Learning Systems – Short Explanation

When searching for learning systems online, you will come across many different variations. You might find scholarly and sales discussions of learning management systems that are used in educational and business institutions. In this context, you will be taught how different learning systems can better facilitate learning.

However, we are exploring learning systems from a different perspective. We will be looking at them from the point of view of complex systems. These systems can independently learn and develop knowledge without significant human intervention. While this definition seems simplistic, its application can be quite complex.

For example, if you were to assume that machines simply need to learn a skill called “reading,” and they would then be able to read books on multiple different subjects, you would be only scratching the surface. In reality, learning requires an understanding of the language first and then an appropriate primer on how the letters combine to form words, after that an explanation of what the words mean. Discussions about sentence structure must follow as meanings change based on how words are combined.

How Learning Systems Work in the World of AI

Machine learning (ML) has been a topic of discussion for decades. At a very high level, it describes how computers learn and process information so that they can make accurate predictions when fed data. While the concept has been around for a while, its implementation has been lacking until recently.

Latest advances in computing technology have helped propel AI and ML into the forefront once again. With progress in deep learning models and the growth of big data, more information is available than ever before. The complexity of learning has not gone away, but the changes in technology have helped to alleviate the burden.

Machine Learning methods are generally classified as supervised or unsupervised. With supervised learning, the algorithms use labeled data examples. In this case, learning systems are trained on known training datasets. After the training, they are able to make predictions which are compared to the intended outputs. Unsupervised learning is very different. As the data is neither labeled nor structured, the learning system is unable to validate whether the output provided is correct or not. What the system does, in this case, is to create inferences from the data to show patterns that would not immediately be obvious.

Machine learning provides companies and businesses with new capabilities for the analysis of massive amounts of data. In addition to the ability to analyze more information, machine learning can do this at a speed and accuracy level that is impossible for humans to match. However, getting the system to that level requires significant amounts of time and resources, including data to ensure that it is properly trained.