What is Machine Learning: how it works and importance

Understand what Machine Learning is, how it works in practice, what its ethical and privacy implications are, as well as the prospects for the future 

Machine Learning (ML) is, simply put, an artificial intelligence technology that uses data and algorithms to learn how humans think and behave .

This technology is behind the famous chatbots (ChatGPT, for example), personalized recommendations on streaming platforms and the operation of major companies in the market, such as Uber and Google. 

The importance of Machine Learning is due to its ability to make predictions and guide decision-making based on the data analyzed - all in an automated way, increasing business productivity and ensuring greater assertiveness. 

How Machine Learning works

The first step is to select and organize the data that will be analyzed - at this stage, the objective of using Machine Learning must be very clear so that the data chosen is the most appropriate. 

After that, the Machine Learning algorithm must be chosen, which will depend on the problem to be solved. Some types of machine learning algorithm are: KNN, Naive Bays, LVQ, SVM, among others. 

The algorithm will then receive the data and be trained, undergoing various tests and evaluations. With the results obtained, the Machine Learning model will be adjusted to achieve the final goal, with the possibility of changing the parameters or even the algorithm itself. 

Machine Learning in practice

To understand these concepts in a more practical way, here are some possible uses for Machine Learning: 

• Speech recognition: Siri, Alexa and other virtual assistants use Machine Learning to recognize users' voices and learn to give more "human" responses.

• Risk and Compliance: banks and institutions from different segments can use Machine Learning to prevent fraud, scams and system intrusions 

• Medical diagnostics: Machine Learning models are increasingly used in the health area, diagnosing diseases, identifying patterns and trends and enabling disease prevention - as is the case with Watson Health

• Sentiment analysis: companies can use machine learning to identify human sentiment in audio and text and understand the user's opinion of products and services - find out more about Scuta.ai

Neural Networks and Deep Learning

When searching for Machine Learning, you often come across terms such as "Neural Networks" and "Deep Learning". Understand these concepts:

• Neural Networks: are a subfield of Machine Learning and represent artificial structures of mathematical models that simulate the functioning of the human brain, consisting of interconnected nodes that process information.

• Deep Learning: this is also a sub-field of Machine Learning and uses several layers of neural networks to work in a more advanced way and learn about more complex data. 

Ethics and Privacy

The use of Machine Learning raises a number of ethical and privacy issues that should be on the radar of companies using this technology. 

One of these is data protection: since ML relies on large amounts of data, all of it must have been obtained with consent. This process is backed by the LGPD, which is already in force in Brazil and has penalized organizations that don't comply with its provisions. 

Furthermore, since Machine Learning seeks to understand human thinking, it is possible that it reproduces existing prejudices in the data analyzed, which can lead to mistaken decisions. 

It is therefore important for companies to be aware of these issues in order to work ethically and transparently, and to be able to make the most of the potential of this technology. 

Future of Machine Learning

The future of Machine Learning is promising and its use will become increasingly common in various economic and social sectors. 

As we are already seeing with ChatGPT, Notion AI and Bing AI, various ML-based artificial intelligences will emerge to increase human productivity in a wide variety of activities. 

Furthermore, one area of research that has received attention recently is Reinforcement Learning, which ensures that Machine Learning is able to solve more complex problems through proactive interaction with the environment. 

It is also expected that, as its use grows, ML will incorporate new privacy and security techniques to ensure that data is protected and used ethically to guide decisions. 

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