SlammedĪ Google engineer, Blaise Aguera y Arcas, blasted the original study early last year, and pointed out various reasons why software should struggle or fail to classify human sexuality correctly. So, Leuner's AI performed better than humans, and better than a fifty-fifty coin flip, but wasn't as good as the Stanford pair's software. Humans got it right 61 per cent of the time for men, and 54 per cent for women, in a comparison study. Not amazing, but not completely wrong.įor reference, the Wang and Kosinski study achieved 81 to 85 per cent accuracy for males, and 70 to 71 per cent for women, using their datasets. A facial morphology classifier, another machine learning model that inspects facial features in photographs, was 62 per cent accurate for males and 72 per cent accurate for females. He found that VGG-Face, a convolutional neural network pre-trained on one million photographs of 2,622 celebrities, when using his own dating-site-sourced dataset, was accurate at predicting the sexuality of males with 68 per cent accuracy – better than a coin flip – and females with 77 per cent accuracy.
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