Generative AI

Models and systems that generate text, images, code, audio or other data in response to user prompts; the Guideline treats these systems as the subject of controls on use, data inputs, provenance and accountability in official work.

Systems that produce content (text, audio, code, video, images) in response to a user's prompt, typically by predicting likely continuations based on training data. The Guide uses this definition to scope covered tools and to distinguish hosting, use-case risk, and data‑provenance considerations.

Models and systems that generate new content (text, images, audio, video or code) in response to user inputs, operating by predicting likely outputs rather than asserting factual truth; the guides use this term to frame risks like hallucinations and outdated information.

Defined as AI systems that produce text, images, audio or other content by learning patterns from large training datasets; the strategy frames generative AI as a class of models requiring specific transparency, licensing and safety considerations for public‑sector use and infrastructure support.

A subset of artificial intelligence capable of producing novel content such as text, images, audio, code, or video. The vision distinguishes generative systems from task‑specific AI and frames generative AI as the focal technology for the report's principles and action lines.

Generative AI refers to machine-based systems that produce novel content — such as text, images, audio, or code — by learning patterns from training data and generating outputs conditioned on inputs. The strategy uses this term to distinguish systems that synthesise new material from other AI that primarily infers or classifies.

AI systems that create new content—such as text, images, audio or synthetic documents/evidence—whose outputs may include hallucinations or fabricated material; the Guidelines require provenance checks, verification and heightened disclosure when such systems materially contribute to legal work.

AI systems capable of producing novel content — including text, images, code, audio, and video — based on training data and user prompts. The roadmap uses this term to capture modalities and applications that have grown rapidly and require specific policy attention.

Systems or models that generate novel content such as text, images, audio, video, or code by producing outputs not directly reproducing a single training exemplar; the Guidelines emphasize risks unique to such outputs (hallucination, synthetic media, bias) and their provenance and traceability requirements.

Systems that produce text, audio, images, video, code, or other media by learning patterns from data; the Guidelines focus on models whose outputs can affect human decisions, rights, safety, or privacy. This definition distinguishes foundation models, fine‑tuned models, and inference‑only services for scope and controls.

AI models that generate new content (e.g., text, images, code); within AI Verify these are distinguished from traditional predictive models and subject to specialized tests such as red‑teaming, content‑safety benchmarks, and contextual safety evaluations to address risks like hallucination and privacy leakage.

Model families and systems designed to generate novel content (text, images, audio, code, etc.) rather than only infer labels; used to refer broadly to architectures, checkpoints and deployments capable of content synthesis.

AI systems capable of producing text, images, code, and other content based on training data; the Action Plan targets development of these models and associated capabilities (e.g., LLMs) for industry and research.

Models or systems that generate text, images, audio, or other media by learning statistical patterns from data rather than retrieving verbatim records; used to create novel or synthesized content based on learned representations.

AI systems defined as producing synthetic outputs — including text, images, audio, video, or models — which are subject to specific governance measures such as notice/labeling of AI-generated content and potential dataset provenance and creator-identification mechanisms.

Models that generate new text, images, audio, or other content outputs rather than merely classifying inputs; includes large language models and image/audio generators. The Model highlights generative AI risks such as memorization, unintended leakage of training data, and content-based harms.

Models or services that autonomously generate content including text, images, audio, and video; the Decree prescribes advance notices, output labeling and watermarking/transparency requirements specific to generative and deepfake outputs.

Generative AI refers to models that produce text, audio, images, or other media outputs by learning patterns from training data; the Action Plan uses this term to frame work on model safety, ethical standards and sectoral guidance for such systems.

AI models that generate new content (text, images, audio, or video) in response to user prompts. The guide frames these models in classroom uses, including content creation, simulations, and feedback, and discusses associated risks and mitigation measures.

Systems capable of producing new content such as text, images, audio, code or video based on learned patterns; the Framework treats generative AI as the primary class of systems covered, with guidance on their capabilities, limitations and appropriate uses.

Models and systems that generate new content or outputs (such as text, images, audio or code) rather than solely classifying or predicting from inputs; in this strategy, generative AI is considered with respect to training data provenance, lawful bases for use, and downstream privacy and rights impacts.

Models and systems designed to generate novel content—such as text, images, audio, code, or other media—rather than solely performing classification or prediction tasks; the Order directs development of companion guidance and testing resources specific to generative AI capabilities and risks.

A set of technologies that leverage large volumes of data and machine learning techniques to produce new content in response to user inputs (prompts); outputs can be written, visual, audio, or video, and the tools are characterized as predictive models rather than human-like intelligence.

AI models that generate new content (e.g., text, images, audio) as outputs; the circular specifically highlights generative AI in the context of prohibitions and safeguards (for example, advising against uploading personal or sensitive data to third‑party generative platforms not contracted or developed by the agency).

Generative AI refers to systems capable of producing novel content—such as text, images, audio, or video—through learned generative processes, including models that synthesize or create outputs rather than only making predictions.

