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Robotic test and inspection practices are being used in a wide range of applications across a diversity of industries. This reflects the many operational and economic benefits arising from their use which include the ability to automate certain laborious manual methods; operation in hazardous locations; uses in inaccessible environments such as within pipelines; the ability to deploy several different techniques simultaneously and thus detect multiple potential defects; reduced workforce costs; and, very often, more rapid testing and greater data acquisition rates than are possible with human operators.


  • Data Validation: The effectiveness of AI systems is largely dependent on the quality of training data, including aspects such as bias and variety.

  • Core Algorithm: The heart of AI systems is built on algorithms, which process data and generate insights. Model validation, learnability, algorithm efficiency and empathy are among the key features of this approach.

  • Non-functional: Performance and Security Testing: Performance and security testing is integral to AI systems. This also includes aspects such as regulatory compliance.

  • Systems Integration Testing: AI systems are designed to operate in the larger context of other systems and to solve specific problems. This requires a holistic assessment of AI systems. Thus, integration testing is of primary importance when multiple AI systems with conflicting goals are deployed together.