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Machine Learning is part of our daily lives, but we don’t see it. Instead, we feel its presence through the infinite solutions that make our lives easier. They seem to know what we need before we ask for it. We interact with chatbots that have the answer to our questions. We chat with Siri or Alexa and even listen to their jokes. It is even possible to consult a virtual assistant about a potential medical diagnosis based on our symptoms. Machines learn from our behavior and prepare to meet our requirements, as simple as that.
Machine learning has made great strides in recent years and continues to advance steadily. It has become the ally of multiple research areas and has refined its ways of learning. A giant derivative of Artificial Intelligence that is here to stay for good.
What is Machine Learning?
The ability of machines to learn without being previously trained to do so is what is known as Machine Learning. They are designed to change their behavior and responses based on the data they are exposed to.
This learning process is based on algorithms that allow analyzing and comparing data to make predictions and establish behavioral patterns. This leads to the autonomous improvement of the system without human intervention.
Its origins date back to the birth of the science of statistics. The establishment of patterns based on data behavior is the key. Artificial Intelligence has been the field that has opened the doors as a branch of this fantastic technology.
For this reason, the beginnings of Machine Learning date back, like AI, to 1950 when Alan Turing wondered about the possibility of machines thinking as humans do.
A milestone that marks the development of machine learning can be found in creating the first artificial neural network system by mathematician Marvin Minsky. Named SNARC (Stochastic Neural Analog Reinforcement Calculator) is considered the first artificial self-learning machine capable of finding its way out of a maze.
Like AI, machine learning faced long periods in which its development was limited due to the lack of available data and computing limitations. But during the 20th century, it managed to take off thanks to the advent of the internet and its immense potential to offer gigantic volumes of data. At the same time, the development of technology in terms of processing capacity served it on a silver platter that is needed to make a great leap forward.
In 1997, IBM marked the history of Machine Learning by presenting its most recent creation: Deep Blue. This system was trained from thousands of successful chess games and managed to beat the world champion of the moment, Garry Kasparov. This success was possible thanks to what is known as deep learning. This learning modality is based on the fact that machines learn from experience and are also capable of educating themselves to improve their performance based on data.
From that moment on, this field has continued to grow in technology, learning, and sophistication and does not seem to stop.
Different types of Machine Learning
We can classify machine learning into three classes according to the treatment of the data it handles for its development:
Reinforcement learning
It is known as learning based on a behavioral model. It responds to trial and error. The system learns its experience from the punishments or rewards it receives for its behavior. In this way, it defines the patterns that will lead it to succeed in similar situations in the future.
Supervised learning
In this type of learning, the characteristics of the data that is introduced change. The information is pre-tagged to build patterns based on the tags and identify new data in the future.
A typical example is the introduction of photos of animals, for instance, with their corresponding tags (cats, dogs, etc.).
The system will recognize them and, in the future, will be able to catalog images of animals based on their similarity to those previously labeled.
Unsupervised learning
In this case, learning goes a step further and no longer requires previous labels. Instead, the systems are trained to look for similarities in the data and catalog them accordingly. An example of this learning can be found in facial recognition systems, which do not look for a specific face but for those faces that share the most significant number of common characteristics.
Deep learning, mentioned above as a particular type of machine learning, deserves special mention. This learning system incorporates the technique of artificial neural networks and aims to simulate the behavior of the human brain. As a result, it is possible to make the computer deal with abstract concepts just as a human being would.
The present and its applications
Machine learning has opened the door to change and improvement of customer-oriented solutions and business in general. The possibility of learning from day to day and improving each system is an invaluable opportunity at this time. For this reason, it has been incorporated into almost all everyday spaces.
Its main applications can be found in the following areas:
Market research and development of predictive models
It allows a more accurate segmentation of the market based on user behavior patterns. It also facilitates the design of demand prediction models based on customer needs and behavior.
Optimization of customer service
Thanks to the analysis of the data obtained from the interaction of the systems with the customer, improving the customer service provided continuously. Offering better recommendations in response to customer requests and personalized options are just some improvements made possible by machine learning.
Improved quality control and fraud detection systems
Continuous process monitoring and data analysis allow machine learning-based systems to respond with greater accuracy and success in quality control and fraud detection. They can detect faults and irregularities in time and launch the necessary alerts.
Automation of processes in companies
Hand in hand with RPA (robotic process automation), machine learning becomes the perfect tool to automate repetitive processes within organizations, minimize human errors, and optimize results. These advances greatly favor areas such as human capital management and access control.
Optimization of production lines
One of the concerns of any manufacturing company is maintaining its production lines, both in terms of time and quality. Machine learning offers the possibility of reducing production costs by continuously improving processes. Moreover, it makes it possible to reduce or prevent future failures, an aspect that favors the competitiveness of companies.
Hypernova Labs for the future
Our development teams prepare daily to build solutions adapted to the future and meet our customers’ needs. We stay on the cutting edge of technology and know that you expect that and much more from us.
Just as machine learning-based systems do, we learn from our experiences and grow stronger as an organization. So come to our offices and discover that the future is no longer a dream but a possible reality. We are waiting for you.
