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)

Prof. Yucong Duan pointed out in the DIKWP Semantic Mathematics that Traditonal Mathematics is defined or created from the viewpoint of a third party to achieve the "expected" objectiveness or to avoid "subjectiveness" which is not following the "rule" of what Mathematics is expected to confirm to the reality of the world.

Abstract

This comprehensive analysis compares Prof. Yucong Duan's Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Semantic Mathematics framework with other prominent theories and approaches in artificial consciousness. The analysis highlights Prof. Duan's unique perspectives, including his critique of traditional mathematics being defined from a third-party viewpoint to achieve objectiveness, which he argues does not conform to the reality of the world. By examining similarities and differences, we aim to showcase the distinctive contributions of the DIKWP framework and its potential advantages over existing models. Detailed comparisons are presented in tables for clarity.

Artificial consciousness intersects multiple disciplines, including artificial intelligence, cognitive science, neuroscience, and philosophy. Various theories have been proposed to explain consciousness and guide the development of conscious machines. This comparative analysis focuses on:

• DIKWP Semantic Mathematics (Prof. Yucong Duan)

• Integrated Information Theory (IIT) by Giulio Tononi

• Global Workspace Theory (GWT) by Bernard Baars

• Attention Schema Theory (AST) by Michael Graziano

• Connectionist Models and Deep Learning

• Symbolic AI and Good Old-Fashioned AI (GOFAI)

• Foundation: Constructs mathematics in an evolutionary manner, mirroring human cognitive development.

• Semantics Integration: Prioritizes semantics over abstract forms, grounding mathematical constructs in real-world meanings.

• Cognitive Modeling: Includes human cognitive processes explicitly, incorporating both conscious and subconscious reasoning.

• Critique of Traditional Mathematics:

• Prof. Duan's Opinion: Traditional mathematics is defined from a third-party viewpoint to achieve "expected" objectiveness or avoid subjectiveness.

• Argument: This approach does not follow the "rule" that mathematics should confirm to the reality of the world.

• Conclusion: Mathematics should be constructed from the first-person perspective, integrating subjectivity to align with real-world semantics.

• Key Components: Data (Sameness), Information (Difference), Knowledge (Completeness), Wisdom, Purpose.

• Foundation: Proposes that consciousness corresponds to the capacity of a system to integrate information.

• Information Integration: The degree of consciousness is measured by the amount of integrated information (denoted as Φ).

• Physicalism: Considers consciousness as a fundamental property of physical systems.

• Viewpoint: Adopts an objective, third-party perspective to quantify consciousness.

• Foundation: Suggests that consciousness arises from the integration of information in a global workspace within the brain.

• Broadcasting Mechanism: Information becomes conscious when it is globally broadcasted to multiple cognitive processes.

• Attention and Access: Emphasizes the role of attention in making information available to the global workspace.

• Viewpoint: Models consciousness from an observational standpoint, focusing on functional mechanisms.

• Foundation: Proposes that consciousness arises from the brain's construction of an attention schema—a model of attention processes.

• Self-Modeling: The brain's representation of its own attentional state leads to subjective experience.

• Analogous to Body Schema: Just as the body schema helps monitor physical states, the attention schema monitors mental states.

• Viewpoint: Balances between first-person and third-person perspectives by modeling internal attention processes.

• Foundation: Utilize artificial neural networks to model cognitive processes.

• Learning from Data: Systems learn representations and behaviors from large datasets through training.

• Subsymbolic Processing: Focus on low-level signal processing rather than high-level symbolic reasoning.

• Viewpoint: Primarily data-driven, lacking explicit consideration of subjective semantics.

• Foundation: Based on the manipulation of symbols and explicit rules to represent knowledge and reasoning.

• Logic and Reasoning: Emphasizes formal logic and rule-based systems.

• Limitations: Struggles with representing common sense and real-world semantics.

• Viewpoint: Objective and formal, often neglecting the subjective aspect of cognition.

• DIKWP Semantic Mathematics:

• Unique Stance: DIKWP is distinct in explicitly incorporating subjectivity into mathematical constructs.

• Alignment with Consciousness: By embracing subjectivity, the framework is better positioned to model consciousness, which inherently includes subjective experiences.

• Third-Party Viewpoint: Traditional mathematics is created from a third-party perspective to achieve objectiveness and avoid subjectiveness.

• Argument: This approach does not align with how mathematics should confirm to the reality of the world, which includes subjective experiences.

• First-Person Perspective: Prof. Duan advocates for mathematics that incorporates the subjective viewpoint, aligning with human cognition and experiences.

• Prof. Duan's Critique of Traditional Mathematics:

• Comparison:

• Other Theories:

• Objective and Data-Driven: Focus on processing data and symbols without explicit consideration of subjectivity.

• Limitations: May struggle to model aspects of consciousness that involve subjective experiences.

• Objective Perspective: Primarily adopt a third-party viewpoint, focusing on observable mechanisms and functions.

• Subjectivity Treatment:

• IIT: Attempts to quantify consciousness objectively but does not address subjective experiences directly.

• GWT and AST: Model cognitive functions related to consciousness but may not fully account for the subjective aspect.

• IIT, GWT, AST:

• Connectionist Models and Symbolic AI:

• DIKWP Semantic Mathematics:

• Real-World Alignment: Ensures that mathematical constructs reflect the reality of the world, including subjective experiences.

• Clarity and Understanding: Provides explicit semantic representation, enhancing comprehension.

