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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.
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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.