"An aspect of this difficulty involves building an intuition for what tool should be leveraged to solve a problem."
While I agree with the good point about debugging, like many others, I am rather worried that we're increasingly deploying AI/ML where we shouldn't be deploying it. Hence, the above quote.
I’m old enough to have learned that the secret to success is much less knowing the tool of the moment than picking the right tool for a job.
The right tool may in fact be the new one, and LLM do open a lot of doors with zero shot capabilities, but oftentimes they can underperform a well tuned heuristic. It’s the ability to pick the right tool that is key.
Want to agree with you, as so many ML apps seem to be solutions looking for problems. But I actually feel that we are rapidly deploying ML in a development context for vastly improved results. The way that good models are built relies on many ML steps, and when the results finally come together the result is superior to what could have been custom designed. Broad adoption of something like probabilistic programming is coming soon.
While I agree with the good point about debugging, like many others, I am rather worried that we're increasingly deploying AI/ML where we shouldn't be deploying it. Hence, the above quote.