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)

This extended analysis delves deeper into 王玉星 (Wang Yuxing)'s master's defense presentation titled "Design and Implementation of Sovereign Artificial Intelligence DIKWP System". Building upon the initial assessment based on Wang's technical report, this analysis integrates insights from his defense record to provide a more nuanced evaluation. Utilizing the Data-Information-Knowledge-Wisdom-Purpose (DIKWP) model and the Four Spaces Framework—comprising Conceptual Space, Cognitive Space, Semantic Space, and Conscious Space—we aim to thoroughly assess the system's design, implementation, and alignment with both technical and ideological standards.

Wang's defense is methodically organized into the following sections:

1. Introduction

2. Background

3. Research Content

• 3.1 Research Objectives

• 3.2 Research Content

• 3.3 Overall Design Scheme

4. Research Progress

• 4.1 DIKWP Definition Standardization

• 4.2 DIKWP Semantic Mathematics

• 4.3 Initial Input Text DIKWP Resource Mapping and Value Conflict Identification

• 4.4 Conceptual, Semantic, and Cognitive Space DIKWP Transformation

• 4.5 DIKWP Human-Machine Interaction Channel Construction

• 4.6 Value Integration and 3-N Problem Resolution

• 4.7 Experimental Design

• 4.8 System Interface Demonstration

• 4.9 Thesis Chapter Arrangement

• 4.10 Stage Achievements

5. Next Steps

Additionally, Wang presented system design diagrams, experimental frameworks, and system interface demonstrations. Feedback from the defense committee highlighted the need for:

• Adherence to institutional reporting standards.

• Enhanced experimental design with clear comparative analyses against existing models.

• Comprehensive integration of ethical and ideological frameworks.

• Robust security measures to prevent data poisoning.

• Inclusion of relevant policy documents and references to support ideological alignment.

Wang's research aims to:

1. Develop a Transparent and Explainable Sovereign AI DIKWP System: Enhancing human-machine interaction efficiency while aligning with local cognitive patterns.

2. Align AI Outputs with User Intent and Socialist Core Values: Ensuring generated content meets user intentions and adheres to mainstream values guided by socialist principles.

The DIKWP model provides a hierarchical framework for understanding cognitive processes. Wang's communication is analyzed through each DIKWP component, integrating both the 3-No Problems (Incompleteness, Inconsistency, Imprecision) and the proposed additional X-No Problems (Relevance, Redundancy, Timeliness, Accuracy, Accessibility, Understandability) to ensure semantic completeness.

Definition: Raw, unprocessed facts or observations.

Application in Communication:

• Research Background: Wang introduces data on the emergence of digital sovereignty, emphasizing network and data sovereignty as critical components. He references global trends, such as the 2024 World Government Summit and the introduction of "Sovereign AI" by NVIDIA's CEO, highlighting the geopolitical significance of AI sovereignty.

• Current Challenges: Identifies data-related issues in generative AI, including discerning user intent, internal value conflicts, data poisoning risks, and lack of explainability.

Assessment of 3-No Problems:

• Incompleteness:

• Strengths: Covers essential data points related to digital sovereignty and AI challenges.

• Areas for Improvement: Lacks specific datasets or empirical data supporting claims, potentially leading to incomplete data representation.

• Inconsistency:

• Strengths: Data presented appears consistent within the context of digital sovereignty and AI challenges.

• Areas for Improvement: Further validation against diverse data sources could enhance reliability.

• Imprecision:

• Strengths: Provides a broad overview of challenges.

• Areas for Improvement: Specificity is lacking in areas such as the exact nature of data conflicts or metrics used to assess data integrity.

Consideration of X-No Problems:

• Relevance:

• Strengths: Data presented is highly relevant to the research topic, focusing on sovereignty in AI and associated challenges.

• Redundancy:

• Strengths: Minimal redundancy; information is presented concisely without unnecessary repetition.

• Timeliness:

• Strengths: Data is current, referencing recent events like the 2024 World Government Summit.

• Accuracy:

• Strengths: Assumes accurate representation of global trends and challenges.

