Active Learning for AI Model Training Data
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Solution Overview
Problem
Current AI systems, particularly chatbots and conversational AI, face limitations in scalability and accuracy when dealing with complex tasks and open-ended conversations due to reliance on rule-based systems and traditional machine learning algorithms, which are not suitable for handling varied and nuanced human inputs.
Innovation Solution
The development of advanced AI models that reuse business conversations as training data, employ deep learning techniques, and combine them with traditional machine learning, using methods like convolutional neural networks and active learning to improve model accuracy and adaptability, along with the use of hybrid models that integrate deep learning and machine learning for enhanced performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rule-based systems and traditional machine learning algorithms are used, then system simplicity is maintained, but scalability and accuracy for complex tasks deteriorate
Solution Approach 1:
The patent combines rule-based systems with machine learning algorithms to create a hybrid architecture. The rule-based component handles simple, well-defined tasks while the machine learning component processes complex, nuanced inputs, allowing the system to maintain simplicity for routine operations while achieving high accuracy for complex conversations and tasks.
Solution Approach 2:
The system integrates multiple processing approaches (rule-based processing and machine learning) into a single universal platform that can adaptively handle both simple and complex tasks. This multi-functional architecture allows the same system to efficiently process varied input types without requiring separate specialized systems.
2Reliability
If larger and more accurate training sets are used, then model accuracy improves, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of training data including data cleaning, feature extraction, and initial model training on subsets of data before final model deployment. This preliminary action prepares the data and models in advance, reducing the time required for final training iterations and enabling faster deployment of accurate models.
Solution Approach 2:
The patent employs techniques such as training on representative subsets of data initially, using data sampling strategies, and implementing incremental training approaches. These methods allow the system to achieve sufficient model accuracy without processing entire massive datasets, thereby reducing training time while maintaining acceptable performance levels.
3Adaptability or versatility
If deep learning techniques are employed, then model adaptability and accuracy improve, but computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the deep learning architecture into multiple specialized components (e.g., separate neural networks for different NLP tasks, modular feature extraction layers). This segmentation allows each component to be optimized independently for its specific function, improving overall adaptability while managing computational complexity through specialized rather than monolithic processing.
Solution Approach 2:
The system applies deep learning techniques selectively to specific parts of the processing pipeline where they provide the most benefit, such as using neural networks for sentiment analysis or entity recognition while employing simpler methods for other tasks. This localized application of complex techniques maintains adaptability where needed while reducing overall computational complexity.
Data Source
AI summary
Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers. Yet another system for utilizing these various classification models is an intent based classification system for action determination. Lastly, it should be noted that any of the above systems may be further enhanced by enabling multiple language analysis.