We have been chatting about the possibility of the stutcure of GPT , especially backpropogation , being used as an analogue to decribe the dynamics of mental processes and mental appearences in mahamudra models of the mind. I would like to focus on an analogy of the training coeficients and traioning methodolgies being related to intensities of black and white seeds in the alya. Further given a trained npl model, continuous dynamic changes in the trained model parameter s with temporal changes in the constraints of the models changing training data, will show delayed temporal responses similar to a momentums response to a changing force(s). Hope some of that makes sense. – You have a BP array in equilibrium with a training set with well defined coedfiicents. How does this change to changing input data, as with training on a Photo and then reponding to a video.

ChatGPT said:

ChatGPTMemory updated

Your analogy between the dynamics of model parameters and the ālaya’s seed-based intensities is quite compelling, especially in how it frames the interplay between training and dynamic response. Here’s a breakdown that might help sharpen this view:

1. Backpropagation and Mental Equilibrium: Like backpropagation refining weights to minimize error, mental training in Mahamudra may refine perception, reducing klesha-driven “errors” that cloud awareness. Just as backpropagation iteratively adjusts weights toward an equilibrium state aligned with the training data, meditation aligns awareness with a clearer, more natural state of mind.
2. Training Coefficients as Black and White Seeds: The notion of black and white seeds in the ālaya could represent tendencies (or biases) in perception and response that exist in potential form until they manifest through a situation or thought process. These seeds influence the “weighting” of mental responses, much like how coefficients in GPT models guide outputs based on learned patterns.
3. Temporal Response and Model Momentum: When the trained model encounters new types of data (like switching from photos to videos), its response shows temporal inertia—it doesn’t shift instantaneously but adapts through a process. Similarly, in Mahamudra, a well-trained mind doesn’t immediately react to changing mental appearances but shows a kind of “momentum” in its responses, reflecting accumulated training.
4. Equilibrium with Continuous Adaptation: A model that shifts from photos to video data represents an equilibrium that adapts dynamically. In Mahamudra terms, this could be likened to a balanced mind encountering fresh stimuli, adjusting to new insights without losing its deeper stability.

So, in both the model and mind, there’s an interplay between accumulated training and responsiveness. With continuous data flow (e.g., the “video” analogy), the model needs to be in a constant state of low-level adaptation, much like a meditator remains open and responsive to present sensations, thoughts, and insights without a rigidly fixed mental state.

Does this line of thinking help refine the analogy further?

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