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 A computer journal for translation professionals

Issue 19-1-296
(the two hundred ninety sixth edition)  
1. What are the Areas in which You See AI Playing a Role in Translation-Related Technology?
1.1 AIT
1.2 Atril
1.3 Datamundi
1.4 iLangL
1.5 Intento
1.6 GT4T
1.7 KantanMT
1.8 Lilt
1.9 MateCat
1.10 memoQ
1.11 Memsource
1.12 Plunet
1.13 SDL
1.14 Smartcat
1.15 Star
1.16 Wordfast
1.17 Xillio
2. The Tech-Savvy Interpreter: Guidelines for Distance Interpreting and the Robot Jobs Test
3. New Password for the Tool Box Archive
The Last Word on the Tool Box
The best-laid plans of mice and men . . .

. . . often go awry. I had everything nicely planned out for this newsletter. I had already conducted several interviews relating to articles and there was some interesting news (such as Google getting into the translation service business), but then, on the spur of the moment, I decided to send out a question to the whole range of technology vendors in the translation world. It turns out I got more than I bargained for, so I decided to scrap what I had originally planned and focus instead on what they had to say. (Some had much more to say than the allotted 150 words, but I decided to leave their answers intact.)

Here was the question:

What are the areas in which you see artificial intelligence playing a role in your technology and/or in translation-related technology of other vendors?

Why ask that? Well, there's been a lot of talk about AI in technology in general; in the world of translation, we've talked about it in relation to machine translation in particular. But of course there's a lot more to say about AI and translation than just machine translation. So I was curious to see what's on the forefront of the minds of the developers of technology that you and I use.

Also -- and this becomes apparent in some of the answers -- the AI that we are encountering in real-life scenarios today is not the AI that science fiction authors have been dreaming (or nightmaring) about. AI as we know it is not able to have any generalized application where it can make "decisions" across different kinds of expertise or domains. AI is being used to enhance decision-making in very narrow fields of expertise (thus "narrow AI"), and it's increasingly good at it. Probably a better way of putting it: developers and users are getting increasingly good at using that kind of technology.

Since I did not edit the answers (aside from grammatical issues) and did not provide any information beyond the prompt above, the answers range all over the place, but I think you'll end up learning a lot in the process (I did). I alphabetized the answers according to company name.  

(And as a bonus, you will find "The Tech-Savvy Interpreter" column dealing with very similar issues.)



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1. What are the Areas in which You See AI Playing a Role in Translation-Related Technology?
1.1 AIT

In 1992, when I first programmed an inventory management system for the local children's hospital, the youngest of the three accountants asked, "Will I be fired?" The fear that I sensed showed the conflict between software and humans in the first place. From the angle of this conflict, I doubt that any of the technologies used by current vendors -- including us -- can be called 'artificial intelligence' in its strict sense, where AI actually replaces humans by 100%. While AnyCount perceives the 38 formats it can handle, and even chooses between several algorithms to make use of the environment (different versions of system, different versions of files), there is still someone who feeds it the data and uses the results of the export. So the PM who counted words manually is not replaced, he just becomes more effective. The accountant from 1992 was also assigned other analytical tasks. True AI fires people.

1.2 Atril

The application of Deep Learning to translation, in the form of Neural Machine Translation (NMT), is clearly the main role that AI is going to have in the translation sector in the short term. The availability of accessible open-source NMT projects has resulted in a proliferation of LSPs adding NMT to their service portfolio -- perhaps as a way of demonstrating their technical prowess. That said, given the vast amounts of training data required to train high-quality NMT systems, it may still take some time for NMT to have a real impact in the industry.

In the short term, we expect NMT to be integrated soon into most competitive CAT tools, with translator workflows slowly shifting to post-editing. We also expect other applications of AI to play a role in two other aspects: a) in the gathering and cleaning of training data for NMT, and b) in more sophisticated QA tools.

1.3 Datamundi (formerly: Fair Trade Translation)

Translation: For some languages it will become hard to tell if a human or a machine has translated the text. Fluency will be misleading and errors will be harder to find; yet they will be worse than today. Meaning will sometimes be lost in perfectly fluent translations.

