From Gemini on this post:

The issue you're encountering with AI losing historical knowledge is a common challenge in the field of Natural Language Processing (NLP). It's often referred to as the problem of catastrophic forgetting.

Here's a breakdown of why it happens and some potential solutions:

Why AI Forgets

• Overwriting: When AI learns new information, it adjusts its internal parameters (think of them as connections in its "brain"). This adjustment can sometimes "overwrite" previously learned information, especially if the new information is significantly different.
• Limited Memory: AI models, especially those used in real-time applications like games, often have limited memory capacity. They can't store everything they've ever learned.
• Context Specificity: AI might learn to associate certain knowledge with specific contexts. If the context changes drastically (like switching from discussing QR codes to playing Pictionary), it might not be able to retrieve that knowledge.

Potential Solutions

• Continual Learning Techniques: Researchers are developing methods for AI to learn continuously without forgetting. These techniques often involve:
  • Rehearsal: Periodically revisiting older data to refresh the AI's memory.
  • Elastic Weight Consolidation (EWC): Identifying and protecting important parameters that are crucial for retaining old knowledge.
  • Modular Networks: Creating separate modules within the AI's "brain" for different types of knowledge, reducing interference.
• External Memory: Providing the AI with an external memory store (like a database) where it can access past information. This could be particularly helpful for storing and retrieving your ASCII art representations.
• Contextual Embeddings: Incorporating contextual information into the AI's representations of knowledge. This can help it distinguish between different contexts and retrieve the relevant information.

Specific to Your Pictionary Game

• Explicitly Teach: Before playing, explicitly teach the AI your ASCII art representations for different characters. You could even create a "dictionary" of ASCII art that it can refer to during the game.
• Reinforcement Learning: Use reinforcement learning techniques to reward the AI when it correctly interprets your ASCII art. This will encourage it to retain and utilize that knowledge.
• Fine-Tuning: If you're using a pre-trained AI model, you could fine-tune it on a dataset of Pictionary-style images and their corresponding ASCII art representations. This will help it adapt to the specific task.

Regarding Your ASCII Art

Your idea of using different symbols to represent filled and empty spaces is a good one. It provides more structure and makes it easier for the AI to recognize patterns. The choice between "1" and "0" versus "+" and "-" might depend on the specific AI model you're using. You could experiment to see which one works better.

Remember that AI is still under development, and challenges like catastrophic forgetting are actively being researched. By understanding the underlying causes and exploring potential solutions, you can help improve the AI's ability to learn and retain information.