Mike MurphyAI Handyman
Subscribe
Back to articles

25 Must-Know AI Terms

December 16, 2025 ·

AIAI Foundations

Introduction

Whether you’re just starting your AI journey or looking to solidify your understanding, this comprehensive guide breaks down 25 essential AI terms that everyone should know. Each term includes a simple definition, an “Explain Like I’m 5” version, real-world examples, and curated AI tools you can use today.

Think of this as your AI reference library - bookmark it, share it, and come back whenever you need a refresher on AI terminology.

Core AI Concepts

Definition:
The simulation of human intelligence in machines, enabling them to perform tasks like reasoning, learning, and problem-solving.

Explain Like I’m 5:
Like teaching a computer to act smart so it can think, learn, or solve problems like a person.

Real-World Example:
Siri uses AI to understand voice commands and answer questions, mimicking human intelligence.

Recommended AI Tools:

Definition:
A type of Machine Learning using neural networks with many layers to process complex patterns, like images or speech.

Explain Like I’m 5:
Deep learning is like teaching a child what a dog is by showing it lots and lots of dogs.

Real-World Example:
A self-driving car recognizes stop signs in images using deep neural networks.

Recommended AI Tools:

  • Descript - AI-powered video and audio editing
  • Lumen5 - AI video creation from text
  • Runway - Creative AI tools for video

Definition:
A subset of AI where systems learn from data to improve performance without being explicitly programmed.

Explain Like I’m 5:
Like helping a computer learn from examples instead of giving it step-by-step instructions.

Real-World Example:
Netflix recommends movies based on your watch history, learning your preferences from data.

Recommended AI Tools:

Definition:
A computing model inspired by the human brain, with interconnected nodes that process data in layers.

Explain Like I’m 5:
Like a team of tiny helpers - each one does a small job, and together they solve big problems.

Real-World Example:
A neural network identifies handwritten numbers by analyzing pixel patterns.

Recommended AI Tools:

Types of Machine Learning

Definition:
Machine Learning where the model is trained on labeled data, predicting outcomes like classification or regression.

Explain Like I’m 5:
Like a student learning with an answer key - it gets told what’s right while it learns.

Real-World Example:
A model trained on labeled cat/dog photos predicts if a new image is a cat.

Recommended AI Tools:

Definition:
Machine Learning where the model finds patterns in unlabeled data, like clustering or dimensionality reduction.

Explain Like I’m 5:
Like sorting a box of toys by color or shape without anyone telling you how - it figures out patterns on its own.

Real-World Example:
Grouping customers by similar shopping habits for targeted ads without prior labels.

Recommended AI Tools:

Definition:
Machine Learning where an agent learns by trial and error, maximizing rewards through actions in an environment.

Explain Like I’m 5:
Like training a dog with treats - it learns by getting rewards or penalties for its actions.

Real-World Example:
A robot learns to navigate a maze by earning points for reaching the exit.

Recommended AI Tools:

AI Application Areas

Definition:
AI techniques for understanding and generating human language, like in chatbots or translations.

Explain Like I’m 5:
Like teaching a computer to read, write, and understand human language.

Real-World Example:
A chatbot answers your questions by understanding and responding in natural language.

Recommended AI Tools:

 

Definition:
AI that enables machines to interpret and understand visual data, like images or videos.

Explain Like I’m 5:
Like giving eyes to a computer so it can understand what it sees in pictures or videos.

Real-World Example:
Facial recognition identifies people in photos for security systems.

Recommended AI Tools:

 

Definition:
AI systems that create content, like text, images, or music, using models like GANs or transformers.

Explain Like I’m 5:
Like a robot that can paint, write, or sing - it creates new stuff from what it learned.

Real-World Example:
An AI generates a painting from a prompt like “sunset over mountains.”

Recommended AI Tools:

  • DALL-E - AI image generation from text
  • Midjourney - AI art and image creation
  • Suno - AI music generation

 

Definition:
Machine Learning model that categorizes data into specific classes based on input features.

Explain Like I’m 5:
Like a sorter that says, “this is a cat” or “this is a dog” based on what it sees.

Real-World Example:
An email classifier labels messages as “spam” or “not spam” based on their content.

Recommended AI Tools:

Working with AI Models

Definition:
AI models trained on massive text datasets to generate human-like responses, like GPT.

Explain Like I’m 5:
Like a giant brain trained on tons of books and websites so it can talk, write, and help answer questions.

Real-World Example:
An LLM writes a story or answers questions based on vast text training.

Recommended AI Tools:

  • ChatGPT - OpenAI’s conversational AI
  • Claude - Anthropic’s advanced AI assistant
  • Gemini - Google’s multimodal AI

Definition:
Designing specific inputs to guide AI models, especially LLMs, to produce desired outputs.

Explain Like I’m 5:
Like asking the perfect question to get the answer you want from an AI.

Real-World Example:
Asking an AI, “Write a 5-line poem about stars” to get a concise poem.

Recommended AI Tools:

Definition:
When a model fails to capture patterns in the training data, leading to poor performance.

