I'll just add that machine learning is learning from what is happening. It's essentially, optimizing itself. That way when the rain comes, it can learn what that means. The first time, it may do nothing and see what impact rain has on it. Then it may seek shelter and see the impact there. It's collecting data and acting on it depending on what it's been told to do (supervised) or what it wants to do (unsupervised).

What we are typically seeing, like with Google for example, the machine learning is referencing lots and lots of historical data. It's solving problems like when you type in a search engine a misspelling and picks up on similar misspellings for what it thinks you may be searching for. The more you say it's right or wrong, hopefully the smarter the optimization is the next time someone like you searches for something. If you ask me, it's been pretty good as of lately. It's even starting to stretch across products like detecting what you are searching for quicker on a few letters based on maybe what you already searched for on YouTube or something.

Another good one is Facebook. I'm almost certain it's measuring the time in seconds you hover over a post on your feed. Then it shows related posts to that content as you continue to scroll downward. For example, if you saw a friend post a video about the recent gun rights discussions, the machine learning algorithms bring those posts up to the forefront as you scroll down from other friends.

To end here, if you feel any of those two problems needs something equivalent to needing to know meteorology to avoid the rain, then I feel you are really overthinking the usage and the intent of ML in the real world. It's not trying to re-create a human brain in every application. It's trying to automate, learn from data, and of course, make smart decisions for particular subset of use cases -- NOT every use case or unknown use cases.

"You only had one job..."