My knee-jerk is to be upset because the risk factor of a poorly functioning pedestrian classifier is obviously higher than something like a sentiment analyzer, but at the same time, this dataset is just for educational purposes, right? Is Udacity actively recommending people use this in production settings?
I think this is the key. As a kid, I remember family member of mine would routinely take their computer into friends at their company's IT department (their personal computer, mind you) because they felt less embarrassed asking a coworker for help than their kids. I try to make sure my parents don't feel awkward or embarrassed for asking me with tech help.
A few years ago I joined a Rails shop, and one thing that always struck me was how many of the engineers didn't know SQL. Most of them had learned to code on Rails, and had always had SQL abstracted away via ActiveRecord.
I know this is not the point of this article, but as data analyst/scientist roles continue to climb in popularity, I'm curious if there won't be a similar trend with Python.
Fascinating, but I'm curious as to how the communication works here. Is it all visual—as in ants see what other ants are doing and adapt—or is there some chemical/pheromone communication going on?