HQ gets a time-based discount for electricity? Time for all the freezers to go into a deeper freeze. HQ gets a message from the Demand Response program asking for a curtailment? Time for the freezers to let the goods come closer to their safe higher temperatures.
The final proof you get from formal methods is often irrelevant in my opinion. Most of the benefit comes from architecting the system so as much as possible can be verified and forcing yourself to make intentional decisions on the edge cases. The results are for other people.
I'm not sure you want to create a record of intentional decisions if you're at Facebook though.
The trouble is the simulation tools are either proprietary and thus have only a small mind share among software developers, or academic, and either way, PLC programmers have not gone near them yet.
But a decent LLM should be able to translate the description of a plant to a TLA+ model (or Alloy, or something similar.)
> My fraternity was more tame and we used to have guys gazing each other in the main room during parties.
Joining a fraternity makes you jointly and severally liable for anything your frat brothers do. It's the very opposite of working or investing in a limited liability company. Makes sense for a fraternity to be more tame...
Which is why I am sitting right now in a meeting and the presenter is logged in from Bangalore and I don't even want to know what `TZ=Asia/Kolkata date` will tell me.
Seems to me these LLMs have a critical mass of Python training data and Rust training data, so there's no advantage for Python there.
So as the article points out, an iterative process that catches the mistakes at compile time is much more suited for an AI than one that catches them at runtime.
HQ gets a time-based discount for electricity? Time for all the freezers to go into a deeper freeze. HQ gets a message from the Demand Response program asking for a curtailment? Time for the freezers to let the goods come closer to their safe higher temperatures.
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