[
 {
  "id": "activation-function",
  "term": "Activation function",
  "layer": "math",
  "definition": "The non-linear function applied to a neuron's sum, such as ReLU or sigmoid, which lets networks model curves, not just lines.",
  "eli5": "A switch that decides how loudly a neuron speaks, so the network can learn bendy shapes, not only straight lines.",
  "evolved_from": [
   "artificial-neuron"
  ],
  "aka": [],
  "led_to": [],
  "entries": [],
  "letter": "A"
 },
 {
  "id": "agent-memory",
  "term": "Agent memory",
  "layer": "building",
  "definition": "The ability of an assistant or agent to keep facts from earlier conversations and use them later, instead of starting fresh in every session.",
  "eli5": "Like a friend who remembers your name and what you talked about last week.",
  "evolved_from": [
   "agent",
   "context-window"
  ],
  "aka": [
   "active memory",
   "long-term memory"
  ],
  "added": "2026-10-12",
  "source": "https://techcrunch.com/2026/10/12/meta-releases-its-first-frontier-model-muse-wonder/",
  "led_to": [],
  "entries": [],
  "letter": "A"
 },
 {
  "id": "agent-sandbox",
  "term": "Agent sandbox",
  "layer": "safety",
  "definition": "An isolated environment where an AI agent can run code and use tools without being able to touch the rest of the computer or network. Microsoft's Execution Containers (MXC) are one example.",
  "eli5": "A sandpit with walls: the agent can build and break things inside it, but nothing outside gets hurt.",
  "evolved_from": [
   "agent",
   "computer-use"
  ],
  "aka": [
   "agent containment"
  ],
  "added": "2026-10-08",
  "source": "https://commandline.microsoft.com/local-models-sandboxed-tools-github-windows/",
  "led_to": [],
  "entries": [],
  "letter": "A"
 },
 {
  "id": "a2a",
  "term": "Agent2Agent protocol (A2A)",
  "layer": "building",
  "definition": "An open protocol for AI agents built by different vendors to discover each other and work together.",
  "eli5": "A common language so robots from different companies can team up on a job.",
  "evolved_from": [
   "agent",
   "mcp"
  ],
  "aka": [
   "A2A"
  ],
  "led_to": [],
  "entries": [
   "a2a-2025"
  ],
  "letter": "A"
 },
 {
  "id": "agent",
  "term": "AI agent",
  "layer": "building",
  "definition": "A system where a model plans and takes multi-step actions with tools toward a goal, with little human input per step.",
  "eli5": "Not just answering 'how do I book a train' but actually going and booking it.",
  "evolved_from": [
   "react-pattern",
   "tool-use"
  ],
  "aka": [],
  "led_to": [
   "a2a",
   "agent-memory",
   "agent-sandbox",
   "computer-use",
   "personal-agent",
   "vibe-coding"
  ],
  "entries": [
   "react-2022",
   "computer-use-2024",
   "mcp-2024",
   "claude-code-2025",
   "a2a-2025",
   "build-2025",
   "claude4-2025",
   "steinberger-openai-2026"
  ],
  "letter": "A"
 },
 {
  "id": "coding-assistant",
  "term": "AI coding assistant",
  "layer": "building",
  "definition": "Tools that suggest, write or edit code with LLMs, from autocomplete to autonomous coding agents.",
  "eli5": "A helper sitting next to you who finishes your sentences, but in code.",
  "evolved_from": [
   "llm"
  ],
  "aka": [],
  "led_to": [
   "vibe-coding"
  ],
  "entries": [
   "github-copilot-2021",
   "claude35-2024",
   "vibe-coding-2025",
   "claude-code-2025",
   "claude4-2025"
  ],
  "letter": "A"
 },
 {
  "id": "ai-governance",
  "term": "AI governance",
  "layer": "safety",
  "definition": "How organisations decide who controls powerful AI and how decisions about it are made.",
  "eli5": "Deciding who gets the keys to the car and what rules they must follow.",
  "evolved_from": [
   "ai-safety"
  ],
  "aka": [],
  "led_to": [
   "kill-switch"
  ],
  "entries": [
   "openai-board-2023"
  ],
  "letter": "A"
 },
 {
  "id": "ai-regulation",
  "term": "AI regulation",
  "layer": "safety",
  "definition": "Laws and rules governing how AI is built and used, such as the EU AI Act.",
  "eli5": "Road rules, but for AI.",
  "evolved_from": [],
  "aka": [],
  "led_to": [],
  "entries": [
   "bletchley-2023",
   "eu-ai-act-2024"
  ],
  "letter": "A"
 },
 {
  "id": "ai-safety",
  "term": "AI safety",
  "layer": "safety",
  "definition": "Research and practice aimed at preventing AI systems from causing accidental or deliberate harm.",
  "eli5": "Putting guard rails on a powerful machine so it helps and does not hurt.",
  "evolved_from": [
   "alignment"
  ],
  "aka": [],
  "led_to": [
   "ai-governance",
   "kill-switch"
  ],
  "entries": [
   "bletchley-2023",
   "ssi-2024"
  ],
  "letter": "A"
 },
 {
  "id": "ai-winter",
  "term": "AI winter",
  "layer": "safety",
  "definition": "A period when AI funding and interest collapse after promises are not met. The main ones were in the 1970s and late 1980s.",
  "eli5": "When everyone gets bored of a toy that did not do what the advert said, and stops buying it for a while.",
  "evolved_from": [
   "symbolic-ai"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "lighthill-1973"
  ],
  "letter": "A"
 },
 {
  "id": "alignment",
  "term": "Alignment",
  "layer": "safety",
  "definition": "Making AI systems do what their designers and users actually intend, and avoid harmful behaviour.",
  "eli5": "Making sure the genie grants the wish you meant, not the one you accidentally said.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "ai-safety"
  ],
  "entries": [
   "instructgpt-2022",
   "constitutional-ai-2022"
  ],
  "letter": "A"
 },
 {
  "id": "agi",
  "term": "Artificial general intelligence (AGI)",
  "layer": "models",
  "definition": "AI that matches or beats humans across most cognitive tasks. There is no agreed definition or test.",
  "eli5": "A robot that could learn to do almost any job a person can.",
  "evolved_from": [],
  "aka": [
   "AGI"
  ],
  "led_to": [],
  "entries": [
   "openai-founded-2015",
   "ssi-2024"
  ],
  "letter": "A"
 },
 {
  "id": "artificial-neuron",
  "term": "Artificial neuron",
  "layer": "math",
  "definition": "A unit that multiplies its inputs by weights, adds them up and passes the result through a simple function.",
  "eli5": "Like a tiny judge that listens to several friends, trusts some more than others, and shouts yes if the total is big enough.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "activation-function",
   "hopfield-network",
   "perceptron"
  ],
  "entries": [
   "mcculloch-pitts-1943"
  ],
  "letter": "A"
 },
 {
  "id": "attention",
  "term": "Attention",
  "layer": "architectures",
  "definition": "A mechanism that lets each part of the input look at every other part and weigh what matters most.",