• Areas for Improvement: Specific sources or data points are not cited, which could bolster credibility.

• Accessibility:

• Strengths: Information is presented clearly, making it accessible to the audience without requiring specialized knowledge.

• Understandability:

• Strengths: Generally understandable, though some technical terms and concepts may require further elaboration for clarity.

Definition: Processed data that is organized and structured to provide context.

Application in Communication:

• System Design: Outlines DIKWP resource mapping and semantic mathematical analysis to transform inputs.

• Value Conflict Handling: Discusses mechanisms to identify and resolve value conflicts within DIKWP components.

Assessment of 3-No Problems:

• Incompleteness:

• Strengths: Information processing steps are outlined.

• Areas for Improvement: Lack of detailed methodologies or algorithmic descriptions could leave gaps in understanding transformation processes.

• Inconsistency:

• Strengths: Processes appear logically structured and consistent.

• Imprecision:

• Strengths: High-level description provides a broad overview.

• Areas for Improvement: Specific techniques or parameters for information processing are not detailed, leading to imprecision.

Consideration of X-No Problems:

• Relevance:

• Strengths: Directly relevant to achieving research objectives of aligning AI outputs with human intent and core values.

• Redundancy:

• Strengths: Information is presented without unnecessary duplication.

• Timeliness:

• Strengths: Aligns with current AI research trends and methodologies.

• Accuracy:

• Strengths: Assumes accurate depiction of information processing.

• Areas for Improvement: Specific techniques or data transformations are not detailed.

• Accessibility:

• Strengths: Information is conveyed in an accessible manner suitable for an academic defense.

• Understandability:

• Strengths: Generally clear, though lack of detailed methodologies may hinder full comprehension.

Definition: Structured information and data representations stored within the ACS, encompassing theoretical models, operational protocols, and learned behaviors.

Application in Communication:

• Knowledge Base Integrity: Emphasizes maintaining a consistent and accurate knowledge repository to prevent corrupted data entries.

• Conflict Resolution Mechanisms: Implements strategies to resolve conflicting information within the knowledge base.

Assessment of 3-No Problems:

• Incompleteness:

• Strengths: Addresses the need for knowledge base integrity and conflict resolution.

• Areas for Improvement: Specific mechanisms or tools employed are not specified, leading to potential incompleteness.

• Inconsistency:

• Strengths: Maintains consistency in discussing knowledge management strategies without internal contradictions.

• Imprecision:

• Strengths: High-level strategies are outlined.

• Areas for Improvement: Descriptions of knowledge management processes are vague, lacking detailed operational frameworks or examples.

Consideration of X-No Problems:

• Relevance:

• Strengths: Ensuring knowledge base integrity is crucial for system reliability and ethical compliance.

• Redundancy:

• Strengths: Information is presented succinctly without unnecessary repetition.

• Timeliness:

• Strengths: Aligns with current best practices in knowledge management within AI systems.

• Accuracy:

• Strengths: Assumes accurate representation of knowledge management challenges and solutions.

• Accessibility:

• Strengths: Presented in an accessible manner suitable for an academic audience.

• Understandability:

• Strengths: Clear in intent but lacks depth, which may affect complete understanding of knowledge management strategies.

Definition: The ACS's ability to apply knowledge judiciously, considering ethical considerations, contextual factors, and long-term implications.

Application in Communication:

• Ethical Integration: Incorporates socialist core values to guide decision-making processes.

• Behavioral Monitoring: Aligns ACS actions with intended goals and ethical standards.

Assessment of 3-No Problems:

• Incompleteness:

• Strengths: Discusses integration of core values.

• Areas for Improvement: Does not elaborate on specific ethical frameworks or operationalization of wisdom within the system.

• Inconsistency:

• Strengths: Ethical integration appears consistent with research objectives.

• Imprecision:

• Strengths: Clear focus on ethical alignment.

• Areas for Improvement: Lacks detailed explanation of how wisdom is measured or enforced, introducing imprecision.