Writing & Editing: AI will check the quality of human work, not only by detecting defects and inconsistencies, but it will also point out where the logic is not clear, or what might be a contradiction in the text. It may also help detect information on the internet that contradicts what is written.

Project Management: AI will predict how much time people in general, but also one person in particular, will spend on a job. This way time will play a more important role in establishing the value of a job again. Post-calculations will train these systems.

1.4 iLangL

I believe artificial intelligence in the translation industry can be used in these areas:

  • estimating the localization quality;
  • help in selecting the best linguist for a particular job in a very short period of time;
  • analyzing the resource workload and helping the project manager manage and optimize the resource pool of linguists; and
  • for complex localization workflows, partially or fully replacing the project manager.
1.5 Intento

We saw machine translation become good enough for general-purpose texts with NMT in 2016 and then for domain-specific texts with custom NMT in 2018, providing 50% less costs and turnaround time. Speech transcription and OCR are moving in the same direction. However, in order to realize those benefits, the localization workflow has to be adjusted to the use of those technologies, and today, with human-powered workflows, it takes so much time. I see a lot of potential for AI at all decision-making stages of the localization workflow: deciding which MT is the right one; sorting segments into different types of review and post-editing; and assigning human experts to review, correct, and teach machines where necessary. Basically, it's the MT adoption that has to be automatized as the gap between the technology capabilities and the industry adoption widens.


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1.6 GT4T

As perhaps the only major improvement in translation technology for decades, machine translation is almost as inevitable for translators as when typing replaced handwriting. Human skill and computing power is a perfect couple. While machines may mishandle really simple sentences that are easy for humans, computers can really do a good job with technical stuff.

GT4T was first created eight years ago when neural MT was non-existent to assist translators in utilizing MT. At that time MT was already a good reference tool. Using GT4T, translators decide when and where to use MT to get hints and suggestions, whether it be a sentence or a phrase. The idea of GT4T is to incorporate MT into translators' workflow, as a reference just like checking a dictionary, rather than to use MT as a first-draft translator for post-editing.

As a reference tool, using MT is as justified as using a dictionary. But people are right to worry that today's MT is already good enough to be abused. A tired translator using too much MT may create errors that are difficult to detect. Translation agencies are attracted by the idea of adopting post-MT editing.

In this fast-paced world, translation is no longer a slow and meticulous endeavor. Half a century ago, a translator may have spent half of his life translating a book. Today's translators' lives are a constant strain of meeting deadlines: steam pages that have to be launched tomorrow, or on-line game translations that go real-time the moment translators hit the Enter key. In today's world, speed is almost as important as quality, if not more. Translators who make good use of MT will be in a better position to meet both speed and quality requirements and are more likely to be successful from the business point of view.

As easy a laughing stock as machine translation is, AI definitely plays a role as a reference tool for translators, and this tool gets better year by year.

1.7 KantanMT

There are numerous areas in which AI and Machine Learning will be used to enhance and improve localisation workflows. More importantly, AI and Machine Learning will be used to improve business efficiencies and operations. These will lead to faster, more intelligent execution of localisation workflows using fewer resources and costs while improving profitability.

Here are a few areas to consider:

  • Demand forecasting -- AI can be used to determine future translation cycles and demands. This can be used to help companies more intelligently manage and predict seasonal cycles in their business and plan resources and asset availability.
  • Predictive workflow planning -- AI and machine learning can be used to implement fluid workflows that are flexible and respond to inbound data types, domains, and quality expectations.
  • Recommendation engines for optimal workflow selection -- AI will be used to present project managers with a choice of workflows and business processes that help deliver customers' expectations based on numerous factors such as fastest speed, lowest cost, or perhaps a balance of both.
  • Alerts & diagnostics from real-time project management monitoring -- Real-time telematics of in-progress workflows will provide alerts and diagnostics to overcome workflow blockages, resource shortages, and other challenges that may appear randomly in a project. Predicting possible workflow problems will help with contingency planning and workflow re-routing to ensure projects are delivered on time, every time.
  • Proactive workflow health management -- AI and Machine Learning can determine not only the optimal workflow, but also optimal resources and relevancy of these resources to avoid any potential project issues. It will re-route and re-provision workflows automatically to ensure projects are delivered on time, every time.
  • Project performance analysis -- Machine Learning and AI will be used to fine-tune workflows based on prior project data and continuously improve project execution, provisioning optimal resources and workflows automatically.
  • Dynamic pricing (based on project factors) -- While the industry has used tiered-pricing for decades, I can envision a time where dynamic pricing can be applied to project execution. AI and Machine Learning will reprovision and re-route workflows to adhere to pricing expectations and thresholds set by the client or project manager. This will ensure projects can be delivered based on an ensemble of project factors.
  • Workflow traffic pattern and congestion management -- AI will be used to monitor all workflows within an organisation and determine if resources need to be reprovisioned or workflows re-routed to optimise the use of assets and adhere to any scheduling constraints.
  • Risk analytics and regulation -- AI will be used to ensure that regulated industries are provisioned with resources and workflows to achieve compliance in line with sector requirements while maintaining minimum risk during project execution.
  • Resource utilisation analysis - AI will be used to classify, monitor, and provision optimal workflows and resources to ensure business execution excellence. These decisions will be made on hard factual data and not biased by human preferences and habits.

While most people think that AI and Machine Learning will be restricted to technological innovation, I firmly believe we are on the cusp of an AI revolution which will fundamentally re-shape our industry and make us rethink many of our business management approaches over the past three decades. The machines are on the rise. :->

1.8 Lilt

We think about AI's role in Lilt's technology in terms of Augmented Intelligence. The idea that AI is somehow able to automate the entirety of translation via pure MT is misguided, especially for content that requires a high level of quality. You can't really "solve" MT until you solve artificial intelligence, which is itself very much an unsolved problem. We need to think about how we can use AI to augment human translation quality and speed. Translation is an art form, and Lilt's focus is on how we can continue to develop technology that uses AI to enable translators to do their best work. 

In short: Machines should work with, and learn from, human translators.

1.9 MateCat

Artificial Intelligence is fascinating and scary. Human language and translation in particular are perhaps the most difficult challenges that machines face. Natural language is a very compressed channel of information that is densely packed with meaning, and it requires contextual information beyond the words themselves in order to be understood.

Language is the greatest challenge that machines face because it is the most human thing there is.

Because of this, automatic translation systems are progressing slowly; nevertheless, they are undeniably progressing.

At Translated, the translation service I co-founded, we have applied artificial intelligence over the past 17 years to help professional translators translate better and faster. We have tried to create a symbiosis between man and machine. We have done this in many ways, but one very important approach has been to provide translators with suggestions (pre-translations) for every sentence. We have developed a translation tool for professional linguists that combines all the professionally translated material available on the web with AI that can predict sentences never seen before. This is the basis for our open-source product called MateCat. (...)

By helping professional translators, we have been able to take advantage of a unique opportunity, namely that of measuring the progress of the AI over a period of many years.

We have measured how much professional translators correct the suggestions provided by the AI, and we have done so day by day, month by month, and year by year.

Back in 2003, with the valuable financial support of the European Commission, we undertook a research project in which we translated several hundred thousand words, and we found that the overall correction rate (post-editing effort) for English > Italian and English > French was around 43%. In 2015, the correction rate was 27% for the same language combinations. The second time around we used a sample of 50 million words translated in MateCat. Thanks to the application of both neural machine translation and MMT, a system which is capable of adapting to the user, we estimate that we will reach a correction rate of between 22% and 26% in 2018.

This improvement has been unstoppable and constant, with just a few small delays and surges due to one technology reaching its maximum potential and another being introduced. There have been two major changes: statistical translation, which entered service in 2006, and deep learning, which was introduced at the end of 2016.

If we continue at this pace, when will we get to the point at which it is no longer necessary to correct the machine translation?

If we just look at the figures, it seems like this could happen between 2030 and 2035.

However, there is another interesting fact that we often forget: humans are not perfect.

When we analysed 20 million words in word-for-word translation suggestions handled by human linguists (called 100% matches), we observed that suggestions from other humans have an average correction rate of 11% rather than 0%. This is because to errare humanum est, and also because each of us has a unique style that we want to promote. When we talk about the singularity, we need to make sure we define the benchmark. Is it absolute perfection? The best translator in the world? Or just the average professional translator?