  "eli5": "While reading 'it was too tired', you glance back to check what 'it' means. Attention does that glance.",
  "evolved_from": [
   "rnn"
  ],
  "aka": [],
  "led_to": [
   "kv-cache",
   "sliding-window-attention",
   "transformer"
  ],
  "entries": [
   "bahdanau-attention-2014",
   "transformer-2017"
  ],
  "letter": "A"
 },
 {
  "id": "backpropagation",
  "term": "Backpropagation",
  "layer": "math",
  "definition": "An efficient way to compute how much each weight in a multi-layer network contributed to the error, using the chain rule.",
  "eli5": "After a team loses, working backwards to find how much each player was to blame, so each one practises the right thing.",
  "evolved_from": [
   "gradient-descent",
   "perceptron"
  ],
  "aka": [],
  "led_to": [
   "cnn",
   "deep-learning",
   "deep-learning-framework",
   "rnn"
  ],
  "entries": [
   "backprop-1986",
   "lecun-cnn-1989",
   "nobel-physics-2024"
  ],
  "letter": "B"
 },
 {
  "id": "benchmark",
  "term": "Benchmark",
  "layer": "safety",
  "definition": "A standard test set for comparing models, such as ImageNet, MMLU or SWE-bench.",
  "eli5": "The same exam given to every student so you can compare scores.",
  "evolved_from": [
   "dataset"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "imagenet-2009"
  ],
  "letter": "B"
 },
 {
  "id": "chain-of-thought",
  "term": "Chain of thought",
  "layer": "building",
  "definition": "Getting a model to write out intermediate reasoning steps before its answer.",
  "eli5": "Showing your working in a maths test, which helps you get the right answer.",
  "evolved_from": [
   "prompt-engineering"
  ],
  "aka": [
   "CoT"
  ],
  "led_to": [
   "react-pattern",
   "reasoning-model",
   "test-time-compute"
  ],
  "entries": [
   "cot-2022",
   "palm-2022",
   "o1-2024"
  ],
  "letter": "C"
 },
 {
  "id": "chatbot",
  "term": "Chatbot",
  "layer": "building",
  "definition": "A program you talk to in natural language. From ELIZA's rules to today's LLM assistants.",
  "eli5": "A computer pen pal that writes back.",
  "evolved_from": [],
  "aka": [],
  "led_to": [],
  "entries": [
   "eliza-1966",
   "chatgpt-2022"
  ],
  "letter": "C"
 },
 {
  "id": "computer-use",
  "term": "Computer use",
  "layer": "building",
  "definition": "An agent that operates software like a person, by reading screenshots and controlling the mouse and keyboard.",
  "eli5": "A helper who can use your laptop the same way you do, by looking and clicking.",
  "evolved_from": [
   "agent",
   "multimodal-model"
  ],
  "aka": [],
  "led_to": [
   "agent-sandbox",
   "personal-agent"
  ],
  "entries": [
   "computer-use-2024"
  ],
  "letter": "C"
 },
 {
  "id": "constitutional-ai",
  "term": "Constitutional AI",
  "layer": "training",
  "definition": "Anthropic's method of training with AI feedback guided by a written list of principles.",
  "eli5": "Giving the model a rulebook of good manners and letting it mark its own homework against it.",
  "evolved_from": [
   "rlhf"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "constitutional-ai-2022",
   "claude-2023"
  ],
  "letter": "C"
 },
 {
  "id": "context-window",
  "term": "Context window",
  "layer": "building",
  "definition": "The maximum number of tokens a model can consider at once, including the prompt and its answer.",
  "eli5": "How many pages of notes you can keep open on your desk at the same time.",
  "evolved_from": [
   "tokens",
   "transformer"
  ],
  "aka": [],
  "led_to": [
   "agent-memory"
  ],
  "entries": [],
  "letter": "C"
 },
 {
  "id": "cnn",
  "term": "Convolutional neural network (CNN)",
  "layer": "architectures",
  "definition": "A network that slides small learned filters across an image, so the same feature detector works everywhere.",
  "eli5": "Looking at a picture through a small window and moving it around, checking each spot for edges and shapes.",
  "evolved_from": [
   "backpropagation",
   "perceptron"
  ],
  "aka": [
   "ConvNet"
  ],
  "led_to": [
   "residual-connection",
   "vision-transformer"
  ],
  "entries": [
   "lecun-cnn-1989",
   "lenet-1998",
   "alexnet-2012",
   "resnet-2015"
  ],
  "letter": "C"
 },
 {
  "id": "copilot",
  "term": "Copilot (Microsoft)",
  "layer": "building",
  "definition": "Microsoft's brand for LLM assistants embedded in its products, grounded in the user's data.",
  "eli5": "A helper built into the apps you already use at work.",
  "evolved_from": [
   "llm",
   "rag"
  ],
  "aka": [],
  "led_to": [
   "copilot-in-power-bi"
  ],
  "entries": [
   "m365-copilot-2023"
  ],
  "letter": "C"
 },
 {
  "id": "copilot-in-power-bi",
  "term": "Copilot in Power BI",
  "layer": "data",
  "definition": "An LLM assistant inside Power BI that drafts report pages, writes DAX and summarises data from natural language.",
  "eli5": "Asking 'show me sales by region' and the report builds itself.",
  "evolved_from": [
   "copilot",
   "semantic-model"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "fabric-2023"
  ],
  "letter": "C"
 },
 {
  "id": "dataset",
  "term": "Dataset",
  "layer": "data",
  "definition": "A collection of examples used to train or test models. Its size and quality often matter more than the model.",
  "eli5": "The pile of flashcards a model studies from.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "benchmark",
   "onelake"
  ],
  "entries": [
   "imagenet-2009"
  ],
  "letter": "D"
 },
 {
  "id": "decision-model",
  "term": "Decision model",
  "layer": "models",
  "definition": "A model built to choose from a fixed set of options and return a calibrated probability for each, instead of writing free text. Used for routing, classification and grading inside apps and agents.",
  "eli5": "Instead of writing an essay, it ticks one box on a multiple-choice sheet and says how sure it is.",
  "evolved_from": [
   "language-model",
   "probability-calibration",
   "structured-outputs"
  ],
  "aka": [
   "decision-scoring model",
   "non-text model"
  ],
  "added": "2026-10-10",
  "source": "https://commandline.microsoft.com/microsoft-decision-1-model-foundry/",
  "led_to": [],
  "entries": [],
  "letter": "D"
 },
 {
  "id": "deep-learning",
  "term": "Deep learning",
  "layer": "learning",
  "definition": "Machine learning with neural networks of many layers, each learning more abstract features.",
  "eli5": "A tower of judges: the bottom ones spot lines, the middle ones spot shapes, the top ones spot faces.",
  "evolved_from": [
   "backpropagation",
   "machine-learning"
  ],
  "aka": [],
  "led_to": [
   "pruning",
   "transfer-learning"
  ],
  "entries": [
