I've worked in computational linguistics:
1) It's 'hard' because you need a lot of 'training data' in order to train models etc.. It's hard to get.
2) 'AI' type interfaces represent a whole new kind of UI challenge. For 'predictive typing' for example, you can optimize an algorithm so that it does better for 90% of the US population, but then it gets 'worse' for the remaining 10%. So it's a paradox. This can have weird effects.
For example, if you have an app in the app-store, you may leave the settings so that it's 'broadly optimal'. You get ok stars.
If you then make it 'better' for those 90%, you might get a little boost in ratings, but you get 1 and 0 star ratings from the 10% for whom it's a sub-par experience. This can destroy your product.
Anyhow - 'there is no right answer' often in AI, and setting expectations can be extremely difficult.
And all of that has nothing even to do with CS.
1) It's 'hard' because you need a lot of 'training data' in order to train models etc.. It's hard to get.
2) 'AI' type interfaces represent a whole new kind of UI challenge. For 'predictive typing' for example, you can optimize an algorithm so that it does better for 90% of the US population, but then it gets 'worse' for the remaining 10%. So it's a paradox. This can have weird effects.
For example, if you have an app in the app-store, you may leave the settings so that it's 'broadly optimal'. You get ok stars.
If you then make it 'better' for those 90%, you might get a little boost in ratings, but you get 1 and 0 star ratings from the 10% for whom it's a sub-par experience. This can destroy your product.
Anyhow - 'there is no right answer' often in AI, and setting expectations can be extremely difficult.
And all of that has nothing even to do with CS.