Description: Guided Learning is a method of training artificial intelligence (AI) systems that involves the supervision of a labeled dataset. This approach aims to ensure that AI models learn ethically and accurately, minimizing bias and promoting fair outcomes. In guided learning, an algorithm is trained using examples where the inputs are clearly defined and assigned specific outputs. This allows the model to identify patterns and relationships in the data, resulting in greater accuracy in its predictions. Ethics in this context refers to the responsibility of AI developers to ensure that the data used does not contain biases that could perpetuate inequalities or injustices. Therefore, guided learning focuses not only on the effectiveness of the model but also on fairness and transparency in its operation. This approach is fundamental in applications where automated decisions can significantly impact people’s lives, such as in job candidate selection, credit granting, or healthcare. In summary, guided learning is a powerful tool that, when applied ethically, can contribute to the creation of fairer and more responsible AI systems.
History: The concept of guided learning dates back to the early days of artificial intelligence in the 1950s when algorithms began to be developed that could learn from data. However, it was in the 1980s that guided learning gained popularity, thanks to the availability of more powerful computers and larger datasets. Over the years, various techniques and algorithms, such as support vector machines and neural networks, have been developed that have improved the effectiveness of guided learning. Today, this approach is fundamental in the development of AI applications across multiple sectors.
Uses: Guided learning is used in a wide variety of applications, including voice recognition, image classification, fraud detection, and disease prediction. In the business sector, it is applied for customer segmentation and marketing campaign optimization. It is also common in recommendation systems, where historical data is used to predict user preferences.
Examples: An example of guided learning is the use of classification algorithms to identify emails as spam or not spam, based on previously labeled features. Another case is the development of medical diagnostic models that analyze X-ray images to detect diseases, using datasets where the images are labeled with confirmed diagnoses.
