Nikhil: Quora, as you probably know, is a question and answer website. We think of ourselves as a knowledge platform. As we became larger (as we get more content and more people), the quality of content was expected to go down but we have managed to keep it high even at a scale of 100M monthly uniques and growing - mostly through machine learning. We do a lot of work behind the scenes using machine learning to keep the content quality high. A big part of this is understanding whether the content quality to start with is good or bad. 

Nikhil: I think that’s the most interesting part of the life cycle of machine learning solution. The problem with these systems is that there is no obvious source of data to train and benchmark. A problem like answer quality prediction is not like ad prediction where you just maximize the clicks themselves. I think this is where the secret sauce is.

Nikhil: I think the most important thing would be using machine learning in the right places. I think a lot of people are just asking machine learning to optimize for the wrong things. Optimizing for the right things is much harder because you might not have the data, and it’s harder to formulate problems like that. I’m hoping that attendees will walk away with a much better understanding of how they can make their machine learning algorithms do more nuance subjectivity predictions than they are doing right now.