Google Gemini replied as follows: The world of AI is often described as a set of Russian nesting dolls—one category sitting inside another. To make sense of it, it helps to look at AI through three different lenses: what it can do (capability), how it works (the engine), and how it behaves (functionality). Here are the major types of AI explained. 1. The "Capability" Levels This is the most common way to categorize AI based on how close it is to (or how much it exceeds) human intelligence. • Artificial Narrow Intelligence (ANI): Also called "Weak AI," this is the only type of AI we have today. It is superhuman at a single task but useless at anything else. • Example: Spotify’s algorithm is a genius at music but can't drive a car; Siri can set a timer but can’t write a novel on its own. • Artificial General Intelligence (AGI): Often called "Strong AI," this is a system that can learn, reason, and apply intelligence across any domain, just like a human. It doesn't exist yet, though 2024–2026 has seen massive leaps toward it. • Example: A robot that can decide to cook a meal, learn to fix a leaky faucet, and then write a poem about the experience without being specifically programmed for any of them. • Artificial Superintelligence (ASI): This is theoretical. It describes an AI that isn't just "human-level," but surpasses the collective intelligence of all humanity in every field—including social skills and creativity. 2. The "Engine" (Machine Learning & Deep Learning) These terms describe the technology used to build the AI. • Machine Learning (ML): Instead of a human writing rigid rules (if X, then Y), ML allows a computer to look at data, find patterns, and make its own rules. • Example: Email spam filters. They "learn" what spam looks like by analyzing millions of examples. • Deep Learning (DL): A sophisticated subset of ML that uses "Neural Networks"—mathematical structures inspired by the human brain. It is the engine behind modern voice recognition and image generation. • Example: Facial recognition on your phone. It "sees" hierarchies of features (edges → shapes → faces) to identify you. 3. The "Creative vs. Doer" (Generative vs. Agentic AI) This is the frontier of current technology (2025–2026). • Generative AI (GenAI): This AI focuses on creation. It uses patterns from its training data to generate brand-new content (text, images, code, or video). It is largely reactive—it waits for your prompt and then gives you an answer. • Example: ChatGPT, Claude, or Midjourney. You ask for a story, and it generates one. • Agentic AI: This is the shift from "AI that talks" to "AI that does." An agent has autonomy. You give it a high-level goal, and it breaks that goal into steps, uses tools, and completes the task without you holding its hand. • Example: Instead of just writing an email (GenAI), an Agentic AI would log into your CRM, find customers who haven't been contacted in 30 days, draft personalized emails, and schedule them to be sent. 4. The Scientific Classifications Academics often categorize AI by its "mindset" or memory. Type Definition Status Reactive Machines No memory. They only react to what is in front of them right now. Common (e.g., Deep Blue chess AI) Limited Memory Can store past data briefly to make better decisions. Standard (e.g., Self-driving cars) Theory of Mind Can understand that humans have feelings and thoughts. Experimental (Social robots) Self-Aware AI Possesses consciousness and its own "self." Science Fiction Summary Checklist • Machine Learning is the engine. • Generative AI is the voice/art. • Agentic AI is the hands/labor. • AGI is the ultimate (and currently distant) goal. Would you like me to show you how a specific "Agentic" workflow might look for your knowledge base project? | 
