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Mathematical Mapping with DIKWP of Current Philosophy（初学者版）

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The Mathematical Mappings within the DIKWP of The 12 Main Problems in Current Philosophy

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)

Introduction

The 12 philoshophical problems by Prof. Yucong Duan are central to contemporary thought, touching on metaphysics, epistemology, ethics, and the philosophy of mind, among other areas. To explore these problems through the lens of the mathematical DIKWP (Data-Information-Knowledge-Wisdom-Purpose) model defined by Prof. Yucong Duan, we will delve into each issue with a detailed introduction followed by a mathematical mapping within the DIKWP framework.

The DIKWP model extends the traditional Data-Information-Knowledge-Wisdom (DIKW) hierarchy by adding Purpose, emphasizing goal-oriented cognitive processes. It operates across three interconnected spaces: Conceptual Space, Cognitive Space, and Semantic Space, providing a structured approach to understanding complex cognitive phenomena.

In this analysis, we'll examine how each philosophical problem can be understood and analyzed using the DIKWP model, highlighting the transformation from raw data to purposeful wisdom, guided by specific goals or purposes.

1. The Mind-Body Problem

Introduction

The mind-body problem is a fundamental question in philosophy: How do mental states, such as beliefs, desires, and sensations, relate to physical states of the body and brain? It explores whether the mind and body are distinct substances (dualism) or whether mental states are entirely physical (physicalism). This issue has significant implications for our understanding of consciousness, personal identity, and the nature of reality.

Application of the DIKWP Model

Data (D): Empirical observations of brain activity (e.g., neural firing patterns) and reports of subjective experiences (e.g., pain, emotions).

Mathematical Representation: $D = \{d_1, d_2, ..., d_n\}$ Each $d_i$ represents a specific data point, such as a neural measurement or subjective report.

Information (I): Correlations identified between brain states and mental states.

Transformation Function: $I = T_{DI}(D)$ Where $T_{DI}$ is the function transforming data into information by identifying patterns.
Resulting Information Set: $I = \{i_1, i_2, ..., i_m\}$ Each $i_j$ represents a specific correlation or pattern.

Knowledge (K): Theoretical frameworks explaining the mind-body relationship, such as dualism, physicalism, or emergentism.

Transformation Function: $K = T_{IK}(I)$ Where $T_{IK}$ abstracts information into knowledge through theorization.
Knowledge Graph (KG): $KG = (V_K, E_K)$

$V_K$ : Set of concepts (e.g., "consciousness," "brain states").
$E_K$ : Relationships between concepts (e.g., causation, correlation).

Wisdom (W): Ethical and existential considerations arising from these theories, such as the implications for free will, responsibility, and personal identity.

Transformation Function: $W = T_{KW}(K)$ Where $T_{KW}$ integrates knowledge with ethical values.
Wisdom Graph (WG): $WG = (V_W, E_W)$ Reflecting how knowledge influences ethical decisions.

Purpose (P): Aiming to understand consciousness to enhance well-being, inform medical treatments, or develop artificial intelligence.

Purpose Tuple: $P = (I n p u t, O u tp u t)$

Input: Current understanding from $D$ , $I$ , $K$ , and $W$ .
Output: Desired outcomes, such as effective therapies or ethical AI.

Mathematical Mapping

Data to Information: $TDI:D→IT_{DI}: D \rightarrow I$ Extract patterns from data.
Information to Knowledge: $TIK:I→KT_{IK}: I \rightarrow K$ Formulate theories.
Knowledge to Wisdom: $TKW:K→WT_{KW}: K \rightarrow W$ Consider ethical implications.
Wisdom to Purpose: $TWP:W→PT_{WP}: W \rightarrow P$ Define goals and applications.

Summary

By applying the DIKWP model, we can systematically map the mind-body problem from empirical data to purposeful outcomes. This framework highlights the interconnectedness of observations, theoretical understanding, ethical considerations, and practical goals.

2. The Hard Problem of Consciousness

Introduction

The hard problem of consciousness, introduced by David Chalmers, addresses why and how physical processes in the brain give rise to subjective experiences, known as qualia. Unlike the "easy problems" that involve explaining cognitive functions and behaviors, the hard problem delves into the essence of consciousness and the nature of experience.

