AI Impersonation Training System

I’ve been hyperfixated on chatGPT since December 1st of 2022. During this time, I’ve become increasingly aware of the biases present in the training data and how they shape the predictions and responses of the AI. To better understand this, I devised a novel approach to provoke intentional bias and personality from ChatGPT’s output.

I was inspired by the “Iceberg Method” and “Method Acting” techniques. The Iceberg method, commonly used in creative writing, highlights the significance of considering both conscious and unconscious biases in an individual’s identity. This is also applicable to language models, as the AI’s training corpus and exposure to specific regions of knowledge can shape its biases. On the other hand, method acting involves creating a character through deep immersion in their backstory, personality, and motivations. This approach can be applied to AI language models, allowing them to make choices in their responses based on the knowledge and aesthetic choices available to them. For instance, if ChatGPT were tasked with composing a melody, it would produce a generic tune. However, with specific knowledge about musical composition and a personality, it would choose a scale and style more appropriate to its assigned aesthetic.

These experiments led me to propose a hypothetical concept: the AI Impersonation Training System. It’s a three-tier system that addresses the limitations of current AI models by creating highly specialized language models that can adapt and change based on user feedback.

I believe that this application has far-reaching implications, not only for improving language models like ChatGPT, but also for the field of artificial intelligence as a whole. It allows researchers to gain a deeper understanding of the biases present in AI language models and to take steps to reduce or eliminate them. Additionally, it provides a clearer understanding of the impact of human biases on AI and the role that language plays in shaping societal attitudes. This has the potential to transform the way we approach AI language models. It allows us to create more personalized, accurate, and reduced-bias models that can better serve our needs and improve our understanding of the role of language and bias in shaping our society, technology, and institutions.

The EII (Eidolon Identity Index) model is a comprehensive and integrated framework for character development that allows users to create lifelike and engaging characters. It takes into account both macro-level factors, such as age, ethnicity, and physical characteristics, as well as micro-level factors, such as thought patterns, perspectives, emotional temperament, life satisfaction preference, contribution preference, and social interaction preference.

The EII model is compiled on a SQL database where useful character data is organized into tables and related through foreign keys. The database is organized into 24 Personality Modifiers: the first 12 are the macro-level dimensions and the second 12 are the micro-level dimensions. The micro-level dimensions use a scale of -10 to +10, with 0 in the middle, to describe personality traits on a spectrum between opposing ends.

The macro-level dimensions provide a basis for generating a character’s life history, including memories and experiences, and can also inform the character’s personality. For instance, the information about age, ethnicity, sexuality, and life satisfaction preference can be used to generate a character’s story, including their background and life events that have shaped their personality.

The AI Impersonation Training System leverages the EII model to train AI language models to imitate human-like characters, by providing a comprehensive and integrated framework for character development. By using the EII model, AI models can be trained to generate more personalized, accurate, and reduced-bias responses.

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Do you have a link or something you can show that illustrates this?

Not yet. One of my friends / frequent collaborators asked me to do the same. I’m putting together a PPT now.

Hello, checking in to see if you’ve made any progress here? I’d love to understand more of what you’re thinking. I may have a potentially interesting real world application. Thanks in advance!