Deep Causal Learning for Memory Data Management
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Solution Overview
Problem
Existing machine learning techniques face challenges in determining which and how much data to keep in memory to operate optimally under constraints of limited data storage, computing power, and latency, especially when assumptions about reward distributions and data stationarity are broken.
Innovation Solution
Deep Causal Learning (DCL) injects randomized controlled signals into electronic memory, computes marginal values of data points, and optimally manages data storage by discarding historical data while acquiring new data, allowing for finite and bounded data storage and processing requirements, and estimating the opportunity cost of reducing the data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passive observational machine learning models are trained with large amounts of historical data, then model accuracy is improved within the historical state space, but the model's external validity decreases when the world changes, resulting in catastrophic consequences
Solution Approach 1:
The patent implements dynamic data management where the system continuously updates which data points to retain based on changing environmental conditions. The data retention policy is not static but adapts over time, allowing the model to maintain accuracy while responding to world changes through active learning mechanisms that dynamically adjust data storage and retraining priorities.
Solution Approach 2:
The system employs feedback mechanisms where model performance is continuously monitored and used to inform data retention decisions. When performance degradation is detected, the system retrieves relevant historical data and triggers retraining, creating a closed-loop system that balances accuracy maintenance with adaptability to changing conditions.
2Measurement precision
If machine learning models are updated frequently to maintain accuracy, then model performance is improved, but large amounts of memory and processing power are required, resulting in significant latency associated with data transfer and retraining
Solution Approach 1:
The patent extracts only the essential data points needed for model updates rather than transferring and retraining on complete datasets. By identifying and retaining only the most valuable data points through marginal value computation, the system reduces data transfer volumes and accelerates retraining processes while maintaining model accuracy.
Solution Approach 2:
The system performs partial retraining using only the subset of data points that have the highest marginal value, rather than retraining on the entire historical dataset. This partial action approach reduces computational overhead and latency while still achieving the necessary model updates to maintain accuracy.
3Quantity of substance
If active machine learning methods accrue data continuously at high frequency, then more data is available for training, but data storage requirements and processing power increase significantly
Solution Approach 1:
The patent implements a selective data retention strategy where low-value historical data points are discarded and only high-marginal-value data points are retained. This discarding and recovering approach allows the system to maintain an effective training dataset without accumulating excessive data, thereby reducing storage requirements and processing power consumption while preserving the essential information needed for model updates.
Data Source
AI summary
Method for active data storage management to optimize use of an electronic memory. The method includes providing signal injections for data storage. The signal injections can include various types of data and sizes of data files. Response signals corresponding with the signal injections are received, and a utility of those signals is measured. Based upon the utility of the response signals, parameters relating to storage of the data is modified to optimize use of long-term high latency passive data storage and short-term low latency active data storage.