Begin with the decision-maker

I enjoyed the article “The first step in AI might surprise you” on AI, ML, Data Science from Cassie so much, I decided to steal some quotes:

Leaders, figure out who’s calling the shots.
If it’s you, then let’s designate you The Decision-Maker for this project.
Otherwise, delegate the position to someone else and ask them to read the rest of this while you play outside in the sunshine.

The right first step is to focus on outputs and objectives.

Spend some time figuring out what looks promising enough to pursue, then come back to machine learning when you’re ready.

Figuring out what problem ML/AI will solve for you is the first and most important step in your project, but unfortunately it’s quite often taken by the wrong people in an organization. While it’s supposed to fall squarely within the decision-makers’ remit, for some reason leaders try to avoid their duties by hiring a bunch of PhDs and sending them off to “Go sprinkle machine learning over the top of our business so… good things happen.” What could possibly go wrong?

It takes business savvy to properly think through what an ML/AI system is supposed to do for you and why it’s worth building. Focus on this first, before getting anywhere near the nitty gritty, including figuring out whether or not the algorithm that’ll solve your problem is considered AI or ML

Build the habit of not taking scientific findings too seriously before you’ve looked up how the metrics are defined.

“Too easy, give us something harder!”
(see also the overconfidence effect.)

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