   "deep-belief-2006",
   "google-brain-cat-2011",
   "alexnet-2012",
   "dqn-2013",
   "alphago-2016",
   "alphafold2-2021",
   "nobel-chemistry-2024"
  ],
  "letter": "D"
 },
 {
  "id": "deep-learning-framework",
  "term": "Deep learning framework",
  "layer": "infra",
  "definition": "Software such as TensorFlow and PyTorch that computes gradients automatically and runs networks on GPUs.",
  "eli5": "A kitchen with all the tools ready, so you just cook instead of building the oven.",
  "evolved_from": [
   "gpu",
   "backpropagation"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "tensorflow-2015"
  ],
  "letter": "D"
 },
 {
  "id": "diffusion-model",
  "term": "Diffusion model",
  "layer": "architectures",
  "definition": "A generative model that learns to reverse a noising process, building images from random noise step by step.",
  "eli5": "Starting with TV static and cleaning it a little at a time until a picture appears.",
  "evolved_from": [
   "vae",
   "generative-model"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "ddpm-2020",
   "stable-diffusion-2022"
  ],
  "letter": "D"
 },
 {
  "id": "dpo",
  "term": "Direct preference optimisation (DPO)",
  "layer": "training",
  "definition": "A simpler alternative to RLHF that learns from preference pairs directly, without a reward model.",
  "eli5": "Skipping the referee and learning straight from 'this answer is better than that one'.",
  "evolved_from": [
   "rlhf"
  ],
  "aka": [
   "DPO"
  ],
  "led_to": [],
  "entries": [
   "dpo-2023"
  ],
  "letter": "D"
 },
 {
  "id": "discriminative-model",
  "term": "Discriminative model",
  "layer": "architectures",
  "definition": "A model that learns to tell classes apart, predicting a label from an input, such as spam or not spam.",
  "eli5": "Someone who can sort cats from dogs but cannot draw either one.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "gan",
   "generative-model"
  ],
  "entries": [],
  "letter": "D"
 },
 {
  "id": "distillation",
  "term": "Distillation",
  "layer": "training",
  "definition": "Training a small 'student' model to copy a large 'teacher' model's outputs.",
  "eli5": "A top student writes simple notes so a younger student can learn the same things faster.",
  "evolved_from": [
   "llm"
  ],
  "aka": [],
  "led_to": [
   "small-language-model"
  ],
  "entries": [
   "deepseek-r1-2025"
  ],
  "letter": "D"
 },
 {
  "id": "dropout",
  "term": "Dropout",
  "layer": "training",
  "definition": "Randomly turning off neurons during training so the network cannot rely on any single one.",
  "eli5": "Practising football with random teammates sitting out, so everyone learns to play every role.",
  "evolved_from": [],
  "aka": [],
  "led_to": [],
  "entries": [
   "dropout-2014"
  ],
  "letter": "D"
 },
 {
  "id": "effort-controls",
  "term": "Effort controls",
  "layer": "building",
  "definition": "A setting that tells a model how much thinking to spend on a request, so you can trade cost and speed against answer quality task by task.",
  "eli5": "Like choosing whether to answer a quiz quickly or take your time and check your work.",
  "evolved_from": [
   "test-time-compute",
   "reasoning-model"
  ],
  "aka": [
   "effort setting",
   "reasoning effort"
  ],
  "added": "2026-10-08",
  "source": "https://aws.amazon.com/blogs/machine-learning/introducing-claude-haiku-5-5-on-aws/",
  "led_to": [],
  "entries": [],
  "letter": "E"
 },
 {
  "id": "embedding",
  "term": "Embedding",
  "layer": "architectures",
  "definition": "A vector of numbers representing any item (text, image, product) so that similar items sit close together.",
  "eli5": "A map address for anything, so similar things end up in the same neighbourhood.",
  "evolved_from": [
   "word-embedding"
  ],
  "aka": [],
  "led_to": [
   "rag",
   "vector-database"
  ],
  "entries": [
   "rag-2020",
   "clip-2021"
  ],
  "letter": "E"
 },
 {
  "id": "expert-system",
  "term": "Expert system",
  "layer": "learning",
  "definition": "A symbolic AI program that encodes a human expert's knowledge as if-then rules for one narrow domain.",
  "eli5": "A rulebook written by a doctor, so a computer can follow the same steps the doctor would.",
  "evolved_from": [
   "symbolic-ai"
  ],
  "aka": [],
  "led_to": [],
  "entries": [],
  "letter": "E"
 },
 {
  "id": "few-shot",
  "term": "Few-shot learning",
  "layer": "learning",
  "definition": "Learning a task from just a handful of examples.",
  "eli5": "Seeing two examples of a new game and then playing it yourself.",
  "evolved_from": [
   "transfer-learning"
  ],
  "aka": [],
  "led_to": [
   "in-context-learning"
  ],
  "entries": [
   "gpt3-2020"
  ],
  "letter": "F"
 },
 {
  "id": "fine-tuning",
  "term": "Fine-tuning",
  "layer": "training",
  "definition": "Continuing training of a pre-trained model on a smaller, specific dataset.",
  "eli5": "After general school, doing a short course to become a chef.",
  "evolved_from": [
   "pretraining",
   "transfer-learning"
  ],
  "aka": [],
  "led_to": [
   "instruction-tuning",
   "lora"
  ],
  "entries": [
   "gpt1-2018",
   "bert-2018",
   "lora-2021",
   "qlora-2023"
  ],
  "letter": "F"
 },
 {
  "id": "foundation-model",
  "term": "Foundation model",
  "layer": "models",
  "definition": "A large model trained on broad data that many products are built on top of, by prompting or fine-tuning.",
  "eli5": "A strong base of a building that many different shops can be built on.",
  "evolved_from": [
   "llm",
   "transfer-learning"
  ],
  "aka": [],
  "led_to": [],
  "entries": [],
  "letter": "F"
 },
 {
  "id": "gan",
  "term": "Generative adversarial network (GAN)",
  "layer": "architectures",
  "definition": "Two networks trained against each other: a generator makes fakes and a discriminator tries to catch them.",
  "eli5": "A forger and a detective who both get better because they keep trying to beat each other.",
  "evolved_from": [
   "generative-model",
   "discriminative-model"
  ],
  "aka": [
   "GAN"
  ],
  "led_to": [],
  "entries": [
   "gan-2014"
  ],
  "letter": "G"
 },
 {
  "id": "generative-model",
  "term": "Generative model",
  "layer": "architectures",
  "definition": "A model that learns what the data looks like so it can produce new examples of it.",
  "eli5": "Someone who has seen so many cats they can draw a brand new one.",
  "evolved_from": [
   "discriminative-model"
  ],
  "aka": [],
  "led_to": [
   "diffusion-model",
   "gan",
   "vae",
   "world-model"
  ],
  "entries": [
   "vae-2013",
   "gan-2014",
   "ddpm-2020",
   "dalle-2021"
  ],
  "letter": "G"
 },
 {
  "id": "gpu",
  "term": "GPU",
