LMAO. and a good example how statistics ≠ understanding.pic.twitter.com/BhKnSaYKkG
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About your comment "not everyone is so and so..." that's exactly the nature of *commonsense models*: stochastic expectations on what are _likely_ to be true, not _necessarily_ true. Analogous to how *language models* are not about which word _must_ follow vs _could_ follow.
you could compare with actual human distributions of data. in this particular case the data seem unconvincing; the system doesn’t recognize that a campfire is different from arson. in other cases , comet does better
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Are you implying in this tweet that the query meant to talk about a campfire? Because I am not sure that _people_ could distinguish that with the amount of info you give in your query. E.g. it talks about a fireplace, and I'd think it would be unlikely to find that when camping
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Also it is pretty difficult to start a campfire or fire in a fireplace by just dropping a match on kindling and log since neither starts burning that quickly/match might just goes out. What you typically do it start burning something somewhat large and then drop that it. But ...
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