such systems conduct analyses based on a large amount of data, which search engines have plenty of due to the millions of visits they receive and, as a result, serve as training.
Another feature depicted there is the continuous data entry, which facilitates the discovery of new patterns. Assume that the word bravo becomes slang for a cultural movement. The search engine will be able to recognize patterns that indicate to the new meaning of the phrase using machine learning and, after some time, will be able to include it in the search results.
Machine learning can be approached in a variety of ways. Deep learning is a well-known term. Large volumes of data are processed using numerous layers of artificial neural networks (algorithms inspired by the organization of neurons in the brain) to handle extremely complex issues like visual object recognition.
Examples of machine learning applications
Artificial intelligence and machine learning
Machine learning is increasingly being used in a wide range of applications. It’s not a whim, but a necessity: many of our current technical resources only work or are viable because of artificial intelligence. Some instances are as follows:
1. Autonomous database
Using machine learning, autonomous databases automate functions previously handled by a database administrator (DBA), allowing the professional to focus on other responsibilities and lowering the chance of the application being unavailable due to human error.
2. Fighting payment system fraud
Every second, thousands of credit card and other payment fraud attempts are made around the world; fortunately, machine learning has enabled fraud-fighting systems to stop the majority of these actions;
Text translation: a translation can never be done exactly to the letter; circumstances, regional expressions, and other factors must all be considered. Automatic translators are becoming increasingly accurate thanks to machine learning.
3. Content recommendation
Machine learning is used by video and audio streaming companies to examine the history of content played or rejected by users in order to make recommendations according to their preferences.
Although artificial intelligence and machine learning have been around for a long time, it is only now that we are seeing widespread adoption of these technologies. But believe me when I say that we are only at the beginning. Imagine how useful and impressive they will be once they have been further trained and refined.
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Source: Nyscinfo