T/SCBDIF 001-2024 Active Group standards

T/SCBDIF 001-2024 AI Large Model Application Capacity Maturity Evaluation Standard

T/SCBDIF 001-2024

Publish Date: 2024-09-30 Implement Date: 2024-12-01 For services related to genuine standard inquiry, procurement, translation, and other related services in China, please Contact Us

Price: $ 20.00

Out of stock

Basic Information

Standard Code: T/SCBDIF 001-2024

Standard Type: Group standards

Standard Status: Active

is_force_gb: no

CCS Name: -

ICS Name: Integrated Information Technology (IT)

Publish Date: 2024-09-30

Implement Date: 2024-12-01

Scope

Scope: This standard aims to provide an evaluation framework and methodology for the maturity of AI large-scale models in different application scenarios, ensuring the rigor, meticulousness, and practicality of the evaluation. This standard is applicable to assessing the performance, stability, reliability, and user satisfaction of AI large-scale models in various application scenarios. Main technical content: The following are the main contents: 1. Standard background and purpose: Emphasizes the cutting-edge status of AI large-scale models in the current technological innovation of artificial intelligence and their role in promoting the digital transformation and intelligent upgrading of various industries. Points out the lack of a unified evaluation system for different types of large-scale models, and emphasizes the positive significance of formulating this standard for promoting the healthy development of the artificial intelligence industry. 2. Scope of application: Applicable to assessing the performance, stability, reliability, and user satisfaction of AI large-scale models in various application scenarios. 3. Terms and definitions: Defines key terms such as "large-scale model", "model development", "model capability", and "application capability", providing a foundation for subsequent evaluations. 4. Evaluation principles: Establishes basic principles for evaluating the maturity of AI large-scale model application capabilities, such as objectivity, fairness, transparency, and operability. 5. Evaluation dimensions: Includes task support, scenario richness, industry coverage, and service maturity, comprehensively covering the application capabilities of AI large-scale models. 6. Overall evaluation method: Adopts both quantitative and qualitative evaluation methods to ensure the accuracy and comprehensiveness of the evaluation. 7. Evaluation methods and processes for each dimension: For different evaluation dimensions, it details the evaluation methods and proc