AI: Machine Learning and AI – What's in a name?

I regularly hear the terms AI and Machine Learning used almost interchangeably, along with a variety of other related terms. I thought it would be useful to add a post that defines some of the common terms and how they differ:

Artificial intelligence (AI) is a fairly generic term. It relates to all intelligent agents that are able to be aware of their environments (in some way), and to take actions where the aim is to achieve a specified goal. Sometimes these goals are terminal ie: they reach a final desired state. Other times, these goals are continuous ie: keep speed at a desired value. It is considered "artificial" intelligence as to an observer, it mimics cognitive functions that humans would imagine other humans performing.

Machine Learning (ML) describes a form of "learning" where a systemĀ  improves its model of a specific behavior (ie: "learns"). It can then use the model to predict future outcomes. Machine Learning is considered a field of Artificial Intelligence. There are many types of Machine Learning.

The most common form of Machine Learning today is Data Mining where the model is trained by analyzing existing outcomes, and then used to predict future outcomes. (This is usually called Predictive Analytics).

The learning can be supervised (ie: here are pictures of dogs, is this other picture a dog?), unsupervised (ie: what are the common types of an object?), or combinations of the two (often called semi-supervised).

Deep Learning is a form of Machine Learning where the models comprise many layers. "Deep" refers to the number of layers, not to any specific ability or insight. These models often do an amazing job, and in some cases are already performing better than humans at specific tasks such as speech to text translation.

Reinforcement Learning is another form of Machine Learning that typically involves working out optimal ways for software agents to operate within defined software environments. Game theory, simulation experiments, etc. often form part of Reinforcement Learning. One common way to represent the environments is as what is known as a Markov Decision Process (a mathematical framework that defines the rules for decision making and the goals and rewards involved).

 

 

 

 

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