Machine Learning (ML) and Artificial Intelligence (AI) are two very popular buzzwords right now. They seem to be appearing everywhere, and are sometimes used interchangeably within various articles. This brings about some confusion, and the question "are they the same thing"? To answer this question it's best to take a look at just what is AI and Machine Learning.
What is Artificial Intelligence
"...intelligence demonstrated by machines, in contrast to the Natural Intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals."
This is best summed up by saying "machines behaving in ways we perceive as smart".
AI can additionally be further grouped into two distinct areas, applied and general. Applied AI, also known as Narrow AI, is the more common grouping and is a type of AI performing a specific task such as driving an autonomous car or trading stocks. General AI is a system that can, at least in theory, perform any task.
What is Machine Learning
General AI is where the most exciting researching is currently being done, and it's what has given rise to Machine Learning. Machine Learning is defined by Wikipedia as:
"a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed."
Back in 1959 there was an idea put forth by Author Samuel that, instead of teaching computers everything it could be possible to teach them to learn for themselves. The biggest problem with this idea is that access to the unbelievable amount of data necessary to develop such a system was impossible, that is until the internet. The internet has given an every growing and astronomically large amount of data for which could be used to train such a system. And so with the idea and the data engineers started working towards a system that mimicked the way humans think and learn, creating what is now known as a Neural Network or Deep Learning.
These Neural Networks have been key to teaching computers to understand the world as we might. Doing so with amazing speed, accuracy, and hopfuly a lack of bias. These systems work by classifying information in a way very similar to how the human brain would. For an example, an autonomous vehicle needs to recognize visual data, such as people, animals, road markings, street signs, etc. A Neural Network churning through billions of images and videos can be trained to recognize all these and more.
Neural Networks basically work on a system of probabilities. This is where a large amount of data is vital. The more data the system has to train itself with, the better it will become at recognizing patters. These patterns can be objects in a photo, or video. The more data the system has, the better the system can make predictions/decisions/statements that have a high degree of certainty.
Some applications of predictions/decisions/statements outcome will be understanding parts of the world we take for granted. Such as listening to an audio recording of a conversation and understanding tone independant of the words. Or even sarcasum. Understanding the emotional response a score of music might have on an individual or crowd, or even compose music to achieve an emotional response.
AI and Machine Learning work together to allow for computers to behave in ways that we would perceive as intelligent. So at it's core, Machine Learning is a way to achieve AI.