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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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Recurrent neural networks and LSTMA recurrent neural network (RNN) is a neural network with feedback connections that carry information from one step of a sequence to the next. Its long short-term memory variant (LSTM) was designed to keep useful memories over longer spans.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.Word embeddingsA word embedding represents each word as a list of numbers learned from text, so that words used in similar ways get similar lists. Embeddings are a basic building block of modern language models.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.Attention and self-attentionAttention lets a neural network weigh the parts of its input by relevance. Self-attention applies this idea inside a single sequence, so each token gathers information from every other token. It is the core operation of the Transformer.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.Reinforcement learning from human feedback (RLHF)Reinforcement learning from human feedback trains a model toward what people prefer, using a reward model learned from human comparisons of its outputs. It became a standard step in building chat assistants.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.Scaling lawsScaling laws are empirical trends showing that a language model's test loss falls smoothly, as a power law, when parameters, data and training compute grow. They help plan large training runs and explain the race for compute.In-context learningIn-context learning is a large language model's ability to perform a new task from instructions and a few examples placed in the prompt, with no weight updates. Named and studied in the GPT-3 paper, it moved adaptation from fine-tuning to prompt writing.State space model (Mamba)A state space model represents a sequence as a hidden state that evolves with the input. S4 brought the idea into deep learning in 2021, and Mamba made its parameters input-dependent, enabling linear-time modeling of long sequences.