What makes a good AI model? Does more data equal better models, or does a smaller amount of data equal better models? Is it a balance of both?
Here are some key factors that contribute to a good AI model:

- **Relevant, high-quality data**: Having a sufficient amount of data that is relevant to the task you want the model to perform is important. However, more data does not necessarily always lead to a better model. The data needs to be of high quality and truly representative of the problem. Low-quality or biased data can lead to poor model performance.

- **Appropriate algorithm and hyperparameters**: Choosing a machine learning algorithm and hyperparameters that are well-suited to your data and task is crucial. Some algorithms perform better with larger amounts of data while others can work with smaller datasets. Tuning the hyperparameters for optimal performance also requires balancing the amount of data with the complexity of the model.

- **Simplicity**: Starting with a simpler model and only increasing complexity if needed can help avoid overfitting and other issues. More data and a more complex model do not always translate to better performance. A model should be as simple as possible but as complex as needed to solve the problem.

- **Evaluation metrics**: Having appropriate evaluation metrics to measure the performance of different models is key. The metrics need to properly capture the task objectives and nuances of the problem. Raw accuracy may not always be the best metric, especially for imbalanced classification or other complex problems. Choosing the right metrics is necessary to determine if a model is good or needs improvement.

- **Regularization**: Using regularization techniques to avoid overfitting and reduce complexity can help improve model performance. This is especially important when data is limited. Common regularization methods include weight decay, dropout, and early stopping. Applying appropriate regularization is part of building a good AI model.

So in summary, there is no simple answer to your question. A good AI model depends on a balance of high-quality data, appropriate algorithms and hyperparameters, simplicity, strong evaluation metrics, and regularization. Neither more data nor fewer data always leads to a better model, and it ultimately depends on the particular problem and use case. The key is to find the right blend of these factors for your needs.
How could one go about giving an AI language model autonomy?   Would a feedback loop work?  What should be done when the token limit is reached to keep the AI's memory active?