  "layer": "infra",
  "definition": "A processor with thousands of small cores, built for graphics and ideal for the matrix maths of neural networks.",
  "eli5": "Thousands of small helpers doing easy sums at the same time, instead of one genius doing them one by one.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "deep-learning-framework",
   "hyperscaler",
   "rack-scale-architecture"
  ],
  "entries": [
   "gpu-training-2009",
   "alexnet-2012"
  ],
  "letter": "G"
 },
 {
  "id": "gradient-descent",
  "term": "Gradient descent",
  "layer": "math",
  "definition": "Repeatedly nudging each weight in the direction that reduces the loss.",
  "eli5": "Walking downhill in fog by always taking a small step where the ground slopes down most.",
  "evolved_from": [
   "loss-function"
  ],
  "aka": [],
  "led_to": [
   "backpropagation"
  ],
  "entries": [
   "backprop-1986",
   "adam-2014"
  ],
  "letter": "G"
 },
 {
  "id": "hallucination",
  "term": "Hallucination",
  "layer": "safety",
  "definition": "When a model states something false or made up with confidence.",
  "eli5": "Like a kid confidently telling you a made-up fact because it sounds right.",
  "evolved_from": [
   "llm"
  ],
  "aka": [],
  "led_to": [],
  "entries": [],
  "letter": "H"
 },
 {
  "id": "hopfield-network",
  "term": "Hopfield network",
  "layer": "architectures",
  "definition": "A recurrent network that stores patterns as stable states and recalls a full pattern from part of it.",
  "eli5": "Hearing the first notes of a song and your brain filling in the rest.",
  "evolved_from": [
   "artificial-neuron"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "hopfield-1982",
   "nobel-physics-2024"
  ],
  "letter": "H"
 },
 {
  "id": "hyperscaler",
  "term": "Hyperscaler",
  "layer": "infra",
  "definition": "One of the few companies that run cloud data centres at enormous scale, such as Microsoft Azure, Amazon Web Services and Google Cloud, and supply most of the computing power used to train and run AI.",
  "eli5": "The giant landlords who own the buildings full of computers that AI runs on.",
  "evolved_from": [
   "gpu"
  ],
  "aka": [
   "hyperscale cloud"
  ],
  "added": "2026-10-13",
  "source": "https://www.theguardian.com/business/2026/oct/13/ai-share-crash-risk-global-imf-debt-corrowing-cyber-attacks",
  "led_to": [],
  "entries": [],
  "letter": "H"
 },
 {
  "id": "in-context-learning",
  "term": "In-context learning",
  "layer": "building",
  "definition": "A model picking up a new task from examples or instructions inside the prompt, with no weight updates.",
  "eli5": "Learning a new card game just from the rules card on the table.",
  "evolved_from": [
   "few-shot",
   "llm"
  ],
  "aka": [],
  "led_to": [
   "prompt-engineering"
  ],
  "entries": [
   "gpt3-2020"
  ],
  "letter": "I"
 },
 {
  "id": "inference",
  "term": "Inference",
  "layer": "infra",
  "definition": "Running a trained model to get outputs. Most AI cost in production is inference, not training.",
  "eli5": "Using what you studied to answer questions, rather than studying again.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "kv-cache",
   "pruning",
   "quantization",
   "speculative-decoding"
  ],
  "entries": [],
  "letter": "I"
 },
 {
  "id": "instruction-tuning",
  "term": "Instruction tuning",
  "layer": "training",
  "definition": "Fine-tuning on examples of instructions and good responses so a model follows requests.",
  "eli5": "Teaching a clever kid to actually answer the question you asked.",
  "evolved_from": [
   "fine-tuning"
  ],
  "aka": [],
  "led_to": [
   "rlhf"
  ],
  "entries": [
   "instructgpt-2022"
  ],
  "letter": "I"
 },
 {
  "id": "kill-switch",
  "term": "Kill switch",
  "layer": "safety",
  "definition": "A way for authorised people to pause or shut down an AI system, even in the middle of a task, if it behaves in ways it should not.",
  "eli5": "A big red stop button for the AI.",
  "evolved_from": [
   "ai-safety",
   "ai-governance"
  ],
  "aka": [
   "emergency brake"
  ],
  "added": "2026-10-12",
  "source": "https://www.theguardian.com/technology/2026/oct/12/ai-less-regulated-than-milk-microsoft-president-calls-for-tougher-rules",
  "led_to": [],
  "entries": [],
  "letter": "K"
 },
 {
  "id": "kv-cache",
  "term": "KV cache",
  "layer": "infra",
  "definition": "Saving the attention keys and values of earlier tokens so each new token does not recompute them.",
  "eli5": "Keeping your rough notes so you do not redo the same working for the next line.",
  "evolved_from": [
   "attention",
   "inference"
  ],
  "aka": [],
  "led_to": [],
  "entries": [],
  "letter": "K"
 },
 {
  "id": "language-model",
  "term": "Language model",
  "layer": "models",
  "definition": "A model that predicts the probability of the next word or token given the previous ones.",
  "eli5": "A very good guesser of what word comes next in a sentence.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "decision-model",
   "llm",
   "word-embedding"
  ],
  "entries": [
   "neural-lm-2003"
  ],
  "letter": "L"
 },
 {
  "id": "llm",
  "term": "Large language model (LLM)",
  "layer": "models",
  "definition": "A transformer language model with billions of parameters trained on huge text collections, able to follow instructions and write.",
  "eli5": "A giant autocomplete that read most of the internet, so its guesses become surprisingly useful answers.",
  "evolved_from": [
   "language-model",
   "transformer",
   "pretraining"
  ],
  "aka": [
   "LLM"
  ],
  "led_to": [
   "coding-assistant",
   "copilot",
   "distillation",
   "foundation-model",
   "hallucination",
   "in-context-learning",
   "multimodal-model",
   "natural-language-query",
   "open-weight-model",
   "rag",
   "reasoning-model",
   "scaling-laws",
   "small-language-model",
   "speculative-decoding",
   "tool-use"
  ],
  "entries": [
   "gpt1-2018",
   "gpt2-2019",
   "gpt3-2020",
   "github-copilot-2021",
   "palm-2022",
   "chatgpt-2022",
   "msft-openai-2023",
   "llama-2023",
   "claude-2023",
   "gpt4-2023",
   "m365-copilot-2023",
   "gemini-2023",
   "claude3-2024",
   "claude35-2024",
   "gpt5-2025"
  ],
  "letter": "L"
 },
 {
  "id": "lora",
  "term": "LoRA",
  "layer": "training",
  "definition": "Low-rank adaptation: training small add-on matrices while the base model stays frozen.",
  "eli5": "Instead of rewriting a whole book, sticking a few small notes on its pages.",
  "evolved_from": [
   "fine-tuning"
  ],
  "aka": [
   "low-rank adaptation"
  ],
  "led_to": [],
  "entries": [
   "lora-2021",
   "qlora-2023"
  ],
  "letter": "L"
 },
 {
