Not really symbolic reasoning. I'm thinking more along the line of classic ML techniques like Random Forest/GLMs have long been successfully applied to business problems.
> I'd also add, maybe controversially, that anyone using "AI" to make some kind of basic prediction (say for personalization, demand planning, medical decisions, really most tabular data) is not going to have a business that lives or dies based on AI.
Unfortunately this rules out the vast majority of business use-cases. ML use-cases on structured data vastly outnumber those of vision/nlp/RL.
Also you seem to be restricting the term "AI" to Neural network-type techniques.
Yes, backend engineers do get paid more in general. I was just wondering why this runs contrary to OP's explanation that "closeness" to user triumphs job difficulty (when it comes to leverage/salaries).
Interesting. I wonder if this distinction applies within software as well. For example, systems programmers getting paid less than application programmers because the latter makes software "closer" to the user, and backend programmers getting paid less than frontend programmers for the same reason.
> If the Federal Reserve sticks to its guns and gets rates up, and keeps them there, it will be unsurprising to see "hard" engineering jobs be valuable again, while all the CSS people suddenly can't find two pennies to rub together.
I was under the impression that "hard" engineering isn't as financially attractive to investors due to the long payoff period as compared to web software. In that case, won't high interest rates actually hurt "hard" engineering due to a greater discount being applied to future cashflows?
> For instance building some state control engine for rockets etc.
Just out of curiosity: are there companies actually writing mission-critical code in a Haskell? My impression is that those normally tend to get written in C++ and the like.