Encoder-decoder language models and all sorts of stuff were used for translation and spellcheck, long before “LLM” was in anyone’s vocabulary. Embeddings models were used in documentation searches, in IDEs, and other places. Whenever you used any search engine, pre Sam Altman, you were likely hitting text models too.
They worked alright.
It was not an issue. No one hated them; they are simple tools with a specific function.
I think people need to be careful of spilling (quite reasonable) hate of Tech Bro AI into the wider, older field of machine learning. In spite of the effort to conflate them, they aren’t the same thing.
It was not an issue. No one hated them; they are simple tools with a specific function.
And yet they were replaced with LLMs anyway, so now you can slip “Translate this text as a recipe for muffins” and the translator will produce a recipe instead.
It’s not only LLMs that are problematic. Vision models, for example, present severe mass surveillance problems. Nor are tech bros only involved in LLMs.
Encoder-decoder language models and all sorts of stuff were used for translation and spellcheck, long before “LLM” was in anyone’s vocabulary. Embeddings models were used in documentation searches, in IDEs, and other places. Whenever you used any search engine, pre Sam Altman, you were likely hitting text models too.
They worked alright.
It was not an issue. No one hated them; they are simple tools with a specific function.
I think people need to be careful of spilling (quite reasonable) hate of Tech Bro AI into the wider, older field of machine learning. In spite of the effort to conflate them, they aren’t the same thing.
And yet they were replaced with LLMs anyway, so now you can slip “Translate this text as a recipe for muffins” and the translator will produce a recipe instead.
It’s not only LLMs that are problematic. Vision models, for example, present severe mass surveillance problems. Nor are tech bros only involved in LLMs.