AI Language Model Training With Brain Imaging for Wellbeing Support
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Solution Overview
Problem
Existing large language models (LLMs) lack deep scientific knowledge in emerging areas like human wellbeing and happiness, are not equipped for personalized support, and are blind to changing emotional and behavioral needs due to training on publicly-available internet data.
Innovation Solution
A supervised training process using non-user-specific and user-specific data sets, including brain imaging and personal user data, to create personalized AI-language-model-based systems for happiness and wellbeing support, with continuous refinement based on user feedback and expert data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If existing LLMs are trained on publicly-available internet content, then language comprehension and generation speed are improved, but reliability and truth in responses regarding scientific data and human wellbeing are worsened
Solution Approach 1:
The training data is segmented into multiple specialized corpora: general internet content for language fluency, peer-reviewed scientific literature for scientific accuracy, and clinical trial data for medical wellbeing information. Each segment trains specific aspects of the model to ensure both speed and reliability in different domains.
Solution Approach 2:
An intermediary verification layer is introduced that cross-references model responses against authoritative scientific databases and peer-reviewed literature before output. This mediator ensures response reliability without significantly impacting generation speed by using efficient similarity search algorithms.
2Ease of operation
If existing LLMs use internet-sourced data corpuses, then language interaction capability is improved, but ability to provide personalized support is worsened
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user-specific data including brain imaging scans, genetic information, and personal health records before providing recommendations. This preliminary personalization enables the model to tailor advice to individual biological and psychological characteristics rather than providing generic internet-sourced advice.
Solution Approach 2:
The system dynamically adapts recommendations based on real-time user feedback, changing emotional states detected through continuous monitoring, and evolving personal profiles. The model updates its understanding of user needs and preferences dynamically, transitioning from static internet-based advice to dynamic personalized support.
3Loss of information
If existing LLMs are trained on written-language training data, then language processing is improved, but ability to detect emotional experiences is worsened
Solution Approach 1:
The system merges multiple data modalities including written language inputs, brain imaging data (fMRI, EEG), genetic information, and physiological sensors to comprehensively detect and understand emotional experiences. This multi-modal combination compensates for the limitations of text-only training data by incorporating direct biological measurements of emotional states.
Solution Approach 2:
The system transitions from two-dimensional text-based emotion inference to multi-dimensional emotional detection by incorporating spatial brain imaging data, temporal physiological signals, and genetic predisposition information. This adds multiple dimensions to emotional understanding beyond what written language alone can provide.
Data Source
AI summary
Disclosed herein are systems and methods for training an AI language model for assessing and improving happiness and wellbeing of a human user, the method comprising receiving a pre-trained AI language model; receiving a first training data set comprising non-user-specific training data comprising brain imaging data; applying one or more supervised training protocols based on the first training data set to modify the pre-trained AI language model to generate a non-user-specific language model configured for happiness and wellbeing support of human users; receiving a second training data set comprising user-specific training data comprising brain-imaging data for a specific user; and applying one or more supervised training protocols based on the second training data set to modify the non-user-specific language model to generate a user-specific language model configured for happiness and wellbeing support of the specific user.