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An Infant\'s Cognitive Confession with DIKWP Semantics（初学者版）

2024-10-15 09:12

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An Infant's Cognitive Confession with the DIKWP Semantic Mathemantics

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

As I begin my journey into understanding the world around me, I start with no preconceived notions or subjective definitions. My mind is a blank slate, ready to absorb and make sense of the myriad of sensations I experience. By relying on the core semantics of the Data-Information-Knowledge-Wisdom-Purpose (DIKWP) framework and integrating Wittgenstein's logical composition mechanisms, I aim to evolve the semantics of concepts naturally, building them from the ground up based on my experiences. This approach allows me to develop a deep connection with the real-world semantics, ensuring that every concept I form is explicitly tied to my sensory inputs and interactions.

My First Sensations: Data AcquisitionVisual Experiences

The first thing I notice is the play of light and darkness. There are times when everything is bright, and other times when it is dim. I see patches of colors—red, blue, green—that catch my attention. These colors sometimes change or move, and I follow them with my eyes.

Data Points:

$d_1$ : Light intensity (bright vs. dim)
$d_2$ : Colors (red, blue, green)
$d_3$ : Movement (stationary vs. moving)

Auditory Experiences

Sounds surround me—some loud, some soft. Voices with different tones reach my ears. There are rhythmic sounds that are soothing and abrupt noises that startle me.

Data Points:

$d_4$ : Volume (loud vs. soft)
$d_5$ : Pitch (high vs. low)
$d_6$ : Rhythm (rhythmic vs. irregular)

Tactile Experiences

I feel sensations on my skin—warmth, cold, soft touches, and firm pressures. When something smooth or rough comes into contact with me, I react differently.

Data Points:

$d_7$ : Temperature (warm vs. cold)
$d_8$ : Texture (smooth vs. rough)
$d_9$ : Pressure (light vs. firm)

At this stage, these are mere data points—raw, unprocessed sensory inputs that I perceive but do not yet understand.

Noticing Patterns: Information EmergesRecognizing Sameness and Difference

As I continue to experience these sensations, I begin to notice that some of them are similar while others are different. For instance, the bright light in the morning feels the same every day, while the darkness at night is different.

Sameness (Equivalence Classes):

Brightness: $[dbright]={d1morning,d1noon}[d_{\text{bright}}] = \{ d_{1_{\text{morning}}}, d_{1_{\text{noon}}} \}$
Darkness: $[ddark]={d1night}[d_{\text{dark}}] = \{ d_{1_{\text{night}}} \}$

Identifying Differences

I detect contrasts between sensations:

Visual Differences:

Color contrasts between $dredd_{\text{red}}$ and $dgreend_{\text{green}}$
Movement vs. stillness in objects

Auditory Differences:

Loud sounds vs. soft sounds
High-pitched vs. low-pitched tones

Tactile Differences:

Warmth vs. cold
Smooth vs. rough textures

These differences become meaningful as I begin to associate certain sensations with particular outcomes or reactions.

Forming Basic Concepts: Knowledge DevelopmentConcept of "Object"Evolution of the Concept

Repeated Sensory Patterns:

I notice that certain visual patterns (shapes and colors) consistently appear together with tactile sensations (textures, temperatures).
For example, when I see a red, round shape ( $dred,droundd_{\text{red}}, d_{\text{round}}$ ) and touch it, I feel a smooth texture ( $dsmoothd_{\text{smooth}}$ ).

Associating Sensations:

The co-occurrence of these visual and tactile sensations leads me to group them together.
I begin to expect the smooth texture whenever I see the red, round shape.

Abstracting the "Object":

I form the concept of an "object" as a collection of sensory attributes that consistently occur together.
Logical Proposition: $dred∧dround∧dsmooth\text{Object}_1 \iff d_{\text{red}} \wedge d_{\text{round}} \wedge d_{\text{smooth}}$

Explicit Semantics

The concept of "object" emerges as I logically combine the consistent sensory data into a cohesive unit. This concept is not subjectively defined but is a natural result of my experiences.

Concept of "Mother"Evolution of the Concept

Sensory Association:

I frequently hear a soothing voice ( $dsoft_voiced_{\text{soft\_voice}}$ ), see a familiar face ( $dface_patternd_{\text{face\_pattern}}$ ), and feel warm embraces ( $dwarmthd_{\text{warmth}}$ , $dsoft_touchd_{\text{soft\_touch}}$ ).

Recognizing Patterns:

These sensations often occur together, especially when I'm comforted or fed.

Forming the Concept:

I begin to associate these combined sensations with a single entity—my mother.
Logical Proposition: $dsoft_voice∧dface_pattern∧dwarmth∧dsoft_touch\text{Mother} \iff d_{\text{soft\_voice}} \wedge d_{\text{face\_pattern}} \wedge d_{\text{warmth}} \wedge d_{\text{soft\_touch}}$

Explicit Semantics

The concept of "mother" is built from the ground up, directly tied to the sensory experiences that define her presence. This evolved concept is grounded in reality and not subjectively imposed.