If we are satisfied with a machine that translates better than the average professional translator, 2025 may be a more plausible date for when we will reach an 11% correction rate in these language combinations. To my mind, that's frighteningly close.

I've been wondering whether I should sell Translated now, since the market for professional translations will shrink significantly, or whether I should try to ride out the change in order to seize an even greater opportunity. In the end, people will probably need more translations, not fewer. I feel a bit like Kodak during the transition from film to digital.

The fact that I'm aware of it is already something, and because of that I have already decided that we'll ride it out.

It is very likely that artificial intelligence will play a key role in every sector in the future. While language is the most difficult thing for machines to tackle, it is possible that the disruption will happen even earlier in many other areas, and this represents an excellent source of startup ideas.


1.10 memoQ

May I first ask what AI is? A popular quote is that "AI is everything that hasn't been done before." Or anything a machine was not expected to be able to do. Let's call it magic? Reliable dictation was considered magic not that long ago, but today it is commonplace and included in memoQ. MT obviously builds heavily on machine learning, and MT seems to be on a steep upward curve in general. In memoQ, we have had the Muse for a good while, which gives you typing suggestions based on what you have translated before. The Muse is great tech that we should make more accessible. We could apply machine learning or related "magic" techniques to make the tool automatically place tags and formatting. The busiest translators and LSPs could gain a lot if the machine could magically tell them which of their many TMs or corpora could be useful for leverage in a new project. Or if it could automatically choose the best translator or vendor based on what it learned from production data. Any data produced in daily work could be the basis of machine learning. But our approach remains that the customer's data is theirs, so any such solution from memoQ will maintain the customer's privacy.

And another perspective from a different member of management at memoQ:

In TEnTs, AI in its MT incarnation is here to stay, but with a clear shift in focus. We'll see technology and processes move from post-editing, where the human editor is an afterthought, to augmented translation, where AI is harnessed to give linguists superpowers. The next exciting things will happen in HMI (human-machine interaction), not in the MT models per se.

Beyond language processing in the strict sense, MT will start cropping up in project management, vendor management, and localization engineering.

Overall, the successful language professional's toolbox will include a growing number of commoditized machine learning and data analysis tools.

AI's future is bleak in one area: QA. If a tool were "smart enough" to assess quality, it would be smart enough to do the job itself better - so we would use it to do the job, not QA.


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1.11 Memsource

At Memsource, AI found its place in our translation management as well as translation tool components. Typically, we look for tasks that are repetitive, need to be performed at scale, and bring high ROI -- these are often perfect candidates for AI. For instance, our MTQE feature identifies high-quality machine translation output that needs no post-editing. Another feature identifies non-translatable content. A very promising area is automation of the localization workflow end to end, from configuring the right project settings to picking the right linguist for the job. On the other hand, AI will struggle with tasks such as a comprehensive review of translated content before delivery to a very demanding customer. For instance, AI will not spot issues that are unique or rare -- and perhaps were not present in the data set on which it was trained. In any case, these are exciting times and AI-powered technology should allow us to focus on creative tasks that are more fun.

1.12 Plunet

As a business management system running your workflows, Plunet could foresee artificial intelligence for vendor allocation, trying to go for the best possible option and then opening up to a broader potential vendor audience. Also, AI could be used to predict deadlines or even possible workflows.

Additionally, we could think about predicting what a user wants to do next and presenting those options to them (UX).

In an ideal world -- given enough metadata and legacy information -- projects could be entirely automated: quoting, setting up the projects, choosing the right CAT tool, vendor allocation, and even some automated exception handling.

1.13 SDL

We see AI in any scenario where it is about increasing productivity and automation. A few examples:

  • Neural MT (obvious a productivity booster) combined with TM, terminology, fragment matching to always provide the best possible match to start from
  • Voice recognition (seeing the increasing quality of AI-based engines such as Google, it becomes more and more compelling to deeply integrate with CAT environments)
  • Project automation, e.g., automatically routing work by analyzing source content and finding the most appropriate resources (TMs, termbases, MT engines) and translators/reviewers with matching skills

Having this set of intelligent tools at their fingertips, there is a certain likelihood that translators will spend more time overseeing and managing a translation process vs. starting to translate from scratch, i.e., a shift from (from scratch) translation to review. (As an aside, I like to avoid the term "post-editing" when it comes to NMT. I prefer the term "review" as I believe this more closely matches the type of work when reviewing output from a well-trained NMT engine.)

