Machine learning (ML) is a data analysis method, the name of which is self-explanatory, “Machine Learning”. It consists of a technique that combines programming, mathematics and the use of business rules to perform systems capable of learning, adapting and creating analytical models from interaction with large amounts of data.
In other words, it is a subfield of artificial intelligence that collects data with the help of algorithms, learns from it and makes predictions, enabling computers to make fast and assertive decisions. This system’s ability to learn and make automated decisions is the foundation of many current inventions, such as self-driving cars and supercomputers.
To explain the benefits of machine learning and its real potential for various corporate aspects, we spoke to Leega, a technology solutions, data analytics and cloud consulting company, who gathered important information about the origin and application of this technique. To follow:
In 1952, the American computer scientist Arthur Samuel created a computer program to play checkers with human beings. With it, the system analyzed the game, movements and learned from the mistakes and successes of the opponent, better predicting the tactics of the matches.
From there, in 1959, Samuel established the term “machine learning” for this technique and defined the method as “a field of study that gives computers the ability to learn without explicit programming.”
Recent examples of machine learning
IBM’s supercomputer, called Watson, has already created recipes, designed clothes and worked in medicine, for example. In 2016, Japanese doctors asked Watson for help identifying a patient’s diagnosis, and within 10 minutes, the machine compared thousands of medical articles and identified a leukemia patient, saving his life.
Another recent case occurred at the Rio Olympics, also in 2016, when the Washington Post newspaper published a story about the games without a person actually writing the content.
In addition, there are already tests of autonomous taxis, cars and trucks that make pre-programmed paths and recognize streets, other cars, traffic lights and other objects that may appear in front of them.
Advantages for companies
Organizations from all sectors have implemented machine learning technology in several areas and such use directly affects business results. The key business advantages of the technology can be shown in five applications:
Real-time chatbot agents
One of the first examples of automation, conversational interfaces such as chatbots allow users to ask questions and receive answers from virtual business assistants or voice command services such as Alexa, Google Assistant and Siri. With the use of machine learning embedded in this context of artificial intelligence, chatbots learn and are supported by algorithms, in a way that improves interaction, predicting responses to user requests and speaking in a way that is increasingly closer to a human being.
More precisely, medical diagnoses
In the health sector, Machine Learning helps, for example, in identifying diagnoses and suggestive treatment prescriptions for patients from the intersection of data from medical studies in a few minutes, which can speed up the recovery of patients and be a decisive factor in saving lives.
Automated data entry jobs can be performed by computers, freeing up HR professionals to focus on higher value work. In addition, the use of machine learning in data entry automation significantly improves some problems such as data duplication and personal data inaccuracies.
Optimized market research and customer segmentation
Retailers can use machine learning to, for example, predict which merchandise will sell best in their region, based on seasonal considerations and demographics for that region. In addition, the method can help with inventory planning and customer segmentation provided by the company’s database to determine prices and assertively deliver items and services at the right time and place.
Machine learning is also used by financial firms because it is a powerful fraud detection tool, thanks to its ability to quickly recognize patterns and identify abnormalities. It is unique because normal bank customer behavior, such as when and where they use their credit card, can be learned through machine learning.
In addition, the technology uses this and other data to quickly distinguish between transactions that match those predicted in the user’s profile and those that may be of fraudulent origin.
Machine learning brings positive effects to both society and the economy. Today, we already encounter the use of ML in a wide variety of tasks, without even realizing it, for example, when activating GPS in traffic or when using word proofreaders when writing e-mails, and this is an increasingly useful tool. crucial for companies in their day-to-day operations, making decision-making even more assertive in line with the purpose of each business.
Have you watched the new videos on YouTube digital look? Subscribe to the channel!