Awesome list. We need more of this kind of stuff to really understand how AI is working and why/when it doesn't. -- Anecdote: I did an experiment in applying

Awesome list. We need more of this kind of stuff to really understand how AI is working and why/when it doesn't. -- Anecdote: I did an experiment in applying machine learning to resumes. The exercise was to create a classifier for resumes of people who were fired or quit within 6 month of starting their job[1]. After several days of getting and cleaning the data I run a bunch of different off-the-shelf algorithms on the data set. To my surprise one of them got 85% accuracy. I was incredulous, because it's a very high number to get on the first try, without optimizations, on pretty fluffy data. So, I started looking into which keywords were the most significant. Turns out, the algorithm learned to detect resumes of interns, who usually went back to school after summer ended. Unfortunately, I never had time to finish that project or do further analysis on my data. -- [1] Yes, I fully understand the ethical implications of doing such classifications. This wasn't going to go in production, I just needed some real-life goals to see whether resumes are predictive of anything at all. -- PS: Mandatory reference: Soma. Great game with some related themes.