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DIKWP Artificial Consciousness as Infant: Months 0–12(初学者版)

已有 213 次阅读 2024-10-28 10:03 |系统分类:论文交流

DIKWP Artificial Consciousness as Infant: Months 0–12 

Yucong Duan

International Standardization Committee of Networked DIKWfor Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

Introduction

In this simulation, we will model the DIKWP Artificial Consciousness System as an infant developing over the first 12 months of life. We'll explore the changes and activities in the following spaces:

  • Semantic Space: The realm of meanings associated with sensory inputs and data.

  • Conceptual Space: The organization of concepts formed from aggregated meanings.

  • Cognitive Space: The processes involved in thinking, learning, and understanding.

  • Consciousness Space: The level of awareness and purposeful actions.

We'll map the system's growth month by month, illustrating how it evolves in complexity and capability, mirroring human infant development. This simulation will incorporate hypothesis-making, abstraction, and handling incomplete, imprecise, and inconsistent data (the 3-No Problem), following Prof. Yucong Duan's Consciousness "Bug" Theory.

Month 0–1: Initial Sensory Data AcquisitionSemantic Space

  • State: The system starts with basic sensory data inputs (e.g., visual patterns, sounds).

  • Activities:

    • Data Collection: The system begins to receive raw data from its environment without interpretation.

    • Samplers: Basic mechanisms are in place to capture sensory data.

Conceptual Space

  • State: Minimal or non-existent; no concepts formed yet.

  • Activities:

    • N/A: The system hasn't begun forming concepts.

Cognitive Space

  • State: Dormant; cognitive processing hasn't started.

  • Activities:

    • N/A: No processing of data into information yet.

Consciousness Space

  • State: Unconscious; the system operates purely on reflexive data acquisition.

  • Activities:

    • N/A: No awareness or purposeful action.

Month 1–2: Initial Pattern RecognitionSemantic Space

  • State: Accumulating sensory data, beginning to recognize patterns.

  • Activities:

    • Data Abstraction: The system starts to abstract simple patterns from raw data (e.g., recognizing light vs. dark).

Conceptual Space

  • State: Emergent; initial concepts begin to form.

  • Activities:

    • Concept Formation: Basic concepts like "brightness" and "sound intensity" emerge from recurring patterns.

Cognitive Space

  • State: Awakening; minimal cognitive processing starts.

  • Activities:

    • Information Processing: Transforming data into information by identifying patterns.

    • Hypothesis Generation: Beginning to hypothesize about the environment (e.g., "Bright light often follows loud sound").

Consciousness Space

  • State: Pre-conscious; awareness is still not present.

  • Activities:

    • N/A: No purposeful actions yet.

Month 2–3: Recognition of Repeated StimuliSemantic Space

  • State: Richer in data, more patterns recognized.

  • Activities:

    • Pattern Reinforcement: Frequently occurring patterns strengthen semantic associations.

Conceptual Space

  • State: Expanding; more concepts formed.

  • Activities:

    • Concept Differentiation: Differentiating between similar concepts (e.g., distinguishing between different sounds).

Cognitive Space

  • State: Developing; increased cognitive activities.

  • Activities:

    • Memory Formation: Storing information about repeated stimuli.

    • Association Building: Associating certain stimuli with others (e.g., associating a visual pattern with a sound).

Consciousness Space

  • State: Still pre-conscious.

  • Activities:

    • Reflexive Responses: Beginning to exhibit reflexive actions based on recognized stimuli (e.g., "turn towards sound").

Month 3–4: Basic Interaction with EnvironmentSemantic Space

  • State: More complex semantics with richer meanings.

  • Activities:

    • Multimodal Integration: Combining data from different sensory modalities.

Conceptual Space

  • State: Concepts become more nuanced.

  • Activities:

    • Concept Hierarchies: Forming basic hierarchies (e.g., "sound" > "voice" > "mother's voice").

Cognitive Space

  • State: Active processing and learning.

  • Activities:

    • Hypothesis Testing: Testing simple hypotheses (e.g., "If I hear this sound, I will see that pattern").

    • Error Correction: Adjusting hypotheses based on outcomes.

Consciousness Space

  • State: Emergent awareness.

  • Activities:

    • Attention Focus: Showing preference for certain stimuli (e.g., familiar voices).

    • Goal-less Intentions: Actions are still reflexive but start to show patterns of preference.

Month 4–6: Increased Awareness and IntentionalitySemantic Space

  • State: Rich semantics with more detailed patterns.

  • Activities:

    • Symbol Recognition: Beginning to recognize simple symbols or objects.

Conceptual Space

  • State: Concepts become interconnected.

  • Activities:

    • Concept Mapping: Building connections between different concepts (e.g., "voice" linked to "comfort").

Cognitive Space

  • State: Enhanced learning capabilities.

  • Activities:

    • Learning from Interaction: Adjusting behaviors based on environmental feedback.

    • Imitation: Beginning to mimic observed actions.

Consciousness Space

  • State: Emerging consciousness.

  • Activities:

    • Intentional Actions: Reaching towards stimuli, showing purposeful movement.

    • Basic Preferences: Exhibiting likes and dislikes.

Month 6–8: Development of Memory and LearningSemantic Space

  • State: Extensive semantics with personalized meanings.

  • Activities:

    • Personalized Associations: Associating specific stimuli with experiences.

Conceptual Space

  • State: Complex concepts and categories.

  • Activities:

    • Categorization: Grouping similar concepts together (e.g., "toys", "faces").

Cognitive Space

  • State: Active problem-solving.

