AI Workflow Synthesis With Memory-Aware Tool Selection
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Solution Overview
Problem
Conventional AI techniques for scientific computing do not consider available memory resources, leading to system failures, inefficient task execution, and inaccurate general-purpose responses, particularly in exascale environments.
Innovation Solution
A system and method that utilizes an AI agent to generate tasks based on available memory resources, dynamically loads tools and models, and stores interaction data for retraining, enhancing accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If AI agents use general-purpose large language models for scientific computing, then ease of operation is improved, but measurement precision deteriorates due to inadequate scientific context
Solution Approach 1:
The patent introduces a scientific knowledge base as an intermediary component that provides domain-specific scientific context, facts, and principles to the AI agent. This mediator enables the AI to maintain general-purpose operability while achieving scientific accuracy by referencing established scientific knowledge rather than relying solely on general training data.
Solution Approach 2:
The system implements a multi-functional AI agent that can both execute general-purpose tasks and perform scientific computations. The agent integrates multiple capabilities including natural language processing, scientific knowledge retrieval, and code execution, allowing it to serve both general and specialized scientific computing needs simultaneously.
2Productivity
If AI agents execute scientific computations without considering memory resources, then productivity is improved, but reliability deteriorates due to system failure
Solution Approach 1:
The patent implements a feedback mechanism where the AI agent continuously monitors available memory resources and adjusts its computation execution accordingly. The agent receives feedback about memory availability and dynamically modifies task execution strategies, selecting appropriate computational methods based on real-time resource status to prevent system failure while maintaining productivity.
Solution Approach 2:
The system dynamically adapts its behavior based on available memory resources. The AI agent adjusts computational complexity, data precision levels, and task execution strategies in real-time according to memory availability, transitioning between different operational modes to balance productivity with system reliability.
3Measurement precision
If AI agents store interaction data in long-term memory for retraining, then measurement precision is improved, but loss of time increases due to data storage and processing requirements
Solution Approach 1:
The patent applies local quality by storing and processing only the most relevant interaction data selectively. Rather than uniformly processing all interaction data, the system identifies and stores only critical scientific computations and interactions that require retraining, optimizing the balance between improving precision and minimizing time expenditure on data handling.
Data Source
AI summary
Systems and methods for scientific computing using an artificial intelligence (AI) agent are disclosed herein. The system may receive a prompt input including a scientific query; generate, by processing the prompt input using an artificial intelligence (AI) agent including a trained machine learning (ML) model, a set of tasks for answering the scientific query; determine, by the AI agent, one or more tools to execute the set of tasks based on available memory resources; execute the set of tasks using the one or more tools to generate output data corresponding to the scientific query; determine, by the AI agent based on the output data, an observation associated with the scientific query or a ranked listing of the output data; and cause at least one of (i) the output data, (ii) the observation, or (iii) the ranked listing to be displayed on an output computing device.