Customized AI Models With Tailored Knowledge for Lower Compute
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Solution Overview
Problem
Existing large language models (LMs) are computationally expensive, difficult to interact with, and require significant resources, making them slow and inefficient for user-specific tasks.
Innovation Solution
Customizable AI models are generated with tailored knowledge bases, capabilities, and specific instructions to enhance computational efficiency and accuracy, reducing the need for extensive inference processes and network interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large language models are used to ensure comprehensive knowledge and capability, then model accuracy and versatility are improved, but computational cost and resource consumption increase significantly
Solution Approach 1:
The patent segments the large language model into multiple smaller specialized models, each trained on specific datasets for particular tasks or domains. This segmentation allows the system to deploy only the necessary model size for each specific task, reducing overall computational cost while maintaining accuracy for targeted functions.
Solution Approach 2:
The patent applies local quality by creating models with specialized knowledge bases tailored to specific domains or tasks. Each model is optimized with relevant data and capabilities for its particular function, rather than using a single general-purpose large model, thereby reducing computational resources while improving targeted accuracy.
2Adaptability or versatility
If large language models are deployed to handle diverse tasks, then model versatility is improved, but interaction complexity and difficulty increase
Solution Approach 1:
The patent segments versatility across multiple specialized models rather than consolidating it in one large model. Each model handles specific task types efficiently, making interactions simpler and more intuitive for users while collectively providing broad versatility through the model ensemble.
3Reliability
If comprehensive training data is used to improve model knowledge, then model capability is improved, but training time and computational resources increase
Solution Approach 1:
The patent segments the training process into multiple smaller training campaigns, each focused on specific datasets and capabilities. This allows parallel training of specialized models with smaller, targeted datasets, reducing individual training times while collectively building comprehensive model capability across the ensemble.
Solution Approach 2:
The patent applies preliminary action by pre-training specialized models on domain-specific datasets before deployment. This preliminary specialization reduces the need for extensive fine-tuning and inference-time computation, thereby reducing overall training time and computational resources while maintaining high capability.
Data Source
AI summary
While AI models, like large language models, are powerful tools with multiple applications, they can be complex to use and can require a lot of resources to operate. The disclosed systems and methods provide tools to generate customized models (or AI agents) that are configured with features like tailored knowledge, capabilities, and instructions that make them faster, more efficient, and use less computational resources. AI agents may offer several technical advantages of improved efficiency, resource use, and connectivity. This disclosure describes systems and methods to configure, evaluate, generate, and deploy the custom models that can more efficiently run specific tasks. Disclosed systems and methods are configured to, for example, receive a query to generate a custom model, generate the AI agent custom model with the information in the query, and then resolve user queries more efficiently using the custom model.