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In recent years, advances have been made in the field of machine learning and artificial intelligence (AI), primarily through developments in deep learning neural networks (DLNN). Challenges however exist with transitioning emerging DLNN algorithms directly for NDE applications. As a counterpoint to AI, intelligence augmentation (IA) refers to the effective use of information technology to enhance human intelligence. While attempting to replicate the human mind has encountered many obstacles over the years, IA has a much longer history of success. All forms of information technology, from writing cuneiform on clay tables to computers and smartphones today, have essentially been developed to enhance the information processing capabilities of the human mind. This paper introduces a series of best practices for intelligence augmentation in NDE, highlighting how the operator should interface with NDE data and algorithms. Algorithms clearly have a great potential to help alleviate the burden of ’big data’ in NDE; however, it is important that operators are involved in both secondary indication review, and the detection of rare event indications not addressed well by typical algorithms. Several past examples of transitioning algorithms for NDE applications are presented, emphasizing the successful interfacing of operator and software for optimal data review and decision making. |
