Programming is solved by LLMs, isn’t it?

AI should virtually eliminate coding and debugging.

This is a direct quote from an IBM report published in 1954 (here, page 2), if you replace AI with Fortran. It didn’t happen, not because Fortran wasn’t revolutionary at the time. It was the first commercial compiler, which took 18 person-years to develop.

Compiling didn’t “solve” it, and neither do LLMs. LLMs help solve (part of) the problem. They don’t solve exception handling. I wrote before about exception handling (or lack thereof) in most machine learning applications. We need to pay more attention to it.

Exception handling is difficult, if not impossible, to automate away because of the complexity and unintended consequences of human-machine (user-model) interactions. LLMs can certainly be useful for generating alternative scenarios and building solutions for them.

We will continue to benefit from the models that are increasingly available to us, including LLMs. Just remembering that the problem is not just pattern recognition, but also exception handling, should help us think about how best to use these models to solve problems.

This essay here is more from a software development perspective. From the essay:

You’d think 15 years into the smart phone revolution most people could operate an order kiosk or self-checkout without help. That’s certainly what stores had hoped. But as these are rolling out you can see how these systems are now staffed by people there to handle the exception. Amazon Go will be surly seen ahead of its time, but those are now staffed full time and your order is checked on the way night. And special orders at McDonalds? Head to the counter 🙂