A more layman way to describe it that avoids too much over simplification is that these learning models try to group things and apply probabilities to sequences of groupings.
E.g.; A word is a grouping of letters, try to find the sequences of letters with the highest probabilities.
A phrase is a grouping of words, with punctuation marks.
Try to find the sequences of words with the highest probabilities.
A sentence is a grouping of phrases. Try to find the highest probability sequences.
A paragraph is a sequence of sentences. And so on and so on.
Within very narrow domains (specific writing styles, say technical or legal writing), these models can be very accurate, since the sequencing of words into phrases, and phrases into sentences, and sentences into paragraphs etc., are very predictable. People call this kind of predictable sequencing a 'style', and it aids us in understanding text more quickly. More generally across all domains, it's much harder to accurately predict these sequences, because AI identify the 'style' of a text, purely from the text itself. No context surrounding the text is given to the AI, and so it guesses.
For example:
a political press release, will be written in one style of writing. And a company marketing press release will be written in a slightly different style of writing. As humans, we can easily distinguish between what is commercial marketing, and what is political, because we are given that information upfront. In latin (the choice language for some mathematicians and logicians for historical reasons), we have the information 'a priori'. A learning algorithm, isn't given that information up front, and must determine only from the text itself, whether it is more likely to be a marketing release selling some product, and therefore it should adopt a certain language style, or a political release selling an ideology and therefore should adopt a slightly different language style.
When we don't know the right answer, and have no way to determine it, the solution that most computers are programmed to adopt is a minimax solution, i.e., minimise the maximum possible error. It does this by sort of mixing and matching both marketing and political styles.
When a human reads it, sometimes it looks very strange and funny. Usually this is because it has some distinguishing feature, that we can immediately recognise as placing it as either a political or marketing document, i.e., a company name, a political party, a corporate or political letterhead, a famous person's name etc. The computer naturally doesn't know who Donald Trump is, since we haven't taught it who or what a Trump is, so it doesn't give it any precedence over any other word on the page. Actually, in the case of Donald Trump, I bet if you took the dates off of all of his tweets, even humans would have a hard time distinguishing if they were political or commercial in nature.