An Interactive Map of AI

Artifipedia launched an interactive map visualizing 56 AI concepts and 150 connections between them, allowing users to drag nodes, trace links, and click for details. The map is embeddable and updates automatically as the encyclopedia grows.

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.