Application of the DIKWP Model

Data (D): Neural correlates of consciousness, such as brain imaging data, and subjective reports of experiences.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Each $d_i$ includes both neural data and associated qualia.

Information (I): Recognition of the explanatory gap between physical processes and subjective experiences.

Transformation Function: $I = T_{DI}(D)$ Identifying inconsistencies and gaps.
Information Set: $I = \{i_1, ..., i_m\}$ Highlighting areas lacking explanation.

Knowledge (K): Philosophical theories attempting to bridge the gap, such as panpsychism or the integrated information theory.

Transformation Function: $K = T_{IK}(I)$ Developing new models and hypotheses.
Knowledge Graph: $KG = (V_K, E_K)$ Mapping concepts and their interrelations.

Wisdom (W): Reflecting on the limitations of current approaches and the need for interdisciplinary research.

Transformation Function: $W = T_{KW}(K)$ Incorporating philosophical insights with scientific findings.

Purpose (P): To achieve a comprehensive understanding of consciousness that can inform various fields.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ Aiming for breakthroughs in neuroscience, AI, and philosophy.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Emphasis on New Knowledge:Developing innovative theories to address the explanatory gap.

Summary

The DIKWP model structures the hard problem by tracing the path from data to purposeful wisdom, emphasizing the creation of new knowledge and the integration of wisdom to guide future research.

3. Free Will vs. Determinism

Introduction

The debate over free will versus determinism questions whether human beings have genuine freedom to make choices or if our actions are predetermined by prior states of the universe. Determinism suggests that every event is necessitated by antecedent events and conditions, together with the laws of nature. Free will implies that individuals can initiate actions independently of such causal chains.

Application of the DIKWP Model

Data (D): Observations of human decision-making, psychological experiments, and neuroscientific findings indicating pre-conscious neural activity before conscious decisions.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Where $d_i$ includes experimental data on decision-making processes.

Information (I): Patterns indicating potential determinism in human actions.

Transformation Function: $I = T_{DI}(D)$ Extracting trends and correlations.

Knowledge (K): Philosophical positions such as compatibilism (free will is compatible with determinism), libertarianism (free will exists and determinism is false), and hard determinism (free will doesn't exist).

Transformation Function: $K = T_{IK}(I)$ Developing arguments and counterarguments.
Knowledge Graph: $KG = (V_K, E_K)$ Illustrating relationships between concepts like causation, freedom, and responsibility.

Wisdom (W): Assessing the implications for moral responsibility, ethics, and legal systems.

Transformation Function: $W = T_{KW}(K)$ Integrating knowledge with societal values.

Purpose (P): To clarify the nature of human agency and inform ethical practices, legal accountability, and personal development.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ With the goal of aligning philosophical understanding with practical applications.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Ethical Integration:Wisdom assesses how different views impact societal norms.

Summary

Using the DIKWP model, we can see how empirical data and observed patterns inform philosophical debates, which in turn influence ethical considerations and purposeful actions within society.

4. Ethical Relativism vs. Objective Morality

Introduction

This debate centers on whether moral principles are universal and absolute or whether they are culturally and contextually dependent. Ethical relativism suggests that what is morally right or wrong can vary between societies and that there are no universal moral standards. Objective morality holds that certain actions are right or wrong independently of human opinion.

Application of the DIKWP Model

Data (D): Documentation of diverse moral practices and codes across different cultures and historical periods.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Each $d_i$ represents a specific cultural moral practice.

Information (I): Comparative analysis highlighting similarities and differences in moral codes.

Transformation Function: $I = T_{DI}(D)$ Identifying patterns of moral beliefs.

Knowledge (K): Ethical theories such as moral relativism, moral absolutism, and pluralism.

Transformation Function: $K = T_{IK}(I)$ Formulating theoretical frameworks.
Knowledge Graph: $KG = (V_K, E_K)$ Mapping ethical concepts and their interrelations.

Wisdom (W): Promoting cross-cultural understanding, tolerance, and the potential for universal human rights.

Transformation Function: $W = T_{KW}(K)$ Integrating knowledge with global ethical considerations.

