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This paper first traces the AI-induced automation of the digitalised and datafied language industry, with a focus on neural machine translation and large language models. The paper goes on to discuss a range of digital literacies that have become increasingly relevant in the language industry in light of these technologies, i.e., machine translation literacy, data literacy and artificial intelligence literacy. After highlighting the interface between these three literacies, the paper drafts an outline of an artificial intelligence literacy framework for translation, interpreting and specialised communication. This framework intends to capture an extensive set of competencies required by stakeholders in the AI-saturated language industry. [ABSTRACT FROM AUTHOR]

This paper is concerned with the following question: to what extent does neural machine translation (NMT) – a relatively new approach to machine translation (MT), which can draw on richer contextual information than previous MT architectures – perform explicitation shifts in translation and how are these shifts realised in linguistic terms? In order to answer this question, the paper attempts to identify instances of explicitation in the machine-translated version of a research report on carbon dioxide capture and storage. The machine-translated text was created using the publicly available generic NMT system DeepL. The human translation of the research report was analysed in a prior research project for instances of explicitation and implicitation (Krüger 2015). After a brief quantitative di scussion of the frequency and distribution of explicitation shifts identified in the DeepL output as compared to the shifts identified in the human translation of the research report, the paper analyses in detail several examples in which DeepL performed explicitation shifts of various kinds. The quantitative and qualitative analyses are intended to yield a tentative picture of the capacity of state-of-the art neural machine translation systems to perform explicitation shifts in translation. As explicitation is understood in this article as an indicator of translational text–context interaction, the explicitation performance of NMT can – to some extent – be taken to be indicative of the "contextual awareness" of this new MT architecture. [ABSTRACT FROM AUTHOR]