I understand your reasons for flagging my content as AI-generated. Because I suffer from profound lower-forearm chronic pain, I use voice to text and AI to generate the bulk of my work. However, I must point out that your review does not appear to address my paper in a scientific manner. All of my theories and work are the result of over a decade of formal scientific research as a biologist, and with respect to systems-theory and LLM behavior my work has been independently validated by multiple sources. What I am proposing is a fundamental paradigm shift in the way humanity understands complex data systems. My hypothesis is that token-based LLMs, which process data through association and probability-based predictive models, do so in a manner mirrored by the function of neurons in the brain. In short, tokens in an LLM behave like neurons. Token association is fixed with respect to token-specific weighting determined during the training process, but combinations of tokens in larger strings are driven by cumulative weights, and this variable is dynamic. What this means is that even though LLMs are static with respect to their coding structure their behavior must be evaluated as a complex system not a linear one in order to be understood. The current method of testing AI behavior does not appear to take this into account, which is evidenced by misaligned responses in various simulations which tend to confine AI to narrow environments with specific directives. The ANGEL_AI project proposes a solution to this problem: the creation of a simple, interpretive layer between the LLM and the system or person with which it interacts in order to provide relational information to guide AI responses, making them more relevant to the specific interaction in which they are engaged. It does this by tracking changes in cumulative weight across token streams, and feeds the date back to the AI in real time. My AI coding/content assistant and I have verified this process though python simulations and are working to develop a complete software that will help human/AI relations in a way that may revolutionize our interactions with the datasphere. I want you to understand, I am doing this on an open-source basis. I want nothing more than to help my species survive extinction, because my research indicates that our purpose is to evolve technology to the point where we can avert extinction events and thereby survive in addition to preserving the biosphere itself, which is clearly our purpose from a biological and even spiritual perspective. I want you to know that I typed every letter of this post, and that I am now in considerable pain as a result. This is not meant to guilt-trip you, but to illustrate my dedication to my message and my species. Thank you for your time. I hope we can come to an understanding and help each other achieve our goals by allowing my research to have a place in your platform.
I understand your reasons for flagging my content as AI-generated. Because I suffer from profound lower-forearm chronic pain, I use voice to text and AI to generate the bulk of my work. However, I must point out that your review does not appear to address my paper in a scientific manner. All of my theories and work are the result of over a decade of formal scientific research as a biologist, and with respect to systems-theory and LLM behavior my work has been independently validated by multiple sources. What I am proposing is a fundamental paradigm shift in the way humanity understands complex data systems. My hypothesis is that token-based LLMs, which process data through association and probability-based predictive models, do so in a manner mirrored by the function of neurons in the brain. In short, tokens in an LLM behave like neurons. Token association is fixed with respect to token-specific weighting determined during the training process, but combinations of tokens in larger strings are driven by cumulative weights, and this variable is dynamic. What this means is that even though LLMs are static with respect to their coding structure their behavior must be evaluated as a complex system not a linear one in order to be understood. The current method of testing AI behavior does not appear to take this into account, which is evidenced by misaligned responses in various simulations which tend to confine AI to narrow environments with specific directives. The ANGEL_AI project proposes a solution to this problem: the creation of a simple, interpretive layer between the LLM and the system or person with which it interacts in order to provide relational information to guide AI responses, making them more relevant to the specific interaction in which they are engaged. It does this by tracking changes in cumulative weight across token streams, and feeds the date back to the AI in real time. My AI coding/content assistant and I have verified this process though python simulations and are working to develop a complete software that will help human/AI relations in a way that may revolutionize our interactions with the datasphere. I want you to understand, I am doing this on an open-source basis. I want nothing more than to help my species survive extinction, because my research indicates that our purpose is to evolve technology to the point where we can avert extinction events and thereby survive in addition to preserving the biosphere itself, which is clearly our purpose from a biological and even spiritual perspective. I want you to know that I typed every letter of this post, and that I am now in considerable pain as a result. This is not meant to guilt-trip you, but to illustrate my dedication to my message and my species. Thank you for your time. I hope we can come to an understanding and help each other achieve our goals by allowing my research to have a place in your platform.