People have always been fascinated by the idea of giving human-like characteristics to inanimate objects. A major component of this over the last century has been the focus on creating “thinking machines.” Advancements in programming and processing have introduced concepts such as machine learning and deep learning into our vocabularies. However, although these terms may be used interchangeably by some, they are different concepts.
It might help to think of these ideas as subsections of concentric circles. For starters, artificial intelligence refers to anything that can perform an intellectual task usually performed by a human. The most basic form of this is “if/then” programming. If a computer encounters a specific parameter in its program, the program tells it what to do next.
Machine learning, on the other hand, means systems can learn to perform tasks by themselves without human intervention through continued exposure to data. Some examples of this principle in action include weather forecasting and antivirus software. The more information these are exposed to, the more accurate their predictions and conclusions will be.
Drilling deeper into this concept is what has come to be known as deep learning. These systems are designed to duplicate many of the processes inside the human brain, enabling them to process more complex relationships between data by themselves. The most common examples of these artificial neural networks are the algorithms used by e-commerce and streaming media platforms. These suggest products, movies and TV shows to users based on what choices the particular user has made in the past. Digital assistants such as Apple’s Siri and Amazon’s Alexa also use these principles to understand human speech or read text.
Understanding how these ideas build off one another is important for understanding artificial intelligence as a whole. For a visualization of these concepts, take a look at the accompanying resource.
Author Bio: Anne Fernandez – Anne joined Accelebrate in January 2010 to manage trainers, write content for the website, implement SEO, and manage Accelebrate’s digital marking initiatives. In addition, she helps to recruit trainers for Accelebrate’s Machine Learning courses and works on various projects to promote the business.