[rpedela]

There are several use cases where ML can help even if it isn't perfect or even just better than random. Here is one example in NLP/search.

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.

[PaulHoule]

I am not against ML. I have built useful ML models.

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.