Machine Translation

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Artificial intelligence | The Babel Wish. Machine translation is fast becoming a solved problem. Making it perfect will be a very hard problem. The Economist, Dec 14, 2024, at page 67.

Note:
(a) translator "Vasco Pedro"

For the Spanish or Portuguese given name Vasco, see surname Vasquez
https://en.wikipedia.org/wiki/Vasquez
("Vasquez means "[son] of Vasco", and Vasco comes from the pre-Roman latinized name 'Velascus [recall Spanish painter Diego Velázquez],' a name of uncertain origin and meaning, but probably [meaning] Basque or Iberian")

(b) "That 'rules-based; approach was superseded in the 1990s by a 'statistical' approach, based on crunching large datasets, which was still the state of the art when Google Translate was launched in 2006. The field exploded in 2016, though, when Google switched to a 'neural' engine—the forebear of todays large language models (LLMs)."
(i) Google Translate
https://en.wikipedia.org/wiki/Google_Translate
("Launched in April 2006 as a statistical machine translation service, it originally used United Nations and European Parliament documents and transcripts to gather linguistic data. Rather than translating languages directly, it first translated text to English and then pivoted to the target language in most of the language combinations it posited in its grid,[8] with a few exceptions including Catalan–Spanish.[9] During a translation, it looked for patterns in millions of documents to help decide which words to choose and how to arrange them in the target language")

This was then.
(ii) large language model (LLM)
(A) "The largest and most capable LLMs are generative pretrained transformers (GPTs)."  en.wikipedia.org for "large language model."
(B) What Are Large Language Models (LLMs)?  IBM, Nov 2, 2023
https://www.ibm.com/think/topics/large-language-models
("LLMs are a class of foundation models, which are trained on enormous amounts of data to provide the foundational capabilities needed to drive multiple use cases and applications, as well as resolve a multitude of tasks [ie, a foundation model is multitasking]. This is in stark contrast to the idea of building and training domain specific models for each of these use cases individually, which is prohibitive under many criteria (most importantly cost and infrastructure), stifles synergies and can even lead to inferior performance")
(C) A bit more details are supplied in
What is LLM (Large Language Model? Amazon Web Services (AWS), undated.
https://aws.amazon.com/what-is/large-language-model/