As I understand it, ML is about the finding of those rules. The big example I can think of is stocks -- what signals are important in determining the fiscal health of a company or stock? Every trader or investor has their own ruleset they believe in, ML (and specifically Quant) is meant to dig through as much data as possible to find those signals that affect the as-best-understood quality of a pick.

For example, a human can think 'oh I will look at numbers like P/E and growth and...' whereas with ML you can feed it all those, and things like number of times the CEO has tweeted in the past week or the number of PR articles or even the language in PRs released to see if there is some strong correlative signal in the bunch, or if it is all just noise.