AI & Machine Learning

Overview of AI & Machine Learning

The B2B and B2C markets are abuzz with the terms artificial intelligence, machine learning, and deep learning. But what do these terms mean, exactly? They’re often used very loosely and you may think they’re interchangeable. But they’re not.

Here’s a short overview of artificial intelligence, machine learning, and deep learning to help you cut through the static to determine which solution is right for you and your business.

Working definitions

Artificial intelligence (AI) is the term for the broad discipline that includes anything related to developing machines that are “intelligent” through programming. This includes many daily items you’re familiar with, from smartphones and marketing software to chatbots and virtual assistants.

Machine learning (ML) refers to machines and systems that can learn from “experience” supplied by data and algorithms. ML is often used interchangeably with AI, but it’s not the same thing — ML is a developmental outgrowth of AI.

Deep learning (DL) is a further developmental outgrowth of ML but applied to even larger data sets. It uses multi-layered artificial neural networks to deliver high accuracy in assigned tasks.

In terms of historical development, AI came first. It serves as the foundational discipline from which ML evolved. And ML is the foundational discipline from which DL evolved. One way to conceptualize their relationship to one another is as nested arenas of AI development along a timeline:

Early artificial intelligence started stirring excitement in the 1950s. By the 1980s, machine learning began to flourish, and by the 2010s, deep learning breakthroughs began driving the AI boom.

Artificial intelligence overview and how it works 

In its broadest sense, AI refers to machines programmed to act according to well-defined rules and responses. The responses are confined to the set of rules that are provided, and the machines can’t deviate from those rules, except if they fail.

A very basic example of AI would be your clothes dryer. You can set a specific time and temperature, and the machine performs the task according to the instructions given. It doesn’t have the ability to make decisions or make any changes by itself.

A more sophisticated example would be configuring your CMS to deliver personalized website experiences. By analyzing a targeted selection of data points about your customer and writing the appropriate logic, your website can display the most relevant content.

In neither case is the machine capable of being more than its programming — even if that programming makes the machine very capable in accomplishing its assigned tasks.


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