// TODO

MAIN

1. prototype with LLM to Websearch ✅
2. training method of one-shot or finetuning
3. batching and cache
4. feedback loop
5. teach the model how to navigate the web

SECONDAY

1. Expand Knowledge Sources:

   • Integrate multiple search engines besides Bing (e.g., Google, DuckDuckGo).
   • Add capabilities to parse and understand academic papers, forums, Q&A sites, books, and other forms of knowledge.
   • Incorporate video and audio content analysis.

2. Improve Text Understanding:

   • Enhance the model to understand the context of the information better.
   • Implement Named Entity Recognition to identify and understand entities in the text.
   • Use semantic analysis to understand the relationships between different pieces of information.

3. Incorporate Feedback Loops:

   • Allow users to provide feedback on the generated summaries.
   • Implement a mechanism to fine-tune the model based on user feedback.
   • Use reinforcement learning to reward good summaries and penalize incorrect or misleading ones.

4. Diversify Model Architecture:

   • Integrate other models alongside BART (e.g., BERT for understanding, GPT for generation).
   • Experiment with newer architectures and techniques as they emerge in AI research.

5. Enhance Interaction:

   • Implement a Natural Language Processing (NLP) interface for users to interact with the system.
   • Develop a voice interface for voice commands and responses.

6. Implement Memory and Continual Learning:

   • Allow the system to remember past interactions and learn from them.
   • Implement mechanisms to prevent catastrophic forgetting when learning new information.

7. Multimodal Learning:

   • Incorporate vision models to understand and generate visual content.
   • Implement auditory models for sound and speech recognition.

8. Expand Domain Knowledge:

   • Specialize in various domains (e.g., medicine, law, engineering) and allow switching between them based on user needs.

9. Real-time Adaptation:

   • Allow the model to adapt in real-time based on the context of the conversation or user requirements.

10. Integrate External APIs and Databases:

    • Connect to various databases and APIs to pull specific, detailed, or up-to-date information.

11. Emotion and Sentiment Understanding:

    • Recognize user sentiment and emotion to adapt responses accordingly.

12. Implement Reasoning Capabilities:

    • Beyond understanding, implement mechanisms for logical reasoning, problem-solving, and decision-making.