The various cutting-edge technologies that are under the umbrella of artificial intelligence are getting a lot of attention lately. As the amount of data we generate continues to grow to mind-boggling levels, our AI maturity and the potential problems AI can help solve grows right along with it. This data and the amazing computing power that’s now available for a reasonable cost is what fuels the tremendous growth in AI technologies and makes deep learning and reinforcement learning possible. With the rapid changes in the AI industry, it can be challenging to keep up with the latest cutting-edge technologies. In this post I want to provide easy-to-understand definitions of deep learning and reinforcement learning so that you can understand the difference.
Both, deep learning and reinforcement learning are machine learning functions, which in turn are part of a wider set of artificial intelligence tools. What makes deep learning and reinforcement learning functions interesting is they enable a computer to develop rules on its own to solve problems. This ability to learn is nothing new for computers – but until recently we didn’t have the data or computing power to make it an everyday tool.
What is deep learning?
Deep learning is essentially an autonomous, self-teaching system in which you use exiting data to train algorithms to find patterns and then use that to make predictions about new data. For example, you might train a deep learning algorithm to recognize cats on a photograph. You would do that by feeding it millions of images that either contain cats or not. The program will then establish patterns by classifying and clustering the image data (e.g. edges, shapes, colours, distances between the shapes, etc.). Those patterns will then inform a predictive model that is able to look at a new set of images and predict whether they contain cats or not, based on the model it has created using the training data.
Deep learning algorithms do this via various layers of artificial neural networks which mimic the network of neurons in our brain. This allows the algorithm to perform various cycles to narrow down patters and improve the predictions with each cycle.
A great example of deep learning in practice is Apple’s Face ID. When setting up your phone you train the algorithm by scanning your face. Each time you log on using e.g. Face ID, the TrueDepth camera captures thousands of data points which create a depth map of your face and the phone’s inbuilt neural engine will perform the analysis to predict whether it is you or not.