Modified Evolutionary DIKWP Semantic Mathematics

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)

Abstract

This document presents a comprehensive and detailed proposal of the modified Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Semantic Mathematics framework, based on the investigations and insights from previous discussions. Prof. Yucong Duan has identified a paradox in traditional mathematics regarding AI semantics and proposes revolutionary changes to align mathematics more closely with real-world semantics. The modified DIKWP Semantic Mathematics framework addresses these issues by constructing mathematics in an evolutionary manner, mirroring human cognitive development, and emphasizing the intrinsic integration of semantics into mathematical constructs. This proposal details the foundational principles, formal definitions, enhancements, implementation strategies, and potential applications of the modified framework, aiming to revolutionize the mathematical foundation for artificial intelligence.

The development of artificial intelligence (AI) has long relied on traditional mathematics as its foundation. However, traditional mathematics often abstracts away from real-world semantics, focusing on pure forms and structures detached from the meanings they represent. Prof. Yucong Duan identifies a paradox in this approach:

Paradox of Mathematics in AI Semantics: Traditional mathematics, based on abstractions of real semantics, aims to achieve genuine AI understanding that inherently requires real semantics. This detachment hinders AI from truly comprehending and interacting with the world as humans do.

To address this paradox, Prof. Duan proposes modifications to the DIKWP Semantic Mathematics framework, emphasizing the need for mathematics to conform to basic semantics and integrate human cognitive processes explicitly.

• Conformity to Basic Semantics: Mathematics should be grounded in the fundamental semantics of the real world.

• Inclusion of Human Cognitive Processes: Recognize that mathematics is a product of human thought and should explicitly consider human cognition and interaction.

• Priority of Semantics over Pure Forms: Semantics should take precedence over abstract forms, ensuring mathematical constructs are meaningful and aligned with reality.

• Evolutionary Construction: Build the mathematical framework in an evolutionary manner, mirroring the cognitive development of an infant, to develop a comprehensive cognitive semantic space.

The modified framework retains the three fundamental semantics but emphasizes their evolutionary development and integration:

1. Sameness (Data): Recognition of shared attributes or identities between entities.

2. Difference (Information): Identification of distinctions or disparities between entities.

3. Completeness (Knowledge): Integration of all relevant attributes and relationships to form holistic concepts.

• Infant Cognitive Development Model: The framework models the cognitive development of an infant, starting from basic sensory experiences and gradually building complex concepts and semantics.

• Cognitive Semantic Space: Constructs a comprehensive space where every concept is formally bundled with semantics evolved from the fundamental semantics.

• Explicit Inclusion of Abstraction: Recognize that abstraction is a cognitive process dependent on human reasoning, both conscious and subconscious.

• "BUG" Theory of Consciousness Forming: Incorporate Prof. Duan's theory that "bugs" or inconsistencies in reasoning contribute to the formation of consciousness, highlighting the importance of cognitive processes in mathematical development.

• Human Interaction: Emphasize the role of human interaction in shaping semantics and knowledge.

• Semantics as the Foundation: Mathematical constructs should emerge from semantics, ensuring they are meaningful and relevant to real-world understanding.

• Alignment with Reality: Mathematics should represent the reality of the world by adhering closely to semantics, rather than abstracting away from them.

1. Perceptual Stage: Initial recognition of sensory inputs (e.g., shapes, sounds).

2. Conceptual Stage: Formation of basic concepts through the association of sensory inputs.

3. Relational Stage: Understanding relationships between concepts (e.g., cause-effect, hierarchy).

4. Abstract Stage: Ability to think abstractly, generalize, and form higher-level concepts.

• Incremental Semantics Building: Start with basic semantic units and gradually build complex constructs through iterative processes.

• Formal Bundling: Each concept is formally associated with its evolved semantics, ensuring that meanings are preserved and explicit.

1. Contextuality: Recognition that meaning is influenced by context.

2. Temporality: Incorporation of temporal aspects, acknowledging that meanings can change over time.

3. Intentionality: Understanding that purposes and intentions influence meanings.

• Contextual Semantics (CS):

  $$CS(e,C)=s$$

  Where e is an expression, C is the context, and s is the semantic representation.

• Temporal Semantics (TS):

  $$TS(e,t)=s$$

  Where t is the time parameter.

• Intentional Semantics (IS):

  $$IS(e,I)=s$$

  Where I represents the intention or purpose behind the expression.

• Abstraction as Completeness: Abstraction is viewed as a cognitive process that seeks completeness, integrating multiple concepts into a generalized form.

