During the interview on Artificial Intelligence with Ben Shneiderman, he begins by asking the undergraduates a few questions about the different methods possible used to make predictions. Specifically the difference between machine learning and statistical method. While he continues to ask the undergraduates multiple questions on their thoughts regarding those to methods, I would be very interested to ask what he believes is a method that would allow us to ensure that the data we use adds to the accuracy of our models or if it is even possible to create such a method? As well what his thoughts on the importance of "good data" to the accuracy of our predictions in relation to the other elements of our models. Especially since this is relevant to the discussions we've been having in class recently about simulations and statistical methods and would help us understand how those differences play a role in machine learning and artificial intelligence.