Section 5 – Technical Methods of Classification

(1) These methods as designated in clause (ii) of sub-section (1) of Section 3 classify artificial intelligence technologies subject to their scale, inherent purpose, technical features and technical limitations such as –

(i) General Purpose Artificial Intelligence Applications with Multiple Stable Use Cases (GPAIS) as described in sub-section (2);

(ii) General Purpose Artificial Intelligence Applications with Multiple Short-Run or Unclear Use Cases (GPAIU) as described in sub-section (3);

(iii) Specific-Purpose Artificial Intelligence Applications with One or More Associated Standalone Use Cases or Test Cases (SPAI) as described in sub-section (4);

(2) General Purpose Artificial Intelligence Systems with Multiple Stable Use Cases (GPAIS) are classified based on a technical method that evaluates the following factors in accordance with relevant sector-specific and sector-neutral industrial standards:

(i) Scale: The ability to operate effectively and consistently across a wide range of domains, handling large volumes of data and users.

(ii) Inherent Purpose: The capacity to be adapted and applied to multiple well-defined use cases within and across sectors.

(iii) Technical Features: Robust and flexible architectures that enable reliable performance on diverse tasks and requirements.

(iv) Technical Limitations: Potential challenges in maintaining consistent performance and compliance with sector-specific regulations across the full scope of intended use cases.

Illustration

An AI system used in healthcare for diagnostics, treatment recommendations, and patient management. This AI consistently performs well in various healthcare settings, adhering to medical standards and providing reliable outcomes. It is characterized by its large scale in handling diverse medical data and serving multiple institutions, its inherent purpose of assisting healthcare professionals in decision-making and care improvement, robust technical architecture and accuracy while adhering to privacy and security standards, and potential limitations in edge cases or rare conditions.

(3) General Purpose Artificial Intelligence Systems with Multiple Short-Run or Unclear Use Cases (GPAIU) are classified based on a technical method that evaluates the following factors in accordance with relevant sector-specific and sector-neutral industrial standards:

(i) Scale: The ability to address specific short-term needs or exploratory applications within relevant sectors at a medium scale.

(ii) Inherent Purpose: Providing targeted solutions for emerging or temporary use cases, with the potential for future adaptation and expansion.

(iii) Technical Features: Modular and adaptable architectures enabling rapid development and deployment in response to evolving requirements.

(iv) Technical Limitations: Uncertainties regarding long-term viability, scalability, and compliance with changing industry standards and regulations.

Illustration

An AI system used in experimental smart city projects for traffic management, pollution monitoring, and public safety. Deployed at a medium scale in specific locations for limited durations, its inherent purpose is testing and validating AI feasibility and effectiveness in smart city applications. It features a modular, adaptable technical architecture to accommodate changing requirements and infrastructure integration, but faces potential limitations in scalability, interoperability, and long-term performance due to the experimental nature.

(4) Specific-Purpose Artificial Intelligence Systems with One or More Associated Standalone Use Cases or Test Cases (SPAI) are classified based on a technical method that evaluates the following factors:

(i) Scale: The ability to address specific, well-defined problems or serve as proof-of-concept implementations at a small scale.

(ii) Inherent Purpose: Providing specialized solutions for individual use cases or validating AI technique feasibility in controlled environments.

(iii) Technical Features: Focused and optimized architectures tailored to the specific requirements of the standalone use case or test case.

(iv) Technical Limitations: Constraints on generalizability, difficulties scaling beyond the initial use case, and challenges ensuring real-world robustness and reliability.

Illustration

An AI chatbot used by a company for customer service during a product launch. As a small-scale standalone application, its inherent purpose is providing automated support for a specific product or service. It employs a focused, optimized technical architecture for handling product-related queries and interactions, but faces limitations in handling queries outside the predefined scope or adapting to new products without significant modifications.