Undoubtedly, artificial intelligence is a component that drives innovation in many areas, from medicine to the automotive industry. And this is just the beginning.
At the core of this technology is machine learning, a process that allows machines to acquire knowledge and improve their performance based on accumulated experience.
However, understanding the different types of learning in the context of artificial intelligence could be overwhelming for many. Did you know that AIs are still fed information?
Therefore, learn about the various approaches used by machines to learn and improve, from supervised to unsupervised, so that you have a solid understanding on this topic.
Learning in Artificial Intelligence
In the context of Artificial Intelligence (AI), learning is the process by which a computer system acquires the ability to improve its performance in a certain task through experience.
Unlike traditional programming systems, in AI learning, this technology automatically learns with the available data, adjusting and improving its behavior while receiving more information.
This AI learning approach is critical for numerous applications, from image classification and speech recognition, to decision-making in autonomous vehicles.
By allowing systems to learn from experience, this provides flexibility and adaptability that contributes to improved performance and the ability to cope with various situations, in dynamic and complex environments.
Types of learning in AI
Learn how an artificial intelligence obtains knowledge:
Supervised Learning
In supervised learning, the AI system receives a set of training data that includes examples of input and the desired responses associated with those inputs.
The system uses this data to learn a function that maps inputs to desired outputs. During training, the system adjusts its parameters to minimize the difference between predicted outputs and actual responses.
Examples of applications include image classification, where the system learns to distinguish between different classes of objects in an image based on previously labeled examples.
While useful when labeled data is available and the desired output is known, it may require a larger amount of labeled data and may not be suitable for problems where the desired output is difficult to define.
Unsupervised learning
Unsupervised learning involves the fact that an AI receives an unlabeled set of input data and must find interesting patterns or structures in this data.
The system seeks to group data into categories or identify relationships between variables without the help of predefined labels.
For example, data clustering allows the system to identify similar data groups based on common characteristics without prior knowledge of the categories.
Although it reveals hidden patterns or trends in the data without the need for labels and is useful for exploring large data sets, interpretation of results can be subjective and performance evaluation can be more difficult without a clear measure of success.
Semi-supervised learning
Semi-supervised learning combines elements of supervised and unsupervised learning, using a dataset that contains both labeled and unlabeled examples.
The system uses the labeled data to learn the structure of the problem and then uses this information to improve its performance on the unlabeled data.
For example, sentiment analysis on social media can benefit from this approach, where some comments are tagged with specific emotions and others are not.
Although you can leverage the information contained in unlabeled data to improve model performance, it requires both labeled and unlabeled data, which can be difficult to obtain in some cases.
Reinforcement learning
In reinforcement learning, the AI system learns through interaction with an environment, receiving feedback in the form of rewards or punishments based on the actions it takes.
The system learns to make optimal decisions to maximize a cumulative reward over time. Examples include training AI agents in games such as chess or Go.
Although you can learn complex strategies from experience, it requires a great deal of time and computing resources to train, and it can be difficult to apply in environments where feedback is scarce or inconsistent.
Relationship between these types of AI learning
Different types of learning in AI offer unique approaches to solving a wide variety of problems.
From supervised learning, which uses labeled data to train accurate predictive models, to reinforcement learning, which allows systems to learn through interaction with their environment, each approach has its own advantages and challenges.
The choice of learning type depends on the specific problem being addressed and the resources available for AI model training.
By understanding the characteristics and applications of each type of learning in artificial intelligence, both developers and professionals can make better decisions and develop more effective solutions in various fields.
Mastering these concepts is therefore critical to harnessing the full potential of this technology, in order to continue driving innovation and progress in the future.
Want to know more about AI? Visit our Artificial Intelligence page and make the most of it.
This post is also available in: Español Français Русский Italiano