AI Edge-Case Resolution Using Human Task Decomposition
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Solution Overview
Problem
Artificial intelligence systems face challenges in resolving edge-cases in real-world scenarios due to the prohibitive amount of training data required to handle infrequent and unusual conditions, leading to potential failure in completing tasks successfully, safely, and in a timely manner.
Innovation Solution
A system and method that involves receiving event data from an intelligent software agent, identifying tasks based on the data, providing these tasks to user clients for user input, determining a remedial action, and sending it back to the agent to resolve the edge-case, utilizing selective compression, encryption, and user specialist profiles for efficient and robust resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is used to train AI systems to handle common situations, then the system can perform well in typical scenarios, but the system cannot reliably handle edge-cases due to the prohibitive amount of training data required
Solution Approach 1:
The patent introduces a human operator as an intermediary component that activates when the AI system encounters an edge-case. The human operator receives information about the edge-case, provides guidance or corrective input, and enables the system to resolve situations that the AI cannot handle independently. This mediator approach allows the system to handle edge-cases without requiring extensive training data, as humans supplement the AI's capabilities in real-time.
Solution Approach 2:
The system performs preliminary actions by detecting edge-cases early in the workflow and activating the human operator before the edge-case resolves itself or causes failure. The human operator is alerted in advance and can provide guidance that prevents the edge-case from escalating into a failure condition. This preliminary intervention allows the system to handle rare situations effectively without needing to train on every possible edge-case scenario.
2Reliability
If the AI system operates autonomously without human intervention, then the system can respond quickly to common situations, but the system fails to resolve edge-cases confidently and safely
Solution Approach 1:
The system dynamically adjusts its level of automation based on the situation at hand. For common situations, the AI operates autonomously with full automation. When an edge-case is detected, the system transitions to a hybrid mode where human operator input is required. This dynamic adjustment allows the system to maintain high automation for typical operations while ensuring safety and reliability when edge-cases occur, avoiding the need for constant human supervision.
Solution Approach 2:
The system incorporates feedback mechanisms where the human operator provides guidance or corrective input when edge-cases are detected. The operator's feedback is processed and used to adjust the system's response, ensuring that edge-cases are resolved safely and correctly. This feedback loop maintains reliability by allowing human expertise to correct or enhance AI decisions in critical situations.
3Productivity
If the system waits for extensive training data to be collected from real-world edge-cases, then the AI can learn from actual occurrences, but the system cannot resolve current edge-cases in a timely manner
Solution Approach 1:
The system takes preliminary action by detecting edge-cases in real-time and activating the human operator immediately, rather than waiting for extensive training data to be collected from real-world occurrences. This preliminary intervention enables the system to resolve current edge-cases quickly and safely, while the data from these resolutions is then used to train the AI for future similar situations. This approach decouples the timing of resolution from the timing of learning.
Solution Approach 2:
The system uses feedback from real-time edge-case resolutions to improve future performance. The data collected from human operator interventions and resolved edge-cases is fed back into the training system to enhance the AI's capabilities for handling similar situations in the future. This feedback mechanism ensures that the system learns from actual occurrences without delaying the resolution of current edge-cases.
4Productivity
If the AI system makes decisions without human input, then the system can operate efficiently and quickly, but the system cannot handle unpredictable edge-cases with confidence
Solution Approach 1:
The system dynamically adjusts its decision-making process based on the situation. For common situations, the AI makes decisions autonomously with high efficiency. When edge-cases are detected, the system transitions to a mode where human operator input is sought, ensuring confidence in handling unpredictable situations. This dynamic approach maintains high productivity for typical operations while ensuring reliability when edge-cases occur.
Solution Approach 2:
The human operator serves as an intermediary that activates when the AI system encounters edge-cases it cannot handle confidently. The operator provides guidance and validation for decisions in these critical situations, ensuring reliability without impacting the efficiency of routine decision-making. The intermediary approach allows the system to maintain high productivity while incorporating human expertise when needed.
Data Source
AI summary
Event data is received from an intelligent software agent controlling an endpoint in an environment, the event data representing an edge-case and including environment information from a time window before the edge-case. Multiple tasks are identified based on the event data. Each task is provided to a user client among more than one user clients. Respective user inputs are received from the more than one user clients, wherein each user input corresponds to the task provided to that user client. A remedial action is determined by combining the user input from each task. Resolution of the edge-case is initiated by the intelligent software agent based on the remedial action.