Your 50-definition guide to artificial intelligence: essential terms from generative AI and the AI Act to keynotes and machine learning, explained in plain English.
The core concepts you need to understand artificial intelligence.
A set of techniques that enable a machine to perform tasks that would normally require human intelligence: understanding, reasoning, decision-making, and creation.
Learn more →A branch of AI in which a model learns rules from data rather than being explicitly programmed.
An approach to machine learning that uses multi-layered neural networks, and lies behind the recent breakthroughs in AI.
A model inspired by the brain, made up of layers of 'neurons' that transform input data into predictions.
A sequence of instructions for solving a problem or completing a task; in AI, it governs how the model learns and makes predictions.
The output of training an algorithm on data: it is what produces predictions or generated content.
The dataset used to teach a model; its quality directly determines the model's performance.
AI that forecasts an outcome (risk, demand, failure) based on historical data.
The vocabulary of generative AI, which sits at the heart of today's keynotes.
A category of AI capable of producing new content — text, images, code, audio — from a given prompt.
Learn more →Large Language Model: a model trained on vast text corpora, capable of understanding and generating natural language.
A conversational AI agent from OpenAI built on a large language model, which brought generative AI to mainstream attention in late 2022.
Learn more →An instruction or question given to a generative AI in order to obtain a result.
The art of crafting effective prompts to obtain accurate and useful responses from a generative AI.
Learn more →When an AI produces a confidently worded but factually incorrect response — the key risk to understand and guard against.
The basic unit (a word or word fragment) processed by a language model; also used to measure the length of texts.
An AI system capable of chaining actions autonomously to complete a task end to end.
Retrieval-Augmented Generation: a technique combining a language model with a document knowledge base to produce reliable, source-backed responses.
The process of specialising a pre-trained model on data specific to a particular use case or domain.
A numerical representation of a piece of text or data that allows an AI to measure its meaning and proximity to other content.
Describes an AI capable of processing multiple types of data (text, images, audio) simultaneously.
An AI assistant integrated into a business tool to support users in their day-to-day tasks.
The branch of AI dedicated to understanding and generating human language (also known as NLP).
Technologies that enable a machine to analyse and interpret images and video.
The formats available for your events.
An expert who presents at events to make artificial intelligence accessible and inspire audiences to act.
Learn more →The headline talk at an event — typically the opening or closing session — designed to make an impact and unite the audience.
Learn more →A session that brings together all participants at an event; the natural setting for a keynote.
An in-depth, interactive session led by an expert, focused on building skills and knowledge.
Learn more →A hands-on format in which participants work directly with tools on real-world use cases.
Learn more →A specialist invited to provide a broader perspective and put topics into context during an event.
A moderated exchange between several speakers on a given theme, often facilitated by an expert.
A presentation delivered online via video conference, as opposed to an in-person event.
The trust framework that underpins every AI project.
European Regulation (EU) 2024/1689 on artificial intelligence — the world's first horizontal legal framework for AI, built on a risk-based approach.
General Data Protection Regulation: the framework governing the processing of personal data in the European Union.
France's national data protection authority (Commission nationale de l'informatique et des libertés), responsible for enforcing data privacy rules.
A distortion in a model that reproduces or amplifies inequalities present in its training data.
The ability to understand and justify the decisions made by an AI system — a key requirement in regulated contexts.
The principle that a human retains control and can intervene in the decisions made by an AI system.
AI designed to be robust, transparent, fair, and respectful of fundamental rights.
An approach that seeks ethical and controlled use of AI, incorporating social and environmental impact alongside regulatory compliance.
The capacity of an organisation or country to maintain control over its data, infrastructure and technology.
Any information relating to an identified or identifiable natural person, protected under the GDPR.
The vocabulary of implementing AI within organisations.
The process of integrating digital technologies into every aspect of an organisation.
A concrete, identified application of a technology to a specific business need.
A prototype designed to validate the feasibility and value of a project before significant investment is committed.
An initiative aimed at familiarising all employees with a technology such as AI.
Learn more →The actions taken to support teams in adopting a new technology or organisational approach.
The efficiency gains achieved when AI assists humans in their tasks.
A French certification attesting to the quality of training programmes, and a prerequisite for public or OPCO funding.
Learn more →A set of practices for deploying, monitoring and maintaining AI models in production reliably.
A keynote to give your teams a real AI culture.
Book a talk → See talk formats