Consideration of X-No Problems:

• Relevance:

• Strengths: Ethical integration is highly relevant, ensuring system outputs align with societal values.

• Redundancy:

• Strengths: Information is presented without unnecessary overlap.

• Timeliness:

• Strengths: Reflects contemporary emphasis on ethical AI development.

• Accuracy:

• Strengths: Presumes accurate alignment with ethical standards.

• Accessibility:

• Strengths: Ethical concepts are conveyed in an accessible manner.

• Understandability:

• Strengths: Clear in purpose but lacks detailed operational insights.

Definition: The overarching goals and objectives guiding the ACS's operations and decision-making processes.

Application in Communication:

• Research Objectives: Aims to create a transparent, explainable Sovereign AI DIKWP system that aligns AI outputs with human intent and socialist core values.

• Goal Alignment: Ensures ACS actions support its defined mission of enhancing human-machine interaction efficiency and value alignment.

Assessment of 3-No Problems:

• Incompleteness:

• Strengths: Purpose is clearly stated.

• Areas for Improvement: Could benefit from more specific objectives or measurable goals to avoid ambiguity.

• Inconsistency:

• Strengths: Purpose aligns consistently with overall research narrative without contradictions.

• Imprecision:

• Strengths: Broad objectives are clear.

• Areas for Improvement: Objectives are broad, lacking specific metrics or milestones, introducing imprecision in defining success criteria.

Consideration of X-No Problems:

• Relevance:

• Strengths: Directly aligns with research focus on sovereign AI and ethical alignment.

• Redundancy:

• Strengths: Information is succinct, avoiding repetition.

• Timeliness:

• Strengths: Reflects current trends in AI ethics and sovereignty.

• Accuracy:

• Strengths: Assumes accurate representation of research goals.

• Accessibility:

• Strengths: Clearly communicates research purpose to the audience.

• Understandability:

• Strengths: Straightforward but could benefit from more detailed articulation of objectives.

Wang's DIKWP system addresses both the foundational 3-No Problems and the expanded X-No Problems to achieve semantic completeness.

Wang's presentation addresses:

1. Incompleteness: Ensures all necessary data, information, and knowledge elements are present for effective AI operation.

2. Inconsistency: Prevents conflicting or contradictory elements within DIKWP components.

3. Imprecision: Avoids vague or ambiguous elements, ensuring clear and specific communication.

To achieve semantic completeness, Wang introduces additional X-No Problems:

1. Relevance (No-Relevant):

• Current Addressing: Ensures AI responses are pertinent to user intent and core values.

• Enhancement Needed: Further elaboration on quantifying and maintaining relevance across different contexts.

2. Redundancy (No-Redundant):

• Current Addressing: Minimizes unnecessary repetition by streamlining knowledge base entries.

• Enhancement Needed: Detailed strategies for identifying and eliminating redundant information within the system.

3. Timeliness (No-Timely):

• Current Addressing: Incorporates up-to-date data through continuous monitoring and updates.

• Enhancement Needed: Specific mechanisms for ensuring real-time data processing and timely response generation.

4. Accuracy (No-Accurate):

• Current Addressing: Focuses on data integrity and knowledge base accuracy.

• Enhancement Needed: Implementation of validation techniques and error-checking algorithms to enhance accuracy further.

5. Accessibility (No-Accessible):

• Current Addressing: Ensures information is accessible through clear communication interfaces.

• Enhancement Needed: Inclusion of accessibility standards and user-centric design principles to cater to diverse user needs.

6. Understandability (No-Understandable):

• Current Addressing: Presents information in a comprehensible manner, aligning with user intent and core values.

• Enhancement Needed: Advanced natural language processing techniques to improve clarity and reduce ambiguity in AI responses.

By incorporating both the 3-No Problems and the additional X-No Problems, Wang's DIKWP model achieves comprehensive coverage of communication deficiencies:

• Set Theory and Information Theory: Ensures that all potential communication challenges are addressed, minimizing uncertainty (entropy) and maximizing mutual information between stakeholders and the AI system.

