With a Basic Commercial Valuation of $355 Million, the DIKWP Artificial Consciousness Model Leads AI Governance Out of the ‘Black Box’ and Embraces the ‘White Box’ EraExecutive Summary
International Standardization Committee of NetworkedDIKWPfor Artificial Intelligence Evaluation(DIKWP-SC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email:[email protected])
The rapid advancement of artificial intelligence (AI) has transformed industries and reshaped societal norms. However, the opacity of AI decision-making processes—often referred to as the “black box” problem—poses significant challenges in trust, transparency, and ethical governance. Professor Yucong Duan’s groundbreaking DIKWP Artificial Consciousness Model offers a revolutionary solution by transitioning AI from opaque “black box” systems to transparent “white box” frameworks. With a comprehensive patent portfolio valued at approximately $355 million, this model not only addresses the critical issues in AI governance but also holds substantial commercial potential.
The DIKWP model extends the traditional Data-Information-Knowledge-Wisdom (DIKW) hierarchy by adding “Purpose,” creating a holistic framework that mirrors human cognitive processes. By incorporating elements such as semantic transformations, ethical reasoning, and intention alignment, the model enables AI systems to be more transparent, interpretable, and ethically aligned with human values.
This report delves into the key innovations of the DIKWP model, its commercial valuation, and how it addresses pressing challenges in AI governance. It explores the transition from black box to white box AI systems and highlights the profound impact this shift has on the AI industry, ethical considerations, and future developments.
Table of Contents
Overview of the DIKWP Model
Background on AI Governance Challenges
The DIKWP Artificial Consciousness Model
Key Innovations by Professor Yucong Duan
Valuation of the DIKWP Patent Portfolio
Challenges with Traditional ‘Black Box’ AI
How DIKWP Enables ‘White Box’ Transparency
Benefits of ‘White Box’ AI Governance
Impact on AI Industry and Governance
Detailed Patent List with Valuations
Summary Analysis Tables
Glossary of Terms
Contact Information
1. IntroductionOverview of the DIKWP Model
The Data-Information-Knowledge-Wisdom-Purpose (DIKWP) model is an evolution of the traditional DIKW hierarchy. It represents a continuum where:
Lack of Transparency: Difficulty in understanding how AI systems arrive at decisions.
Ethical Concerns: Potential biases and unethical outcomes without clear accountability.
Regulatory Compliance: Challenges in meeting transparency and fairness requirements set by regulators.
Trust Deficit: Users and stakeholders may lack confidence in AI systems they cannot understand or scrutinize.
These challenges necessitate a shift toward “white box” AI systems that are transparent, interpretable, and ethically aligned.
2. The DIKWP Artificial Consciousness ModelKey Innovations by Professor Yucong Duan
Professor Yucong Duan has made pioneering contributions to the DIKWP model, enhancing its applicability and depth across various domains, including artificial intelligence, cognitive science, philosophy, and medicine. His key innovations include:
2.1 Invention of the DIKWP Graphs: Extending the Knowledge Graph
Overview:
Professor Duan extended the traditional Knowledge Graph by developing the DIKWP Graphs, which include:
Data Graph (DG)
Definition: Represents raw data elements and their direct relationships based on shared attributes.
Function: Organizes data into structured formats, facilitating efficient retrieval and management.
Structure:
Example: In a smart city sensor network, nodes represent sensor readings, and edges represent spatial or temporal relationships.
Information Graph (IG)
Definition: Captures patterns, anomalies, and insights derived from data.
Function: Represents “differences” and meaningful associations, highlighting significant relationships and trends.
Structure:
Example: In social media analytics, nodes represent trending topics, and edges represent influence relationships.
Knowledge Graph (KG)
Definition: Structures information into a network of interconnected concepts and entities.
Function: Ensures “completeness” by integrating all relevant information, enabling reasoning and inference.
Structure:
Example: In healthcare, nodes represent diseases, symptoms, and treatments, with edges indicating relationships like “causes” or “is treated by.”
Wisdom Graph (WG)
Definition: Incorporates ethical values, experiences, and judgment into the knowledge structure.
Function: Guides decision-making by integrating ethical considerations, representing “wisdom.”
Structure:
Example: In autonomous vehicle decision-making, nodes represent safety protocols and ethical dilemmas, with edges guiding actions in critical situations.
Purpose Graph (PG)
Definition: Represents overarching goals and objectives guiding the system’s actions.
Function: Aligns all processes with the defined purpose, ensuring coherence and direction.
Structure:
Example: In corporate strategy, nodes represent goals like market expansion, with edges connecting strategic initiatives.
Impact and Significance:
Conscious Space
Cognitive Space
Semantic Space
Conceptual Space
Detailed Explanation:
Inner DIKWP Model: Represents the AI’s internal processing and reasoning.
Outer DIKWP Model: Represents external inputs, environment, or other agents.
Interaction: Models exchange data, information, knowledge, wisdom, and purpose, refining each other’s outputs.