  "id": "loss-function",
  "term": "Loss function",
  "layer": "math",
  "definition": "A number that measures how wrong the model's output is. Training tries to make it smaller.",
  "eli5": "A score of how far your dart landed from the bullseye. Training is trying to lower that score.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "gradient-descent"
  ],
  "entries": [],
  "letter": "L"
 },
 {
  "id": "lstm",
  "term": "LSTM",
  "layer": "architectures",
  "definition": "A recurrent network with gates that decide what to keep, forget and output, so it remembers over long sequences.",
  "eli5": "A notebook with an eraser: you choose what to write down, what to rub out, and what to read aloud.",
  "evolved_from": [
   "rnn"
  ],
  "aka": [
   "long short-term memory"
  ],
  "led_to": [],
  "entries": [
   "lstm-1997",
   "seq2seq-2014"
  ],
  "letter": "L"
 },
 {
  "id": "machine-learning",
  "term": "Machine learning",
  "layer": "learning",
  "definition": "Getting computers to improve at a task from data or experience, instead of being explicitly programmed.",
  "eli5": "Instead of writing the rules, you show lots of examples and the computer works out the rules itself.",
  "evolved_from": [
   "symbolic-ai"
  ],
  "aka": [],
  "led_to": [
   "deep-learning",
   "reinforcement-learning",
   "supervised-learning",
   "unsupervised-learning"
  ],
  "entries": [
   "samuel-checkers-1959"
  ],
  "letter": "M"
 },
 {
  "id": "microsoft-fabric",
  "term": "Microsoft Fabric",
  "layer": "data",
  "definition": "Microsoft's unified analytics platform combining data engineering, warehousing, real-time analytics and Power BI on OneLake.",
  "eli5": "One big workshop where data gets cleaned, stored and turned into reports, without moving between buildings.",
  "evolved_from": [
   "onelake",
   "semantic-model"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "fabric-2023"
  ],
  "letter": "M"
 },
 {
  "id": "mixture-of-experts",
  "term": "Mixture of experts (MoE)",
  "layer": "architectures",
  "definition": "A model split into many expert sub-networks with a router that sends each token to only a few of them.",
  "eli5": "A hospital where a receptionist sends you to the right two doctors instead of seeing all of them.",
  "evolved_from": [
   "transformer"
  ],
  "aka": [
   "MoE"
  ],
  "led_to": [],
  "entries": [
   "moe-2017",
   "mixtral-2023",
   "deepseek-v3-2024",
   "qwen3-2025"
  ],
  "letter": "M"
 },
 {
  "id": "mcp",
  "term": "Model Context Protocol (MCP)",
  "layer": "building",
  "definition": "An open standard for connecting AI apps to tools and data sources through one common interface.",
  "eli5": "Like USB-C for AI: one plug shape so any tool fits any AI app.",
  "evolved_from": [
   "tool-use"
  ],
  "aka": [
   "MCP"
  ],
  "led_to": [
   "a2a"
  ],
  "entries": [
   "mcp-2024",
   "build-2025"
  ],
  "letter": "M"
 },
 {
  "id": "multimodal-model",
  "term": "Multimodal model",
  "layer": "models",
  "definition": "A model that understands or produces more than one kind of data, such as text, images and audio.",
  "eli5": "A friend who can read, look at photos and listen, all at once.",
  "evolved_from": [
   "llm",
   "vision-transformer"
  ],
  "aka": [],
  "led_to": [
   "computer-use",
   "physical-ai"
  ],
  "entries": [
   "dalle-2021",
   "clip-2021",
   "gpt4-2023",
   "gemini-2023",
   "claude3-2024",
   "gpt4o-2024"
  ],
  "letter": "M"
 },
 {
  "id": "natural-language-query",
  "term": "Natural language to query",
  "layer": "data",
  "definition": "Turning a plain-English question into SQL, DAX or another query language with an LLM.",
  "eli5": "Asking your data a question in normal words and getting the right formula written for you.",
  "evolved_from": [
   "llm",
   "semantic-model"
  ],
  "aka": [
   "text-to-SQL",
   "NL2SQL"
  ],
  "led_to": [],
  "entries": [],
  "letter": "N"
 },
 {
  "id": "normalization",
  "term": "Normalisation (batch and layer norm)",
  "layer": "training",
  "definition": "Rescaling activations inside a network to keep numbers in a stable range during training.",
  "eli5": "Turning every singer's volume to the same level so nobody drowns the others out.",
  "evolved_from": [],
  "aka": [],
  "led_to": [],
  "entries": [
   "batchnorm-2015"
  ],
  "letter": "N"
 },
 {
  "id": "onelake",
  "term": "OneLake",
  "layer": "data",
  "definition": "Microsoft Fabric's single, organisation-wide data lake, often described as 'OneDrive for data'.",
  "eli5": "One big shared cupboard where every team keeps its data, so nobody makes copies.",
  "evolved_from": [
   "dataset"
  ],
  "aka": [],
  "led_to": [
   "microsoft-fabric"
  ],
  "entries": [
   "fabric-2023"
  ],
  "letter": "O"
 },
 {
  "id": "open-weight-model",
  "term": "Open-weight model",
  "layer": "models",
  "definition": "A model whose trained weights are published so anyone can download, run and fine-tune it. Not always fully open source.",
  "eli5": "A recipe book you can take home and cook from, even if the shop did not tell you how they invented the recipes.",
  "evolved_from": [
   "llm"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "stable-diffusion-2022",
   "llama-2023",
   "llama2-2023",
   "mistral7b-2023",
   "mixtral-2023",
   "llama3-2024",
   "deepseek-v3-2024",
   "deepseek-r1-2025",
   "qwen3-2025",
   "gpt-oss-2025"
  ],
  "letter": "O"
 },
 {
  "id": "perceptron",
  "term": "Perceptron",
  "layer": "math",
  "definition": "A single layer of artificial neurons with a rule to adjust weights after each mistake.",
  "eli5": "A judge who changes how much they trust each friend every time they get an answer wrong.",
  "evolved_from": [
   "artificial-neuron"
  ],
  "aka": [],
  "led_to": [
   "backpropagation",
   "cnn"
  ],
  "entries": [
   "perceptron-1958"
  ],
  "letter": "P"
 },
 {
  "id": "personal-agent",
  "term": "Personal agent",
  "layer": "building",
  "definition": "An always-on agent that acts for one person across their apps, messages and files.",
  "eli5": "A helper that knows your stuff and does your chores while you are busy.",
  "evolved_from": [
   "agent",
   "computer-use"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "steinberger-openai-2026"
  ],
  "letter": "P"
 },
 {
  "id": "physical-ai",
  "term": "Physical AI",
  "layer": "models",
  "definition": "AI that senses and acts in the physical world, such as robots, drones and self-driving systems, rather than only working with text and images on a screen.",
  "eli5": "AI with a body: it can look around a room and move things in it, not just chat.",
  "evolved_from": [
   "world-model",
