Take a pre-woke version of the Oxford English Dictionary and make it's definitions the heart of an LLM and figure out how it can be used to make an AGI.
Answer
Creating an AGI (Artificial General Intelligence) using the framework of a "pre-woke" Oxford English Dictionary (OED) as the core linguistic model would be an intriguing approach. Here's how one might conceptualize this:
1. Lexical Foundation:
- Deep Semantic Understanding: The OED provides not just definitions but etymologies, historical uses, and examples. This rich context can help in understanding language at a profound level, potentially allowing the AI to grasp nuances in human communication better than models trained on more contemporary, possibly less nuanced datasets.
2. Knowledge Representation:
- Ontology Development: Use the definitions and relationships described in the OED to create an ontology of concepts. This ontology would serve as a structured framework for knowledge, where each word and its meanings become nodes connected by various relationships like synonymy, antonymy, hyponymy, etc.
3. Contextual Learning:
- Textual Analysis: By analyzing how words have been used over time (as documented in the OED), the AI could learn context not just from current data but from centuries of written language. This historical perspective might offer a unique depth in understanding context, intent, and evolution of language.
4. Reasoning and Inference:
- Logic from Language: The definitions in the OED can be used to teach the AI logical reasoning. For example, understanding definitions can help in deducing properties of objects or concepts (if "A is a type of B" and "B has property C", then "A likely has property C").
5. Building AGI:
- Integration with Machine Learning:
- Hybrid Model: Combine this linguistic model with machine learning algorithms. The OED-based model would provide a semantic backbone, while ML models handle pattern recognition, prediction, and decision-making based on real-time data.
- Adaptive Learning: The AI could start with a pre-woke understanding of language but adapt through interaction, learning new words and meanings but always referencing back to its foundational understanding to avoid drift into misunderstandings or overly contextless interpretations.
- Ethical and Bias Considerations:
- By using a "pre-woke" dictionary, there's a risk of encoding outdated or biased views. However, this can be mitigated by:
- Critical Update Mechanism: Allow the system to flag and update definitions or connotations that are recognized as biased or harmful in modern contexts, but do so through a controlled process that maintains the historical integrity of the original OED where appropriate.
- By using a "pre-woke" dictionary, there's a risk of encoding outdated or biased views. However, this can be mitigated by:
- Multidisciplinary Approach:
- Cross-reference with Other Knowledge: Integrate this linguistic core with databases from other fields (science, art, history) to enable the AGI to draw connections across different domains, enhancing its general intelligence.
6. Human-like Interaction:
- Conversational Depth: With a deep understanding of language evolution, the AGI could engage in more nuanced conversations, understanding slang, jargon, and cultural references from different eras, making interactions feel more 'human' or historically informed.
Challenges:
- Modern Contextual Relevance: The AI would need mechanisms to learn and integrate contemporary language use, slang, and new terminology not covered in an older OED edition.
- Computational Intensity: Processing and reasoning based on such a vast and detailed lexical database could be computationally intensive.
Conclusion:
This approach would create an AGI with a unique linguistic and conceptual foundation, potentially leading to an AI with a rich, historical understanding of human language. However, it would need to be balanced with mechanisms for updating its knowledge base to remain relevant and sensitive to modern ethical standards. This blend of old and new could potentially lead to an AGI that not only understands language deeply but can also apply this understanding in novel, insightful ways.