When looking up projects to study I had discovered an interesting report on algorithms used to detect gender in computer vision. As someone who is particularly interested in working with computer vision, I suggested we go from there, but Billy brought up a good point in that, although interesting, there was pretty limited usage for such a function. He, in turn, suggested us to study NMT or Neural Machine Translation. NMT is what most digital keyboards, translation services, etc use now to predict text. It is considered a significant improvement from its predecessor, Statistical Machine Translation, as it uses far less memory and predicts individual words based off context, intent, and relation between certain words as opposed to the statistical usage of words in phrases, which requires a lot of subcomponents to work together. As we discussed about NMT further, I started to see a lot of usage for this system, both practically and artistically. A project idea I had come up with was a robot that cuts you off as you try to talk, and attempts to finish your sentences for you. Because NMT tends to predict words based off it’s perceived intent and context of only a single sentence, chances are it would be very off-base with its prediction, though legible and grammatically correct. I’ve had many conversations with friends where it seems like we are on the same page but are actually in entirely different head-spaces, so when they finish my sentences I’m taken slightly aback by what they think I’m trying to say. That feeling is something I would try to replicate with this project idea.