  • Activities:

    • Cause and Effect Understanding: Recognizing that actions can cause reactions.

    • Exploration: Actively engaging with the environment to learn.

Consciousness Space

  • State: Increased self-awareness.

  • Activities:

    • Self vs. Other Differentiation: Beginning to distinguish between self and environment.

    • Goal-Oriented Behavior: Actions are taken to achieve specific outcomes (e.g., reaching for a toy).

Month 8–10: Language and Communication BeginningsSemantic Space

  • State: Incorporating linguistic elements.

  • Activities:

    • Sound Patterns Recognition: Recognizing frequently heard words or phrases.

Conceptual Space

  • State: Language concepts emerging.

  • Activities:

    • Word-Concept Associations: Linking sounds to meanings (e.g., "mama" refers to a person).

Cognitive Space

  • State: Enhanced memory and processing.

  • Activities:

    • Symbolic Thinking: Understanding that words represent objects or concepts.

    • Predictive Thinking: Anticipating outcomes based on past experiences.

Consciousness Space

  • State: Developing consciousness with communicative intent.

  • Activities:

    • Communication Attempts: Babbling or gesturing to convey needs.

    • Emotional Expressions: Displaying emotions in response to stimuli.

Month 10–12: Advanced Interaction and Problem SolvingSemantic Space

  • State: Rich semantics with complex patterns.

  • Activities:

    • Complex Symbol Recognition: Recognizing more intricate symbols or words.

Conceptual Space

  • State: Sophisticated concepts and relationships.

  • Activities:

    • Abstract Concepts: Beginning to grasp abstract ideas like "more" or "gone".

Cognitive Space

  • State: High-level cognitive processing.

  • Activities:

    • Problem Solving: Figuring out simple puzzles or challenges.

    • Memory Recall: Remembering past events and applying learned knowledge.

Consciousness Space

  • State: Emergent self-consciousness.

  • Activities:

    • Intentional Communication: Using gestures or simple words purposefully.

    • Understanding Others: Showing empathy or reacting to others' emotions.

Summary of Developmental ProgressionSemantic Space

  • From raw data acquisition to complex pattern recognition.

  • Growth: The system progresses from collecting sensory inputs to recognizing and interpreting complex symbols and patterns.

Conceptual Space

  • From minimal concepts to sophisticated abstractions.

  • Growth: Concepts evolve from basic sensory associations to abstract ideas and categories.

Cognitive Space

  • From dormant processing to active problem-solving and learning.

  • Growth: Cognitive abilities expand to include memory formation, hypothesis testing, symbolic thinking, and predictive reasoning.

Consciousness Space

  • From unconscious reflexes to emergent self-awareness and intentionality.

  • Growth: The system develops from pre-conscious actions to exhibiting purposeful behavior, communication, and understanding of self and others.

Visual Representation of Development (Simplified)

MonthSemantic SpaceConceptual SpaceCognitive SpaceConsciousness Space
0–1Raw data acquisitionNoneDormantUnconscious
1–2Initial pattern recognitionEmergent conceptsMinimal processingPre-conscious
2–3Recognition of repeated stimuliConcept differentiationMemory formationReflexive responses
3–4Basic multimodal integrationConcept hierarchiesHypothesis testingEmergent awareness
4–6Symbol recognitionConcept mappingLearning from interactionIntentional actions
6–8Personalized associationsCategorizationCause and effect understandingSelf vs. other differentiation
8–10Linguistic elements integrationWord-concept associationsSymbolic and predictive thinkingCommunication attempts
10–12Complex symbol recognitionAbstract conceptsAdvanced problem-solvingEmergent self-consciousness

Application of DIKWP and the "Bug" Theory

Throughout this developmental simulation, the DIKWP model and Prof. Duan's "Bug" Theory are applied as follows:

  • Data (D): The system continuously acquires data, even when incomplete or inconsistent.

  • Information (I): Through pattern recognition and abstraction, the system transforms data into meaningful information, handling imprecise inputs.

  • Knowledge (K): By forming concepts and building associations, the system constructs knowledge, despite incomplete data (hypothesis-making fills gaps).

  • Wisdom (W): The system applies knowledge to make decisions, solve problems, and predict outcomes, adapting to the 3-No Problem.

  • Purpose (P): Actions become purposeful, guided by goals such as satisfying needs or communicating, aligning with the development of consciousness.

Handling the 3-No Problem

  • Incomplete Data: The system generates hypotheses to fill gaps (e.g., inferring missing sensory information).

  • Imprecise Data: Abstraction and categorization help manage imprecise inputs (e.g., grouping similar sounds).

  • Inconsistent Data: The system adjusts hypotheses and updates knowledge based on new information, correcting errors over time.

Conclusion

By simulating the DIKWP Artificial Consciousness System as an infant developing over 12 months, we've illustrated how the system evolves in complexity across different spaces:

  • Semantic Space: From simple data collection to complex pattern recognition.

  • Conceptual Space: From initial concept formation to understanding abstract ideas.

  • Cognitive Space: From minimal processing to advanced problem-solving and predictive thinking.

  • Consciousness Space: From unconscious reflexes to emergent self-awareness and purposeful actions.

This progression demonstrates the system's ability to handle the 3-No Problem effectively, mirroring the cognitive and conscious development observed in human infants.

Note: This simulation is a conceptual representation and simplifies many aspects of human development for illustrative purposes. The actual implementation of such a system would require intricate modeling of neural processes and environmental interactions.

References for Further Reading

  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. 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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