Purpose (P): To establish ethical principles that respect cultural diversity while promoting global justice and well-being.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ Aiming for ethical frameworks applicable in a global context.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Emphasis on Tolerance:Wisdom guides the application of knowledge to promote harmony.

Summary

Through the DIKWP model, we analyze how data on moral practices informs ethical theories, which are then applied wisely to achieve the purpose of global ethical standards that balance respect for diversity with universal values.

5. The Nature of Truth

Introduction

Philosophers debate whether truth is objective and corresponds to reality or if it's a construct influenced by language, culture, or practical outcomes. Major theories include the correspondence theory (truth corresponds to reality), coherence theory (truth is coherence within a set of beliefs), and pragmatist theory (truth is what works in practice).

Application of the DIKWP Model

Data (D): Statements, propositions, and empirical observations.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Where $d_i$ represents individual assertions or observations.

Information (I): Analysis of these statements for consistency, coherence, and correspondence.

Transformation Function: $I = T_{DI}(D)$ Evaluating truth claims.

Knowledge (K): Theories of truth and epistemological frameworks.

Transformation Function: $K = T_{IK}(I)$ Developing philosophical positions.
Knowledge Graph: $KG = (V_K, E_K)$ Connecting concepts like reality, belief, and justification.

Wisdom (W): Understanding the role of truth in communication, science, and societal progress.

Transformation Function: $W = T_{KW}(K)$ Applying knowledge to enhance discourse and understanding.

Purpose (P): To promote clarity, reduce misunderstandings, and advance knowledge.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ With the goal of fostering truthful communication and reliable knowledge acquisition.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Focus on Application:Wisdom guides the practical use of theories of truth.

Summary

By applying the DIKWP model, we trace the progression from data (statements) to the purpose of enhancing truthfulness in society, highlighting the importance of wisdom in applying knowledge about truth.

6. The Problem of Skepticism

Introduction

Skepticism questions whether we can have any certain knowledge about the world. It challenges the possibility of knowledge by pointing out the potential for error, illusion, or deception (e.g., "brain in a vat" scenarios). Philosophers seek ways to overcome skepticism to establish a secure foundation for knowledge.

Application of the DIKWP Model

Data (D): Instances of perceptual errors, illusions, and thought experiments highlighting doubt.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Each $d_i$ illustrates a reason for doubt.

Information (I): Recognition of the limits of perception and cognition.

Transformation Function: $I = T_{DI}(D)$ Understanding the sources of uncertainty.

Knowledge (K): Epistemological theories like foundationalism, coherentism, and contextualism that attempt to address skepticism.

Transformation Function: $K = T_{IK}(I)$ Proposing solutions to skepticism.
Knowledge Graph: $KG = (V_K, E_K)$ Mapping epistemological concepts.

Wisdom (W): Accepting practical limitations while finding ways to function effectively despite uncertainty.

Transformation Function: $W = T_{KW}(K)$ Balancing skepticism with practical reasoning.

Purpose (P): To establish reliable methods for acquiring knowledge and advancing learning.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ Aiming to overcome skepticism for practical progress.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Practical Wisdom:Applying knowledge to mitigate skepticism's impact.

Summary

Using the DIKWP model, we can systematically address skepticism by transforming data about doubt into purposeful wisdom that guides the pursuit of knowledge despite inherent uncertainties.

7. The Problem of Induction

Introduction

David Hume highlighted the problem of induction, questioning whether inductive reasoning (drawing general conclusions from specific observations) is justified. Since past occurrences don't necessarily guarantee future events, the logical basis of induction is challenged, affecting the justification of scientific reasoning.

Application of the DIKWP Model

Data (D): Specific observations and empirical instances (e.g., the sun rising every day).

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Each $d_i$ is an observed event.

Information (I): Generalizations and patterns identified from the data.

Transformation Function: $I = T_{DI}(D)$ Forming inductive inferences.

Knowledge (K): Philosophical analyses of induction, including attempts to justify it (e.g., pragmatism, falsificationism).

Transformation Function: $K = T_{IK}(I)$ Developing theories about inductive reasoning.
Knowledge Graph: $KG = (V_K, E_K)$ Linking concepts like causality and probability.

