Producing a good machine-learning model depends both on methods that maximize performance on a given dataset and on the quality of the data.

“Yeah. So I think when we're talking about producing a good machine learning model, there's two dimensions. One is given a data set, you know, what are the training algorithms, neural architectures, and all that to maximize the model's performance on that data set.”

“Um, but then the other axis is to make a better model is just to have better data. More data, uh, what type of data, what kind of reward signals in that data, and obviously, you need both of those, uh, doing well in order to actually have a good model at the end of the day.”