Not sure you understand what machine learning is? It can sound a little scary and confusing, but it's not. It's just using data to answer questions.

Machine-learning and artificial intelligence are still growing technologies, but they aren't passing trends. See how ML and AI can help with sales and marketing for your B2B or B2C company.

Broadly speaking, artificial intelligence is a machine that thinks like a person. If that seems like a broad definition, it is. A better, and common, way to determine when something artificial is intelligent is the famous Turing test: If you didn’t know you were communicating with a computer, would you be able to tell?

If artificial intelligence is any machine that thinks like a human, the specific way machine learning “thinks like a human” is by learning. Computers that use machine learning don’t just take in information and spit it back out. They can look at different facts, the way a human does, turn those facts into concepts, and use those concepts to compute better and faster.

Machine learning is already in use in some high-profile projects. Here are a few examples you’ll probably recognize:

Autonomous vehicles (probably the most commonly cited example of ML)
The “did you mean” function when you misspell something in Google
The ranking of “top things to do” in a Google search for a given topic
Speech recognition capabilities (such as Siri)

Predicting user churn to drive greater engagement
Incorporating bots for improved customer experiences
Scaling personalized content
Demonstrating marketing ROI

It’s important that marketers understand the difference between ML and Artificial Intelligence (AI), the latter of which is – at this point – aspirational for marketing. Machine learning is a foundational aspect to AI, and you need to ensure the technology you purchase is implementing ML right.

Functioning properly, machine learning provides the algorithms that make machines smarter but keep in mind that a lot of these algorithms are commoditized – many products use them. It’s how they are used in combination that often differentiates a solution from others. 

There’s a reason ML is used for attribution – it helps improve the accuracy of the analysis. Consider the amount of historical data you need to analyze; no human can accurately analyze all that data and come back with realistic insights. The same for predictive analysis. And ML can learn as the data grows.

Intelligence tools are one part of the marketing stack, and that can make it challenging to do attribution and other analysis. This is starting to change.