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Concepts
Concepts
How AI works, in plain language: from the artificial neuron to the Transformer, pre-training and reasoning models.
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Information theoryA framework that treats information as a measurable quantity to be encoded and transmitted, founded in Shannon's 1948 paper; it gave the field the bit, entropy and channel capacity.k-means clusteringk-means clustering splits n observations into k groups by repeatedly sending each point to its nearest centroid and recomputing each centroid as its cluster's mean. It is simple and scales to large data, but k must be fixed in advance.Support vector machineA supervised maximum-margin model for classification and regression that separates classes by the widest possible margin and uses kernel functions to handle non-linear data.Cross-validationA model validation technique that repeatedly splits data into folds, trains on some parts and validates on others, then averages the results to estimate how well a model or statistical result will generalise to unseen data.AdaBoostAdaBoost, short for Adaptive Boosting, is a statistical classification meta-algorithm introduced by Yoav Freund and Robert Schapire in 1995. It reweights training examples, trains weak classifiers in sequence and combines their outputs into one weighted classifier.Topic modelTopic models represent documents as mixtures of latent topics, giving a compact statistical description of text collections. The 2003 Latent Dirichlet Allocation paper formalized this idea as a hierarchical Bayesian model.Retrieval-augmented generationRetrieval-augmented generation (RAG) lets a language model fetch relevant documents from an external store and put them into its prompt before answering, so it can use knowledge beyond its training data and cite where an answer came from.Instruction tuningInstruction tuning is a supervised fine-tuning stage that trains a large language model to follow user instructions; the FLAN paper showed it substantially improves zero-shot performance on unseen tasks.Prompt injectionPrompt injection is an attack in which text inside a model's input overrides its original instructions, letting an adversary hijack a language-model application — including, as researchers showed in 2023, through content the application retrieves on its own.Computer useComputer use is an Anthropic capability, released in public beta in October 2024, that lets an AI model operate a computer the way a person does — reading the screen, moving a cursor, clicking and typing — so it can drive ordinary software built for humans.Supervised learningSupervised learning trains a model to map inputs to outputs using paired labelled examples, so that it can predict answers for new, unseen data. Its central measure of success is how well the model generalises beyond the training set.Loss functionA loss function turns the cost of a prediction into a single number, and training a model means making that number as small as possible. Its choice decides which mistakes a model is punished for.OverfittingOverfitting is when a model fits its training data so closely that it performs poorly on new data. It is one half of the bias–variance trade-off and is usually spotted by comparing training and validation performance.RegularizationRegularization is the practice of making a problem's answer simpler, usually by adding a penalty term to the objective, so that ill-posed problems become solvable and models avoid overfitting.Decision tree learningDecision tree learning builds a classification or regression tree from data by repeatedly splitting it into more homogeneous groups. It is a widely used supervised method whose rules can be read directly, and whose main weaknesses are instability and a tendency to overfit.Feature engineeringFeature engineering is the preprocessing step that turns raw data into more useful inputs for machine learning and statistical models, shaping, cleaning and trimming the attributes a model learns from.AutoencoderAn autoencoder is an unsupervised neural network that compresses its input into a low-dimensional code and reconstructs it back, learning a latent representation useful for dimensionality reduction, denoising and anomaly detection.AI agentAn AI agent is a system that perceives its environment and acts autonomously toward goals. The term covers everything from a thermostat to LLM-driven software that directs its own tool use.Multi-agent systemA multi-agent system is a computing system made of several interacting intelligent agents that coordinate to solve problems too hard for a single agent. Recent frameworks built on large language models let such agents talk to each other.World modelA world model is a machine learning system that learns an internal representation of an environment and predicts how it changes in response to actions, letting agents plan inside a learned simulator instead of by trial and error.