Fundamentals
The foundation vocabulary to discuss AI in the executive committee. Without these shared terms, the rest of the glossary only reads halfway.
- EmbeddingAn embedding is the numerical representation of a word, a sentence, or a document, in the form of a vector in a space of several hundred or thousand dimensions. Two texts semantically close have geometrically close embeddings, which enables search by meaning rather than by keywords.
- Foundation modelA foundation model is a generalist AI model trained on massive, diversified data, which serves as a reusable base for dozens or even hundreds of different business applications, by adaptation (fine-tuning, RAG, prompt) rather than by specific training.
- Generative AIGenerative AI is a family of artificial intelligence models capable of producing new content (text, image, sound, video, code) from a natural-language instruction, as opposed to traditional AI which was limited to classifying, predicting, or detecting.
- InferenceInference is the usage phase of an AI model, during which the model computes a response from a given prompt. It is the operation billed by API providers, distinct from training which is an initial fixed cost.
- LLM (Large Language Model)An LLM (Large Language Model) is a type of artificial intelligence trained on text corpora of several hundred billion words, which produces natural language by predicting, word by word, the most probable continuation of a given text.
- MultimodalA multimodal AI model is a model capable of processing and producing several types of content simultaneously: text, image, audio, video, code. The same model can analyse a photo, understand a voice query, read a document, and reply in writing, without any intermediate pipeline.
- ParameterA parameter of an AI model is a numerical value learned during training that determines the strength of connections between the artificial neurons of the network. The parameter count measures the model's capacity to store associations between the elements of its training corpus.
- TokenA token is the elementary unit of text that an AI model manipulates, generally a word fragment equivalent to 3 or 4 characters in English. It is both the model's unit of computation and the AI providers' unit of billing, counted separately on input and output.
- TrainingTraining is the construction phase of an AI model, during which the model ingests a massive corpus and adjusts its billions of internal parameters to learn the statistical regularities of that corpus. It is the most expensive operation in a model's life cycle.
- TransformerThe transformer is the neural-network architecture, introduced by Google in 2017, that underpins nearly all current generative AI models. Its central innovation is the attention mechanism, which lets the model dynamically weigh the relative importance of words in a sequence.