I think the most crucial thing not noted here is that there are still many non-AI translation jobs available simply for the reason that not all companies want their confidential and proprietory data to become some AI/machine translation company's property and grist for their algorithms via being fed to a MT application, such as by some unaware and careless translator.
Firstly, with the advent of generative AI like ChatGPT, we are likely to see more and more non-native speakers confidently producing their own texts in foreign languages or post-editing MT output themselves. The demand for translations may well decrease considerably – especially for translations into and out of English.
Secondly, the efficiency of MT is a myth.
While some translators find that editing MT output increases their productivity, many experienced translators find that post-editing takes them more time than a normal translation (see e.g. Kränzler, Artificial Intelligence in Technical Translation, table 2 on page 5, 201217_sof8_ai-in-technical_translation.pdf). Post-editing MT output requires comparing two existing texts, assessing the quality of the target text and, if found to be necessary, editing it. This activity is more complex than grasping the meaning of just one text and rendering it in the target language in the grammatically and stylistically most appropriate way. Ironically, as MT technology gets better, it produces more plausible output and spotting mistakes in the MT output requires more concentration rather than less.
Any claims about the efficiency of MT in comparison to professional human translation appear even more doubtful if you consider the enormous amount of resources that go into building and operating MT systems – not just in terms of computing power (hardware and electricity) and data, but also in terms of brain power for programming and training.
So far, it is not clear how to make money with artificial intelligence based on Large Language Models. Still, investors betting on what they hope will prove to be a revolutionary technology are pouring huge amounts of money into this technology – disrupting and distorting the translation market.
MT systems are expensive. Training and fine-tuning them is costly. Language Services Providers (LSPs) feel the need to invest in MT to stay competitive, but struggle to recoup their costs – last, not least because their customers have come to expect paying 40% less for translations. The only cost factor left for making savings appears to be translators’ pay. The beauty of being hit with a 30-40% rate reduction is that 10% inflation barely register.
By introducing new benchmarks (“adequate” or “fit for purpose” translations) LSPs’ (ab)use of MT has driven down translation quality and prices as well as translators’ rates and job satisfaction. Translators are considering career changes and students’ interest in translation courses is in decline.
This development could perhaps be halted if translators’ rates were not determined depending on how the target text is produced, but depending on the expected quality of the translation. According to the relevant ISO standard 18587:2017 “full post-editing” is the “process to obtain a product comparable to a product obtained by human translation“.
On principle, a product of the quality of a human translation should always be remunerated like a human translation. Whether translating from scratch or doing full post-editing, professional translators deliver what MT will never be able to do. Only humans are able to grasp and render the full meaning of a text, to explain their translation decisions and to accept liability for their mistakes. It should go without saying that their work needs to be remunerated accordingly.
Few people would expect their hairdresser to charge them less if they supplied him with a pair of scissors – or presented him with the results of a robot’s first attempt at cutting their hair. If translation is to remain a sustainable profession, translators need to take a page from the hairdressers’ book.
What % of other people in the AI space are as optimistic as you? I fully admit that I am a raging AI pessimist due to being extremely anxiety-prone and intolerant of uncertainty, coupled with a complete lack of computer/software engineering knowledge. So I just go off of what I’m reading on the internet and it is TRULY all over the place, haha!
Consider programming. AI definitely helps programmers, but is it really any different than access to Stack Overflow, optimizing compilers, common libraries and APIs, or, going farther back, to structured programming and compilers instead of assembly, to IDEs and debuggers instead of punch cards.
All those things have meant that people who don't really understand computational complexity and Big O notation, or pointer arithmetic, or other topics, are perfectly able to program to solve other tasks. The resultant code may be less perfectly optimized and efficient than the most talented coder could do, but I don't think it's resulted in a lack of jobs for top coders, rather in an explosion in the number of places where software is used.
People have been lamenting the death of the Real Programmer for as long as their have been computers.
Do you not worry about giving workers false confidence here? AI performance is accelerating more than linearly, no? More generally, what evidence are the assertions in your final paragraph based on?
One specific note: the finding that median translator pay fell by 8% from 2010 to 2021 is already adjusting for inflation. That is, it is already netting out the consumer benefits of automation for those workers, the fact that "automation in other industries lowers the cost of the goods and services we buy."
edit: My own conclusion paragraph would have reflected on your subtitle: "Productivity is up and real wages are down." Econ 101 teaches the intuition that productivity and wages are proportional, but that's not true when the technology in question is more *substituting* for human labor than *complementing* it. Historically, technological progress has eventually led to the marginal human having a higher marginal product of labor (albeit after a painful adjustment period, perhaps), but there is nothing in economic theory that guarantees that will continue to be the case. Instead, the signs are pointing in the opposite direction, and it is imperative to recognize those signs and plan ahead, as a society.