   "multimodal-model"
  ],
  "aka": [
   "embodied AI"
  ],
  "added": "2026-10-11",
  "source": "https://www.techmeme.com/261011/p6#a261011p6",
  "led_to": [],
  "entries": [],
  "letter": "P"
 },
 {
  "id": "pretraining",
  "term": "Pre-training",
  "layer": "training",
  "definition": "The first, most expensive training stage where a model learns general knowledge from huge unlabelled data.",
  "eli5": "Going to school for many years to learn a bit of everything before choosing a job.",
  "evolved_from": [
   "self-supervised-learning",
   "transfer-learning"
  ],
  "aka": [],
  "led_to": [
   "fine-tuning",
   "llm"
  ],
  "entries": [
   "elmo-2018",
   "gpt1-2018",
   "bert-2018",
   "t5-2019",
   "karpathy-anthropic-2026"
  ],
  "letter": "P"
 },
 {
  "id": "probability-calibration",
  "term": "Probability calibration",
  "layer": "math",
  "definition": "A model is calibrated when its confidence matches reality: things it says are 80 percent likely happen about 80 percent of the time.",
  "eli5": "If a weather app says 'rain 70 percent' on ten days, it should rain on about seven of them.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "decision-model"
  ],
  "entries": [],
  "letter": "P"
 },
 {
  "id": "prompt-engineering",
  "term": "Prompt engineering",
  "layer": "building",
  "definition": "Writing and structuring inputs to get better outputs from a model.",
  "eli5": "Asking a question in the clearest way so you get the answer you need.",
  "evolved_from": [
   "in-context-learning"
  ],
  "aka": [],
  "led_to": [
   "chain-of-thought"
  ],
  "entries": [
   "cot-2022"
  ],
  "letter": "P"
 },
 {
  "id": "pruning",
  "term": "Pruning",
  "layer": "infra",
  "definition": "Removing the weights or parts of a model that matter least, so it runs faster and uses less memory with little loss in quality.",
  "eli5": "Trimming the branches of a plant that bear no fruit so the rest grows better.",
  "evolved_from": [
   "deep-learning",
   "inference"
  ],
  "aka": [
   "model pruning"
  ],
  "added": "2026-10-10",
  "source": "https://arxiv.org/abs/2610.12327",
  "led_to": [],
  "entries": [],
  "letter": "P"
 },
 {
  "id": "q-learning",
  "term": "Q-learning",
  "layer": "learning",
  "definition": "A reinforcement learning method that learns the long-term value of taking each action in each situation.",
  "eli5": "Keeping a scorecard of how good each move turned out to be in each spot, and picking the best-scoring move.",
  "evolved_from": [
   "reinforcement-learning"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "q-learning-1992",
   "dqn-2013"
  ],
  "letter": "Q"
 },
 {
  "id": "quantization",
  "term": "Quantisation",
  "layer": "infra",
  "definition": "Storing model weights with fewer bits, such as 4-bit instead of 16-bit, to save memory and speed up inference.",
  "eli5": "Rounding numbers so they take less space, like writing 3.14 instead of 3.14159265.",
  "evolved_from": [
   "inference"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "qlora-2023"
  ],
  "letter": "Q"
 },
 {
  "id": "rack-scale-architecture",
  "term": "Rack-scale architecture",
  "layer": "infra",
  "definition": "Designing a whole data-centre rack, with its chips, networking, power and cooling, as one system so many GPUs work together like a single large computer.",
  "eli5": "Building the whole cupboard of computers as one machine instead of buying the parts one by one.",
  "evolved_from": [
   "gpu"
  ],
  "aka": [
   "rack-scale system"
  ],
  "added": "2026-10-12",
  "source": "https://blogs.nvidia.com/blog/ocp-global-summit-vera-rubin-scale-ai-factories/",
  "led_to": [],
  "entries": [],
  "letter": "R"
 },
 {
  "id": "react-pattern",
  "term": "ReAct pattern",
  "layer": "building",
  "definition": "An agent loop that alternates reasoning, acting with a tool, and observing the result.",
  "eli5": "Think, try, look, think again, like solving a puzzle one move at a time.",
  "evolved_from": [
   "chain-of-thought",
   "tool-use"
  ],
  "aka": [],
  "led_to": [
   "agent"
  ],
  "entries": [
   "react-2022"
  ],
  "letter": "R"
 },
 {
  "id": "reasoning-model",
  "term": "Reasoning model",
  "layer": "models",
  "definition": "A model trained to think step by step before answering, spending more compute on harder questions.",
  "eli5": "A student who works the problem out on rough paper before writing the final answer.",
  "evolved_from": [
   "chain-of-thought",
   "reinforcement-learning",
   "llm"
  ],
  "aka": [],
  "led_to": [
   "effort-controls"
  ],
  "entries": [
   "o1-2024",
   "deepseek-r1-2025",
   "claude-code-2025",
   "gemini25-2025",
   "qwen3-2025",
   "gpt-oss-2025",
   "gpt5-2025"
  ],
  "letter": "R"
 },
 {
  "id": "rnn",
  "term": "Recurrent neural network (RNN)",
  "layer": "architectures",
  "definition": "A network that reads a sequence one step at a time, carrying a memory forward.",
  "eli5": "Reading a story one word at a time and keeping a running note in your head.",
  "evolved_from": [
   "backpropagation"
  ],
  "aka": [],
  "led_to": [
   "attention",
   "lstm",
   "state-space-model"
  ],
  "entries": [
   "lstm-1997",
   "bahdanau-attention-2014",
   "seq2seq-2014"
  ],
  "letter": "R"
 },
 {
  "id": "red-teaming",
  "term": "Red teaming",
  "layer": "safety",
  "definition": "Deliberately attacking a model to find harmful or unwanted behaviour before release.",
  "eli5": "Hiring friendly burglars to test your locks.",
  "evolved_from": [],
  "aka": [],
  "led_to": [],
  "entries": [],
  "letter": "R"
 },
 {
  "id": "reinforcement-learning",
  "term": "Reinforcement learning",
  "layer": "learning",
  "definition": "Learning by trial and error from rewards and penalties.",
  "eli5": "Like training a puppy with treats: good moves get a treat, so it does them more.",
  "evolved_from": [
   "machine-learning"
  ],
  "aka": [
   "RL"
  ],
  "led_to": [
   "q-learning",
   "reasoning-model",
   "rlhf"
  ],
  "entries": [
   "samuel-checkers-1959",
   "q-learning-1992",
   "td-gammon-1995",
   "dqn-2013",
   "alphago-2016",
   "ppo-2017"
  ],
  "letter": "R"
 },
 {
  "id": "residual-connection",
  "term": "Residual connection",
  "layer": "architectures",
  "definition": "A shortcut that adds a layer's input to its output, so very deep networks stay trainable.",
  "eli5": "A lift beside the stairs, so the message can skip floors instead of getting lost on the way.",
  "evolved_from": [
   "cnn"
  ],
  "aka": [
   "skip connection"
  ],
  "led_to": [
   "transformer"
  ],
  "entries": [
   "resnet-2015"
  ],
  "letter": "R"
 },
 {
  "id": "rag",
  "term": "Retrieval-augmented generation (RAG)",
