This article highlights a number of examples where a user is acting as a trainer for a computer that then acts as an image capture or generation device. The article seems to imply that in the future of image capture we may cede control and authorship to our computational tools.
The article begins by presenting the example of common icons for and 128,706 sketches of the camera — each of which tends to look like a classic point and shoot camera. The article begins this way so that, in it’s larger discussion of contemporary imaging, it can begin to show that the concept of the camera – or the things that we take images with – is shifting towards devices with more self agency and intelligence.
The key subjects that the article traverses through are YOLO Real-Time Object Detection, Google Clip, Pinterest visual search, and machine learning tools for image generation. What becomes evident is that the key paradigm shift is more about computers’ ability to interpret and modify image produced by cameras. In the case of the object detection the Google camera and Pinterest are using algorithms to understand the contents of the image. In these cases the computer generates a different result depending on the contents of the images is sees. The end of the article is where some more troubling subjects are explored. The article highlights how machine learning algorithms can generating photo real images. This computational power not only has the possibility of altering our understanding of cameras and photo equipment, bus also could begin to drastically alter our understanding of the truth of images.