Computer vision (CV) is a wide set of computational approaches to image processing and the use of computational intelligence to infer domain knowledge from input images, whether photographs, drawings, or video. Not all CV is AI (and not all AI is related to CV), so this is "just another application area" in many senses, like NLP. Images are relevant to both classical AI (for example diagnostic systems — is the tumor in this medical image benign? — or self-driving cars — is there a pedestrian about to cross the road?) and for GenAI (creating realistic-seeming pictures of settings and situations that do not exist in response of a text prompt from a user).
This module will help you do the following:
First, browse this opinion piece (and keep it in mind for Module 11, too). Then, pick at least one of the following textbooks to browse before the in-class discussion:
Based on the opinion piece and the textbook(s) you browsed, think about possible applications (one that you can envision, please, instead of ones you looked up on the internet) of facial recognition and whether any of them feel like perfectly safe to apply or are all likely to have dangeous caveats that could jeopardize a well-meaning deployment of such technology?
After this module, you should be familiar with the following concepts:
Remember that you can always look concepts up in the glossary. Should anything be missing or insufficient, please report it.