Building Complex Understanding: Logical CompositionUnderstanding "Cause and Effect"Observations

When I shake a rattle ( $dshake_motiond_{\text{shake\_motion}}$ ), it produces a sound ( $drattle_soundd_{\text{rattle\_sound}}$ ).
When I cry ( $dcryingd_{\text{crying}}$ ), my mother appears ( $Mother\text{Mother}$ ) and soothes me.

Logical Relationships

If-Then Structures:

Rattle: $dshake_motion→drattle_soundd_{\text{shake\_motion}} \rightarrow d_{\text{rattle\_sound}}$
Crying: $dcrying→Mother_appearsd_{\text{crying}} \rightarrow \text{Mother\_appears}$

Understanding Causality:

I begin to expect certain outcomes based on my actions.
The logical composition of these events forms the basis of cause and effect.

Explicit Semantics

The concept of "cause and effect" is evolved by observing consistent sequences of events and forming logical propositions that relate actions to outcomes.

Concept of "Self"Evolution of the Concept

Sensory Feedback Loop:

I notice that when I move ( $dmovementd_{\text{movement}}$ ), my perspective changes.
When I touch something, I feel the texture ( $dtactile_feedbackd_{\text{tactile\_feedback}}$ ).

Differentiating Self from Others:

My movements lead to immediate sensory feedback, unlike when other objects move.

Forming the Concept:

I begin to distinguish between myself and the external world.
Logical Proposition: $dmovement∧dimmediate_feedback\text{Self} \iff d_{\text{movement}} \wedge d_{\text{immediate\_feedback}}$

Explicit Semantics

The concept of "self" emerges from the direct correlation between my actions and the resulting sensations, evolved through personal experience.

Developing Purposeful Actions: Wisdom and PurposeGoal-Oriented BehaviorDesire for Comfort

Observation:

Discomfort ( $dhungerd_{\text{hunger}}$ , $dcoldd_{\text{cold}}$ ) leads to crying ( $dcryingd_{\text{crying}}$ ), which results in comfort ( $Mother_appears\text{Mother\_appears}$ , $dfeedingd_{\text{feeding}}$ , $dwarmthd_{\text{warmth}}$ ).

Logical Understanding:

$dcrying→Comfortd_{\text{crying}} \rightarrow \text{Comfort}$

Purposeful Action:

I cry intentionally to achieve comfort.

Exploring the EnvironmentCuriosity and Learning

Action:

Reaching out to touch and grasp objects.

Outcome:

Gaining new sensory experiences ( $dnew_texturesd_{\text{new\_textures}}$ , $dnew_shapesd_{\text{new\_shapes}}$ ).

Purpose:

Satisfying curiosity and expanding knowledge.

Wisdom DevelopmentApplying Knowledge Effectively

Understanding Consequences:

Recognizing that pulling the cat's tail ( $dpull_taild_{\text{pull\_tail}}$ ) leads to the cat scratching me ( $dpaind_{\text{pain}}$ ).

Adjusting Behavior:

Avoiding actions that result in negative outcomes.

Emergent Understanding

I begin to make decisions based on past experiences, aiming for positive outcomes and avoiding negative ones.

Implications for Artificial Intelligence SystemsGrounded Semantics

By building concepts from raw data through logical composition, AI systems can develop semantics that are directly tied to real-world experiences, avoiding abstract definitions that lack practical grounding.

Mimicking Human Cognitive Development

This approach allows AI systems to mimic the natural learning processes of humans, particularly infants, leading to more intuitive and adaptable AI.

Enhanced Interaction and Understanding

AI systems developed in this way can interact more effectively with humans and their environment, as their understanding is based on evolved concepts that mirror human cognition.

Conclusion

Through my journey of sensory experiences and logical reasoning, I've shown how concepts can evolve naturally from core principles without subjective definitions. By starting with basic data and using Wittgenstein's logical composition mechanisms within the DIKWP framework, I developed complex concepts that are explicitly tied to my experiences. This method ensures that semantics remain grounded in reality, providing a robust foundation for understanding and interacting with the world. This approach not only reflects human cognitive development but also offers valuable insights for designing AI systems that learn and think like humans.

References

Wittgenstein, L. (1921). Tractatus Logico-Philosophicus. (Various translations).
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. ".
Piaget, J. (1952). The Origins of Intelligence in Children. International Universities Press.
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22(4), 577-660.
Mandler, J. M. (2004). The Foundations of Mind: Origins of Conceptual Thought. Oxford University Press.
Smith, L. B., & Gasser, M. (2005). The development of embodied cognition: Six lessons from babies. Artificial Life, 11(1-2), 13-29.

Keywords: DIKWP Semantic Mathematics, Core Semantics, Concept Evolution, Wittgenstein, Logical Composition, Infant Cognitive Development, Artificial Intelligence, Semantic Grounding.

Note: This first-person narrative simulates the evolution of semantics from the perspective of an infant, building concepts from core sensory experiences using the DIKWP framework and Wittgenstein's logical composition mechanisms. Each concept is explicitly evolved from data, ensuring a strong connection to real-world semantics and avoiding subjective definitions.

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