I think the most impressive part is that it figured out that bar is something that could be an obstacle that you would have to duck. The other things like potential wordplay it can kind of try to bruteforce to see which words are most probabilistically used in order to create wordplay.

If it develops connections between words like "bar" => "sometimes an obstacle" => "can be ducked", that could explain it.

Like it would have all these attributes for each of those words, or concepts that relate to that word. "bar" has properties like "means multiple things", "often used in jokes", "could be an obstacle". But it must have quite a bit of properties.

I'm not sure how this AI is built internally, but if it works something that has this network of words and concepts that it can have weighted connections to each other, then you could retroactively come up with how the AI could have got to that result.

So user first says that they want a joke to be explained. It adds "joke" as a concept.

Joke has strong network connections to something like "puns", "unexpected punchlines", "absurdity", etc. And then when a joke is presented it will try to find connections with strongest weights and least concepts travelled. Like if it's a pun joke, it will go through all the words and will see that "bar" => "multiple meanings" => "pun" => "joke".

But then is it pre programmed to consider both meanings when it tries to analyse a joke that is a pun or has it somehow learned to come to it on its own?