AI Output definition
AI Output means any data, work of authorship, text or other content that is generated by any AI Technologies.
AI Output means output or data that is derived from an Authorized User’s prompts or submissions to an AI Product and may contain Customer Content.
AI Output means the Meeting Notes, Transcripts and Recordings generated by the Subscription Service on behalf of Customer.
Examples of AI Output in a sentence
The Company has implemented processes to ensure that (i) no AI Input or AI Output contains any Personal Information, and (ii) no Company AI Product is used by the Company in furtherance of a decision that produces a legal or similarly significant effect concerning individuals, or for the purpose of “automated decision-making” or “profiling” of individuals, as such terms are defined under Privacy Requirements or other applicable Laws.
More Definitions of AI Output
AI Output content, predictions, recommendations, or decisions that can influence the physical or virtual environments with which they interact.
AI Output means any output, content, decision, prediction, recommendation or other information that is generated or output by any AI Technologies. “AI Technologies” means, in addition to any definition for “artificial intelligence” or “artificial intelligence system” or any similar term under applicable Legal Requirements, engineered or machine based techniques, models and technologies (including Software) that can mimic human intelligence or infer from any input received how to generate outputs such as predictions, content, recommendations or decisions, and that may influence physical or virtual environments, including (i) deep learning, machine learning, natural language processing (or large language models), automation and other artificial intelligence models or technologies, or (ii) Software, or systems that make use of or employ neural networks, statistical learning algorithms (like linear and logistic regression, support vector machines, random forests, k-means clustering) or reinforcement learning. For clarity, AI Technologies includes Generative AI Tools. “Anti-Corruption Laws” means: all laws, rules and regulations relating to bribery or corruption, including: (a) the U.S. Foreign Corrupt Practices Act of 1977, as amended (“FCPA”); (b) the UK Bribery Act 2010; (b) the anti-corruption laws of the country of incorporation of the Company; and (c) any other applicable anti-corruption or anti-bribery laws.
AI Output means all text, images, materials and other data (a) generated by 3M's use, or the use by Supplier's personnel, agent(s), authorized recipient(s), customer(s) or end user(s) of an AI System and/or (b) produced by an AI System for 3M pursuant to the Agreement. AI Output is deemed 3M Confidential Information.
AI Output means all output (of whatever kind) generated using AI in, or in connection with, the provision of any Services to, or receipt or use of any Services by or on behalf of, ▇▇▇▇▇▇ ▇▇▇▇▇▇▇ or any of its Affiliates, Personnel or other authorized users. AI Output shall be deemed to be ▇▇▇▇▇▇ ▇▇▇▇▇▇▇’▇ Confidential Information. For the avoidance of doubt, all AI Output is deemed to be Deliverables (whether or not identified as such in the applicable Order).
AI Output means content, data, information or anything else generated by: (a) SUSE AI in response to Customer’s or an End Customer’s input of prompts into SUSE AI; or (b) Third Party Integrations with SUSE AI in response to input of prompts into SUSE AI or into those Third Party Integrations.
AI Output means the results generated by AI, e.g., text, images, videos, code, tests, predictions, recommendations or decisions.
AI Output means any data, work of authorship, text or other content that is generated by any AI Technologies. “AI Technologies” means any Technology related to deep learning, machine learning, self-improving, generative artificial intelligence or other artificial intelligence fields including, but not limited to, those that use of or employ neural networks, statistical learning algorithms (like linear and logistic regression, support vector machines, random forests, k-means clustering), or reinforcement learning.