• Mutual Exclusivity and Collective Exhaustiveness: Each No Problem addresses distinct aspects of communication deficiencies, ensuring no overlap and comprehensive coverage of the semantic space.

The Four Spaces Framework—Conceptual Space, Cognitive Space, Semantic Space, and Conscious Space—provides a multidimensional perspective on the cognitive and ethical dimensions of ACS. Below is an analysis of how Wang's communication aligns with each space.

Definition: The theoretical constructs and models that the ACS uses to interpret and understand its environment and operations.

Application in Communication:

• DIKWP Model Integration: Utilizes the DIKWP model and semantic mathematical analysis as foundational constructs for system design.

• Value Conflict Handling: Incorporates socialist core values into the conceptual framework to guide AI behavior.

Assessment:

• Alignment with Reality: Wang's conceptual models align with the current focus on ethical AI and digital sovereignty.

• Guiding Hypotheses: The integration of core values serves as a hypothesis for aligning AI outputs with societal norms.

Strengths:

• Robust Theoretical Foundation: Strong integration of the DIKWP model with ethical considerations.

• Clear Value Alignment: Emphasizes the importance of aligning AI with national values.

Areas for Improvement:

• Detailing Theoretical Constructs: Further elaboration on how the DIKWP model specifically addresses ethical alignment and value conflicts would enhance understanding.

Definition: The mental processes, computational functions, and cognitive architectures within the ACS that enable perception, reasoning, and decision-making.

Application in Communication:

• Resource Mapping and Semantic Analysis: Describes the cognitive processes involved in transforming inputs through DIKWP resource mapping.

• Conflict Resolution: Discusses handling value conflicts across DIKWP components, indicating cognitive decision-making processes.

Assessment:

• Process Monitoring: Describes high-level cognitive processes without delving into specific algorithms or mechanisms.

• Performance Metrics: Lacks specific metrics for evaluating cognitive function efficiency and accuracy.

Strengths:

• Integration of Cognitive Processes: Demonstrates an understanding of how data transforms into actionable knowledge within the system.

• Conflict Resolution Mechanism: Addresses the need for resolving value conflicts, indicating thoughtful cognitive architecture design.

Areas for Improvement:

• Specificity in Cognitive Processes: Detailed descriptions of algorithms or computational functions used in cognitive processes would provide deeper insights.

• Performance Evaluation: Introducing metrics or methods for assessing cognitive performance would enhance the framework's robustness.

Definition: The language, symbols, and meaning-making processes that the ACS uses to communicate and interpret data.

Application in Communication:

• Semantic Mathematical Analysis: Employs semantic mathematical techniques to parse and understand input content.

• Language Processing: Ensures generated responses are semantically consistent with user inputs and aligned with core values.

Assessment:

• Disorganized Communication Prevention: Aims to prevent incoherent or nonsensical outputs through semantic analysis.

• Semantic Drift Control: Maintains language processing accuracy over time by aligning outputs with predefined value frameworks.

Strengths:

• Robust Semantic Processing: Utilizes advanced semantic analysis to ensure accurate interpretation and response generation.

• Alignment with Core Values: Ensures that semantic outputs align with ethical and societal norms.

Areas for Improvement:

• Operational Details: More detailed explanations of the semantic mathematical methods and their implementation would provide clearer insights into the system's capabilities.

• Handling Ambiguities: Strategies for managing ambiguous or multifaceted inputs could be elaborated to enhance semantic robustness.

Definition: The ACS's self-awareness, ethical considerations, and alignment with societal norms and values.

Application in Communication:

• Ethical Decision-Making: Integrates socialist core values into decision-making algorithms to ensure ethical compliance.

• Behavioral Monitoring: Aligns ACS actions with intended goals and ethical standards through continuous monitoring.

Assessment:

• Ethical Lapses Prevention: System designed to prevent decisions that violate ethical standards by aligning with core values.

• Self-Awareness Maintenance: Implements mechanisms for the system to evaluate its own state and detect anomalies, enhancing self-awareness.