Having said all of the above, though, language is language -- which always means "unexpected intricacies" that make it key to design any piece of technology in such a way that translators are in control of the resources they are working with, rather than shoehorning users into a certain way of working. Flexibility will be key. In our focus groups, users keep telling us that this is a vital aspect. 

1.14 Smartcat

We use AI to make translation and translation management stress-free and efficient.

For linguists, this means suggesting jobs relevant to their expertise; adapting translation suggestions to their style and voice; helping with tedious things like tags, number formats, and non-translatables; and handling invoices, payment, and everything related to running their own accounting. These tasks often take up to 30% of translators' time, which they could use for translating, thus making more money and being happier in general.

For project managers, this means suggesting the best-matching linguists for specific projects, automatically setting deadlines, choosing the most relevant MT engines, tracking progress, and notifying the PM if anything goes wrong.

In the near future, AI will become the brain of multiple smart personal assistants for project managers and linguists in Smartcat, helping them work more efficiently and creatively by taking on repetitive, non-productive tasks.


5 Translation Technology Trends to Watch Out for in 2019

memoQ's Trend Report is a curious look into the most important developments influencing the landscape of translation technology and related fields in the coming year.

This year's report debates five trends that have managed to break out of the silos of the translation industry and establish themselves as prominent themes worldwide. Beyond machine translation and automation, these articles also explore the way people consume content on-demand, use speech recognition in our everyday lives, or the need for actionable insights for businesses that aspire to conquer global markets.

Have a read and share at! We'd love to hear your thoughts!

1.15 Star

The two AI technologies -- Semantic Information Management and Machine Learning -- play a critical role in STAR technologies and will also have a significant impact in the evolution of those technologies.

Semantic Information Management and Processing

Semantic information represented in knowledge graphs and ontologies is key for opening the "content black box" in technical communications making content interpretable. This enables smart services and processing of tech coms in digitized processes. Semantic information is the base for intelligent assistants, chat bots, voice assistants, and intelligent AR/VR scenarios. The semantic authoring and component content management solution GRIPS and the smart service server PRISMA by STAR are based on a knowledge graph model developed by STAR over the last 20 years.

STAR terminology management technologies (TermStar and WebTerm) will morph into ontology management solutions, which will also manage relations between terms and multilingual ontologies. Ontologies enable smart assistance and smart answers to questions.

Machine Learning (ML)

ML is the base for machine translation and is the core of the STAR MT machine translation solution supporting translators with machine-based translation suggestions. ML-based neural networks will also support

  • Intelligent (predictive) text completion in authoring memories
  • Higher degrees of automation for quality assurance, error detection, and correction in translations
  • AI-based post-editing of machine translations
  • Optimized terminology recognition and checking in morphology-rich languages
  • Higher automation of alignment corrections
1.16 Wordfast

Translators and linguists are the quintessential knowledge workers of the post-industrial era. Artificial Intelligence seeps into the translation/localization industry from almost every possible opening. I'll address the hiring process here.

Very few translators actually understand how, prior to them being hired for a particular project, a complex web of algorithms was at work. I'll paint a gloomy picture of the situation. There is no escaping AI. But you can beat the Matrix when you understand it.

Modern project management systems rely on rich databases of potential translators, which are detailed beyond what we imagine. The intrusive level of information is made possible not only by hunters' greed or skills, but by the voluntary submission of the game. Linguists maintain profiles at translation portals, as well as social networks, and on their homepages. All that information is harvested by HR at LSPs. Not much of a problem so far; it's been that way ever since the nineties. We want it; our universities even train us to do so. But the next step is more disturbing, and AI is driving it.

