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Like I've indicated, I work in a field - translation - where machine learning applications have already been commonplace for over a decade, seeing the development of machine translation from substandard early Google Translate effort that many people still associate with machine translation with the sophisticated effort put in by DeepL and other such engines. (ChatGPT also produces an OK effort, but as recounted here, still makes some more obvious mistakes than DeepL, at least when translating to Finnish.)
Furthermore, translation is a classic example of a field where overtly enthusiastic tech types have been predicting "human worker replacement by machine" for ages and ages now. An anecdote I often recount is when, during student days, I had been drinking with a friend who also studied translation and we went to a pizza place at the end of the night. We were accosted by a drunken engineer who started explaining how translation is a dead-end field and machines are going to replace translators any day now. I replied that when there's going to be a machine to replace the translators, it's also going to replace the engineers, and he got quiet and left. (At this moment, I wouldn't be surprised if the engineers weren't replaced before the translators.)
How has machine learning affected my workload? Well, the amount of money I've been making has if anything increased during the recent years, though this is also probably natural career development (and also a necessity to answer the rising inflation). For a long time, fair amount of my work has been checking and editing machine translation, a job that is common enough to just generally be referred by an acronym in the field (MTPE, Machine Translation Post-Editing). At the same time, I'm still able to charge 2/3 of my usual word-based rate for MTPE, evidence that there's still a lot of work to be done to not only fix the various errors that even advanced models make but get the "smell of machine translation" off the text.
Obviously, this is an issue of margins. While a lot of text is translated from one language to another, vastly greater amounts of texts getting produced right now aren't, including in commercial applications. Translator workloads getting lighter and translation getting cheaper has thus far just meant that more and more texts get translated now than previously. We'll see if the wall hits at some point.
Like I've also recounted previously, during the past few years I have, if anything, got less MTPE than previously, even though machine translation has improved. This is partly just random chance (ie. I've worked in projects which just aren't that suitable for modern MT applications) but also because some customers explicitly forbid translators from using MT, presumably because end-client companies are afraid that some trade secrets end up in Google's files. Of course I can't be sure they have means to actually check if I use MT if I just edit it good enough, but I can't not be sure of that, either, and getting caught for something like this would be a good way to lose a regular client.
From what I've learned, the biggest game changer in the field - from the point of view of a working translator - was when electronic communication enabled the translation from regular in-company jobs to enterpreneuer-based freelancing. This was actually going on while I was at the university - the teachers still mentioned in-company jobs as something to strive for, but often acknowledged they probably wouldn't be forthcoming and this would (negatively) affect pay for translators. Furthermore, even before machine translation, as such, got common, there have been the so-called translation memory programs, fairly simple tools that mostly replicate existing translations to new ones, and have done their share in making translation faster. Even in white-collar fields, the automation of gruntwork is hardly a new concept.
Of course we'll have to see in the coming years how, exactly, LLM's affect this field. One particular potential field for advancement would be when we get models that can, with some reliability, provess image, audio and text at the same time, since this might have a considerable effect on subtitling (or dubbing, but that is pretty rare in Finland, outside of children's programs). On the other hand, I work in a co-operative office and regularly chat with another translator who basically does not do MTPE at all, rarely uses MT in general and is generally not particularly aware of the developments in the machine-learning field. She seems to get by quite fine, nevertheless.
I edit highly technical documents, fixing grammar and spelling, and I've long thought a bot would eventually do my job. One of the companies I work for even uses a bot, sends us the bot-fixed papers, and then we double-check the bot, fixing its mistakes. Despite this going on for years, they are no closer to replacing us, in part because the standards keep getting raised. Like a recent update to the style guide suggests we should be using wherein in appropriate spots. Using where to mean in which has been normal in English for like forever, but it seems now that the bots are doing the easy stuff, we're expected to do the harder stuff too.
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