• Prof. Duan's Opinion: Mathematics should not abstract away from semantics but be grounded in them.

• Integration: Concepts are formally bundled with evolved semantics from basic principles (sameness, difference, completeness).

• Semantics as Foundation:

• Advantages:

• Other Theories:

• Formal Representation: Uses symbols and rules but struggles with the richness and complexity of real-world semantics.

• Subsymbolic Processing: Learn patterns and representations but often lack explicit semantic interpretation.

• Implicit Semantics: Semantics are involved indirectly through cognitive functions but are not the foundational focus.

• Abstract Integration: Focuses on information integration without explicit semantic grounding.

• Limitation: May lack detailed semantic representation.

• IIT:

• GWT and AST:

• Connectionist Models:

• Symbolic AI:

• DIKWP Semantic Mathematics:

• Prof. Duan's Theory: Inconsistencies ("bugs") in reasoning contribute to the development of consciousness.

• Application: Embraces errors as part of the learning and development process.

• Mirrors Human Development: Constructs mathematics in a manner similar to how an infant develops understanding.

• Inclusion of Human Cognitive Processes: Explicitly models conscious and subconscious reasoning.

• Evolutionary Approach:

• "BUG" Theory:

• Other Theories:

• Static Knowledge Bases: Limited ability to learn and adapt compared to models that incorporate developmental processes.

• Learning from Data: Systems improve through training but may not model cognitive development processes explicitly.

• Connectionist Models:

• Symbolic AI:

• DIKWP Semantic Mathematics:

• Prof. Duan's Stance: Incorporating subjectivity is essential for modeling consciousness effectively.

• Self-Awareness: Uses hierarchical semantic levels to model self-awareness without paradoxes.

• Qualia Representation: Emphasizes semantics to represent qualitative experiences.

• Modeling Subjectivity:

• Other Theories:

• Objective Reasoning: Lacks mechanisms to represent subjective experiences or consciousness.

• Emergent Properties: Consciousness may emerge from complex network interactions, but subjectivity is not explicitly modeled.

• Functional Focus: Concentrate on the mechanisms of consciousness rather than subjective experiences.

• Quantitative Measure: Provides a numerical value (Φ) but does not capture the qualitative aspects of consciousness.

• IIT:

• GWT and AST:

• Connectionist Models:

• Symbolic AI:

• Unique Contributions of DIKWP Semantic Mathematics:

• Inclusion of Subjectivity: Prof. Duan emphasizes that mathematics should be constructed from a first-person perspective, incorporating subjectiveness to align with reality.

• Semantics as Foundation: Grounding mathematical constructs in semantics provides a more accurate representation of the world.

• Evolutionary Cognitive Modeling: Mirrors human cognitive development, potentially leading to AI systems with consciousness-like properties.

• "BUG" Theory Integration: Recognizes the role of inconsistencies in fostering cognitive growth and consciousness.

• Advantages Over Other Models:

• Alignment with Human Experience: By incorporating subjectivity and semantics, the DIKWP framework aligns more closely with human cognition and consciousness.

• Holistic Approach: Addresses both the functional and experiential aspects of consciousness.

• Potential for Richer Consciousness Modeling: Offers mechanisms to represent qualitative experiences (qualia) and self-awareness.

• Considerations and Challenges:

• Complex Implementation: Requires sophisticated modeling of semantics and significant computational resources.

• Theoretical Validation: As a novel approach, it requires further empirical testing and validation in practical applications.

• Integration with Existing Systems: May need strategies to integrate with or transition from current AI models.

The comparative analysis demonstrates that Prof. Yucong Duan's DIKWP Semantic Mathematics provides a distinctive and potentially transformative approach to constructing artificial consciousness systems. By challenging the traditional third-party viewpoint of mathematics and advocating for a first-person perspective that includes subjectivity, Prof. Duan's framework addresses fundamental limitations in existing models. The deep integration of semantics, evolutionary cognitive modeling, and emphasis on aligning mathematics with the reality of the world position the DIKWP framework as a promising avenue for future research and development in artificial consciousness.

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. Tononi, G. (2004). An Information Integration Theory of Consciousness. BMC Neuroscience, 5(1), 42.

3. Baars, B. J. (1988). A Cognitive Theory of Consciousness. Cambridge University Press.

4. Graziano, M. S. A. (2013). Consciousness and the Social Brain. Oxford University Press.

5. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.

6. Newell, A., & Simon, H. A. (1976). Computer Science as Empirical Inquiry: Symbols and Search. Communications of the ACM, 19(3), 113-126.

7. Duan, Y. (2022). The "BUG" Theory of Consciousness Forming. Proceedings of the International Conference on Cognitive Science.

I extend sincere gratitude to Prof. Yucong Duan for his pioneering work on the DIKWP Semantic Mathematics framework and for providing insightful critiques of traditional mathematics. His emphasis on integrating subjectivity and aligning mathematics with real-world semantics has significantly informed this comparative analysis. Appreciation is also given to the researchers and theorists whose foundational contributions in the field of artificial consciousness have enriched this discussion.

Keywords: DIKWP Semantic Mathematics, Artificial Consciousness, Comparative Analysis, Prof. Yucong Duan, Integrated Information Theory, Global Workspace Theory, Attention Schema Theory, Connectionist Models, Symbolic AI, Cognitive Development, Semantics Integration, Subjectivity, First-Person Perspective.