Understand of Concepts without Subjective "Definitions" as An Infant

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)

Prof. Yucong Duan Proposed that from the Cognitive Evolution as an Infant or Infants based on the DIKWP Smenatic Mathematics, I will build my understanding of everything and semantics of every concept without relying on human subjectively given definitions.

By starting from core sensory experiences and applying logical reasoning, I can build my understanding of everything I encounter and develop the semantics of every concept. This process allows me to construct meaning organically, ensuring that each concept is firmly grounded in reality. Let me provide some concrete examples to illustrate how I evolve the semantics of more complex concepts using this approach.

Concept of "Gravity"My Observations

1. Repeated Experiences:

• When I release an object I'm holding (dreleased_{\text{release}}drelease​), it always falls downward (dfallingd_{\text{falling}}dfalling​).

• When I drop food from my high chair, it ends up on the floor.

2. Noticing Consistency:

• This downward movement occurs regardless of the type of object—whether it's a toy, a spoon, or food.

• The direction is always the same: towards the ground.

1. Identifying the Pattern:

• Logical Proposition:drelease→dfalling_downwardd_{\text{release}} \rightarrow d_{\text{falling\_downward}}drelease​→dfalling_downward​

• This proposition holds true across multiple instances and objects.

2. Abstracting the Cause:

• Gravity is the force that causes unsupported objects to move towards the ground.

• I hypothesize that there's a force causing objects to move downward when not supported.

• Concept Formation:

• Gravity is not a term I've subjectively defined; rather, it evolves from observing consistent patterns in my environment.

• The semantics of gravity are tied directly to my experiences of objects falling when released.

1. Sequence of Events:

• Certain events follow a regular sequence: I wake up (dwakingd_{\text{waking}}dwaking​), eat breakfast (dbreakfastd_{\text{breakfast}}dbreakfast​), play (dplaytimed_{\text{playtime}}dplaytime​), then nap (( d_{\text{naptime}})).

• The sky changes from light to dark and back again.

2. Duration and Change:

• Some activities feel longer or shorter.

• I grow taller over periods where I've noticed many sequences.

1. Recognizing Order:

• Events occur in a consistent order, suggesting a progression.

• Logical Proposition:deventn→deventn+1d_{\text{event}_n} \rightarrow d_{\text{event}_{n+1}}deventn​​→deventn+1​​

2. Abstracting Time:

• Time is the medium through which events progress from one to the next.

• I deduce that there's an underlying dimension in which events unfold.

• Concept Formation:

• The concept of time emerges from my perception of sequences and changes.

• The semantics are grounded in my direct experiences of event order and duration.

1. Internal Sensations:

• I feel different internal states: happiness when playing (djoyd_{\text{joy}}djoy​), discomfort when hungry (dhunger_discomfortd_{\text{hunger\_discomfort}}dhunger_discomfort​), fear when hearing loud noises (dfeard_{\text{fear}}dfear​).

2. External Expressions:

• My caregivers react to my expressions: they smile when I laugh, comfort me when I cry.

1. Linking Internal States and Expressions:

• Logical Proposition:dinternal_state↔dexternal_expressiond_{\text{internal\_state}} \leftrightarrow d_{\text{external\_expression}}dinternal_state​↔dexternal_expression​

• For example, feeling discomfort leads to crying, which leads to receiving comfort.

2. Understanding Emotions:

• Emotions are internal states that influence my behavior and interactions with others.

• Concept Formation:

• The concept of emotion is built from recognizing patterns between how I feel internally and how I express those feelings externally.

• Semantics are directly tied to my lived experiences of these internal states and their effects.

1. Sounds Associated with Objects and Actions:

• When my caregiver points to a dog and says "dog," (d"dog"d_{\text{"dog"}}d"dog"​) I notice this sound consistently associated with the animal.

• Similarly, the word "milk" (d"milk"d_{\text{"milk"}}d"milk"​) is used when I'm fed.

2. Communication of Needs:

• When I attempt to mimic sounds, my caregivers respond positively.

