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Current approaches to chemical map generation from hyperspectral images are based on models such as partial least squares (PLS) regression, generating pixel-wise predictions that do not consider spatial context and suffer from a high degree of noise. This study proposes an end-to-end deep learning approach using a modified version of U-Net and a custom loss function to directly obtain chemical maps from hyperspectral images, skipping all intermediate steps required for traditional pixel-wise analysis. The U-Net is compared with the traditional PLS regression on a real dataset of pork belly samples with associated mean fat reference values. The U-Net obtains a test set root mean squared error that is 7% lower than that of PLS regression on the task of mean fat prediction. At the same time, U-Net generates fine detail chemical maps where 99.91% of the variance is spatially correlated. Conversely, only 2.37% of the variance in the PLS-generated chemical maps is spatially correlated, indicating that each pixel-wise prediction is largely independent of neighboring pixels. Additionally, while the PLS-generated chemical maps contain predictions far beyond the physically possible range of 0%-100%, U-Net learns to stay inside this range. Thus, the find - ings of this study indicate that U-Net is superior to PLS for chemical map generation.

Fujitsu and others have developed a device that uses artificial intelligence technology to judge the fat content of frozen albacore tuna, a widely used indicator to determine the quality of the fish. Without relying on trained human visual inspections, the automated inspection device makes it possible to quickly determine whether frozen tuna portions should be distributed and labeled as high-quality, fatty bintoro tuna or used to make processed products. It is thus expected to help expand the distribution of albacore tuna that can be eaten raw, the developers said Wednesday. The companies will launch the device in Japan in June, targeting seafood processing firms and others. They aim to broaden the scope of automatic judgments to also cover other fish species with high distribution volumes, such as yellowfin tuna and bonitos, enabling assessments of the freshness, texture and taste of the fish as well.

Explainable artificially intelligent (XAI) systems form part of sociotechnical systems, e.g., human+AI teams tasked with making decisions. Yet, current XAI systems are rarely evaluated by measuring the performance of human+AI teams on actual decision-making tasks. We conducted two online experiments and one in-person think-aloud study to evaluate two currently common techniques for evaluating XAI systems: (1) using proxy, artificial tasks such as how well humans predict the AI's decision from the given explanations, and (2) using subjective measures of trust and preference as predictors of actual performance. The results of our experiments demonstrate that evaluations with proxy tasks did not predict the results of the evaluations with the actual decision-making tasks. Further, the subjective measures on evaluations with actual decision-making tasks did not predict the objective performance on those same tasks. Our results suggest that by employing misleading evaluation methods, our field may be inadvertently slowing its progress toward developing human+AI teams that can reliably perform better than humans or AIs alone.