TL;DR

AI (Artificial Intelligence) helps computers do tasks that usually need human smarts—like understanding language, recognizing images, or making decisions. Modern AI learns from examples rather than following rigid rules, which makes it surprisingly good at messy, real-world problems.

Why it matters

AI is already in your pocket (voice assistants), your inbox (spam filters), and your news feed (recommendations). Understanding the basics helps you use these tools wisely, spot their limits, and make informed choices about when to trust them.

What is AI, really?

Artificial Intelligence is a broad term for making machines act intelligently. That could mean:

  • Playing chess better than humans
  • Translating languages in real-time
  • Generating human-like text or images
  • Diagnosing medical conditions from scans

The key idea: instead of programming every rule manually ("if this, then that"), we train AI systems on examples. They find patterns in data and learn to make predictions or decisions on their own.

Machine Learning: The Core Idea

Machine Learning (ML) is how most modern AI works. Instead of explicit instructions, you:

  1. Gather data (thousands of cat photos, for example)
  2. Train a model (the AI learns what features make something a "cat")
  3. Use the model (it can now identify cats in new photos)

Think of it like teaching a child. You don't explain "cats have pointy ears and whiskers" in formal logic—you just show them lots of cats, and they figure it out.

Jargon: "Model"
A trained AI system. It's the result of feeding data into a learning algorithm. The model contains all the patterns it learned, stored as millions (or billions) of numbers called parameters.

Large Language Models (LLMs)

Large Language Models are AI systems trained on vast amounts of text (books, websites, articles). They learn to:

  • Predict the next word in a sentence
  • Answer questions
  • Write essays, code, emails
  • Translate languages
  • Summarize documents

Examples: ChatGPT, Claude, Google Bard, and others.

These models don't "understand" like humans do—they're incredibly sophisticated pattern-matchers. But the patterns are so rich that the results often seem human-like.

Jargon: "Parameters"
Numbers inside the model that determine its behavior. A model with 175 billion parameters has 175 billion numbers it tweaks during training. More parameters usually mean more capability—but also more computing power needed.

What AI is good at

  • Pattern recognition: Spotting trends in data, recognizing faces, detecting spam
  • Language tasks: Translation, summarization, chatbots, writing assistance
  • Creative generation: Writing stories, creating images, composing music
  • Automation: Handling repetitive tasks like scheduling, data entry, customer support
  • Prediction: Forecasting weather, stock trends, equipment failures

What AI struggles with

  • True understanding: AI doesn't "know" things the way you do—it matches patterns
  • Common sense: It might confidently give nonsense answers (called hallucinations)
  • Reasoning about new situations: It's great with familiar patterns, weak with truly novel problems
  • Explaining itself: Often it's a "black box"—hard to know why it made a decision
  • Ethics and values: It doesn't have morals or judgment—it reflects its training data, biases and all

How does AI learn?

The most common approach is supervised learning:

  1. Input data: Show the AI examples (e.g., photos labeled "cat" or "dog")
  2. Make a guess: The AI tries to predict the label
  3. Check the error: Compare the guess to the real answer
  4. Adjust: Tweak the model's parameters to improve
  5. Repeat: Do this millions of times until it gets good

Over time, the model learns which features matter (whiskers, ears, tails) and which don't (background color).

Key terms (quick reference)

  • AI (Artificial Intelligence): Machines performing tasks that typically require human intelligence
  • ML (Machine Learning): Training computers to learn from data instead of explicit programming
  • Model: The trained AI system containing learned patterns
  • Parameters: Numbers inside the model that get adjusted during training
  • LLM (Large Language Model): AI trained on massive text datasets to understand and generate language
  • Training: The process of feeding data to an AI so it learns patterns
  • Hallucination: When AI confidently generates false or nonsensical information

Use responsibly

  • Don't paste private info into public AI tools unless you're sure it's safe
  • Double-check important facts—AI can sound confident even when wrong
  • Be aware of bias—AI learns from human-created data, which includes human biases
  • Use as a starting point, not the final word

What's next?

Now that you know the basics, you might explore:

  • How Chatbots Work (no math required)
  • Prompting 101: How to ask AI tools for better results
  • AI Safety Basics: Using AI responsibly in your family or team
  • Embeddings & RAG: How AI tools search and retrieve information