• Cognitive Functions:

• Conscious Reasoning: Deliberate thought processes contributing to abstraction.

• Subconscious Processing: Implicit cognitive functions influencing understanding.

• Definition: "Bugs" are inconsistencies or gaps in reasoning that prompt cognitive growth.

• Role in Consciousness: These "bugs" lead to reflection and adaptation, contributing to the development of consciousness.

• Application in the Framework:

• Error Detection: Identifying inconsistencies in semantic representations.

• Adaptive Learning: Adjusting and refining semantics based on detected "bugs."

• Semantic Foundations: Mathematical constructs are developed based on semantic relationships and meanings.

• Form Following Function: The form of mathematical expressions is determined by the semantics they represent.

• Set Theory with Semantics:

• Traditional set theory focuses on elements and their membership in sets.

• In the modified framework, sets are defined not only by their elements but also by the semantic relationships between them.

• Function Representation:

• Functions are not just mappings from inputs to outputs but are associated with the semantics of the transformation they perform.

• Entities (E): Basic units with inherent semantics.

• Attributes (A): Properties or characteristics of entities.

• Relations (R): Semantic connections between entities.

• Entity:

  E={ei∣ei is an entity with semantic content si}E = \{ e_i \mid e_i \text{ is an entity with semantic content } s_i \}E={ei​∣ei​ is an entity with semantic content si​}

• Attribute:

  A={aj∣aj is an attribute of entities in E}A = \{ a_j \mid a_j \text{ is an attribute of entities in } E \}A={aj​∣aj​ is an attribute of entities in E}

• Relation:

  R={rkl∣rkl is a relation between ek and el}R = \{ r_{kl} \mid r_{kl} \text{ is a relation between } e_k \text{ and } e_l \}R={rkl​∣rkl​ is a relation between ek​ and el​}

1. Aggregation (AGG): Combining entities or attributes to form a composite entity.

   AGG(e1,e2,...,en)=ecompositeAGG(e_1, e_2, ..., e_n) = e_{composite}AGG(e1​,e2​,...,en​)=ecomposite​

2. Specialization (SPEC): Deriving a more specific entity from a general one by adding attributes.

   SPEC(egeneral,aadditional)=especificSPEC(e_{general}, a_{additional}) = e_{specific}SPEC(egeneral​,aadditional​)=especific​

3. Association (ASSOC): Establishing a relationship between entities.

   ASSOC(ek,el,rkl)=Relationship instanceASSOC(e_k, e_l, r_{kl}) = \text{Relationship instance}ASSOC(ek​,el​,rkl​)=Relationship instance

• Consistency Constraints: Ensure that semantic representations do not contradict established meanings.

• Contextual Constraints: Meanings must be consistent within the given context.

• Nodes: Represent entities with semantic content.

• Edges: Represent semantic relations between entities.

• Semantic Connectivity: Degree to which entities are semantically related.

• Semantic Distance: Measure of dissimilarity between entities based on their attributes and relations.

• Initialization: Start with basic semantic elements derived from fundamental experiences.

• Iteration: Apply semantic operations to build more complex concepts.

• Evaluation: Assess semantic representations for consistency and completeness.

• Supervised Learning: Use human feedback to guide semantic development.

• Unsupervised Learning: Allow the system to discover patterns and relationships autonomously.

• Reinforcement Learning: Employ rewards and penalties to shape semantic evolution.

• Communication Protocols: Establish standards for semantic representation to ensure alignment between humans and AI systems.

• Shared Cognitive Development: Facilitate AI systems to develop semantics in ways similar to human cognitive processes.

• Continuous Learning: Incorporate human feedback to refine semantics continually.

• Error Correction: Use detected "bugs" to adjust and improve the system's understanding.

• Modular Design: Break down the cognitive semantic space into manageable modules.

• Distributed Computing: Utilize parallel processing and cloud computing to handle large-scale semantic data.

• Semantic Compression: Reduce redundancy by identifying and merging similar semantic elements.

• Indexing and Retrieval: Implement efficient data structures for quick access to semantic representations.

• Natural Language Understanding (NLU): Improved comprehension of language through semantically rich representations.

• Context-Aware Systems: AI that can interpret and respond appropriately based on context.

• Ontologies: Development of more accurate and semantically meaningful ontologies.

• Automated Reasoning: Enhanced ability to draw inferences and conclusions from semantic data.

• Intuitive Interfaces: Systems that interact with users in more natural and meaningful ways.