Strengths:

• Strong Ethical Integration: Clear emphasis on aligning AI behavior with societal and national ethical standards.

• Behavioral Monitoring: Continuous oversight ensures actions remain within ethical and operational boundaries.

Areas for Improvement:

• Detailed Ethical Frameworks: Providing explicit ethical guidelines or frameworks used in decision-making processes would enhance clarity.

• Self-Monitoring Mechanisms: More information on how the system self-monitors and rectifies ethical deviations would strengthen the conscious space integration.

Definition: The seamless incorporation of ethical and cultural considerations into the ACS's operations and decision-making processes.

Application in Communication:

• Cultural Competence: Ensures the ACS can interpret data within diverse cultural contexts and adapt to cultural nuances.

• Ethical Safeguards: Aligns ACS actions with human values and implements mechanisms to resolve ethical dilemmas.

Assessment:

• Cultural Misinterpretations Avoidance: Systems are designed to accurately interpret culturally specific data, preventing erroneous actions.

• Ethical Violations Prevention: Ensures that all actions comply with established ethical guidelines, maintaining trust and accountability.

Strengths:

• Comprehensive Ethical Alignment: Robust mechanisms to ensure AI actions are ethically sound and culturally appropriate.

• Adaptive Learning: Plans to enable the system to learn and adapt to cultural nuances over time.

Areas for Improvement:

• Implementation Details: Specific methods or algorithms used to integrate cultural and ethical considerations need further elaboration.

• Conflict Resolution Mechanisms: Detailed strategies for resolving ethical dilemmas or conflicts within the system would provide deeper insights.

During the defense, several key pieces of feedback were provided by the committee members. Wang has addressed some of these points, while others remain areas for further development.

Feedback:

• 文斌教授 (Professor Wen Bin): Noted that Wang's Word document did not adhere to the school's mid-term assessment format and emphasized the need to follow institutional guidelines strictly.

Response:

• Wang's Action: Acknowledged the oversight and committed to reviewing and aligning his report with the school's standards. Emphasized plans to incorporate detailed experimental sections and comprehensive comparative analyses to form a complete framework.

Recommendation:

• Ensure Compliance: Wang should meticulously follow the institution's guidelines for report formatting and content inclusion to avoid administrative issues and ensure clarity.

Feedback:

• 邱钊教授 (Professor Qiu Zhao): Emphasized the importance of comparative experiments against established large language models (LLMs) to validate the DIKWP system's effectiveness. Highlighted the need to clearly specify which models will be compared to ensure scientific rigor.

Response:

• Wang's Action: Plans to include comparisons with both domestic (e.g., GLM series, 文心一言) and international models (e.g., GPT-4) to assess the DIKWP system's performance in value alignment and conflict resolution.

Recommendation:

• Define Comparison Models: Clearly specify the selected models for comparative analysis, providing rationale for their selection based on their prominence and relevance to value alignment.

Feedback:

• 朱绵茂教授 (Professor Zhu Mianmao): Highlighted the necessity of integrating national ideological frameworks and ethical considerations into the system design. Emphasized the inclusion of chapters addressing ideological alignment, ethical and legal standards, and mechanisms to prevent data poisoning.

Response:

• Wang's Action: Introduced a socialist core value alignment module within the DIKWP system to ensure outputs conform to national values. Implemented data integrity mechanisms to detect and mitigate data poisoning, referencing relevant policy documents to support ethical and ideological alignment.

Recommendation:

• Expand Ethical Frameworks: Provide detailed descriptions of the ethical guidelines and ideological frameworks integrated into the system. Include specific algorithms or processes that enforce these standards during data processing and content generation.

Feedback:

• 朱绵茂教授: Noted the absence of comprehensive references and suggested including key national policy documents related to ideological strengthening.

Response:

• Wang's Action: Committed to incorporating relevant policy documents and ethical guidelines into the reference section to ensure the research's alignment with national standards.

Recommendation:

• Comprehensive Referencing: Ensure all references, especially those related to national policies on digital sovereignty and AI ethics, are thoroughly cited to support the system's design and implementation.

