A Solution for New Operational Environments in AI

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A science-based certification methodology is being proposed to meet the challenge of the opaque nature of AI’s black-box models. The methodology will assess the viability of employing pre-trained data-driven models in new operational environments. Researchers at Tennessee State University as posted by arXiv have come up with a methodology that introduces tools to facilitate the development of secure engineering systems that will provide decision-makers with confidence and trustworthiness and safety of AI-based models across diverse environments characterized by limited training data and dynamic, uncertain conditions. The study illustrates, through the use of simulation results, how the proposed methodology efficiently quantifies physical inconsistencies exhibited by pre-trained AI models.
Artificial Intelligence (AI) has gained an enormous amount of growth and application across many fields and has completely changed the engineering sector over the last ten years. Engineers have been able to solve complex problems and expedite operations within several engineering disciplines. The automation of tasks has changed engineering while improving designs and enabling predictive analysis. Fields such as finance and healthcare are too delicate to be accepted by current AI models such as Artificial Neural Networks, Ensemble Approaches, etc.
An Artificial Neural Network allow programs to see patterns and solve problems in artificial intelligence. It is an interconnected group of nodes, much like neurons in a brain (). Ensemble Approaches use multiple learning algorithms to get better performance in a predictive sense than could be gained from any constituent learning algorithms by themselves.
Traffic state estimation, or TSE, is important for certification to ensure their effective performance across various environments. Applications such as robots in work spaces shared with humans are important in this estimation. The team’s findings in the study have implications for developing the TSE area and advance knowledge of certifying deep learning models for safety-critical applications.
In these scenarios, new operational environments refer to the real-world settings where an AI system is deployed and differ from those used to develop and test such a system, which expose it to unfamiliar situations and inputs. There is a focus on transparency, which encourages a better understanding of model judgments and fosters public confidence and facilitates regulatory compliance.
There are several examples of artificial intelligence anticipating scenarios such as natural disaster prediction for wildfires, earthquakes, or hurricanes. They need to be certified in order to gain the ability to deliver accurate early warnings. AI certification is necessary to address new operational contexts in aerospace and aviation such as weather disturbances, air traffic changes and emergency scenarios. In the fields of manufacturing and industrial robotics, AI models need to be certified in order to operate in various production environments in anticipation of unforseen production difficulties. AI models in security and surveillance particularly need certification in unpredictable circumstances. The abrupt volatility seen in financial markets are another reason for the need of certification in new operational settings. The other areas highlighted by the team include Power Grid Management and applications in space exploration.
The issue of ethics may arise if AI models are not certified or vetted without knowing their limitations. The understanding of pre-trained models can be tricky due to their complexity and difficulty in interpretation.
The team provides information about traffic flow physics and its relevance to AI models within the context of traffic data certification. The conservation of vehicles principle based on mass conservation is introduced as a fundamental physical concept that governs traffic flow. The emphasis is on deep learning models in forecasting traffic conditions along with the importance of labeled training data and computational resources. The mathematical measure known as the cost function, or objective function, is one of the most important measures in deep learning that quantifies the disparity between a neural network’s expected output and the actual target values.
By comparing physical constraints and the laws of physics, the team aims to augment the reliability, robustness and trustworthiness of deep learning models that have been designed by traffic state estimation. Predicted values for velocity, density and predicted flow and their alignment with actual influx and outflow of vehicles will insure adherence to fundamental principles.
Science-based certification of AI models ensure the system’s correctness and dependability. The Lax-Hopf method is used for generating the synthetic datasets for traffic, adhering to Greenshields’ model and traffic conservation laws. Greenshields’ model is a model of uninterrupted traffic flow that predicts and explains trends observed in real traffic flows.
The team was able to present a method to validate the machine learning model’s traffic state estimations and identify inconsistencies by benchmarking it against well-defined traffic behavior consistent with fundamental laws of physical conservation. By applying the science-based metrics, their approach offers a standardized method that is reproducible to certify AI systems for safety and reliability when in environments beyond their training scope.