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Probably bias on the author's side where he would select those words over the nonsense outputs or the unfunny ones.

The names are done on a letter basis rather than a word basis judging from some of the outputs.

Even still, the algorithm has no knowledge of these words. The likelihood of producing them is infinitesimal. Why would it produce real words more often than gibberish, or words in every other language on earth? I don't think this argument holds.

If the algorithm is trained on English words, it will learn English phonotactics (i.e., syllable structure, what sounds are allowed at the beginning of words, etc). That makes it much more likely to generate valid words, as the set of strings that follow English phonotactic rules << the set of gibberish strings.

Agreed, but I can't imagine with a sample space of just 7700 paint names that it would generate turdly any more frequently than bvlogd or a gazillion other marginally pronounceable six letter strings, without some additional bias not mentioned in the article.

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