Feedback:

• 段玉聪教授 (Professor Duan Yucong): Raised concerns about the structural integration of the 3-No Problems within the DIKWP framework, questioning the transition from "3-No Problems" to "Non-3-No Problems" and the scientific validity of such an approach.

Response:

• Wang's Action: Clarified that the introduction of X-No Problems complements the existing 3-No Problems to achieve semantic completeness. Emphasized maintaining the integrity of the original 3-No Problems while addressing additional communication deficiencies.

Recommendation:

• Maintain Clarity: Clearly map each No Problem to specific DIKWP components and Four Spaces to ensure mutual exclusivity and collective exhaustiveness. Provide mathematical justification and empirical evidence to support the expanded framework's validity.

1. Conference Paper Publication:

• Published a paper titled "Resource Adjustment Processing on the DIKWP Artificial Consciousness Diagnostic System" as the first author in the 2023 IEEE International Conference on High Performance Computing & Communications.

2. Competition Achievement:

• Secured the second prize in the National Artificial Intelligence Application Scenario Innovation Challenge.

3. System Development:

• Completed the initial construction of the DIKWP system, including resource mapping, human-machine interaction channels, and preliminary value alignment mechanisms.

1. System Optimization:

• Enhance the DIKWP semantic mathematics processing.

• Deepen the resolution of the 3-No Problems (Incompleteness, Inconsistency, Imprecision).

2. Experimental Design:

• Refine experimental setups to include comprehensive comparative analyses against established LLMs.

• Implement DIKWP white-box evaluation standards to assess system performance.

3. Ethical and Ideological Alignment:

• Incorporate detailed ethical frameworks and national policy references.

• Develop robust mechanisms to prevent data poisoning and ensure data integrity.

4. Documentation:

• Align the thesis structure with institutional guidelines.

• Ensure comprehensive referencing of all relevant policy documents and foundational research.

1. Comprehensive Framework Integration:

• Integration of the DIKWP model with the Four Spaces Framework provides a robust, multidimensional approach to diagnosing and mitigating hallucinations in ACS.

2. Emphasis on Ethical Alignment:

• Strong focus on aligning ACS outputs with socialist core values ensures the system adheres to national and societal ethical standards.

3. Transparent and Explainable Design:

• The proposal emphasizes transparency and explainability, enhancing trust and accountability in ACS operations.

4. Multidisciplinary Approach:

• Incorporates technical, ethical, and cognitive insights, fostering comprehensive diagnostic practices.

5. Mathematical Rigor:

• Employs mathematical theories, including set theory and information theory, to rigorously define and justify communication deficiencies.

6. Stage Achievements:

• Successful publication of a conference paper and winning a national competition demonstrate practical progress and recognition.

7. System Development:

• Completed initial system construction with core functionalities, providing a solid foundation for further development and optimization.

1. Ethical and Ideological Integration:

• Detailed Ethical Framework: Expand on how the system manages ethical dilemmas and maintains value alignment beyond core value integration.

• Implementation Details: Provide explicit ethical guidelines or frameworks used in decision-making processes.

2. Data Poisoning and Security Measures:

• Data Integrity Mechanisms: Detail specific technical measures to detect and mitigate malicious data inputs.

• Security Protocols: Implement comprehensive security protocols to safeguard against unauthorized data manipulation.

3. Experimental Design and Comparative Analysis:

• Clear Comparison Targets: Define specific existing models (both domestic and international) for comparative analysis to validate the DIKWP system's effectiveness.

• Detailed Experimental Metrics: Elaborate on the metrics and evaluation standards used in the experiments to assess system performance accurately.

4. Documentation and Reference Inclusion:

• Comprehensive References: Ensure all relevant policy documents, ethical guidelines, and foundational research are included in the reference section.

• Structured Report Adherence: Align the report structure with institutional guidelines, incorporating all required sections and adhering to formatting standards.

5. System Optimization and Functionality Details:

• Enhance System Features: Provide more details on system functionalities, especially regarding the DIKWP cognitive interaction channel and value alignment mechanisms.

