The AI Terms Cheat Sheet [Easy Explainer of AI Terminology]

The field of Artificial Intelligence (AI) is comprised of many disciplines, technologies and subfields.

There are dozens of terms that are used to describe AI technologies, and the definitions can be complex and confusing.

A large part of our focus with the Marketing AI Institute is to make AI more approachable and actionable.

To do that, we've created this AI terms cheat sheet, which features easy, accessible definitions of core AI terminology.
We encourage you to skip to the term you're curious about. But, the terms are also in specific order to help you build on each piece of knowledge.

algorithm

An algorithm is a series of steps used to solve a problem or perform an action.
Human programmers write algorithms. Then, machines follow them to produce an outcome.
Almost every piece of software you use consists of a machine following instructions written by a human.
That includes AI. Except that AI uses different algorithms in different ways to do things your typical software can't do.

Artificial Intelligence (AI)

AI is the science of making machines smart.
That definition comes from AI expert and CEO of DeepMind Demis Hassabis .
Here's what we mean by making machines smart...
Your typical software can only follow the instructions it's given.
That is helpful for automation. Even basic software makes our lives easier by doing things for us better and faster.
But typical software is static. It only does what it is told to do, over and over. And it only becomes better when a human programmer upgrades it.
In short, typical software cannot adapt to real-time or changing conditions in data or environment.
That makes it unsuitable for fast-changing situations or markets.
AI is different. It allows us to teach machines to become more human-like.

We give them the ability to see, hear, speak, move, and write. We even give them the ability to understand and make predictions.
In some cases, these smart machines can teach themselves to get better at the tasks listed above.
That gives AI abilities that typical software doesn't have. It can respond, react, and recommend in real-time—without a human explicitly telling it what to do.
This makes AI suitable for a wide-range of intelligent tasks that were typically reserved only for humans.

Today, AI can:

Navigate roads, cities, and warehouses.
Predict what you want to buy or watch next.
Predict how different actions will impact a business.
Create new solutions to problems.
And much, much more.

Now, "artificial intelligence" isn't one technology that does all of these smart tasks.
It's actually an umbrella term for a collection of technologies.
Some of these technologies include natural language generation (NLG), natural language processing (NLP), Machine Learning, deep learning, and neural networks.

Machine Learning

Machine learning is the core subset of AI that makes its most advanced capabilities possible.
Machine learning is how AI technology learns and gets smarter on its own.
In machine learning, a human trains a machine to achieve an outcome, using data prepared by the human.
Using what it learned from the human, the machine then goes and tries to achieve the outcome using data it's never seen before.
Every time the machine tries to achieve the outcome, it learns from the results—even if they're bad. And it applies these learnings to its next attempt.

In this way, the machine uses machine learning to rapidly improve at a task without direct human involvement.
In the process, it might discover new, creative, or counterintuitive ways to achieve the outcome that humans never thought to try.
This is why AI enabled by machine learning is so powerful. Once trained, it can very quickly outpace humans, as well as find solutions and patterns we're unable to see.

As an example, Demis Hassabis (mentioned above) taught a machine learning program how to beat video games from the 1980s.