Introduction The last decade has seen artificial intelligence (AI) deliver impressive results in areas ranging from image detection to self-driving cars, and most people are familiar with AI performing tasks on our behalf like powering online customer support as seen on Zendesk, Amazon Alexa or even recommending products as we do so on e-commerce platforms like Shopify. As we go deeper into the AI journey, there are bound to be more AI-based applications making their way into every aspect of our lives. From improving medical and medical research, powering customer service chats to answering security questions, these AI platforms are going to continue enabling us to get more out of our working days while freeing us from menial tasks that distract us from the bigger picture. It wasn’t long ago that computers on our desktops and in our pockets could merely process ones and zeroes. Currently, computers can recognize images and detect motion. They can understand natural language, detect our emotions and face, as well as make sophisticated predictions about the world around them. This has taken AI far beyond what people once thought was possible and set the world on a path to realize the possibilities of man(machine)kind. “Artificial Intelligence”, “Machine Learning”, and “Deep Learning”. You often see them used interchangeably in media outlets, but what do these terms actually mean? Are the three really the same, in essence? This article will attempt to explain how each of these terms is connected — more precisely what is the difference between them — and how each of them has been instrumental in advancing recent achievements in AI development.