Conscious Space: The AI’s awareness of its own existence, states, and processes.
Cognitive Space: The processing area where perception, memory, learning, and problem-solving occur.
Semantic Space: The network of meanings, concepts, and relationships that the AI understands.
Conceptual Space: The abstract realm where high-level concepts and ideas are formed.
Impact and Significance:
Traditional TRIZ: Focuses on patterns of invention documented in patents, consisting of 40 inventive principles and contradiction matrices.
Integration with DIKWP Model:
Comprehensive Problem-Solving: Addresses technical, ethical, and purpose-driven aspects.
Innovation Enhancement: Encourages creative solutions that are socially responsible.
Strategic Alignment: Ensures that innovations contribute to organizational goals.
2.4 Initiation of White-Box Testing of AI through the DIKWP Model
Overview:
Developed a method for white-box testing of AI systems by replacing natural language interfaces with the DIKWP model, enabling transparent and interpretable communication between testers and AI systems.
Detailed Explanation:
Bidirectional Communication: AI exposes internal processing at each DIKWP layer.
Interpretation without Natural Language: Structured outputs reduce ambiguity.
Traceability: Allows testers to trace the flow of information and identify errors.
Impact and Significance:
Need for Semantic Mathematics: Traditional mathematics in AI lacks the ability to represent and manipulate semantic meanings effectively.
Components:
Bridging Gaps: Connects numerical computation with semantic reasoning.
Advancement in AI Capabilities: Allows AI to process language and concepts with mathematical precision.
Innovation in AI Research: Opens new avenues for research in AI and cognitive sciences.
2.6 Extension of Blockchain Content and Operations to DIKWP Semantic Content
Overview:
Extended blockchain technology to handle DIKWP semantic content and operations, enhancing how information is stored, shared, and utilized in decentralized systems.
Detailed Explanation:
Semantic Content Storage: Records data, information, knowledge, wisdom, and purpose on the blockchain.
Enhanced Smart Contracts: Capable of interpreting and acting upon semantic content.
Decentralized Knowledge Management: Participants contribute to and access a collective knowledge repository.
Impact and Significance:
Semantic Communication with DIKWP:
Challenges: Complexity, lack of transparency, slow responsiveness.
DIKWP-Based Approach: Utilizes data-driven policies, informed decision-making, and purpose alignment.
Impact and Significance:
DIKWP Framework & Applications: 25 patents
Artificial Consciousness & Ethical AI: 15 patents
DIKWP-TRIZ and Semantic Mathematics for AI: 12 patents
White-Box AI Testing via DIKWP: 10 patents
DIKWP in Blockchain Operations: 10 patents
Semantic Communication & Digital Governance: 10 patents
Other AI & Machine Learning Applications: 9 patents
Valuation Methodology
The total valuation of approximately $355 million is based on:
Income Approach: Estimating the present value of expected future income streams.
Market Approach: Comparing with similar patented technologies and their market transactions.
Cost Approach: Estimating the cost required to develop a similar technology from scratch.
Option-Based Valuation: Considering the patents as options for future developments.
Key Patents and Their Valuations
Some notable patents include:
Valuation: $6 million
Significance: Foundational patent for the DIKWP framework; high market impact.
Cross-DIKW-Mode Ambiguity Processing Method Oriented to Essential Computing and Reasoning (CN202011103480.6)
Valuation: $6.5 million
Significance: Embeds ethical reasoning within AI governance frameworks.
Consensus Method for Blockchain Based on DIKWP Model (CN202111658319.X)
Valuation: $7.5 million
Significance: Applies DIKWP models to interactions within the metaverse.
An aggregate synergy premium of 15% was applied, considering the integrated nature of the portfolio, leading to the final valuation.
4. Transition from ‘Black Box’ to ‘White Box’ in AI GovernanceChallenges with Traditional ‘Black Box’ AI
Structured Communication: AI exposes internal processing at each DIKWP layer.
Bidirectional Interaction: Facilitates communication between AI and testers or users.
Interpretation without Natural Language: Reduces ambiguity by using structured outputs.
Traceability: Allows tracing the flow of information and identifying errors.
Benefits of ‘White Box’ AI Governance
Healthcare
Loan Approval: Transparent and fair decision-making processes.
Investment Strategies: Ethical investment algorithms considering social responsibility.
Autonomous Vehicles
Semantic Smart Contracts: Contracts that interpret and act upon semantic content.
Supply Chain Management: Detailed tracking with semantic context for transparency.
Education
E-Government Services: Transparent and accessible governmental services.
Policy Development: Data-driven and ethically aligned policy-making.
Ethical AI Development
Interpretability: Stakeholders can understand AI decision pathways, enhancing trust.
Accountability: Clear traceability of decisions allows for accountability and correction.
User Adoption: Increased trust leads to broader acceptance and utilization of AI technologies.
6. Conclusion
The DIKWP Artificial Consciousness Model represents a significant leap forward in AI development and governance. By addressing the limitations of black box AI systems and promoting transparency through white box methodologies, it paves the way for more ethical, trustworthy, and effective AI applications.
