Artificial intelligence is increasingly part of our daily lives, and the examples are as diverse as possible. From face and speech recognition technology, Chat GPT, Google LLaMA, Chat Bots, to suggestions of items to buy according to our personal interests.
When we think of companies, the influence of artificial intelligence and its technologies on the daily lives of workers is also quite clear.
One great example is analyzing data and extracting useful information from it. But it is also possible to have repetitive tasks automated by using state-of-the-art robots, for example.
All of this is only possible because artificial intelligence has two pillars that support it: Machine Learning and Deep Learning. These pillars make it possible for machines to think like human beings, which is the main goal of AI.
Keep reading this article to better understand how each of the pillars works, in addition to practical examples of their uses in the area of cybersecurity and virtual assistant.
The concept of machine learning
Are you in doubt about what is Machine Learning in a simple way? So now you will have your answer.
In a nutshell, Machine Learning technology allows a machine with artificial intelligence to provide patterns and make decisions autonomously.
And then, over time, through experience and data analysis, the machine learns and evolves the way it performs tasks on its own.
In this way, there are numerous possibilities to use Machine Learning technology. These include processing data in real-time, identifying market forecasts, automating tasks, segmenting groups, etc.
Consequently, there are some clear benefits to using machine learning. Whether to solve people's personal problems such as recommending a movie for the weekend, or for professional issues as mentioned above.
Advantage over competitors
To work correctly, Machine Learning uses data and past results. From these sources, it is possible to make reliable market forecasts.
Naturally, when a company knows how the market will develop, it becomes much easier to adapt the business model to this future scenario. In this way, the company differentiates itself from competitors and continues to grow over time.
Adjust strategies whenever necessary
Machine learning technology performs analysis and data processing in real-time. And because of this characteristic, it is possible to set agile and flexible strategies for companies.
The result is that the business will be able to remain competitive and keep pace with changes in the market in which it operates.
High assertiveness in predictions
Sometimes the way machine learning works is not clear and can even generate fear in people. However, once you understand the power of this technology, it becomes much easier to take advantage of it and realize its benefits.
First of all, high assertiveness in the predictions that are made is essential. After all, for people to really entrust their tasks to machines, they need to realize that there are few or no errors in the predictions.
As machine learning happens constantly, algorithms start to evolve and predictions practically exclude human error from the process.
Customer groups for sales
In summary, everything related to machine learning has something related to data pattern analysis.
Therefore, the machine is able to create smaller groups of customers according to their patterns and behavior online. Therefore, a very big advantage that happens is the more personalized and individual sales approaches.
Machine Learning in Cybersecurity
We constantly come across headlines about scandals involving company data leaks. Likewise, technology is often used to make digital scams more sophisticated and convincing.
By understanding this reality, it is important for people to have cybersecurity solutions in their professional and personal lives. And yes, even if you are “just” an analyst doing your job day after day, you can be the target of an attack and be harmed by it.
Some common attacks are using deep fakes to imitate other people's voices and hacking networks to steal data. In addition, it is common for scammers to invade systems looking for credit card numbers to buy products online.
Therefore, using machine learning in your favor helps to reduce the problems related to cybersecurity. An algorithm can, for example, find patterns and solve problems with high precision and efficiency.
Some examples of using machine learning to improve cybersecurity are the detection of intrusions and the identification of spam and phishing. Likewise, technology can identify malware families and assess risks in general.
Machine Learning in Virtual Assistants
As you already know, the purpose of artificial intelligence is to teach machines to perform tasks and think like humans.
In this sense, we have virtual assistants, or chatbots as they are also known. These robots are taught to interact with people mainly to reduce the workload in companies with customer service.
Thus, access to some documents or basic information that is saved in a system can be easily sent by a virtual assistant.
This is the basic use of virtual assistants. But it is also possible to apply machine learning to these robots.
Within artificial intelligence, there is an application called NLP (Natural Language Processing). This area seeks to understand how machines can interact with the different languages used by humans to communicate.
When a chatbot or virtual assistant uses NLP in its code, the machine is able to interact with humans in a very natural way.
And from machine learning, systems gain a new ability which is automatic learning through contact with people and their behavior.
Discover the 4 methods of Machine Learning
For machine learning to happen, there are 4 methods that are currently used.
Supervised learning
In this method, the algorithm has a model that receives new data and needs to find ways to reach the same inputs and outputs.
For this, the machine will identify the data that was received, understand what their patterns are, and then make predictions.
At first, the machine will not perform as expected. However, as the machine learns autonomously, after a certain time this reality changes, and human interference is no longer necessary.
Semi-supervised learning
In this machine learning method, the data are called “labeled” and “unlabeled”.
In labeled data, there is labeled information, with pairs of inputs and outputs. And it is from this label information that the algorithm is able to develop knowledge and classify the unlabeled data.
Unsupervised learning
For this method, there is no human presence in the operation. The operation happens by sending large volumes of data, and the machine will try to structure this data so that it can be used later.
Structuring can happen through clustering (union of groups with similar information) and dimension reduction (the smallest possible number of variables is determined to find information).
Learning Reinforcement
In this method, there are particular rules that the machine needs to take into account. They are sets of actions, criteria, and values that are added to the algorithm so that it achieves the best result by observing different possibilities.
Machine Learning vs Deep Learning: understand the difference
In this article, you understood in detail how machine learning works, its examples, and the methods that are used. Despite this, we also have deep learning technology, which is part of machine learning.
For deep learning to happen, it is necessary to use artificial neural networks that simulate the human brain.
Unlike simple machine learning, which uses algorithms with predefined equations, deep learning has pattern recognition in several layers of processing.
It is this technology that allows companies to be able to process a large volume of data to learn our personal preferences. As a result, more assertive recommendations and products according to what we are looking for on the internet come to us.
After reading this article, the intention is that you can understand the concept of machine learning, its uses, and methods so that it can exist. And finally, a brief explanation so you can identify some specific differences that exist between machine learning and deep learning.
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