In the age of big data, image perception is no longer a bodily cognitive process. Computer algorithms play a significant role in the pipeline of image generation, management, distribution, and reception. Below are a few examples that illustrate how image, and its associated algorithms, pose new challenges.
By facing such challenges, we have become increasingly dependent on the use of social media platforms to archive our everyday life images at both individual and collective levels. At the same time, the design of the information organization strategies, such as hashtag, personal relationship, and geographical data, helps shape the way in which we store and retrieve such large number of images. On the other hand, we envision more sophisticated machine learning algorithms to recognize and classify our images with accuracy that supersedes our human counterparts. Nevertheless, a significant number of such algorithms rely on supervised learning. The ‘supervision’ of such machine learning activities is actually funded and performed by large corporations, government, and academic institutions. It may be time to consider if we can put the ‘supervision’ back in public scrutiny. The theme of this year’s Conference on The Image aims to trigger responses and critical discussions of how we deal with image in this age of big data and artificial intelligence.
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