• User Interface Demonstrations: Offer comprehensive demonstrations of system interfaces to showcase practical applications and user interactions.

6. Addressing the 3-No Problems with X-No Problems:

• Operationalization: More detailed descriptions of how each No Problem is detected, measured, and mitigated within the system.

• Integration with DIKWP: Clear mapping of each No Problem to DIKWP components and the Four Spaces to ensure comprehensive coverage.

7. Technical Depth:

• Algorithmic Details: Provide in-depth explanations of the algorithms and mathematical models used for DIKWP resource mapping, semantic analysis, and value conflict resolution.

• Performance Metrics: Define and utilize specific performance metrics to evaluate the effectiveness of each system component.

Wang Yuxing's master's defense presents a meticulously structured approach to designing and implementing a Sovereign Artificial Intelligence DIKWP system aimed at aligning AI outputs with human intent and socialist core values. By integrating the DIKWP model with the Four Spaces Framework and addressing both the 3-No Problems and the additional X-No Problems, the proposal offers a comprehensive diagnostic and mitigation strategy for hallucinations in Artificial Consciousness Systems (ACS).

• Holistic Assessment: Combines data integrity, information processing, knowledge representation, decision-making wisdom, and purpose alignment.

• Multidimensional Perspective: Incorporates theoretical constructs, cognitive functions, language use, and ethical considerations.

• Enhanced Reliability and Safety: Proactively identifies and mitigates hallucinations to prevent operational disruptions and safety risks.

• Ethical Operation: Aligns ACS behavior with ethical standards and societal norms, fostering trust and accountability.

• Interdisciplinary Collaboration: Encourages the integration of technical, ethical, and cognitive insights, promoting comprehensive diagnostic practices.

• Mathematical Rigor: Ensures semantic completeness and robust communication deficiency coverage through mathematical frameworks.

1. Incorporate Ethical and Ideological Considerations: Expand on ethical frameworks and ideological alignment as per supervisory feedback.

2. Enhance Security Measures: Implement robust data integrity and security protocols to prevent data poisoning.

3. Conduct Comparative Experiments: Clearly define and engage with existing models for comparative analysis to validate system effectiveness.

4. Ensure Comprehensive Documentation: Align the report structure with institutional guidelines and include all necessary references and documentation.

5. Optimize System Functionalities: Continue refining system features and provide detailed demonstrations of user interactions.

6. Operationalization of X-No Problems: Develop and integrate algorithms and metrics based on the expanded set of No Problems to ensure comprehensive coverage and mitigation of communication deficiencies.

By addressing the identified areas for improvement and continuing to refine the system based on empirical data and expert feedback, Wang Yuxing can ensure the successful implementation and operational efficacy of his Sovereign AI DIKWP system.

(Ensure all relevant policy documents, ethical guidelines, and foundational research are included here as per Wang's communication and feedback received.)

1. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379–423.

2. Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory. Wiley-Interscience.

3. Fano, R. M. (1961). Transmission of Information: A Statistical Theory of Communication. MIT Press.

4. Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-34.

5. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. Springer.

6. ISO/IEC 25012:2008. Software engineering — Software product Quality Requirements and Evaluation (SQuaRE) — Data quality model.

7. Relevant National Policy Documents:

• 中共中央关于加强意识形态工作的意见 (Central Committee of the Communist Party of China on Strengthening Ideological Work).

• 国家互联网信息办公室发布的互联网信息服务算法推荐管理规定 (Regulations on Algorithmic Recommendations for Internet Information Services issued by the National Internet Information Office).

Final Remarks

This comprehensive analysis underscores the strengths of Wang Yuxing's master's defense while highlighting critical areas for enhancement. By addressing both the foundational 3-No Problems and integrating the additional X-No Problems, the DIKWP model achieves semantic completeness, ensuring robust and ethical communication within the Sovereign AI system. Continuous refinement, comprehensive ethical integration, and rigorous experimental validation are essential for the successful realization of this research.

References for Further Exploration

• 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

• 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. ".