📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
🤖 AI BASICS FOR BEGINNERS FREE
Day 3 of 5 · 60% complete
Did you know that when you scroll through TikTok, at least three completely different types of AI are working at the same time? One AI recognises what’s in each video (computer vision). A second AI predicts which videos you personally want to watch next (recommendation AI). And if there’s text or captions in the video, a third AI is reading and understanding them (language AI).
Most people think “AI” is one thing. It’s not. It’s a whole family of different technologies — each one built differently, trained differently, good at different things, and broken in completely different ways.
Today I’m giving you the complete guide to all six types of AI running around in your daily life. By the end, you’ll never say “that app uses AI” without immediately knowing which type — and what that means for how it could fail.
🎯 What You’ll Learn in Day 3
✅ The six different types of AI — with real examples of each
✅ How to spot which type is running in any app or feature
✅ The main weakness of each type (in plain language)
✅ How different AI types can be combined in one product
✅ Your first multi-type attack chain, designed from scratch
⏱ 25 min read · 3 exercises · Browser needed for exercises 1 and 3
📋 Before You Start:
- Completed Day 1: What Is AI? and Day 2: How Does AI Learn?
- Remember: AI finds patterns in examples and makes guesses
- Remember: training data is the foundation — corrupt it, you corrupt the AI
Types of Artificial Intelligence — Day 3 of 5
- Type 1: Large Language Models — The AI That Talks
- Type 2: Computer Vision — The AI That Sees
- Type 3: Recommendation AI — The AI That Predicts What You Want
- Type 4: Voice AI — The AI That Listens and Speaks
- Type 5: Generative AI — The AI That Creates Things
- Type 6: Anomaly Detection AI — The AI That Guards the Gate
- The Big Picture — All Six Types and Their Weak Points
- Questions and Answers
Days 1 and 2 built your foundation — what AI is and how it learns. Today is the taxonomy lesson. I want you to be able to look at any app, any website, any feature and say “that’s Type 3, and here’s how it can fail.” The LLM hacking course and the how hackers attack AI guide both assume you know these types. Let’s lock them in.
Type 1: Large Language Models — The AI That Talks
You probably already know this one — ChatGPT, Claude, Gemini, Copilot. These are called large language models, or LLMs. They’re the most talked-about type of AI right now.
What they do: LLMs are trained on enormous amounts of text — basically a massive chunk of everything ever written on the internet, plus millions of books. They learned the patterns of how human language works. When you type something, they predict — word by word — what a useful response looks like. They can write, answer questions, summarise documents, write code, translate languages, and hold conversations.
Where you find them: ChatGPT (obviously). The AI in Google Search that writes those summary boxes. GitHub Copilot that helps programmers write code. The chatbot in most company websites. Gmail’s “Smart Compose” that finishes your sentences. Microsoft Copilot in Word and Excel.
How they can be tricked: The main weakness of LLMs is that they can’t properly separate “instructions telling me what to do” from “content someone sent me to process.” If you put instructions inside a message, the AI might follow those instructions instead of doing its job. This is called prompt injection — and it’s the most important AI attack to learn. We’ll go deep on this in Day 4.
💡 Quick identification test: Can you type any question or instruction in natural language and get a useful response? Does it follow varied requests rather than just fixed commands? That’s an LLM.
Type 2: Computer Vision — The AI That Sees
Computer vision AI processes images and video. It can look at a photo and tell you what’s in it, find specific objects, read text, recognise faces, track movement, and detect things out of place. It’s trained on millions of labelled images until it learns the visual patterns that distinguish one thing from another.
Where you find it: Face unlock on your phone (recognises your face). Google Photos tagging your friends automatically. Instagram’s automatic alt text on photos. Security cameras that detect people. TikTok understanding what type of content is in each video. Snapchat’s face filters. Self-driving car cameras. The AI that checks if your ID photo matches your face during age verification.
How it can be tricked: Computer vision learns patterns in pixels — not the “meaning” of what it sees, the way you understand a photo. That means tiny, invisible changes to an image’s pixels can completely fool it. A photo that looks exactly like a cat to you might look like a dog to the AI after changing just a few pixels in a very specific way. This is called an adversarial example. Researchers have also printed special sticker patterns that make security cameras fail to detect people walking right past them. Wild, right?
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