Because machine-learning practitioners have finite data and face the curse of dimensionality, they may need to interpolate between data points and smooth and amortize the process to support prediction.

“You know, we're machine learning people, we have a finite sample of data,”

“and our purposes might be, you know, at some point, we've got to interpolate between these data points,”

“we've got the curse of dimensionality and so on. So we have to put some kind of, I mean, we're amortizing this process, and we have to smooth it and make it amenable to the types of prediction that we're doing.”