With a few lifeboats still available, too many translators are both cursing and holding onto a sinking ship.

Numerous translators are actively discussing in various online venues the problems with AI translation and are saying that clients will come back to them when they discover the problems of AI. Although these discussions provide opportunities for bonding among colleagues, they serve no other identifiable purpose, and they certainly do nothing to impede the obvious headlong race into a world in which translation is viewed as a commodity by both translation brokers and their translation-consuming clients.

The underlying, persistent reality is that translation is a business.

The amount of money translation brokers have needed to pay translations they purchase for resale has been a constant profit-diluting annoyance to the LSB (language service broker) community. In response, brokers have employed numerous devices over the years to lower their translation purchase price. One device is the mandatory use of broker-specified CAT tools, with an accompanying discounting of rates that can be received by translators. Another is forcing translators to work on hamster-wheel online translation platforms in order to receive work.

But now the brokers on which most translators depend have a new way to lower (or almost eliminate) the cost of obtaining translations to sell, this being the elimination of professional translators from the translation process step.

And there is abundant evidence that they are succeeding at doing just that.

One reason for the brokers’ success is that the good-enough paradigm has been widely adopted and is working for a huge portion of the translation market.

Another reason is more serious for freelance translators and needs to be recognized by translators wishing to survive:

Brokers conduct themselves based on the correct understanding that very few translators from which they purchase translations can compete with them in acquiring direct clients themselves. Most translators don’t even know who their potential direct clients are. And, even if they do know, they generally don’t know who to approach at those clients or how to approach them. Many, for a variety of reasons, do not have the ability to access potential direct clients.

The adoption of AI by brokers succeeds largely by the monetization of their control of customers, combined with the inability of most translators to compete with brokers. It succeeds because good enough is good enough and, more critically, because most translators are trapped, with little ability to compete with brokers and no alternative income-earning path.

To survive by translating for earnings anywhere near what they previously could expect to earn, translators will need to acquire direct clients. For most translators, that will not be possible.

That is where broker-dependent freelance translators are, and it is essentially the end of the road for most translators wishing to pursue translation as a way to earn a living.

矮 in the world is this happening? All dwarfs are not created (or translated) equally.

In the field of astronomy, the term dwarf star has a long history. That history dates back far before the word police would raise their eyebrows and raise a fuss about dwarf being offensive.

That history has followed the term into the Japanese language, where the expression 矮星 has long been used and is still used to refer to dwarf stars, including on numerous pages of the website of the National Astronomical Observatory of Japan.

In the age of the word police, however, the demotion of Pluto to the status of dwarf planet presented an opportunity for the authorities (at least here in Japan) to make allow the politics of correctness to intrude into language. The result is that the term is treated differently between Japanese and English.

Whereas 矮星 is accepted for dwarf star in Japanese, 矮惑星 for dwarf planet is avoided, surely for fear that the word police would coming knocking on the door of offensive transgressors. The NAOJ website gives a nod to the dwarf planet use in English, but avoids mention of 矮惑星 in Japanese, preferring to use the safer English term dwarf planet in running Japanese text, rather than use the dreaded 矮 character.

One example, from a FAQ page of the NAOJ website:

太陽系のdwarf planetとは、「太陽の周りを回り」「十分大きな質量を持つために自己重力が固体としての力よりも勝る結果、重力平衡形状（ほぼ球状）を持ち」「その軌道近くから他の天体が排除されていない」「衛星でない」天体である。

[from https://www.nao.ac.jp/faq/a0508.html]

Another method used to avoid 矮 is to call these dwarf planets 準惑星.

I guess the only thing that language realists can be thankful for is that the language revisionists have not banned 矮星 for dwarf star, but perhaps the day will come when we will see that character banned in dwarf stars as well. Time will tell.

Thoughts on stock photos and AI-generated photos

You often see company websites with photos of what are intended to look like groups of employees, sometimes sitting in a meeting room or standing around chatting. These are almost all stock photos, purchased for the purpose of decorating a company website with attractive photos of attractive people who have no connection with the company using the photo.

A typical stock photo of a group includes:

  • handsome males,
  • beautiful females, and
  • a woke makeup of genders, ethnicities, and ages.

Some people might look at the photo and believe that these are actually people who work at the company or are customers for the company’s products or services. Many will not. Is that an honest way to present the company? Perhaps some people would say no.

Now take an example of a company using a typical AI-generated photo depicting the same type of group, which includes:

  • handsome males,
  • beautiful females, and
  • a woke makeup of genders, ethnicities, and ages.

There are still people who would say this is dishonest, but there is an aspect of the photo that would disclose clearly to visitors to the website that what they are viewing is fake. One out of five of the people depicted will have the wrong number of fingers on one of their hands or have their left or right hand attached to the end of the wrong arm.

There you have it, honesty restored by embracing one of the strengths of AI, anatomical hallucination.

(On the occasions we might use AI for photos (we never use it for translation), we flag that fact by using a mouseover text that indicates the source.)

It’s not that difficult: Translators, Interpreters, and Linguists

A surprising number of people seem to misunderstand the distinctions between translators, interpreters, and linguists. Worse yet is the misunderstanding that any of these categories of professionals should be expected to be able to do the job of the others.

Admittedly, even respected dictionaries leave room for—and can be accused of promoting—confusion between these terms. People spending large budgets on language services, however, should reasonably be expected to distinguish between these three terms of art in the field of language services. The differences are not difficult to grasp.