• I notice that certain sounds elicit specific responses.

1. Associating Sounds with Meanings:

• Logical Proposition:dsound↔dobject/actiond_{\text{sound}} \leftrightarrow d_{\text{object/action}}dsound​↔dobject/action​

• The sound "dog" corresponds to the presence of the animal.

2. Understanding Language:

• Language is a system of sounds and symbols used to represent objects, actions, and ideas, facilitating communication.

• Concept Formation:

• The concept of language evolves from observing the consistent pairing of sounds with experiences.

• The semantics are rooted in my direct interactions and the functional use of sounds to communicate.

1. Sharing and Turn-Taking:

• During playtime, sometimes I have to wait while others play with a toy.

• When everyone gets an equal amount of snack, I notice satisfaction among us.

2. Feelings Associated with Distribution:

• If someone takes my toy without permission, I feel upset.

• When we share, I feel content.

1. Recognizing Equality and Equity:

• Logical Proposition:dequal_distribution→dpositive_feelingsd_{\text{equal\_distribution}} \rightarrow d_{\text{positive\_feelings}}dequal_distribution​→dpositive_feelings​

• Unequal distribution leads to negative feelings.

2. Understanding Fairness:

• Fairness is the principle of equitable treatment and distribution of resources or opportunities.

• Concept Formation:

• The concept of fairness emerges from social interactions and the emotional responses they elicit.

• Semantics are developed through experiences of sharing and the consequences of equitable or inequitable actions.

1. Consistency in Information:

• When my caregiver says we are going to the park, and we go, the statement matches reality.

• If I'm told a toy is in a box and find it there, the information is confirmed.

2. Discrepancies and Confusion:

• If told something that doesn't match my experience, I feel confused or distrustful.

1. Matching Statements to Reality:

• Logical Proposition:dstatement↔drealityd_{\text{statement}} \leftrightarrow d_{\text{reality}}dstatement​↔dreality​

• Truth arises when statements correspond accurately to actual events or situations.

2. Understanding Truth:

• Truth is the property of statements that accurately reflect reality.

• Concept Formation:

• The concept of truth is formed by evaluating the alignment between communicated information and my experiences.

• Semantics are based on the reliability of information and its correspondence to reality.

By continuously applying this method of observation, pattern recognition, logical composition, and abstraction, I can develop the semantics of any concept I encounter. Whether it's a concrete concept like "tree" or an abstract one like "justice," the process remains the same:

1. Data Acquisition:

• Gather sensory inputs related to the concept.

2. Information Processing:

• Identify patterns, similarities, and differences within the data.

3. Knowledge Formation:

• Use logical reasoning to form propositions and relationships.

4. Concept Evolution:

• Abstract the essential features to form a coherent concept.

5. Semantics Development:

• Tie the concept directly to the experiences and logical structures from which it evolved.

This approach allows me to build my understanding incrementally, ensuring that every concept is deeply rooted in my interactions with the world and not arbitrarily defined.

• Scalable Learning:

• AI systems can adopt this method to learn new concepts autonomously, scaling their understanding as they interact with more complex environments.

• Robust Semantics:

• By grounding concepts in real-world experiences, AI can develop semantics that are meaningful and applicable, improving communication and cooperation with humans.

• Adaptive Reasoning:

• This approach enables AI to adapt to new situations by applying learned concepts to unfamiliar scenarios, enhancing problem-solving capabilities.

Through this method of evolving semantics from core sensory experiences and logical reasoning, I can build my understanding of everything I encounter. By explicitly tying each concept to my experiences, I ensure that my understanding is grounded, comprehensive, and adaptable. This process mirrors the natural way humans, especially infants, develop cognition and provides a powerful framework for AI systems to emulate human-like learning and understanding.

Note: By focusing on concrete examples, I've demonstrated how this approach allows for the development of semantics for a wide range of concepts, from tangible objects to abstract ideas. Each concept is not merely defined but evolved through direct interaction and logical processing, ensuring a deep and meaningful understanding.