Exploring AI part five: What is unsupervised learning?
As part of our exploring Artificial Intelligence (AI) series, we have already explored exactly what AI is, looked into Natural Language Processing and delved deeper into explainable AI. In our latest blog post, we're taking a look at unsupervised learning. What is it and how can it be applied? Getting a better understanding will help you to understand where AI and machine learning could fit within your organisation.
There are numerous ways to play with LEGO. We can follow the instructions included in the box, step by step. Or we can ignore the manual and instead make up our own process keeping in mind the aim is to build a spaceship. Or we can go totally free format and try to figure out what is the best way to use available pieces.
Why are we talking about LEGO in this blog? Because those three approaches are analogous to three key categories of machine learning algorithm. Supervised Learning models learn from datasets that contain the right answers for every example (where the next brick should go, given how the previous bricks have been used) and the training process aims directly to reproduce those answers.
Reinforcement learning also has a target outcome in mind, but instead of case-by-case, guidance is based on a reward system that quantifies an overall goal (Are the bricks configured as a spaceship?) and the algorithm must learn which steps to take to maximise reward. In Unsupervised Learning the goal is to explore patterns and features of the data (what configurations of the bricks are possible).