To be sure, there are a small number of people who cross the boundaries between the professions, but these are quite rare, and a translator should not be assumed capable of interpreting, or an interpreter of translating.

Translators

A translator engages in translation, which is the production of a text written in a target-language from a text written in a source language. Translators write words, but work without uttering a word that they are translating. A Japanese-to-English translator works from a Japanese source text, translating it into an English target text. Only a small portion of Japanese-to-English or English-to-Japanese translators are capable of interpreting between those languages, and most do not even want to be interpreters.

Interpreters

An interpreter engages in interpreting (rarely, but confusingly, sometimes called interpretation), which is the expression of a message spoken originally in the source language as a message spoken in the target language. While there are exceptions, most Japanese/English interpreters consider themselves exclusively interpreters and do not actively seek out translation assignments. Many of them would not be good translators.

Linguists

The term linguist is just a bit more problematical, because of a range of meanings. Strictly speaking, a linguist is a specialist in, not surprisingly, linguistics, which deals with the characteristics of language, including aspects such as structure, syntax, semantics, and origins.

In many years of serving the commercial translation market, we have encountered only a small number of working commercial translators who were also linguists, and have met very few linguists who are actively translating or who are even capable of translating or wish to translate as a profession. That separation is even greater when we consider linguists who might interpret. There are very few such people. Similar to the case of translators, interpreters and linguists are two quite distinct groups.

People who should know better, but don’t, misuse the term linguist, and some who know better, purposefully misuse the term.

You often see translation companies (particularly the ones more accurately characterized as translation brokers) boasting of all the “linguists” they have. This makes one wonder why they would talk about a group of professionals not generally engaged in or proficient at translation when they are trying to sell translation services.

Perhaps they think it makes the people they sell translations to feel better that their documents are being translated by people called linguists. Or perhaps they think that the translators they purchase translations from will feel better working for low rates if they can wear the title of linguist.

To be fair, there is the argument that linguist just means someone who is good at a number of languages, but professional translators realize that being “good at a number of languages” doesn’t mean you can translate.

There you have it, a short description of three often-confused professions. Although it might be optimistic for language professionals to expect people outside these fields never to confuse them, when a non-specialist such as a client gets it right, we feel more comfortable than when we need, for example, to inform an interpreting client that will we not be translating in their meeting or deposition.

Where did the chatbot hear that?

The buzz over more than the last year in cyberspace has been arguably buzzier than we’ve seen in a while. It is the buzz about AI chatbots, the highest profile one at the moment being ChatGPT and its peripheral functions, created by OpenAI.

The buzz has been triggered by ChatGPT’s abilities in several areas. One is ChatGPT’s ability to come up with plausible answers to questions, and in English bordering on human-created text.

Another is its amazing ability to come up with things in diverse styles such as haiku and rap on demand.

Yet another is ChatGPT’s ability to make breathtakingly stupid factual mistakes, some being total fabrications, which have come to be called hallucinations, but that could still fool unwary and credulous chatbot-struck users. A related problem is its own credulity in believing leading questions and producing responses that rely on falsehoods and mischaracterizations in questions put to it.

These aspects of ChatGPT’s behavior aside, the appearance of such chatbots means that humans must pay more attention to credibility and accountability than ever before.

If a human friend tells you something that is not only shocking but incredible in the true sense of the word, you can ask the friend “Where in the world did you hear that?” And if your friend says she heard it from YouTube, you might be just a bit skeptical. If she learned it from a certain highly opinionated podcaster known for promoting conspiracy theories, you might start to wonder about the trustworthiness of that friend’s statement, including statements about other subjects. But you should be thankful that your human friend is at least willing and able to reveal the source of her information, enabling you to evaluate it. That’s where AI chatbots part ways with the real world.

ChatGPT and its like collect information from countless Internet sources, some good, some not-so-good, and some totally wrong. The learning process is an opaque and impenetrable black box. You might wonder what sources were used to generate a totally fabricated and factually incorrect account of events that you know is wrong; or about what sources were used to generate a true, useful response. You might not care if you know the answer to the question you asked and are only window-shopping for chatbot failure stories to post online.

But what about when you ask ChatGPT or its now-multiplying wannabe clones a non-trivial question you don’t know the answer to? If the chatbot gives you a plausible-sounding answer, you or others might believe it and could make decisions based on the chatbot response.

I have experimented numerous times with some leading questions I know the answers to; ChatGPT failed miserably in too many cases to repair the damage already done to its reputation with me. Getting facts wrong about events that are not likely to affect our lives or fortunes is one thing. Fabricating answers to questions that are more important, however, is potentially very dangerous.

Since AI chatbots learn from what humans have written on the Internet, the quality of what the humans write is even more important than before. When you consider that much of what is written on the Internet is not even written by fully identified humans, the potential problems come into focus. It is important to be able to know and evaluate the sources of an AI chatbot’s learning. But before that, it would be better if the chatbot itself could know and evaluate the sources of the information from which it is learning, thereby front-loading quality into its knowledge base and, by extension, its responses. The anonymity and lack of accountability that has long been a characteristic of Internet information makes that quite difficult.

That anonymity and lack of accountability is a problem even when chatbots are learning from human-sourced information. But when chatbots start flooding the Internet with their own content, sometimes helped along by humans who trusted them, will chatbots effectively start learning from other chatbots that themselves have learned from not-very-learned humans or even from other chatbots? The image of multiplying mops in Disney’s Sorcerer’s Apprentice comes to mind. Let the believer beware.