An Interactive Map of AI

An Interactive Map of AI

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.

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56 concepts · 150 connections · this map is generated from the "connects to" links on every entry, so it grows as the encyclopedia does.

Core Concepts

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.

Intelligence - The word underneath "artificial intelligence" - used constantly, defined by nobody, and the reason the field's biggest arguments never resolve.

Neural Network Fundamentals

Neural Network - A system of simple connected units that learns patterns from examples - the foundation underneath deep learning and modern AI.

Deep Learning - Machine learning using neural networks with many layers - the approach behind nearly every recent AI breakthrough.

Backpropagation - The algorithm that works out which weights caused a mistake and by how much - the reason neural networks can learn at all.

Gradient Descent - Walking downhill on the error surface, one small step at a time - how a model's weights actually get updated.

Loss Function - The number that says how wrong the model is - and therefore the definition of what it's trying to become.

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.

Architecture & Mechanisms

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.

Attention - The mechanism that lets an AI decide which other words matter when interpreting each word - the core idea behind transformers.

Context Window - The maximum amount of text an AI can consider at once - its short-term working memory, measured in tokens.

Token - The small piece of text an AI reads and writes - usually a chunk of a word, not a whole word.

Embeddings - Turning words (or images, or anything) into lists of numbers, arranged so that similar meanings end up close together.

Vector Database - A database built to store embeddings and find the most similar ones fast - the search engine behind meaning-based retrieval.

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.

Learning Paradigms

Machine Learning - Getting computers to learn patterns from data and improve at a task, instead of being explicitly programmed with rules.

Supervised Learning - Teaching an AI by showing it labelled examples - inputs paired with the correct answers - so it can predict answers for new inputs.

Unsupervised Learning - Finding structure in data nobody labelled - useful, underrated, and much harder to know if you got right.

Reinforcement Learning - Learning by trial and error through rewards - the way you'd train a pet, applied to software.

RLHF (Reinforcement Learning from Human Feedback) - Training a model on human preferences rather than correct answers - the step that turned text predictors into assistants.

Transfer Learning - Starting from a model that already learned something general, instead of from random numbers - why small teams can build real AI.

Fine-tuning - Continuing a model's training on your own examples so its behavior changes - baked into the model, not supplied at answer time.

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.

Train/Test Split - Holding back data the model never sees, so you can find out whether it learned anything or just memorised.

Feature Engineering - Reshaping raw data into things a model can actually use - still where most of the accuracy comes from outside deep learning.

Models & Generation

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.

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.

GAN (Generative Adversarial Network) - Two networks trained against each other - one faking, one detecting - until the fakes pass. The technique diffusion largely replaced.

Open-Weight Models - Models whose weights you can download and run yourself - often called open source, usually not quite.

Temperature - A single setting that controls how random or predictable an AI's output is - low for focused, high for creative.

Chain-of-Thought - Getting a model to reason step by step before answering - which dramatically improves its performance on hard problems.

Hallucination - When an AI produces something fluent and confident that is simply false - fluency is not the same as accuracy.

Computer Vision

CNN (Convolutional Neural Network) - A network that slides small filters across an image to find local patterns - the architecture that made computer vision work.

Image Classification - Getting an AI to look at an image and say what it is - the foundational task of computer vision.

Object Detection - Finding where objects are in an image and what they are - drawing a labelled box around each one.

Image Segmentation - Labelling every pixel rather than drawing a box - what you need when the exact shape matters.

OCR (Optical Character Recognition) - Turning pictures of text into text - solved for clean documents, still genuinely hard for everything else.

Data & Clustering

Clustering - Grouping things that resemble each other - and the fact that the algorithm always returns groups, whether or not any exist.

Systems & Deployment

Training vs Inference - Building the model versus using it - two completely different activities with different costs, hardware, and constraints.

Inference API - Renting a model by the request - how nearly everyone actually uses AI, and the dependency that comes with it.

System Prompt - The standing instructions a model gets before the conversation starts - influential, invisible to users, and not a security boundary.

Prompt Engineering - The craft of writing instructions that get the best, most reliable output from an AI model.

Tool Use - Letting a model call real software - a search, a calculator, your database - instead of trying to answer everything from memory.

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.

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.

Safety & Ethics

Bias & Fairness - The problem of AI systems producing unfair or discriminatory outcomes - usually by absorbing biases present in their training data.

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.

Jailbreaking - Getting a model to do what it was trained to refuse - and the structural reason it keeps working.

Red-teaming - Attacking your own system on purpose, before someone else does it for free.

Explainability - Getting a model to show its working - and the uncomfortable fact that most methods explain the explanation, not the decision.

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.