Hey, thanks for the thoughtful response. We're not doing any reinforcement learning, we just fine tune the net as we get more data (and occasionally train from

Hey, thanks for the thoughtful response. We're not doing any reinforcement learning, we just fine tune the net as we get more data (and occasionally train from scratch when we add a lot of new classes). In regards to (A), we plan to start skipping the selection steps for predictions that we're really confident in and will just add them by default. I think once we have enough data we might even be able to predict what users will eat before they take a picture. I eat practically the same thing for breakfast every day of the week so it could just log it for me without requiring me to do any work. Same goes for things like coffee shops, we don't ask for location right now but if you always get the same thing when you walk into a coffee shop, we could just log it for you based on the fact that you were there. B. We keep track of our predictions and what users end up logging so we can tell what our weaknesses are. When we train new models we prioritize the weakly performing classes, especially if they're popular among our users.