Hybrid Human-Machine Platform for Sensor Data Classification
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Solution Overview
Problem
Current AI systems struggle with solving AI-complete problems in their full generality due to brittleness when faced with unexpected circumstances, lacking commonsense knowledge, and failing to perform well in real-world scenarios that require human-like understanding and contextual judgment.
Innovation Solution
A 'human-in-the-loop' approach is implemented, where human intelligence is integrated with machine decision-making systems to augment AI capabilities by querying a pool of agents for tasks that require human judgment, using a platform that combines machine and human intelligence to classify sensor data and improve decision-making accuracy through real-time feedback and training of predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine computation components are used to solve AI-complete problems, then processing speed and automation are improved, but reliability and accuracy deteriorate due to brittleness and lack of commonsense knowledge
Solution Approach 1:
The patent merges machine computation components with human agents into a hybrid system. The machine component handles initial processing and can solve straightforward problems autonomously, while human agents are invoked for complex or uncertain cases. This combination allows the system to maintain high processing speed for routine tasks while achieving high reliability through human judgment when needed, effectively resolving the contradiction between speed and accuracy.
Solution Approach 2:
The patent introduces a confidence assessment mechanism as an intermediary between the machine computation component and the final decision. When the machine's confidence in its answer falls below a threshold, the system invokes human agents as intermediaries to provide additional judgment. This mediator approach allows the system to maintain automation for high-confidence cases (preserving speed) while ensuring reliability for low-confidence cases through human intervention.
2Reliability
If human agents are used to provide judgment and analysis, then reliability and accuracy are improved, but processing time and system complexity increase
Solution Approach 1:
The patent applies partial action by having human agents intervene only when necessary, rather than involving them in all cases. The machine computation component handles the majority of straightforward cases autonomously, and human agents are selectively invoked only for cases where the machine's confidence is below the threshold. This selective involvement minimizes the time loss from human intervention while maintaining reliability for complex cases.
Solution Approach 2:
The patent segments the decision-making process into two distinct stages: an initial machine computation stage that handles routine processing, and a subsequent human agent stage that handles only the uncertain or complex cases. This segmentation allows the system to achieve high reliability for difficult cases through human judgment while maintaining fast processing for the majority of cases that the machine can handle confidently, thereby reducing overall processing time.
3Measurement precision
If more agents are queried to achieve required confidence, then decision accuracy is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent uses partial action by querying human agents selectively based on the machine's confidence assessment. Rather than querying a fixed number of agents for all cases, the system queries agents only when the machine's confidence falls below a threshold. The number of agents queried is adjusted dynamically based on the specific case's uncertainty, minimizing system complexity while achieving the required confidence level for accurate decision-making.
Data Source
AI summary
Data is received characterizing a request for agent computation of sensor data. The request includes a required confidence and required latency for completion of the agent computation. Agents to query are determined based on the required confidence. Data is transmitted to query the determined agents to provide analysis of the sensor data. Related apparatus, systems, techniques, and articles are also described.