Machine Learning is guiding Artificial Intelligence capabilities.
Image Classification, Recommendation Systems, and AI in Gaming, are popular uses of Machine Learning capabilities in our everyday lives. If we breakdown machine learning further, we find that these 3 Machine Learning examples are powered by different types of machine learning:
How can we better understand Supervised, Unsupervised, and Reinforcement Learning?
Let’s start with Supervised Learning, which makes up most of the uses for Machine Learning today. In Supervised Learning, the machine already knows the output of the algorithm before it starts working on it. The algorithm is taught through a training data set that guides the machine, and the machine works out the steps from input to output. Supervised learning is used for image classification or identity fraud detection, and for weather forecasting. But how is Unsupervised Learning different?
Well first off, with Unsupervised Learning, the system does not have any concrete data sets, and the outcomes are also mostly unknown. Unsupervised Learning has the ability to interpret and find solutions to a limitless amount of data. Now when you log onto Hulu or Netflix, you have personalized recommendations because of Unsupervised Learning.
Lastly, there is Reinforcement Learning. Reinforcement Learning is different, because it gives a high degree of control to software agents and machines, which are determining what the behavior within a context should be. People are helping the machine to grow by maximizing performance, providing feedback to the machine, helping it to learn its behavior.
Reinforcement Learning requires the use of tons of different algorithms, giving control to the agent as they decide the best action based on the current results. When you are gaming on PC, Xbox, Playstation, or Nintendo, and you witness AI in Gaming, this is because of Reinforcement Learning.