Unlike traditional programming, where the machine is given a set of specific instructions to perform a task, Machine Learning (ML) allows the machine to learn on its own… How is it possible to achieve this…?
ML is a branch of A.I. that deals with the study and development of algorithms and techniques that allow machines to learn and improve their skills autonomously without being specifically programmed to do so.
There are different types of ML, but in general they can be classified into two broad categories:
1. Supervised learning. This consists of providing the machine with a set of labeled data (already classified or categorized). The machine uses these to learn how to perform the classification task itself, and then is subjected to a set of unlabeled data to see how it performs.
2. Unsupervised learning is when the machine is given an unlabeled data set and left to discover patterns and relationships on its own.
A common example of ML is the automatic email spam filter. In this case, the machine learns to distinguish between unwanted emails (spam) and legitimate emails through the analysis of examples of each type of email.
Another example of ML is speech recognition as machines can learn to recognize and transcribe human speech very accurately. This is achieved by training the machine with a large set of recordings, then testing it with new voices to evaluate its accuracy.
ML is also used in health data analysis, weather prediction, social network trend analysis, and many other areas.
Although ML can be very useful, it also has some limitations. One of the main problems is the need for a large amount of labeled data of sufficient quality to train the machine, which can be costly and laborious to achieve.
Another limitation of ML is that it is sometimes difficult to understand how it arrived at a particular decision or prediction. This can be especially problematic in critical applications, such as medical diagnostics or financial analysis, where it is important to understand the decision-making process.
Despite these challenges, ML is increasingly becoming an integral part of many industries that are leveraging this machine learning to extract better quality information, increase productivity, reduce costs and get more value from their data.
Nubeprint, with 5 registered patents, offers a managed MPS solution with dynamic algorithms and filters. In 2013, it develops the first A.I. engine for MPS and, since 2017, it has a Machine Learning (ML) developed specifically for MPS: through this machine learning, the system develops pattern recognition and the ability to learn continuously, with predictions based on Big Data, after which it makes the necessary adjustments without having been specifically programmed to do so. Nubeprint, with a clear vocation for the future, is committed to technological advances and invests 30% of its resources in R&D&I, obtaining the Innovative SME certificate (Innovative SME, AENOR EA 0047) by the end of 2022.
SOURCE: tecon.es/ Nubeprint