With a robust patent portfolio valued at approximately $355 million, Professor Yucong Duan’s innovations stand at the forefront of this transformation. The integration of semantic understanding, ethical reasoning, and purposeful alignment within AI systems not only solves current challenges but also sets new standards for the future of AI governance.
As industries and governments grapple with the complexities of AI implementation, the DIKWP model offers a comprehensive solution that aligns technological advancement with human values and societal goals. Embracing this model will lead to AI systems that are transparent, accountable, and beneficial to society as a whole.
7. References
Duan, Yucong. DIKWP Artificial Consciousness Authorized Invention Patent Comprehensive Business Report.
International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation (DIKWP-SC). (2024). DIKWP Semantic Mathematics Standardization for International Testing and Evaluation Standards Based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Model.
Duan, Yucong. (2022). “The End of Art – Subjective Objectification in DIKWP Philosophy.” ResearchGate.
Duan, Yucong. (2023). “Mathematical Paradoxes in AI Semantics.”
Additional patents and publications by Professor Yucong Duan related to the DIKWP model and its applications.
8. Appendices8.1 Detailed Patent List with Valuations
(Refer to the full patent list provided in the original document, detailing each patent’s number, title, category, valuation, and reasoning.)
8.2 Summary Analysis Tables
Table 1: Valuation Summary by Patent Category
| DIKWP Framework & Applications | 25 | $86,000,000 | $3,440,000 |
| Artificial Consciousness & Ethical AI | 15 | $54,500,000 | $3,633,333 |
| DIKWP-TRIZ and Semantic Mathematics | 12 | $42,500,000 | $3,541,667 |
| White-Box AI Testing via DIKWP | 10 | $51,500,000 | $5,150,000 |
| DIKWP in Blockchain Operations | 10 | $58,500,000 | $5,850,000 |
| Semantic Communication & Digital Governance | 10 | $30,000,000 | $3,000,000 |
| Other AI & Machine Learning Applications | 9 | $32,000,000 | $3,555,556 |
| Total | 91 | $355,000,000 |
Table 2: High-Value Patents (Valuation Over $5 Million)
(Refer to the key patents listed in section 3.)
Table 3: Revenue Projections Over Three Years
| Licensing Agreements | $857.5 million | $857.5 million | $857.5 million | $2,572.5 million |
| Product Development & Sales | $50 million | $100 million | $150 million | $300 million |
| Consulting Services | $20 million | $20 million | $20 million | $60 million |
| Research Grants & Funding | $10 million | $10 million | $10 million | $30 million |
| Total Revenue | $937.5 million | $987.5 million | $1,037.5 million | $2,962.5 million |
Table 4: Cost Projections Over Three Years
| Patent Maintenance & Legal Fees | $3 million | $3 million | $3 million | $9 million |
| Research & Development | $30 million | $35 million | $40 million | $105 million |
| Operational Expenses | $15 million | $20 million | $25 million | $60 million |
| Marketing & Sales | $20 million | $25 million | $30 million | $75 million |
| Contingency & Miscellaneous | $5 million | $5 million | $5 million | $15 million |
| Total Costs | $73 million | $88 million | $103 million | $264 million |
Table 5: Net Profit Calculations Over Three Years
| Year 1 | $937.5 million | $73 million | $864.5 million |
| Year 2 | $987.5 million | $88 million | $899.5 million |
| Year 3 | $1,037.5 million | $103 million | $934.5 million |
| Total | $2,962.5 million | $264 million | $2,698.5 million |
8.3 Glossary of Terms
DIKWP Model: An extended framework of Data-Information-Knowledge-Wisdom-Purpose.
Artificial Consciousness: AI systems that simulate aspects of human consciousness using DIKWP interactions.
Ethical AI: AI systems that incorporate ethical reasoning into their decision-making processes.
TRIZ: A problem-solving, analysis, and forecasting tool derived from the study of patterns of invention in patents.
DIKWP-TRIZ: An innovative problem-solving methodology integrating the DIKWP framework into the TRIZ model.
Semantic Mathematics for AI: A mathematical framework tailored for AI development within the DIKWP model.
White-Box Testing: A testing methodology examining the internal workings of an application.
Semantic Communication: Communication leveraging semantic understanding to enhance clarity and efficiency.
Metaverse: A collective virtual shared space, created by the convergence of virtually enhanced physical reality and persistent virtual reality.
8.4 Contact Information
Professor Yucong Duan
Email: [email protected]
Affiliations:
International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation (DIKWP-SC)
World Artificial Consciousness CIC (WAC)
World Conference on Artificial Consciousness (WCAC)
This report provides a comprehensive analysis of the DIKWP Artificial Consciousness Model, its innovations, commercial valuation, and impact on AI governance. By embracing the ‘white box’ era, the model sets a new standard for transparency, ethical alignment, and purposeful AI development.