What is AI and ML?
Artificial intelligence (AI) is a discipline that deals with the development of programs that are able to perform actions commonly associated with the human being, such as learning, reasoning, making decisions and solving problems.
Machine learning (ML) is a specific branch of artificial intelligence that focuses on the development of algorithms and models that are trained using large amounts of data to give computers the ability to emulate the way in which the human brain learns. These models are developed to perform certain tasks and/or actions without being explicitly programmed to do so. An example would be an ML model for detecting spam in an e-mail box, classifying each e-mail as spam or non-spam. The training of these models can take place using three main paradigms: supervised, unsupervised and reinforcement training.
In supervised learning, the model learns to map inputs to targets using a labeled dataset, that is, a set of input-label pairs. In the spam detector example, a pair consists of the text of the email and the label that can be 'spam' or 'not spam'. A model trained in an unsupervised way, on the other hand, uses mathematical techniques mainly to find hidden patterns and/or group similar data.
Finally, in reinforcement learning, the learning process is guided by a mechanism of rewards and punishments.
What are the advantages?
- Automation and performance improvement: it is possible to automate repetitive and laborious tasks, allowing people to focus on more creative and strategic activities. In addition, machine learning models can analyze large amounts of data to identify less obvious patterns and trends, leading to more informed decisions and improved performance.
- Adaptability and speed: machine learning algorithms can adapt and continuously improve their performance based on experience and new data, making them suitable for a wide range of constantly changing applications. In addition, they are able to process large amounts of data much faster than humans, allowing decisions to be made in real time and improving operational efficiency.
- Customization and error reduction: AI systems can be used to personalize user experiences, such as product recommendations, tailored content and virtual assistance, improving user engagement and satisfaction. Finally, by automating complex tasks, AI and machine learning can reduce human errors and improve the accuracy and reliability of operations.
How do AI and ML work?
Machine learning algorithms process data to detect meaningful information. Here's a general idea of how they work:
- Data collection: starts with the collection of a vast amount of data relevant to the problem that you intend to solve. This data can come from various sources, such as sensors, databases, text files, images, videos, etc.
- Preparing the data: the data are then processed and prepared for analysis. This may include cleaning the data to remove missing or inconsistent values, normalizing to put the data on a common scale, and dividing the data into training sets and test sets.
- Choice of model: then, an appropriate machine learning model is selected for the specific problem. This could include models such as artificial neural networks, decision trees, support vector machines, linear regression, etc.
- Model training: the model is then trained using the training dataset. During the training process, the model seeks to identify patterns in the data and to learn from the relationships between variables so that it can make predictions or make decisions.
- Evaluation of the model: after training, the model is evaluated using the test data set to determine its performance. This allows you to evaluate the accuracy of the model and identify any areas where it can be improved.
- Optimization and deployment: finally, the model can be further optimized to improve its performance and then implemented in a production environment for use in real situations.
This is just a preview of how AI and machine learning work. The specific techniques and implementation details can vary greatly depending on the problem and the context in which they are used.
