Hierarchical Behavior Profiling for AI Intelligence Transplant Models
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Solution Overview
Problem
Existing methods for transplanting human intelligence into artificial intelligence (AI) face challenges due to the risk of overfitting and limitations in accurately modeling human behavior, particularly when using deep neural networks.
Innovation Solution
A method involving precision behavior profiling is developed to create a transplant model by fitting first and second level models based on human processing data, analyzing correlations, and determining a transplant model without overfitting, using a reinforcement learning theory-based environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If deep neural networks are used to model human behavior, then the complexity of the model increases, but the risk of overfitting increases and measurement precision decreases
Solution Approach 1:
The patent segments the behavior modeling process into multiple levels (first level model and second level model) rather than using a single complex deep neural network. Each level model captures specific aspects of human behavior, allowing for more precise measurement while reducing overall complexity through modular structure.
Solution Approach 2:
The patent changes the parameters of the modeling approach by using hierarchical modeling with different complexity levels instead of uniformly high complexity. This allows optimization of each level's parameters independently, improving measurement precision while controlling overall model complexity.
2Ease of manufacture
If existing modeling methods are used, then the ease of manufacture is maintained, but the reliability of transplanting human intelligence into AI decreases
Solution Approach 1:
The patent performs preliminary behavior profiling and model fitting before the actual intelligence transplantation process. By pre-establishing the hierarchical model structure and fitting it to behavioral data, the system ensures reliability in the transplantation while maintaining ease of implementation through a structured, step-by-step approach.
Solution Approach 2:
The patent incorporates feedback mechanisms where the fitted models are validated against actual human behavior data. This feedback loop ensures that the transplantation model reliably captures human intelligence characteristics while allowing for adjustments that maintain implementation ease.
Data Source
AI summary
Provided are an electronic device for precision behavior profiling for transplanting humans' intelligence into AI and an operating method thereof, which may be configured to theoretically design at least one environmental factor, fit a first level model from human's processing data for a task based on the environmental factor, fit a second level model from processing data of the first level model for the task based on the environmental factor, and determine the second level model as a transplant model for humans' intelligence based on a correlation between the first level model and the second level model through profiling for the first level model and the second level model. According to various embodiments, the human's processing data may include at least any one of behavioral data or a brain signal generated while the human processes the task.