Artificial Intelligence System

A system that processes data and produces outputs such as classification, prediction, recommendation, or decision-support by algorithmic means; the defined scope for regulatory obligations and risk assessment under the bill.

Introduced by the Act (with the detailed statutory definition to be prescribed by regulation) as machine-based systems that infer from input to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments; such systems are subject to disclosure, oversight, risk management, and potential technical standards under the EDSTA and its regulations.

A system that, by design, performs tasks, decisions, or predictions through algorithmic processing, including machine learning models, trained models, and associated components used to generate outputs or take automated actions.

Any machine-based system that, for any explicit or implicit objective, infers from the inputs the system receives how to generate outputs, including content, decisions, predictions, or recommendations, that can influence physical or virtual environments.

Any data system, software, application, tool, or utility that uses static or dynamic machine learning algorithms or other forms of AI, whether as a primary purpose or an embedded capability. The definition is operationalized to determine coverage under the statute.

A broadly defined, OECD-derived concept of a machine-based system that, for explicit or implicit objectives, infers from input how to generate outputs (predictions, content, recommendations or decisions) that may influence physical or virtual environments. Systems covered vary in autonomy and adaptiveness across their capabilities and deployment contexts.

Any machine-based system capable of performing tasks that require human-like intelligence, including learning, reasoning, problem-solving, perception, and decision-making. The term covers systems used to automate or assist decisions and actions affecting individuals or society.

A machine-based system that, for explicit or implicit objectives, infers from input how to generate outputs such as predictions, content, recommendations or decisions that may influence physical or virtual environments; a technology-neutral definition used to delimit the Convention's scope.

A technology-neutral concept covering automated decision systems, including statistical, machine‑learning and rule‑based systems, whose functions and impacts are subject to the Act's scope and obligations.

For Convention purposes, machine-based technologies capable of making predictions, recommendations, or decisions that influence real or virtual environments, exhibiting capabilities typically associated with human intelligence such as perception, reasoning, learning, and autonomous action.

Refers to a technological system designed with capabilities to generate various forms of products, content, or data. Under the bill, such systems are required to integrate watermarking features for their outputs.

Any machine-based system that, for a defined set of objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. This includes generative artificial intelligence systems.

Any machine-based system capable of making predictions, recommendations, or decisions that influence real or virtual environments, based on a given set of human-defined objectives. This includes various forms of machine learning and automated decision-making technologies.

The use of machine learning and related technologies that use data to train statistical models for the purpose of enabling computer systems to perform tasks normally associated with human intelligence or perception, such as computer vision, speech or natural language processing, and content generation.

Encompasses a wide range of technologies capable of performing tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. This broad definition is intended to be technology-neutral and future-proof.

A technological system that, autonomously or partly autonomously, processes data related to human activities through techniques such as genetic algorithms, neural networks, or machine learning, to generate content, make decisions, recommendations, or predictions. This broad definition aims to encompass various evolving AI technologies.

An artificial intelligence system is defined as any data system, software, hardware, application, tool, or utility that operates in whole or in part using artificial intelligence. This includes the entire technological infrastructure enabling AI functionality.

Any machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. This definition is intended to be technology-neutral and comprehensive, covering a wide array of AI applications from simple algorithms to complex machine learning models.

Any machine learning-based system that can, for any given set of objectives, generate outputs that include images, audio content, video content, digitally generated text, three-dimensional models, predictions, recommendations or decisions influencing physical or virtual environments.

Generally understood as a machine-based system that operates with varying levels of autonomy and can, for explicit or implicit objectives, generate outputs such as predictions, recommendations, or decisions that influence physical or virtual environments. This definition is central to the EU AI Act.

An 'artificial intelligence system' encompasses any data system, hardware, tool, or utility that operates, in whole or in part, using artificial intelligence.

As defined within the Act, this refers to a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. It encompasses various methodologies, including machine learning, deep learning, and expert systems, designed to perform tasks that typically require human intelligence. The bill's focus is on ensuring the development and deployment of such systems in a manner that fosters public trust and ethical considerations.