Along with @maciejwolczyk we’ve been training a neural network that learns how to play NetHack, an old roguelike game, that looks like in the screenshot. Recently, something unexpected happened.
Along with @maciejwolczyk we’ve been training a neural network that learns how to play NetHack, an old roguelike game, that looks like in the screenshot. Recently, something unexpected happened.
Their problem:
The model wasn’t trained on a full moon. They had a system to set up the environment for replicable results but it didn’t include modifying the system time.
It reminds me of another bug with the system time, which a friend of mine encountered. He was working on hardware and he was getting a lot of units that worked fine at the factory, immediately failed at the client’s location, and then worked again when they were returned to the factory. It turned out that when these machines were turned on, their embedded OS automatically queried some server to update the current time. The client’s internet connection had such high latency that the server’s response only came back after the machine was already in use. This generated a huge delta-t value that triggered the sanity checks and shut the machine down. The factory had a much lower-latency connection and so the race condition could never be replicated there.
As for the weirdest bug I ever encountered myself: a compiler generating bad machine code. I have often said that the worst part of programming is that the computer always does exactly what you tell it to, but that was the one and only time in twenty years that the computer actually didn’t.
That reminds me of The case of the 500-mile email