Explain Like I’m 5:
Like not studying enough - it doesn’t learn the patterns well at all.

Real-World Example:
A house price predictor using one feature misses trends and gives bad estimates.

Recommended AI Tools:

Definition:
Adjusting a pre-trained AI model with new data for a specific task.

Explain Like I’m 5:
Like teaching an already smart robot to be even better at one specific thing.

Real-World Example:
Tweaking a language model with medical texts to answer health questions better.

Recommended AI Tools:

Definition:
When a model learns the training data too well, including noise, and performs poorly on new data.

Explain Like I’m 5:
Like memorizing practice test answers too well - it struggles with anything new.

Real-World Example:
A model memorizes quiz answers but fails on new questions about the same topic.

Recommended AI Tools:

Technical Foundations

Definition:
A set of rules an AI follows to learn from data or solve a problem.

Explain Like I’m 5:
Like a recipe the computer follows to solve a problem or make a decision.

Real-World Example:
A sorting algorithm arranges numbers from smallest to largest for an AI model.

Recommended AI Tools:

Definition:
A tool that lets different software systems, like AI models, communicate with each other.

Explain Like I’m 5:
Like a waiter that takes your order and brings back what you asked from the kitchen (the app or tool).

Real-World Example:
An app uses an AI API to add voice-to-text without building it from scratch.

Recommended AI Tools:

Definition:
A collection of data used to train, validate, or test AI models.

Explain Like I’m 5:
Like a big notebook full of examples that the AI uses to learn.

Real-World Example:
A set of labeled flower images trains an AI to identify roses vs. daisies.

Recommended AI Tools:

AI Responsibility & Advanced Concepts

Definition:
Principles addressing fairness, bias, transparency, and accountability in AI development and use.

Explain Like I’m 5:
Like teaching robots to be fair, honest, and not hurt people.

Real-World Example:
Testing facial recognition across diverse groups to avoid biased outcomes.

Recommended AI Tools:

Definition:
When an AI generates outputs that are confidently incorrect or made-up, like an LLM spinning a convincing but totally fictional story.

Explain Like I’m 5:
Like when AI confidently makes stuff up that isn’t true.

Real-World Example:
An AI claims “dinosaurs roamed in 2020” with no factual basis.

Recommended AI Tools:

Definition:
Using a pre-trained model on a new task, adapting it with minimal additional training.

Explain Like I’m 5:
Like learning to ride a scooter after learning to ride a bike - it reuses what it already knows.

Real-World Example:
Using a pre-trained image model to identify new types of flowers with less data.

Recommended AI Tools:

Definition:
The process of selecting and transforming data features to improve machine learning model performance.

Explain Like I’m 5:
Like picking the most helpful clues so the AI can make better guesses.

Real-World Example:
Choosing a customer’s age and purchase history as features to predict their buying preferences.

Recommended AI Tools:

Definition:
Techniques to assess how well an AI model performs, using metrics like accuracy or confusion matrices.

Explain Like I’m 5:
Like grading a test to see how well the AI learned.

Real-World Example:
Measuring a spam filter’s accuracy by checking how many emails it correctly labels.

Recommended AI Tools:

Definition:
Methods to make AI decisions understandable to humans, improving trust and accountability.

Explain Like I’m 5:
Like making sure we understand why the AI made a decision - not just what it said.

Real-World Example:
A tool explains why an AI denied a loan, showing which factors influenced the decision.

Recommended AI Tools:

Conclusion

Understanding these 25 AI terms gives you a solid foundation for navigating the rapidly evolving world of artificial intelligence. Whether you’re building AI applications, making business decisions about AI adoption, or simply staying informed about technology trends, this reference guide has you covered.

What’s Next?

  • Bookmark this page for future reference
  • Try out some of the AI tools mentioned in each section
  • Share this guide with anyone looking to understand AI better

Want to learn more about AI? Follow my journey at mikemurphy.co where I break down complex AI concepts into practical, actionable insights.

FAQ: Common Questions About AI Terms

Q: What’s the difference between AI, Machine Learning, and Deep Learning?
A: AI is the broadest concept (machines acting intelligently), Machine Learning is a subset of AI (learning from data), and Deep Learning is a subset of ML (using multi-layered neural networks).

Q: Do I need to be a programmer to understand AI?
A: No! While programming helps if you want to build AI systems, understanding AI concepts doesn’t require coding knowledge. This guide is designed for everyone.

Q: Which AI term should I learn first?
A: Start with “Artificial Intelligence” and “Machine Learning” as foundation concepts, then explore the specific areas that interest you most (like NLP for language tasks or Computer Vision for image tasks).

Q: Are AI hallucinations dangerous?
A: They can be if relied upon for critical decisions. Always verify AI outputs, especially for important information. This is why Explainable AI and proper Model Evaluation are crucial.

Q: What’s the best AI tool for beginners?
A: ChatGPT and Google Teachable Machine are excellent starting points - both are user-friendly and help you understand AI capabilities hands-on.