AI and ML in Testing
AI and ML have provided some great opportunities for assisting speed and quality of testing, when such large amounts of data require analysis, and highlighted the importance of a robust testing strategy for AI systems. While AI can effectively analyze and digest large datasets, ensuring the quality of the AI algorithm and the accuracy of its model is paramount. Factors such as avoiding biases, balancing accuracy with computational costs, and considering on-board processing limitations must be carefully addressed. How can AI assist when an aircraft is in flight, no longer ‘connected’? Qualifying an AI tool involves defining parameters, setting targets, and identifying patterns within the data. How can accuracy be achieve with several AI algorithms running concurrently?