Detailed Mechanism
Collection of Empirical Data:
When Strant executes a task (e.g., predicting stock prices, analyzing cash flow, or evaluating real estate values), the entire process and its outcomes are recorded. This recorded information comprises:
+ Input Data: The data utilized to generate the prediction.
+ Predicted Result: The result generated by Strant AI, for example: "Stock A will increase by 5% next week."
+ Actual Result: The actual outcome, for example: Stock A only increased by 3%, resulting in a discrepancy.
Evaluation and Comparison of Results:
The actual results are compared with the predicted results to assess the level of accuracy:
+ Discrepancy Identification: Strant AI calculates the magnitude of the difference between the prediction and the actual outcome.
+ Identification of Unaccounted Factors:
If the AI predicts stock prices based on news and charts but does not account for an unexpected economic event, the system will register this omission.
Feedback and Retraining Loop:
The actual results and newly identified factors are fed back into the system:
Adjustment of Weights within Fusion Modules:
If a Data Learning (DL) Module produces an inaccurate prediction, its weight will be reduced in future similar tasks.
Retraining of DL Modules:
The modules are updated to incorporate the new empirical data, thereby enhancing their analytical capabilities.
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