I have written about this myself from the perspective of a human translator: https://alakasa.substack.com/p/how-will-gpt-4-affect-white-collar
I think the most crucial thing not noted here is that there are still many non-AI translation jobs available simply for the reason that not all companies want their confidential and proprietory data to become some AI/machine translation company's property and grist for their algorithms via being fed to a MT application, such as by some unaware and careless translator.
This article misses a few crucial points.
Firstly, with the advent of generative AI like ChatGPT, we are likely to see more and more non-native speakers confidently producing their own texts in foreign languages or post-editing MT output themselves. The demand for translations may well decrease considerably – especially for translations into and out of English.
Secondly, the efficiency of MT is a myth.
While some translators find that editing MT output increases their productivity, many experienced translators find that post-editing takes them more time than a normal translation (see e.g. Kränzler, Artificial Intelligence in Technical Translation, table 2 on page 5, 201217_sof8_ai-in-technical_translation.pdf). Post-editing MT output requires comparing two existing texts, assessing the quality of the target text and, if found to be necessary, editing it. This activity is more complex than grasping the meaning of just one text and rendering it in the target language in the grammatically and stylistically most appropriate way. Ironically, as MT technology gets better, it produces more plausible output and spotting mistakes in the MT output requires more concentration rather than less.
Any claims about the efficiency of MT in comparison to professional human translation appear even more doubtful if you consider the enormous amount of resources that go into building and operating MT systems – not just in terms of computing power (hardware and electricity) and data, but also in terms of brain power for programming and training.
So far, it is not clear how to make money with artificial intelligence based on Large Language Models. Still, investors betting on what they hope will prove to be a revolutionary technology are pouring huge amounts of money into this technology – disrupting and distorting the translation market.
MT systems are expensive. Training and fine-tuning them is costly. Language Services Providers (LSPs) feel the need to invest in MT to stay competitive, but struggle to recoup their costs – last, not least because their customers have come to expect paying 40% less for translations. The only cost factor left for making savings appears to be translators’ pay. The beauty of being hit with a 30-40% rate reduction is that 10% inflation barely register.
By introducing new benchmarks (“adequate” or “fit for purpose” translations) LSPs’ (ab)use of MT has driven down translation quality and prices as well as translators’ rates and job satisfaction. Translators are considering career changes and students’ interest in translation courses is in decline.
This development could perhaps be halted if translators’ rates were not determined depending on how the target text is produced, but depending on the expected quality of the translation. According to the relevant ISO standard 18587:2017 “full post-editing” is the “process to obtain a product comparable to a product obtained by human translation“.
On principle, a product of the quality of a human translation should always be remunerated like a human translation. Whether translating from scratch or doing full post-editing, professional translators deliver what MT will never be able to do. Only humans are able to grasp and render the full meaning of a text, to explain their translation decisions and to accept liability for their mistakes. It should go without saying that their work needs to be remunerated accordingly.
Few people would expect their hairdresser to charge them less if they supplied him with a pair of scissors – or presented him with the results of a robot’s first attempt at cutting their hair. If translation is to remain a sustainable profession, translators need to take a page from the hairdressers’ book.
What % of other people in the AI space are as optimistic as you? I fully admit that I am a raging AI pessimist due to being extremely anxiety-prone and intolerant of uncertainty, coupled with a complete lack of computer/software engineering knowledge. So I just go off of what I’m reading on the internet and it is TRULY all over the place, haha!
Consider programming. AI definitely helps programmers, but is it really any different than access to Stack Overflow, optimizing compilers, common libraries and APIs, or, going farther back, to structured programming and compilers instead of assembly, to IDEs and debuggers instead of punch cards.
All those things have meant that people who don't really understand computational complexity and Big O notation, or pointer arithmetic, or other topics, are perfectly able to program to solve other tasks. The resultant code may be less perfectly optimized and efficient than the most talented coder could do, but I don't think it's resulted in a lack of jobs for top coders, rather in an explosion in the number of places where software is used.
People have been lamenting the death of the Real Programmer for as long as their have been computers.
Do you not worry about giving workers false confidence here? AI performance is accelerating more than linearly, no? More generally, what evidence are the assertions in your final paragraph based on?
One specific note: the finding that median translator pay fell by 8% from 2010 to 2021 is already adjusting for inflation. That is, it is already netting out the consumer benefits of automation for those workers, the fact that "automation in other industries lowers the cost of the goods and services we buy."
edit: My own conclusion paragraph would have reflected on your subtitle: "Productivity is up and real wages are down." Econ 101 teaches the intuition that productivity and wages are proportional, but that's not true when the technology in question is more *substituting* for human labor than *complementing* it. Historically, technological progress has eventually led to the marginal human having a higher marginal product of labor (albeit after a painful adjustment period, perhaps), but there is nothing in economic theory that guarantees that will continue to be the case. Instead, the signs are pointing in the opposite direction, and it is imperative to recognize those signs and plan ahead, as a society.