• Personalized Experiences: AI that understands individual user's semantics for tailored interactions.

• Cognitive Modeling: Better understanding of human cognition through modeling semantic development.

• Educational Tools: AI systems that adapt to learners' semantic understanding.

• Challenge: Handling the vastness and complexity of natural language semantics.

• Solution: Employ hierarchical structures and modular approaches to manage complexity.

• Challenge: Aligning the modified framework with existing mathematical constructs.

• Solution: Use the modified framework as a foundational layer, upon which traditional mathematics can be built, ensuring that semantics are preserved.

• Challenge: Ensuring the correctness and reliability of semantic representations.

• Solution: Implement rigorous testing, peer review, and formal verification methods.

• Challenge: Addressing concerns related to AI ethics, bias, and privacy.

• Solution: Incorporate ethical guidelines into the development process, ensure transparency, and involve diverse stakeholders.

• Example: Formation of the concept "Bird"

1. Perceptual Stage: Recognition of sensory inputs (e.g., feathers, wings, flight).

2. Conceptual Stage: Associating these inputs to form the concept of "bird."

3. Relational Stage: Understanding relationships (e.g., "birds lay eggs," "birds can fly").

4. Abstract Stage: Generalizing to include flightless birds (e.g., "penguins are birds").

• Formal Representation:

• Entity: E_bird

• Attributes: {a_feathers, a_wings, a_beak}

• Relations: R_canFly(E_bird), R_layEggs(E_bird)

• Scenario: Two AI systems developed under the modified framework communicating about "bank."

• Financial Context: CS("bank", C_finance) = E_financialInstitution

• Environmental Context: CS("bank", C_environment) = E_riverBank

• Contextual Semantics:

• Outcome: Both systems correctly interpret "bank" based on the shared context, avoiding misunderstandings.

The modified DIKWP Semantic Mathematics framework represents a significant shift in how mathematics is approached, particularly in the context of artificial intelligence. By grounding mathematical constructs in fundamental semantics and modeling cognitive development, the framework addresses the paradox identified by Prof. Yucong Duan, where traditional abstract mathematics fails to achieve semantic-rich AI understanding.

Key aspects of the modified framework include:

• Evolutionary Construction: Building semantics in a manner that mirrors human cognitive development.

• Integration of Human Cognitive Processes: Recognizing and incorporating the role of human cognition in mathematical development.

• Priority of Semantics: Ensuring that semantics take precedence over pure forms in mathematical constructs.

• Alignment with Reality: Mathematics is closely tied to real-world semantics, enhancing AI's ability to comprehend and interact meaningfully with the world.

By addressing the limitations of traditional mathematics and emphasizing the intrinsic integration of semantics, the modified DIKWP Semantic Mathematics framework holds promise for advancing AI development, improving human-AI interaction, and contributing to our understanding of cognition and knowledge representation.

• Software Implementation: Develop prototypes to test and refine the framework's practical applicability.

• Pilot Studies: Conduct experiments to evaluate the framework's effectiveness in real-world scenarios.

• Cognitive Scientists: Collaborate to ensure the framework aligns with current understanding of human cognition.

• Linguists: Work together to capture the nuances of natural language semantics.

• AI Researchers: Integrate the framework into AI systems and assess performance improvements.

• Feedback Integration: Incorporate insights from practitioners and users to refine the framework.

• Scalability Enhancements: Explore methods to handle larger and more complex semantic datasets.

1. 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. ".

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5. Chalmers, D. J. (1995). Facing Up to the Problem of Consciousness. Journal of Consciousness Studies, 2(3), 200-219.

6. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

7. Smith, B., & Mark, D. M. (2003). Do Mountains Exist? Towards an Ontology of Landforms. Environment and Planning B: Planning and Design, 30(3), 411-427.

8. Sowa, J. F. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole.

I extend sincere gratitude to Prof. Yucong Duan for his pioneering work on the DIKWP Semantic Mathematics framework and for proposing the modifications that have inspired this detailed proposal. Appreciation is also given to researchers in cognitive science, philosophy, artificial intelligence, and related fields whose contributions have informed and enriched this work.

For further discussion on the modified DIKWP Semantic Mathematics framework and its applications, please contact [Author's Name] at [Contact Information].

Keywords: DIKWP Semantic Mathematics, Modified Framework, Cognitive Semantic Space, Evolutionary Construction, Fundamental Semantics, Human Cognitive Processes, Semantics Priority, Prof. Yucong Duan, Artificial Intelligence, Knowledge Representation, Mathematical Revolution