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

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.

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

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.

Machine Learning

This is how AI technology learns and gets smarter on its own. 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. The machine learns from the results, and applies these learnings to its next attempt

Natural Language Generation (NLG)

Natural language generation is when AI writes or speaks human-sounding language.

Natural language generation powers everything from writing tools to smart home assistants to chatbots. It makes it possible to converse with machines.

Computer Vision

Computer vision is when AI accurately identifies objects in videos or real-time visual feeds.

Computer vision takes image and facial recognition further. It’s when AI can actually recognize moving objects, either in a video or out in the world.

Artificial General Intelligence (AGI)

Artificial general intelligence doesn’t exist, but describes an AI system that can learn and understand any intelligent task.

Right now, AGI doesn’t exist and isn’t close to existing. The potential creation of AGI raises fundamental questions about the benefits and dangers of technology, as well as what it means to be human.

Robotics

Robots and robotics are not AI—they’re powered by AI.

Robotics combines image recognition, facial recognition, and computer vision to power a physical body. (If a robot talks, it may also use NLG and NLP.)

A robot itself is not AI, but it relies heavily on AI software to function

Artificial Intelligence (AI)

AI is the science of making machines smart.

That definition comes from AI expert and CEO of DeepMind Demis Hassabis. 

• It allows us to teach machines to become more human-like by giving them the ability to see, hear, speak, move, and write, and even make predictions
• This makes AI suitable for a wide-range of intelligent tasks that were typically reserved only for humans
• Now, “artificial intelligence” isn’t one technology that does all of these smart tasks, but a collection of technologies

Facial Recognition

Facial recognition is when AI accurately identifies human faces in photos and videos.

You use facial recognition any time you use the Face ID function on your iPhone. AI is able to recognize your face, then use that information to confirm your identity.

Deep Learning

Deep learning is a subset of machine learning that unlocks superhuman AI performance.

While machine learning makes AI’s advanced capabilities possible, deep learning pushes the very boundaries of what’s possible with AI.

Deep learning seeks to mimic how the human brain works. It does that by using “neural nets,” a collection of interconnected artificial neurons.

Artificial Narrow Intelligence (ANI)

All AI systems are artificial narrow intelligence, which means they only perform narrowly defined tasks.

They may perform these narrowly defined tasks at superhuman levels. 

Pattern Recognition

Pattern recognition is when machines detect pattens in data.

These patterns help machines better optimize towards outcomes, which makes pattern recognition a key function in machine learning.

Natural Language Processing (NLP)

Natural language processing is when AI interprets what human language means.

To do NLG, a machine must use natural language processing to first understand written or spoken language.

Sentiment Analysis

Sentiment analysis is when AI understands the tone and emotion of human language.

Sentiment analysis takes NLP one step further. It not only understands language, but also understands its tone and emotion.

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