[BooglyWoo]

I'm not sure that quite addresses the problem here.

After all there is no clear definition of 'what dogs look like' (in the sense of a collection of logical rules), but deep learning models excel at detecting them, when provided with enough positive examples.

If it's possible for humans to agree on whether a given article is clickbait or not, we should be able to put together an adequate dataset for training a system to classify them too. From the linked article I am unable to discern how the training dataset was labelled.

In other words, the fact that 'clickbait' is a nebulous concept shouldn't preclude machine learning from being able to detect it.

[astrodust]

Just as "dogness" is a factor, so is "clickbaityness". You're right, this is all about thresholds.

[kristianc]

I often wonder what Wittgenstein would have made of today's models of machine learning / deep learning https://en.m.wikipedia.org/wiki/Family_resemblance

[BooglyWoo]

Me too. My reading of the Blue and Brown books led me to believe Wittgenstein's conception of meaning is inextricably tied up with the notion of "learning" and exposure to language and its use. Rather than meaning being contingent on 'hard' logico-mathematical derivations of formal semantics.

This contrast seems somewhat reminiscent of the complementary approaches of hard-coded rule based AI vs machine learning.