In “Network, Machine, Computation, Formula for the Future: Outlining the Contours of the Post-Cyber Digital Generation,” I discussed how people have been encouraged to think about the relationship between the exterior appearances of machines and human beings through several contemporary examples, including the defeat of the best human Go player in the world by Alpha Go, the Go-playing computer program based on machine learning; the autonomous responses to the host’s tease when attending Good Morning Britain by the AI humanoid robot, Sophia, who now has a Saudi Arabian citizenship; the robotic double of the Osaka University professor, Hiroshi Ishiguro, who created the robot based on his own image that amazed the world; and Erica, allegedly the most beautiful humanoid robot in the world.

In the original vision of artificial intelligence (AI) in 1950s, the goal was to teach computer to perform a range of cognitive tasks. They included playing chess, solving mathematical problems, understanding written and spoken language, recognizing content of images, and so on. Today, AI (especially in the form of supervised machine learning) has become a key instrument of modern economies employed to make them more efficient and secure: making decisions on consumer loans, filtering job applications, detecting fraud, and so on.

What has been less obvious is that AI now plays an equally important role in our cultural lives, increasingly automating the realm of the aesthetic. Consider, for example, image culture. Instagram Explore screen recommends images and videos based on what we liked in the past. Artsy.net recommends the artworks similar to the one you are currently viewing on the site. All image apps can automatically modify captured photos according to the norms of "good photography." Other apps "beatify" selfies. Still other apps automatically edit your raw video to create short films in the range of styles. The App The Roll from EyeEm automatically rates aesthetic quality of you photos.
(. . . )

Does such automation leads to decrease in cultural diversity over time? For example, does automatic edits being applied to user photos leads to standardization of “photo imagination”? As opposed to guessing or just following our often un-grounded intuitions, can we use AI methods and large samples of cultural data to measure quantitatively diversity and variability in contemporary culture, and track how they are changing over time?