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DIKWP Artificial Consciousness as Infant: Months 0–12（初学者版）
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DIKWP Artificial Consciousness as Infant: Months 0–12
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
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)
| Month | Semantic Space | Conceptual Space | Cognitive Space | Consciousness Space |
|---|---|---|---|---|
| 0–1 | Raw data acquisition | None | Dormant | Unconscious |
| 1–2 | Initial pattern recognition | Emergent concepts | Minimal processing | Pre-conscious |
| 2–3 | Recognition of repeated stimuli | Concept differentiation | Memory formation | Reflexive responses |
| 3–4 | Basic multimodal integration | Concept hierarchies | Hypothesis testing | Emergent awareness |
| 4–6 | Symbol recognition | Concept mapping | Learning from interaction | Intentional actions |
| 6–8 | Personalized associations | Categorization | Cause and effect understanding | Self vs. other differentiation |
| 8–10 | Linguistic elements integration | Word-concept associations | Symbolic and predictive thinking | Communication attempts |
| 10–12 | Complex symbol recognition | Abstract concepts | Advanced problem-solving | Emergent 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
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. ".
https://blog.sciencenet.cn/blog-3429562-1457292.html
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