Artificial Intelligence terms to learn for beginners

Feels like all of us having to figure out new AI terminology, but cuter

Learning about artificial intelligence (AI) and how it works often means learning a new vocabulary. So here’s a quick reference guide for AI terms that pop up in discussion about AI in the workplace. This list gets updated frequently, so bookmark it for reference.

And if you need an explainer on the difference between AI and generative AI, we have that too.

If you want to go even further and learn more about AI in the workplace, check out our online AI literacy course for beginners.

General terms

• artificial intelligence (AI) - technology that makes machines think and learn like humans

• artificial neural network - a system in AI inspired by how the brain works

• augmented intelligence - AI that helps humans, not replaces them

• generative AI - AI that creates new things like text, images, or music

• machine learning (ML) - a type of AI where computers learn from data

• deep learning - a more advanced type of machine learning using neural networks

• transformer - a model that helps AI understand and generate language better

• LLM (large language model) - a big AI model trained on lots of text to understand language

• model - the AI program that learns from data and gives answers

• NLP (natural language processing) - AI that understands and works with human language

• conversational AI - AI that talks to people, like chatbots

• training - the process of teaching an AI model by exposing it to data so it can learn patterns, relationships, and rules for a specific task

• data - raw information, such as text, images, audio, or numbers, used to train, test, and evaluate AI models

AI tools and techniques

• GPT (generative pre-trained transformer) - a popular type of generative AI for creating text

• prompt engineering - writing good questions or instructions for AI

• grounding - making sure AI gives answers connected to real facts

• hallucination - when AI makes up information that isn’t true

• parameters - the numbers inside an AI model that it learns from data

Learning types

• AI training and learning - teaching an AI model using data

• supervised learning - AI learning with labeled data (correct answers provided)

• unsupervised learning - AI learning patterns without labeled data

• reinforcement learning - teaching AI through rewards and punishments

AI testing and validation

• validation - checking if the AI works correctly

• safety - ensuring AI doesn’t cause harm

• toxicity - detecting harmful or offensive AI-generated content

• zero data retention - ensuring AI doesn’t save any data after use

Ethics in AI

• AI ethics - rules about using AI fairly and safely

• ethical AI maturity model - a guide for making AI ethical in stages

• explainable AI (XAI) - AI that shows how and why it makes decisions

• transparency - being open about how AI works and what it does

• human in the loop (HITL) - involving humans in AI decisions for better control

• machine learning bias - when AI unfairly favors one group over another

• anthropomorphism - thinking of AI as if it has human emotions or thoughts

AI in practice

• CRM with AI - using AI in customer relationship management, like for marketing or sales

• sentiment analysis - AI understanding emotions in text, like happy or sad

• prompt defense - protecting AI from harmful or tricky inputs

• red-teaming - testing AI by trying to break or fool it

Check out our AI basics course for beginners, no technical background required