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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.Backpropagation and gradient descentBackpropagation is the procedure that lets a multilayer neural network learn by sending output errors backward to adjust its weights, using gradient descent. A 1986 Nature paper made it widely known, and it is standard in most neural networks today.TokenizationTokenization splits text into the tokens a language model actually reads; most modern systems do it with byte pair encoding, a 1994 compression method adapted for translation in 2015.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.Deep learning on GPUsGraphics processing units (GPUs) run many arithmetic operations in parallel, which suits neural-network training. Their use from 2010 onward made deep learning practical at much larger scales.DropoutA regularisation technique for neural networks: during training it randomly zeroes the inputs or outputs of some neurons so that units cannot co-adapt, which reduces overfitting.Batch normalizationA technique that re-centres each layer's inputs to zero and rescales them to unit variance over a mini-batch, making deep networks faster and more stable to train.Residual connectionA residual connection adds a block's input to its output, so the block learns only the change it needs to make. This makes very deep networks trainable, and it is a standard part of today's transformers.Knowledge distillationKnowledge distillation trains a small model to reproduce the outputs of a larger model or an ensemble, so that much of the predictive performance is kept at lower compute and deployment cost.Positional encodingA component of sequence models, especially transformers, that encodes each token's position as a vector, giving order-blind attention a sense of sequence; its forms range from the original transformer's sinusoids to RoPE and ALiBi.Mixture of experts (MoE)A mixture of experts model contains many sub-networks but routes each token to only a few of them. This lets total model size grow while the compute spent on each token stays much smaller than the total.Tensor CoresTensor Cores are specialised units inside NVIDIA GPUs that perform a small matrix multiply-and-add in one step, making the matrix arithmetic behind neural-network training and inference dramatically faster.TransformerThe Transformer is a neural network architecture introduced in 2017 that relies on self-attention instead of recurrence or convolution. Most of today's large language models are built on it.Pre-training and fine-tuningPre-training trains a large model on huge amounts of unlabeled text, and fine-tuning then adapts it to a specific task with a small labeled dataset. This two-stage recipe is the basis of the term foundation model.Model parallelismModel parallelism splits one model's parameters and computation across multiple accelerators, so networks too large for a single device can still be trained. NVIDIA's 2019 Megatron-LM paper showed an intra-layer version built in native PyTorch.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.