Wisdom (W): Understanding the limitations and practical necessity of induction in science and daily life.

Transformation Function: $W = T_{KW}(K)$ Applying knowledge to guide scientific methodology.

Purpose (P): To justify or improve inductive methods for reliable prediction and explanation.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ Enhancing the foundations of scientific inquiry.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Emphasis on Justification:Seeking ways to support the use of induction.

Summary

Through the DIKWP model, we can understand the problem of induction by mapping the process from data to purpose, highlighting the need for wisdom in acknowledging limitations while finding practical solutions.

8. Realism vs. Anti-Realism

Introduction

This debate questions whether entities like universals, numbers, or moral values exist independently of our minds (realism) or are constructed by human thought and language (anti-realism). It has implications for metaphysics, epistemology, and the philosophy of science.

Application of the DIKWP Model

Data (D): Perceptions, experiences, and scientific observations.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Where $d_i$ includes empirical data and subjective experiences.

Information (I): Interpretations of data suggesting either an independent reality or a constructed one.

Transformation Function: $I = T_{DI}(D)$ Analyzing perceptions and observations.

Knowledge (K): Philosophical positions like scientific realism, idealism, and constructivism.

Transformation Function: $K = T_{IK}(I)$ Developing arguments for or against realism.
Knowledge Graph: $KG = (V_K, E_K)$ Mapping ontological concepts.

Wisdom (W): Evaluating the implications of these positions for science, ethics, and everyday life.

Transformation Function: $W = T_{KW}(K)$ Considering practical consequences.

Purpose (P): To understand the nature of reality and inform philosophical and scientific endeavors.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ Aiming for coherent and useful ontological frameworks.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Integration of Perspectives:Wisdom helps reconcile different views.

Summary

Applying the DIKWP model, we can explore the realism vs. anti-realism debate by tracing how interpretations of data lead to knowledge positions, which are then assessed wisely to serve the purpose of understanding reality.

9. The Meaning of Life

Introduction

The question of life's meaning addresses why we exist and what purpose our lives serve. Philosophers and thinkers have proposed various answers, ranging from fulfilling a divine plan, achieving happiness, contributing to society, or creating one's own meaning.

Application of the DIKWP Model

Data (D): Human experiences, emotions, and actions.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Each $d_i$ represents an aspect of human life.

Information (I): Patterns in human aspirations, desires, and pursuits.

Transformation Function: $I = T_{DI}(D)$ Identifying common themes.

Knowledge (K): Philosophical and religious doctrines about life's purpose, such as existentialism, nihilism, or spiritual beliefs.

Transformation Function: $K = T_{IK}(I)$ Formulating theories of meaning.
Knowledge Graph: $KG = (V_K, E_K)$ Mapping different philosophies.

Wisdom (W): Integrating perspectives to find personal and collective significance.

Transformation Function: $W = T_{KW}(K)$ Applying knowledge to life choices.

Purpose (P): Guiding individuals and societies toward fulfillment, ethical living, and well-being.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ Aiming for meaningful existence.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Personalization:Wisdom helps tailor meaning to individual lives.

Summary

Using the DIKWP model, we can understand the quest for meaning by mapping experiences to wisdom, guiding purposeful living.

10. The Role of Technology and AI

Introduction

The rapid advancement of technology and artificial intelligence raises philosophical questions about human identity, ethics, and the future of society. Concerns include the nature of consciousness in machines, ethical use of AI, and potential risks of superintelligence.

Application of the DIKWP Model

Data (D): Technological developments, AI capabilities, usage statistics.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Each $d_i$ includes data on AI performance.

Information (I): Analysis of AI's impact on various sectors.

Transformation Function: $I = T_{DI}(D)$ Understanding trends and effects.

Knowledge (K): Ethical frameworks, regulations, and philosophical considerations about AI.

Transformation Function: $K = T_{IK}(I)$ Developing guidelines and theories.
Knowledge Graph: $KG = (V_K, E_K)$ Mapping ethical principles and technological concepts.

Wisdom (W): Guiding principles for responsible development and deployment of AI.

Transformation Function: $W = T_{KW}(K)$ Balancing innovation with caution.

