What’s the Difference Between Machine Learning and AI?

The terms are often used interchangeably

When a machine can tell the difference between objects and make a choice to discard or accept them, AI is born.
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If you’re like most marketers, you’re probably trying to get in on a little AI action to raise your game and keep up with your competition. And if you’re like most marketers, you might not understand exactly how it all works yet. Join the club.

As you discover new smart tools for your company, the first step towards making smart buying decisions is to understand the difference between machine learning and artificial intelligence. These terms are often used interchangeably, but they are definitely not the same thing.

“AI is any technology that enables a system to demonstrate human-like intelligence,” explained Patrick Nguyen, chief technology officer at [24]7.ai. “Machine Learning is one type of AI that uses mathematical models trained on data to make decisions. As more data becomes available, ML models can make better decisions.”

Let’s break that down, shall we?

Machine learning

You don’t have to have a smart home to come into contact with machine learning. In fact, companies like Facebook and Google have been using it for some time now to organize Big Data, speed up search or optimize advertising.

According to the University of Maastricht, “Machine learning algorithms are widely employed and are encountered daily. Examples are automatic recommendations when buying a product or voice recognition software that adapts to your voice.” Sounds familiar, right?

Machine learning is based on what is known as “neural networks.” If it sounds complicated, that’s because it is. But in a nutshell, neural networks are built for training and learning. They rely on certain factors of importance to determine the probable outcome of a situation and need to be programmed by humans first.

A neural network programmer must adjust the factors of importance (otherwise known as weights) in the outcome until the network reaches the required result from the information it has.

Now, just imagine a human programmer manually setting up a neural network for every possible outcome of a Google search! That’s where machine learning comes in.

Once the neural network has been perfected and the machine understands how to adjust the factors of importance on its own, it can train itself to improve accuracy without human intervention. And once the machine is trained, it can sort new inputs through the network and produce accurate results in real-time (think voice search).

It’s an incredibly complex and clever technique, but still, machine learning doesn’t possess any real intelligence.

Artificial intelligence

Algorithms don’t need to understand why they self-correct and improve, they are merely programmed to do so. However, once machine learning reaches a point where it can reflect and interact with humans in a convincing way and make decisions by itself, that’s when artificial intelligence is at play.

The reason we hear the two definitions interchanged is that AI cannot exist without machine learning—although machine learning can exist without AI. Think about an algorithm that can identify patterns in data based on specific weighted factors, or perhaps identify all types of images that are the same.

“If we plug several photos of cats doing different things or in different places into a computer, but all the photos are still tagged as cats, then the computer will learn from each photo it is shown,” said Kamelia Aryafar, Ph.D., director of machine learning at Overstock. “Eventually, it will recognize that the cat is the common denominator in each set of data, in turn helping the computer learn to identify cats.”

There isn’t really anything humanly intelligent about that. However, when that algorithm is connected to cameras and speakers, detecting objects in front of it and given a voice that responds to questions, it mimics human intelligence. It has become artificial intelligence.

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