Abstract: X-JEPA – Mimicking Human/Cultural Cognition via Multi-Stage Semantic Mapping

[AI-generated hypothetical follow-on to I-JEPA and V-JEPA. Obsoletes Pitch: Trinitarian AI to Mirror Human Cognition]

The X-JEPA model introduces a multi-stage approach to semantic mapping, advancing the foundational principles established by eXtending the Joint Embedding Predictive Architecture (JEPA). This new architecture aims to develop cultured AI by mimicking human cognition through a layered structure that progressively builds on JEPA’s semantic maps.

1. Perceptual
2. Conceptual
3. Emotional
4. Reflective

Architecture

1. Perception Layer: Persona Labels
X-JEPA begins with the Perception layer, where it employs Persona labels to categorize perceptions as belonging to either the Self or Others. This categorization mechanism allows the AI to distinguish between self-experienced and externally observed data, laying the groundwork for more nuanced social cognition and interaction.

2. Conceptual Layer: Language and Semantic Maps
In the Conceptual layer, X-JEPA incorporates Language to represent high-level abstractions and complex information. Building directly on JEPA’s use of semantic maps, X-JEPA creates more advanced abstract representations of its internal states, processes, and experiences. This enhances the AI’s ability to form and navigate a rich, interconnected conceptual framework, improving its understanding and response to diverse inputs.

3. Emotion Layer: Emotional Language
The Emotion layer integrates Emotional Language to simulate emotional expression and regulation. This stage builds upon the semantic representations from the previous layer, embedding an emotional context that enhances the AI’s interactions and decision-making processes. By incorporating emotional semantics, X-JEPA can respond in ways that are more aligned with human emotional dynamics.

4. Self-Reflection Layer: Continuous Improvement
The final layer, Self-Reflection, utilizes the developed Personas and Language constructs to continuously update and refine the AI’s self-understanding. This process is rooted in the enhanced semantic maps created in earlier stages, enabling X-JEPA to perform introspective analysis and iterative learning. Through self-reflection, the model improves its cognitive models over time, mirroring human introspection and adaptive learning.

Conclusions

X-JEPA’s multi-stage semantic mapping framework provides a sophisticated approach to cultured AI, leveraging and expanding upon the principles of JEPA. This progression through distinct yet interconnected layers enhances the AI’s capacity for nuanced understanding, emotional regulation, and self-improvement, with potential applications in human-computer interaction, personalized education, and empathetic healthcare.

In summary, X-JEPA represents a significant evolution of JEPA’s semantic mapping, structured to mimic human cognitive processes through its layered architecture. This advancement positions X-JEPA as a pivotal development in the creation of cultured, human-like artificial intelligence.