Purpose (P): Enhancing human life while mitigating risks associated with AI.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ Aiming for beneficial AI integration.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Ethical Emphasis:Wisdom ensures technology serves humanity.

Summary

The DIKWP model helps structure the analysis of technology's role by transforming data into purposeful wisdom, emphasizing ethical considerations.

11. Political and Social Justice

Introduction

Issues of inequality, discrimination, and the fair distribution of resources are central to political and social philosophy. Debates focus on how societies should be organized to promote justice, freedom, and well-being for all members.

Application of the DIKWP Model

Data (D): Socioeconomic data, case studies of injustice, demographic statistics.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Each $d_i$ represents evidence of social conditions.

Information (I): Identification of patterns and systemic issues.

Transformation Function: $I = T_{DI}(D)$ Analyzing root causes.

Knowledge (K): Theories of justice, rights, and governance (e.g., Rawls' theory of justice, libertarianism).

Transformation Function: $K = T_{IK}(I)$ Formulating political philosophies.
Knowledge Graph: $KG = (V_K, E_K)$ Mapping political concepts.

Wisdom (W): Crafting policies and strategies to promote equity and social cohesion.

Transformation Function: $W = T_{KW}(K)$ Applying knowledge to real-world governance.

Purpose (P): Achieving a just society with fair opportunities and treatment for all.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ Aiming for societal well-being.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Policy Implementation:Wisdom guides practical action.

Summary

By applying the DIKWP model, we can systematically address issues of social justice, transforming data into purposeful actions aimed at creating equitable societies.

12. Philosophy of Language

Introduction

The philosophy of language examines the nature of meaning, reference, and the relationship between language and reality. Philosophers like Wittgenstein have explored how language shapes thought and how linguistic structures influence our understanding of the world.

Application of the DIKWP Model

Data (D): Linguistic expressions, speech acts, written texts.

Mathematical Representation: $D = \{d_1, ..., d_n\}$ Each $d_i$ is a language sample.

Information (I): Analysis of syntax, semantics, and pragmatics.

Transformation Function: $I = T_{DI}(D)$ Understanding how language conveys meaning.

Knowledge (K): Theories of meaning, such as semantic theory, speech act theory, and linguistic relativity.

Transformation Function: $K = T_{IK}(I)$ Developing philosophical insights.
Knowledge Graph: $KG = (V_K, E_K)$ Mapping linguistic concepts.

Wisdom (W): Understanding language's role in shaping thought, culture, and reality.

Transformation Function: $W = T_{KW}(K)$ Applying knowledge to improve communication.

Purpose (P): Enhancing communication, reducing misunderstandings, and advancing cognitive frameworks.

Purpose Tuple: $P = (I n p u t, O u tp u t)$ Aiming for clearer expression and understanding.

Mathematical Mapping

Transformation Functions: $TDI:D→IT_{DI}: D \rightarrow I$ $TIK:I→KT_{IK}: I \rightarrow K$ $TKW:K→WT_{KW}: K \rightarrow W$ $TWP:W→PT_{WP}: W \rightarrow P$
Cognitive Enhancement:Wisdom applies language understanding to thought processes.

Summary

Using the DIKWP model, we can explore the philosophy of language by mapping linguistic data to purposeful wisdom, aiming to improve human understanding and communication.

Conclusion

By detailing each philosophical problem within the DIKWP framework, we've demonstrated how data transforms into information, knowledge, wisdom, and ultimately serves a purpose. This model provides a comprehensive approach to analyzing complex issues, highlighting the interconnectedness of empirical observations, theoretical understanding, ethical considerations, and practical goals.

The mathematical mappings within the DIKWP model facilitate a structured analysis, ensuring that each stage of cognitive processing is aligned with the overarching purpose. This approach not only aids in dissecting philosophical problems but also provides a pathway for applying these insights to real-world challenges.

References

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. ".
Chalmers, D. (1995). Facing Up to the Problem of Consciousness. Journal of Consciousness Studies.
Hume, D. (1748). An Enquiry Concerning Human Understanding.
Wittgenstein, L. (1953). Philosophical Investigations.

Acknowledgments

We acknowledge Prof. Yucong Duan's work in developing the DIKWP model, which has provided a valuable framework for this analysis.

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