That's not a problem at all! In the fortune 500 there are plenty of companies with "problems" that used to be solved with boring things like "averages" and "business logic", but now you can replace those things with LSTMs and Deep NLP models and get half the performance with several orders of magnitude more complexity! The best part is the people building these systems have none of that annoying "engineering background" baggage that will mean they don't worry about stupid stuff like support and maintenance, or even basic debugging: if the model breaks you just build a brand new one!

As an added bonus, people with these skills cost x3 as much as a standard engineer. HR will love it!

Who needs a mean when you can train a model to calculate means....

This made me lol. The comic is seriously a great intro learning resource; it didn't occur to me the subtext might be "don't attempt ML if you're not at Google".

The message should be, whether intended or not, for Martha to send this comic to her boss and begin scheduling brainstorming meetings. With the vast plethora of introductory resources for ML today, there's no good excuse for both supervisors and engineers to understand what tools it might provide.

The phrase "we need to add AI to everything" should be met with as much consternation as "we need to promote synergy". Precision is important. It's the difference between spending 6 months developing an deploying trained neural nets and 60 minutes for linear regression.

You'd be surprised how many investment proposals still contain 'AI' just to sex them up. Even if it isn't applicable. Even if the application suggested is way beyond what current AI capabilities are. Case in point: received just such a proposal last week.

> The comic is seriously a great intro learning resource

No, it isn't. It's horrible. It read like advertising copy to sway dumb investors into pouring their life savings into Google stock.

Well she was warned not to open that door.

Managed healthcare companies have huge data and they have begun to use ML to classify their documents as medical and non-medical, as quality of care and quality of service etc., based on the text they extract from their members' health insurance claims. Generally the extracted text needs a lot of preprocessing.

... so that puts her about par in the industry, no?

Maybe by the end of part 3.

I just had a flashback and blacked out for 15 minutes in a spat of PTSD from your comment, please sir use your words more carefully they are powerful and over the target.