2018 Special Focus - Artificial Images and Visual Intelligence: Seeing in the Age of Big Data

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.

  • In 2015, Google revealed its “Deepdream” example software to visualize its internal artificial neural networks for image recognition. Creative coders adopted the software to generate hallucinatory and psychedelic imageries that were both real and surreal. Can computer software now claim the originality of such artificial images?
  • In February 2018, major international news websites posted a photo of a Chinese policewoman wearing a pair of sunglasses attached with a surveillance camera, claiming that the device was equipped with facial recognition technology to identify suspected criminals in crowds. Will the plots in the popular science fiction film “Minority Report” come true in the near future?
  • Controversial research in face reading artificial intelligence demonstrated the ability to predict political views and other personality traits from facial features. In the coming years, will machine learning algorithms understand us better than we understand ourselves?

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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