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The main questions are how machines can mimic human responsiveness to situations and navigate various contexts using adaptable reference frames. This approach is rooted in understanding that our perceptions and experiences shape our interpretations of situations.

A key concept introduced is the “dynamical context,” which describes how a situation interacts with an individual’s mental frameworks, affecting their responses. The proposed “Quadranym Model” aims to represent commonsense knowledge and enhance machines’ awareness of human reactions. The idea of the dynamical context suggests that a situation interacts with an individual’s existing mental frameworks, including their psychological state and expectations. When faced with a specific situation, these preexisting factors create a sort of resonance, influencing how the individual perceives and responds to it.

Overall, the project seeks to create a model for understanding how words connect to human experiences and improve machine comprehension, ultimately contributing to advancements in artificial intelligence and commonsense reasoning. Feedback and collaboration are encouraged.

Key Concepts:

1. Word-Sensibility:
   • This approach looks at how the meanings of words are grounded in human experiences and expectations. It goes beyond simple definitions, examining how people use words based on their real world responsiveness  to situational contexts.
   • The goal is to enable machines to interpret words in a way that reflects human-like understanding.
2. Commonsense Knowledge:
   • One of the central challenges in AI is that machines lack the commonsense knowledge that humans intuitively possess. The project seeks to bridge this gap by modeling how humans respond to various situations.
3. Human Responsiveness:
   • The project emphasizes the ability of humans to respond quickly and effectively to situations, which informs their understanding of words and concepts. Machines need to replicate this responsiveness to enhance their language processing capabilities.
4. Dynamical Context:
   • This concept refers to how a person’s existing mental frameworks (including emotions and expectations) interact with specific situations. When faced with a scenario, these frameworks influence how an individual perceives and responds to it.
   • It underscores the importance of context in shaping understanding and meaning.
5. Quadranym Model:
   • This model proposes a structured way to represent commonsense knowledge through a system of “quadranyms,” which consist of four term-facets that capture different dimensions of meaning. The framework consists of active-actual states (subject using energy) and passive potential-states (energy being utilized).
   • These quadranyms serve as units of context that can help machines analyze and understand language by organizing concepts into manageable frameworks.

Objectives of the Project:

• Text Analysis: Develop methods that focus on human responsiveness to improve text analysis capabilities.
• Modeling Interaction: Explore how dynamic interactions with the environment shape word meanings and responses.
• Commonsense Grounding: Create a model that provides a foundation for understanding metaphoric structures in language.
• Sharing Viewpoints: Enable machines to better simulate the sharing of perspectives, enhancing their ability to understand social interactions.

Importance of Sensibility:

• The term “sensibility” is key to the project, referring to an acute awareness and responsiveness to the world. The model distinguishes between sensibility (emotional and social responses) and rationality (logical reasoning), suggesting that a deeper understanding of human sensibility can inform AI development.

Research and Collaboration:

• The project is still in the exploratory phase, inviting feedback and collaboration to refine its theories and applications. It draws from diverse fields such as cognitive science, philosophy of mind, and semiotics, aiming for a comprehensive approach to commonsense reasoning in AI.

Conclusion:

The ultimate aim is to create a robust model for word-sensibility that enhances machine understanding of human language, making AI systems more intuitive and effective in processing natural language. By focusing on how humans interact with their environments and make sense of their experiences, the project aspires to contribute significantly to advancements in artificial intelligence.

By Dane Scalise

Summary assisted by ChatGPT