how can we incorporate mental formations to large language model to create the first prototype of AGI?
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Mental formations, also known as mental models or cognitive models, are an important aspect of human intelligence, and incorporating them into a large language model could be a step towards creating the first prototype of Artificial General Intelligence (AGI). Here are some ways to do that:

1. Create a hierarchical model of knowledge: Mental formations are built on top of each other, forming a hierarchical structure. This structure can be incorporated into a language model by creating a hierarchical model of knowledge. Each level of the hierarchy can represent a different mental formation, and the model can be trained to understand how these formations relate to each other.

2. Train the model on diverse datasets: Mental formations are influenced by a wide range of factors, such as culture, language, and personal experiences. To incorporate these factors into the model, it should be trained on diverse datasets that cover a wide range of topics and perspectives. This will help the model to understand how mental formations vary across different contexts.

3. Incorporate feedback loops: Mental formations are not fixed, and they can change over time as new information is learned. To incorporate this aspect into the model, it can be designed to incorporate feedback loops. For example, the model can be trained to adjust its mental formations based on new data, or to seek out new data that challenges its existing mental formations.

4. Emulate human reasoning: Mental formations are a key aspect of human reasoning, and incorporating this type of reasoning into the model can help to create an AGI prototype. One way to do this is to create a model that can reason by analogy, using mental formations to identify similarities and differences between different concepts.

5. Incorporate emotional intelligence: Mental formations are not just about knowledge, but also about emotions. To create a truly human-like AGI, the model should also be trained to understand and express emotions. This can be achieved by training the model on datasets that include emotional content, or by using techniques such as sentiment analysis to identify emotional content in text.

Overall, incorporating mental formations into a large language model is a complex and challenging task, but it is an important step towards creating an AGI prototype that can truly emulate human intelligence.
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