Grammar’s resistance to artificial intelligence
Artificial-intelligence systems like Grammarly are trained with data; for instance, translation software is fed sentences translated by humans. Grammarly’s training data involve a large number of standard error-free sentences (so it knows what good English should look like) and human-corrected sentences (so the software can find the patterns of fixes that human editors might make). Developers also manually add certain rules to the patterns Grammarly has taught itself. The software then looks at a user’s prose: if a string of words seems ungrammatical, it tries to spot how the putative mistake most closely resembles one from its training inputs.
All this shows how far artificial “intelligence” is from the human kind (which Grammarly wants to correct to “humankind”). Computers outpace humans at problems that can be cracked with pure maths, such as chess. Advances in language technology have been impressive in, for example, speech recognition, which involves another sort of statistical guess—whether or not a stretch of sound matches a certain string of words. One Grammarly feature that works fairly well is sentiment analysis. It can rate the tone of an email before you send it, after being trained on texts that have been assessed by humans, for example as “admiring” or “confident”.