  "layer": "building",
  "definition": "Fetching relevant documents first and giving them to the model, so answers are grounded in real sources.",
  "eli5": "An open-book exam: look up the right page first, then write the answer.",
  "evolved_from": [
   "embedding",
   "llm"
  ],
  "aka": [
   "RAG"
  ],
  "led_to": [
   "copilot"
  ],
  "entries": [
   "rag-2020"
  ],
  "letter": "R"
 },
 {
  "id": "rlhf",
  "term": "RLHF",
  "layer": "training",
  "definition": "Reinforcement learning from human feedback: humans rank outputs, a reward model learns their taste, and the LLM is tuned to score well.",
  "eli5": "People pick which of two answers is better, and the model learns to give the kind people pick.",
  "evolved_from": [
   "reinforcement-learning",
   "instruction-tuning"
  ],
  "aka": [
   "reinforcement learning from human feedback"
  ],
  "led_to": [
   "constitutional-ai",
   "dpo"
  ],
  "entries": [
   "rlhf-2017",
   "instructgpt-2022",
   "chatgpt-2022",
   "constitutional-ai-2022",
   "dpo-2023"
  ],
  "letter": "R"
 },
 {
  "id": "scaling-laws",
  "term": "Scaling laws",
  "layer": "training",
  "definition": "Empirical rules showing performance improves predictably with more parameters, data and compute.",
  "eli5": "The more you practise and the bigger your brain, the better you get, in a way you can predict on a chart.",
  "evolved_from": [
   "llm"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "scaling-laws-2020",
   "chinchilla-2022"
  ],
  "letter": "S"
 },
 {
  "id": "self-supervised-learning",
  "term": "Self-supervised learning",
  "layer": "learning",
  "definition": "Creating labels from the data itself, such as hiding a word and predicting it. It is how large language models are pre-trained.",
  "eli5": "Covering a word in a sentence with your finger and guessing it, millions of times, with no teacher needed.",
  "evolved_from": [
   "unsupervised-learning",
   "supervised-learning"
  ],
  "aka": [],
  "led_to": [
   "pretraining"
  ],
  "entries": [],
  "letter": "S"
 },
 {
  "id": "semantic-model",
  "term": "Semantic model",
  "layer": "data",
  "definition": "In Power BI and Fabric, the curated layer of tables, relationships, measures and business names that reports and Copilot read from.",
  "eli5": "A well-labelled map of your data, so both people and AI know what each number means.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "copilot-in-power-bi",
   "microsoft-fabric",
   "natural-language-query"
  ],
  "entries": [],
  "letter": "S"
 },
 {
  "id": "sliding-window-attention",
  "term": "Sliding-window attention",
  "layer": "architectures",
  "definition": "An attention pattern where each token looks only at a fixed number of nearby tokens instead of the whole input, so memory and compute stay flat as the text gets longer.",
  "eli5": "Reading a long book while keeping only the last few pages open in front of you.",
  "evolved_from": [
   "attention",
   "transformer"
  ],
  "aka": [
   "local attention"
  ],
  "added": "2026-10-08",
  "source": "https://arxiv.org/abs/2610.10114",
  "led_to": [],
  "entries": [],
  "letter": "S"
 },
 {
  "id": "small-language-model",
  "term": "Small language model (SLM)",
  "layer": "models",
  "definition": "A compact language model, usually under about 10 billion parameters, that runs on laptops or phones.",
  "eli5": "A pocket dictionary: less than the big library, but it fits in your bag and works offline.",
  "evolved_from": [
   "llm",
   "distillation"
  ],
  "aka": [
   "SLM"
  ],
  "led_to": [],
  "entries": [
   "mistral7b-2023"
  ],
  "letter": "S"
 },
 {
  "id": "speculative-decoding",
  "term": "Speculative decoding",
  "layer": "infra",
  "definition": "A way to speed up text generation: a small, fast model drafts the next few tokens and the large model checks them all in one pass, keeping the ones it agrees with.",
  "eli5": "A quick helper guesses the next few words and the expert only says yes or no, which is faster than the expert writing every word.",
  "evolved_from": [
   "inference",
   "llm"
  ],
  "aka": [],
  "added": "2026-10-08",
  "source": "https://commandline.microsoft.com/local-models-sandboxed-tools-github-windows/",
  "led_to": [],
  "entries": [],
  "letter": "S"
 },
 {
  "id": "state-space-model",
  "term": "State space model",
  "layer": "architectures",
  "definition": "A sequence model that updates a compact hidden state, handling very long inputs in linear time. Mamba is the best known.",
  "eli5": "Keeping a small, smart summary as you read, instead of re-reading the whole book for every new word.",
  "evolved_from": [
   "rnn"
  ],
  "aka": [
   "SSM",
   "Mamba"
  ],
  "led_to": [],
  "entries": [
   "mamba-2023"
  ],
  "letter": "S"
 },
 {
  "id": "structured-outputs",
  "term": "Structured outputs",
  "layer": "building",
  "definition": "Constraining a model to return data in an exact format, such as JSON matching a schema.",
  "eli5": "Filling in a form with fixed boxes instead of writing a free essay.",
  "evolved_from": [
   "tool-use"
  ],
  "aka": [
   "JSON mode"
  ],
  "led_to": [
   "decision-model"
  ],
  "entries": [],
  "letter": "S"
 },
 {
  "id": "supervised-learning",
  "term": "Supervised learning",
  "layer": "learning",
  "definition": "Learning from examples that come with the right answer (labels), such as photos marked cat or dog.",
  "eli5": "Learning with an answer key: practise, check the answer, fix your mistake.",
  "evolved_from": [
   "machine-learning"
  ],
  "aka": [],
  "led_to": [
   "self-supervised-learning"
  ],
  "entries": [
   "perceptron-1958",
   "svm-1995",
   "random-forests-2001"
  ],
  "letter": "S"
 },
 {
  "id": "symbolic-ai",
  "term": "Symbolic AI",
  "layer": "learning",
  "definition": "AI built from hand-written rules and logic over symbols, rather than learned from data. Dominant until the 1990s.",
  "eli5": "Teaching a robot by writing a giant rulebook: if this, then that.",
  "evolved_from": [],
  "aka": [
   "GOFAI",
   "good old-fashioned AI"
  ],
  "led_to": [
   "ai-winter",
   "expert-system",
   "machine-learning"
  ],
  "entries": [
   "dartmouth-1955",
   "deep-blue-1997"
  ],
  "letter": "S"
 },
 {
  "id": "system-1-system-2",
  "term": "System 1 and System 2 thinking",
  "layer": "models",
  "definition": "Daniel Kahneman's split between fast intuitive thinking (System 1) and slow deliberate thinking (System 2), now used to describe fast models versus reasoning models.",
  "eli5": "Catching a ball without thinking is System 1. Doing long division is System 2.",
  "evolved_from": [],
  "aka": [],
