This work is an evaluation of machine translation engines completed in 2018 and 2021, inspired by Isabelle, Cherry & Foster (2017), and Isabelle & Kuhn (2018). The challenge consisted of testing MTs Google Translate and Bing and DeepL in the translation of certain linguistic problems normally found when translating from Spanish into English. The divergences representing a “challenge” to the engines were of morphological and lexical-syntactical types. The absolute winner of the challenge was DeepL, in second place was Bing from Microsoft, and Google was the engine that was the poorest in the management of the linguistic problems. In terms of time, when comparing the engines three years apart, it was found that DeepL was the only one that enhanced its performance by correcting a problem it had before in a test sentence. This was not the case for the other two, on the contrary, their translations were of lower quality. These machines do not seem to be consistent in the manner in which they are improved. These findings may be valuable for translators who may work with these systems as pre or post-editors so that their efforts may be better directed.

An exploratory study about the use of translation technologies in translation programs in Mexico reported that few professors teach technology in few translation courses. Some reasons for this were that they had not been well trained in their academic programs when they were students, or they lacked a more comprehensive knowledge of these technologies (Peña Aguilar 2018). Effective training was not possible for most of these instructors as students, and they seemed to be reproducing similar learning insufficiencies with future translators. Because of this, another survey-based project was devised to identify the use that professionals who graduated from Mexican translation programs are making of translation technologies. What could be their disposition towards the use of translation technologies? The results indicate that professional translators do not resort to the use of 'core' translation technologies very often, but do use other electronic resources useful for accomplishing their tasks. One in two translators thinks their income has increased due to their technology knowledge, and they learn about these technologies on their own. Additionally, they are partly enthusiastic and neutral about using translation tools of this type. Professional translators think they could have learned about translation environment tools (TEnTs) at university (and they wish they had), but university instructors are still not teaching these technologies as much. So, there is a need reported by a few professionals, but not being dealt by some university programs. This could tell about the need to change or revise translation programs or, at the very least, the need to have a change of attitude on the side of university instructors. In contrast, those that are doing their part would hopefully find some reasons to keep up.