# An Interactive Map of AI > Source: > Published: 2026-07-16 03:53:55+00:00 Navigate # The map of AI. Every concept on Artifipedia, and every link between them. Drag a node, hover to trace its connections, click to read. ### Embed the map Free to use on any site. Paste this where you want it to appear — it stays current as the encyclopedia grows. 56 concepts · 150 connections · this map is generated from the "connects to" links on every entry, so it grows as the encyclopedia does. ## Every concept ### Agent Memory Giving an AI a way to remember across conversations, since the model itself forgets everything the moment a session ends. ### AGI (Artificial General Intelligence) A hypothetical system with broad human-level capability across domains — undefined enough that people can argue about whether it's arrived. ### AI Agent Software that pursues a goal by taking its own steps — deciding, acting, and reacting — instead of answering once and stopping. ### AI Alignment The problem of making AI systems actually do what people intend — reliably pursuing the goals we want, not just the ones we accidentally specified. ### Artificial Intelligence The field of making machines do things that seem to require intelligence — a definition that has moved every time the machines succeed. ### Attention The mechanism that lets an AI decide which other words matter when interpreting each word — the core idea behind transformers. ### Backpropagation The algorithm that works out which weights caused a mistake and by how much — the reason neural networks can learn at all. ### Bias & Fairness The problem of AI systems producing unfair or discriminatory outcomes — usually by absorbing biases present in their training data. ### Chain-of-Thought Getting a model to reason step by step before answering — which dramatically improves its performance on hard problems. ### Clustering Grouping things that resemble each other — and the fact that the algorithm always returns groups, whether or not any exist. ### CNN (Convolutional Neural Network) A network that slides small filters across an image to find local patterns — the architecture that made computer vision work. ### Context Window The maximum amount of text an AI can consider at once — its short-term working memory, measured in tokens. ### Deep Learning Machine learning using neural networks with many layers — the approach behind nearly every recent AI breakthrough. ### Diffusion Model How most AI image tools work — starting from random noise and removing it step by step, guided by a prompt, until a picture appears. ### Embeddings Turning words (or images, or anything) into lists of numbers, arranged so that similar meanings end up close together. ### Explainability Getting a model to show its working — and the uncomfortable fact that most methods explain the explanation, not the decision. ### Feature Engineering Reshaping raw data into things a model can actually use — still where most of the accuracy comes from outside deep learning. ### Fine-tuning Continuing a model's training on your own examples so its *behavior* changes — baked into the model, not supplied at answer time. ### GAN (Generative Adversarial Network) Two networks trained against each other — one faking, one detecting — until the fakes pass. The technique diffusion largely replaced. ### GPU The chip that made deep learning possible — thousands of small cores doing the same maths at once, which is exactly what neural networks need. ### Gradient Descent Walking downhill on the error surface, one small step at a time — how a model's weights actually get updated. ### Guardrails The checks around a model that decide what it's allowed to receive, say, and do — the part that stops a demo becoming an incident. ### Hallucination When an AI produces something fluent and confident that is simply false — fluency is not the same as accuracy. ### Image Classification Getting an AI to look at an image and say what it is — the foundational task of computer vision. ### Image Segmentation Labelling every pixel rather than drawing a box — what you need when the exact shape matters. ### Inference API Renting a model by the request — how nearly everyone actually uses AI, and the dependency that comes with it. ### Intelligence The word underneath "artificial intelligence" — used constantly, defined by nobody, and the reason the field's biggest arguments never resolve. ### Jailbreaking Getting a model to do what it was trained to refuse — and the structural reason it keeps working. ### Large Language Model (LLM) An AI trained on enormous amounts of text to predict the next piece of writing — the technology behind chatbots like ChatGPT and Claude. ### Loss Function The number that says how wrong the model is — and therefore the definition of what it's trying to become. ### Machine Learning Getting computers to learn patterns from data and improve at a task, instead of being explicitly programmed with rules. ### Multi-Agent Systems Several AI agents working together on one problem, each with a role — powerful in demos, awkward in production. ### Multimodal AI Models that handle more than one kind of input — text and images, sometimes audio and video — in a single shared representation. ### Neural Network A system of simple connected units that learns patterns from examples — the foundation underneath deep learning and modern AI. ### Object Detection Finding *where* objects are in an image and *what* they are — drawing a labelled box around each one. ### OCR (Optical Character Recognition) Turning pictures of text into text — solved for clean documents, still genuinely hard for everything else. ### Open-Weight Models Models whose weights you can download and run yourself — often called open source, usually not quite. ### Overfitting When a model memorizes its training data instead of learning the general pattern — so it looks great in training but fails on new data. ### Privacy & PII Personal data going into AI systems, coming back out of them, and the fact that a trained model is very hard to un-train. ### Prompt Engineering The craft of writing instructions that get the best, most reliable output from an AI model. ### Quantization Storing a model's numbers with less precision so it fits in less memory and runs faster — usually at a surprisingly small cost in quality. ### Red-teaming Attacking your own system on purpose, before someone else does it for free. ### Reinforcement Learning Learning by trial and error through rewards — the way you'd train a pet, applied to software. ### Retrieval-Augmented Generation (RAG) Letting an AI answer from a specific set of documents by looking them up as it responds — instead of relying only on what it memorized. ### RLHF (Reinforcement Learning from Human Feedback) Training a model on human preferences rather than correct answers — the step that turned text predictors into assistants. ### Supervised Learning Teaching an AI by showing it labelled examples — inputs paired with the correct answers — so it can predict answers for new inputs. ### System Prompt The standing instructions a model gets before the conversation starts — influential, invisible to users, and not a security boundary. ### Temperature A single setting that controls how random or predictable an AI's output is — low for focused, high for creative. ### Token The small piece of text an AI reads and writes — usually a chunk of a word, not a whole word. ### Tool Use Letting a model call real software — a search, a calculator, your database — instead of trying to answer everything from memory. ### Train/Test Split Holding back data the model never sees, so you can find out whether it learned anything or just memorised. ### Training vs Inference Building the model versus using it — two completely different activities with different costs, hardware, and constraints. ### Transfer Learning Starting from a model that already learned something general, instead of from random numbers — why small teams can build real AI. ### Transformer The neural-network architecture behind almost every modern AI model — built around attention, which lets it weigh every word against every other, all at once. ### Unsupervised Learning Finding structure in data nobody labelled — useful, underrated, and much harder to know if you got right. ### Vector Database A database built to store embeddings and find the most similar ones fast — the search engine behind meaning-based retrieval.