One of the wild things about how these models work is how often things that aren't sampled directly end up a variable in the model via secondary signal.
They aren't keying queries by phase of the moon. But if, for example, more people talk about camping outdoors when the moon is full, and they're using conversation topic and timestamp as signal in what eventually becomes training data, it's not impossible the model has learned something about moon-phases.
That's the kind of thing that's hard to prove had no impact on an answer.
How sure are you that the phase of the moon is not an input to the system somewhere? http://www.catb.org/jargon/html/P/phase-of-the-moon.html
One of the wild things about how these models work is how often things that aren't sampled directly end up a variable in the model via secondary signal.
They aren't keying queries by phase of the moon. But if, for example, more people talk about camping outdoors when the moon is full, and they're using conversation topic and timestamp as signal in what eventually becomes training data, it's not impossible the model has learned something about moon-phases.
That's the kind of thing that's hard to prove had no impact on an answer.