AI systems capable of producing new content such as text, images, or other data based on patterns learned from existing data; in this strategy the term is used specifically for language models and related tools (e.g., ChatGPT, open-source LLMs) deployed for teaching, research, and administration in universities.

Models and systems capable of creating novel content (for example text, images, audio or code), such as large language models and multimodal systems; the Guidelines treat these as higher‑risk in public service contexts and subject them to specific data and security controls.

Software that produces content (text, images, video, audio) in response to user prompts—typically implemented using large language models—and which may fabricate or hallucinate outputs unless verified.

Machine learning models that generate new content by learning patterns from training data, including large language models and generative image/audio models. The draft highlights these models as a distinct class due to their content-creation capabilities and associated risks (misinformation, IP, hallucinations).

Generative AI is defined as systems that can create new content — including text, images, audio or code — often by predicting or sampling sequences of tokens; the guidance treats it as an umbrella term for models used for drafting, summarisation, and multimodal outputs.

Generative AI refers to artificial intelligence systems designed to produce novel outputs, including text, images, video, or audio, based on patterns learned from training data. This guidance applies to all forms of generative AI used by state employees.

A kind of artificial intelligence capable of generating new content such as code, images, music, text, simulations, or 3D objects. These systems learn patterns from existing data to create novel outputs that resemble the training data.

A type of artificial intelligence that can produce various types of content, including text, images, audio, and synthetic data.

AI that can generate text, images, or other media in response to prompts.

Generative AI refers to artificial intelligence systems capable of producing novel content, including text, images, audio, and video, often in response to prompts or existing data. Technologies like deepfakes are a product of generative AI, enabling the creation of synthetic media.

Generative AI refers to a class of artificial intelligence models capable of generating new data, such as text, images, audio, or video, that resembles real-world data. Technologies like large language models and deepfake generators fall under this category, posing challenges addressed by the ELVIS Act.

Generative AI models learn patterns from existing data to produce novel outputs. This technology raises specific concerns regarding data privacy, intellectual property, and potential misuse, making its regulation a key focus for policymakers.

Artificial intelligence that can generate new content such as text, images, audio, and video that resembles what humans can produce. It is effective at recognising patterns (in video, audio, text or images) and emulating them when tasked with producing something.

A subset of artificial intelligence capable of producing new content, such as text, images, or other data, that is similar to the data it was trained on. The Telangana strategy recognizes its transformative potential for applications like content creation, personalized citizen services, and automated problem-solving.

Generative AI (생성형 AI) specifically denotes AI models capable of producing new content, such as text, images, or data, based on learned patterns. The plan highlights its strategic use for tasks like developing public data chatbots and automating content creation.

Artificial intelligence systems capable of creating new content, such as text, images, or other data, often with significant societal and economic impacts.

AI systems designed to generate new content, such as text or images, based on the patterns learned from training data.

A type of artificial intelligence that can produce various types of content, including text, images, audio, and synthetic data, often in response to prompts.

Defined as AI systems that generate various outputs like text, sound, images, or videos by mimicking the structure and characteristics of input data, requiring specific transparency and safety obligations.

Generative AI refers to artificial intelligence systems that can produce new content, such as text, images, or code, based on learned patterns. HB 273 specifically restricts students from using generative AI tools for academic assignments unless explicitly authorized by a teacher for a defined instructional purpose.

Generative AI refers to AI systems that can create novel outputs, often used in contexts requiring disclosure when interacting with individuals in regulated occupations. The Act holds companies accountable for violations stemming from its use.

Specifically denotes AI systems capable of creating new content, including text, images, audio, video, or code, in response to prompts or inputs. These systems are often trained on vast datasets, raising significant copyright considerations.

An AI system that generates diverse outputs, including text, sound, images, or video, by mimicking the structure and characteristics of input data.

Generative AI is defined as artificial intelligence that mimics the structure and features of input data to produce various new outputs, such as text, sound, images, or video, and is subject to specific labeling requirements under the AI Basic Act.

Generative AI refers to artificial intelligence systems that can produce novel content, such as images, text, audio, or video, based on patterns and structures learned from large datasets. These technologies are at the forefront of creating realistic simulations of human attributes, including voices and likenesses.

Generative AI refers to artificial intelligence models capable of producing novel outputs, such as new code, images, music, text, or 3D objects, rather than merely analyzing existing data. Tools like ChatGPT are examples of generative AI.

Generative AI systems are defined as those capable of creating new content—such as text, images, audio, or video—by learning patterns from extensive datasets and responding to user prompts. Examples include Large Language Models (LLMs).

A type of artificial intelligence system capable of generating new content, such as text, images, or other media, often by learning patterns from large datasets.

Generative AI refers to a class of artificial intelligence models that can produce novel outputs, rather than just classifying or predicting existing data. The Expanded ASEAN Guide specifically addresses the unique governance and ethical considerations for these advanced systems.

Artificial intelligence systems capable of generating new content, such as text, images, audio, or code, often based on patterns learned from large datasets.

AI models that can create text, images, audio, and videos in response to a user prompt.