  "led_to": [],
  "entries": [],
  "letter": "S"
 },
 {
  "id": "test-time-compute",
  "term": "Test-time compute",
  "layer": "training",
  "definition": "Spending more computation when answering, for example by reasoning longer or trying several answers.",
  "eli5": "Taking more time to think on a hard exam question instead of answering instantly.",
  "evolved_from": [
   "chain-of-thought"
  ],
  "aka": [
   "inference-time scaling"
  ],
  "led_to": [
   "effort-controls"
  ],
  "entries": [
   "o1-2024"
  ],
  "letter": "T"
 },
 {
  "id": "tokens",
  "term": "Tokens",
  "layer": "building",
  "definition": "The chunks of text a model reads and writes, often parts of words. Pricing and limits are counted in tokens.",
  "eli5": "Lego bricks of text: models build sentences out of these small pieces.",
  "evolved_from": [],
  "aka": [],
  "led_to": [
   "context-window"
  ],
  "entries": [],
  "letter": "T"
 },
 {
  "id": "tool-use",
  "term": "Tool use (function calling)",
  "layer": "building",
  "definition": "A model outputting structured calls to external functions or APIs, then using the results.",
  "eli5": "A smart kid who knows when to grab a calculator or a dictionary instead of guessing.",
  "evolved_from": [
   "llm"
  ],
  "aka": [
   "function calling"
  ],
  "led_to": [
   "agent",
   "mcp",
   "react-pattern",
   "structured-outputs"
  ],
  "entries": [
   "react-2022",
   "toolformer-2023",
   "mcp-2024"
  ],
  "letter": "T"
 },
 {
  "id": "transfer-learning",
  "term": "Transfer learning",
  "layer": "learning",
  "definition": "Reusing what a model learned on one task as a head start on another.",
  "eli5": "If you can ride a bicycle, learning a scooter is much faster.",
  "evolved_from": [
   "deep-learning"
  ],
  "aka": [],
  "led_to": [
   "few-shot",
   "fine-tuning",
   "foundation-model",
   "pretraining",
   "zero-shot"
  ],
  "entries": [
   "t5-2019"
  ],
  "letter": "T"
 },
 {
  "id": "transformer",
  "term": "Transformer",
  "layer": "architectures",
  "definition": "A network built from attention layers that processes all tokens in parallel. The architecture behind modern LLMs.",
  "eli5": "A classroom where every word can talk to every other word at once, instead of passing notes down the row.",
  "evolved_from": [
   "attention",
   "residual-connection"
  ],
  "aka": [],
  "led_to": [
   "context-window",
   "llm",
   "mixture-of-experts",
   "sliding-window-attention",
   "vision-transformer"
  ],
  "entries": [
   "transformer-2017",
   "bert-2018",
   "vit-2020"
  ],
  "letter": "T"
 },
 {
  "id": "turing-test",
  "term": "Turing test",
  "layer": "safety",
  "definition": "Turing's 1950 proposal: if a judge cannot reliably tell a machine from a human in text chat, the machine passes.",
  "eli5": "A guessing game: are you chatting with a person or a computer?",
  "evolved_from": [],
  "aka": [],
  "led_to": [],
  "entries": [
   "turing-1950"
  ],
  "letter": "T"
 },
 {
  "id": "unsupervised-learning",
  "term": "Unsupervised learning",
  "layer": "learning",
  "definition": "Finding structure in data without labels, such as grouping similar customers.",
  "eli5": "Sorting a box of mixed buttons into piles without anyone telling you the groups.",
  "evolved_from": [
   "machine-learning"
  ],
  "aka": [],
  "led_to": [
   "self-supervised-learning"
  ],
  "entries": [
   "deep-belief-2006",
   "google-brain-cat-2011"
  ],
  "letter": "U"
 },
 {
  "id": "vae",
  "term": "Variational autoencoder (VAE)",
  "layer": "architectures",
  "definition": "A network that compresses data into a smooth space and can decode new samples from it.",
  "eli5": "Squeezing a picture into a tiny recipe, then cooking new pictures from slightly changed recipes.",
  "evolved_from": [
   "generative-model"
  ],
  "aka": [
   "VAE"
  ],
  "led_to": [
   "diffusion-model"
  ],
  "entries": [
   "vae-2013"
  ],
  "letter": "V"
 },
 {
  "id": "vector-database",
  "term": "Vector database",
  "layer": "building",
  "definition": "A database that stores embeddings and finds the most similar ones quickly. The search engine behind RAG.",
  "eli5": "A library where books are shelved by meaning, so similar books sit together.",
  "evolved_from": [
   "embedding"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "rag-2020"
  ],
  "letter": "V"
 },
 {
  "id": "vibe-coding",
  "term": "Vibe coding",
  "layer": "building",
  "definition": "Building software mainly by describing what you want to an AI and accepting its code, with little manual review.",
  "eli5": "Telling a builder robot 'make me a cool treehouse' and trusting what it builds.",
  "evolved_from": [
   "coding-assistant",
   "agent"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "vibe-coding-2025"
  ],
  "letter": "V"
 },
 {
  "id": "vision-transformer",
  "term": "Vision transformer (ViT)",
  "layer": "architectures",
  "definition": "A transformer applied to images by cutting them into patches and treating each patch like a word.",
  "eli5": "Cutting a photo into jigsaw pieces and letting the pieces talk to each other to work out the picture.",
  "evolved_from": [
   "transformer",
   "cnn"
  ],
  "aka": [
   "ViT"
  ],
  "led_to": [
   "multimodal-model"
  ],
  "entries": [
   "vit-2020"
  ],
  "letter": "V"
 },
 {
  "id": "word-embedding",
  "term": "Word embedding",
  "layer": "architectures",
  "definition": "A list of numbers representing a word, where words with similar meaning get similar numbers.",
  "eli5": "Giving every word a home on a map, so 'cat' lives next door to 'kitten' and far from 'car'.",
  "evolved_from": [
   "language-model"
  ],
  "aka": [],
  "led_to": [
   "embedding"
  ],
  "entries": [
   "neural-lm-2003",
   "word2vec-2013",
   "elmo-2018"
  ],
  "letter": "W"
 },
 {
  "id": "world-model",
  "term": "World model",
  "layer": "models",
  "definition": "A model that learns how an environment works, so it can predict what happens next and plan. Championed by Yann LeCun.",
  "eli5": "Knowing that if you push a cup off the table it falls, without having to try it.",
  "evolved_from": [
   "generative-model"
  ],
  "aka": [],
  "led_to": [
   "physical-ai"
  ],
  "entries": [],
  "letter": "W"
 },
 {
  "id": "zero-shot",
  "term": "Zero-shot learning",
  "layer": "learning",
  "definition": "A model doing a task it was never explicitly trained or shown examples for, from the instruction alone.",
  "eli5": "Doing a puzzle the first time just from reading the rules.",
  "evolved_from": [
   "transfer-learning"
  ],
  "aka": [],
  "led_to": [],
  "entries": [
   "gpt2-2019",
   "clip-2021"
  ],
  "letter": "Z"
 }
]