This is the first part in a beginner-to-beginner blog series, a series from a developer new to artificial intelligence and machine learning to other beginners seeking to learn more about artificial intelligence.
When I first began thinking of ways I could apply my neuroscience degree to my newfound love of programming, I was immediately drawn to the study of artificial intelligence. It only took some hours of cursory research for me to stumble upon the term machine learning. At first, AI and machine learning seemed indistinguishable from each other, and this is the attitude often taken in media and by companies claiming to use AI technology in their products. In actuality, they are related, but quite distinct in both meaning and function.
On the surface, machine learning is one of many branches within AI. The following diagram is a simple visual of how machine learning falls inside of AI. It is a part, but far from the entire story of how we create machine intelligence.
Let’s begin from machine learning and use that understanding to make a distinction from AI. A popular definition for machine learning was said by a pioneer in the field, Arthur Samuel, who defined it as a “field of study that gives computers the ability to learn without being explicitly programmed.” What machine learning is trying to prove is the concept that given sets of data, machines can learn on their own, independent of humans.
You can read the full blog post on IBM Developer.
Originally published at https://developer.ibm.com.