Why DIKWP Needs to Be Standardized?

Yucong Duan

International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

Professor Yucong Duan proposes an International Test and Evaluation Standard for Artificial Intelligence based on the Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Model. This model aims to address misunderstandings and uncertainties in human-machine communication by eliminating subjective human definitions and establishing a standardized, mathematical framework for AI evaluation.

Key Points of the DIKWP Model:

1. Extension of DIKW Model: The traditional Data-Information-Knowledge-Wisdom (DIKW) model is expanded by adding "Purpose," forming the DIKWP model. This addition emphasizes the intentionality behind data processing and decision-making.

2. Networked Relationships: Unlike the hierarchical nature of the traditional DIKW model, the DIKWP model emphasizes networked relationships among its components, reflecting the complex interactions in cognitive processes.

3. Mathematical Standardization: Each component of the DIKWP model is defined mathematically to eliminate subjective interpretations:

• Data (D): Seen as specific manifestations of the same semantics in cognition, recognized through shared semantic attributes.

• Information (I): Corresponds to one or more "different" semantics, highlighting differences between new inputs and existing cognitive objects.

• Knowledge (K): Represents one or more "complete" semantics, formed through abstraction and generalization.

• Wisdom (W): Involves ethics, social morals, and human values, guiding decision-making beyond technical considerations.

• Purpose (P): Denotes goal-oriented processing, defined as a tuple of input and desired output semantics.

4. Cognitive and Semantic Spaces: The model distinguishes between conceptual space, cognitive space, semantic space, and consciousness space, emphasizing how concepts and semantics interact within cognitive processes.

5. Addressing Uncertainties in AI Communication:

• Human-Machine Misunderstandings: By standardizing semantics mathematically, the model aims to reduce confusion arising from subjective definitions in human language.

• AI Evaluation Standards: Provides a unified framework for assessing AI models, ensuring comparability and consistency across different systems.

6. Ethical Considerations: The model places significant emphasis on aligning AI technologies with ethical principles and social values, promoting fairness and reducing biases.

7. Application in AI Testing:

• Overcoming Black-Box Limitations: Moves beyond traditional black-box testing by providing functional white-box evaluation results.

• Comprehensive Assessment: Evaluates AI capabilities across data understanding, information processing, knowledge representation, wisdom application, and purpose alignment.

Implications of the DIKWP Model:

• Enhanced Communication: By eliminating subjective definitions, the model facilitates clearer human-machine interactions.

• Improved AI Alignment: Encourages the development of AI systems that align more closely with human ethics and societal values.

• Standardized Evaluation: Offers a robust framework for benchmarking AI systems globally, addressing the limitations of existing evaluation benchmarks like GLUE, SuperGLUE, and MMLU.

Conclusion:

Professor Duan's DIKWP model presents a comprehensive approach to standardizing AI evaluation by focusing on networked semantic relationships and ethical considerations. It seeks to improve understanding between humans and machines by providing clear, mathematically defined semantics for each component of cognitive processing.

References

1. International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation (DIKWP-SC),World Association of Artificial Consciousness(WAC),World Conference on Artificial Consciousness(WCAC). Standardization of DIKWP Semantic Mathematics of International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP ) Model. October 2024 DOI: 10.13140/RG.2.2.26233.89445 .  https://www.researchgate.net/publication/384637381_Standardization_of_DIKWP_Semantic_Mathematics_of_International_Test_and_Evaluation_Standards_for_Artificial_Intelligence_based_on_Networked_Data-Information-Knowledge-Wisdom-Purpose_DIKWP_Model

2. Duan, Y. (2023). The Paradox of Mathematics in AI Semantics. Proposed by Prof. Yucong Duan:" As Prof. Yucong Duan proposed the Paradox of Mathematics as that current mathematics will not reach the goal of supporting real AI development since it goes with the routine of based on abstraction of real semantics but want to reach the reality of semantics. ".