It is useful if you don't care if the product is right.
Most engineering managers would think "this is great!" but the customer won't agree. The CEO will agree until the customers revolt.
Most engineering managers would think "this is great!" but the customer won't agree. The CEO will agree until the customers revolt.
Let's say you have a product search engine and you analyzed the logged queries. What you find is a very long tail of queries that are only searched once or twice. In most cases, the queries are either misspellings, synonyms that aren't in the product text, or long queries that describe the product with generic keywords. And the queries either return zero results or junk.
If text classification for the product category is applied to these long tail queries, then the search results will improve and likely yield a boost in sales because users can find what they searched for. Even if the model is only 60% accurate, it will still help because more queries are returning useful results than before. However you don't apply ML with 60% accuracy to your top N queries because it could ruin the results and reduce sales.
Knowing when to use ML is just as important as improving its accuracy.
I am against GPT-3.
For that matter I was interested in AGI 7 years before it got ‘cool’. Back then I was called a crackpot, now I say the people at lesswrong are crackpots.
As someone mentioned above, language models for embedding generation has improved dramatically with these newer MLM/GPT techniques, and even with improvement to F-score/auc/etc. for one use case can generate enormous utility.
Nay-saying really doesn't make you look intelligent.
I also have strong ethical feelings and have walked away from clients who wanted me to introduce methodologies (e.g. Word2Vec for a medical information system) where it was clear those methodologies would cause enough information loss that the product would not be accurate enough to put in front of customers.