Translators in an LSP database are ranked not just on the surface features that linguists voluntarily provide: sex, age, education, location, expertise, price. Artificial Intelligence works on more juicy parameters. Are you conciliatory or irritable? Confrontational or consensual? Mentally fragile or sturdy? Do you live alone? Let's dig deeper. How many PMs have you trashed or exhausted? How many translation teams or projects have you crashed? We can know that. None? All right, How many translation projects have you completed, as opposed to how many have you walked away from? We can know that. Can you accept criticism and act on it? We can know that. Are you committed? If required, can you work an extra weekend? We can know that.

That type of information is at the core of strategic metrics such as profitability, usability, return on investment. But how does Artificial Intelligence profile me? It's so easy. Social network noise -- or lack thereof. Forum posts, evaluations, reviews, Blue Board notices. Not just in the translation industry (discussion groups, fora, conferences and events, published resumes, etc.) but in life in general (TripAdvisor, Amazon, Yelp... tell so much about you). Those GAFAM golden boys (Google Apple Facebook Amazon Microsoft, etc.) never sell or share their database? You must be joking. Read the news. And the data is up for grabs, anyway. Then there's third-party noise. (You don't post? Others oblige, and gleefully mention you. Less harvested volume, but extremely telling.) AI excels at doing that. Crunching stats, weighing vocabulary, analyzing mood swings in fora, gauging character. A decade of digital footprint lays you bare. They even know when you take your vacation, with whom and where, and most importantly -- if you take any. (Tight on money, eh? Good to know, we can cut rates.)

OK, we think, "I'm using a pseudo, I'm protected, the Matrix won't know." You bet. The AI used by headhunters has pseudo-busting night-vision goggles. They even see through paywalls. Most pseudos still have a profile (language pair, tools owned, expertise, and the host will add IP, number of posts, stats). That pseudo is then paired with the written expression in posts, preferred words, cumbersome syntax, repeated typos, to create a unique fingerprint. For a machine that analyses a million words a second, that anonymous fingerprint is soon matched with another known fingerprint in a homepage, or a non-pseudo account. Job done. foo_kitten29 (usual IP: 223.56.34.xx) is Mary-Ann Forthswaith of 29 Oak Alley, Plumridge, Herts.

I once had a German translator show me his homepage, which had a rather verbose English version, written by him. I noted two frequent syntactic structures, a linguistic footprint that was typical of his "native German" English. Using his CV, it took me a dozen searches to land on a post in a translation forum where he had mentioned his alma mater, and his area of expertise. His linguistic footprint gave him away 100%. I pointed it out to him: "There you are!" He was using a pseudo. Schrecklich! Wie hast du das gefunden? The illusion of protection.

So, depending on budget, quality expectations, and rush level, the translator will be cold-bloodedly selected as a pig for slaughter: the right fat/meat ratio, the right price per pound, the firmness of the bacon. AI can accurately profile a translator by character. The docile, efficient, reliable linguist is easily spotted.

Artificial Intelligence is harnessed by large LSPs. It was called Business Intelligence in the 2000s, and it used to rely on dumb categories such as language pair, price range. Very limited. Now with the increase in manpower, and the increase in profiling big data, headhunters of the 2010s and 2020s select us on those intimate and invisible qualities that translate into profitability.

So you thought "profiling" was a nice subject for a TV series ten years ago? In our wildly competitive age, we're all guilty until proven profitable.

1.17 Xillio

Xillio Localization Hub connects any CMS to any translation tool. I see a great potential in applying AI in various areas of this application that would help automate and improve the workflows and quality. To give you some ideas:

  • Workflow automation - AI-based rules to govern project distribution and flows.
  • Quality assessment and measurement - both on the source and the target.
  • AI-based classification and metadata management.

These components could be applied and used directly in the Localization Hub, or in the connected applications.


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2. The Tech-Savvy Interpreter: Guidelines for Distance Interpreting and the Robot Jobs Test (Column by Barry Slaughter Olsen)

January 2019 so far has been a whirlwind. In the United States, the Democratic Party has taken control of the lower house of Congress. And with that change has come renewed scrutiny of current US President Donald Trump's interpreted interactions with President Vladimir Putin of Russia. What has ensued is a largely unprecedented interest from the mass media in how interpreters do what we do. What's the difference between simultaneous and consecutive interpreting? How do we take consecutive notes? Why can't they be used as a stenographic record? And perhaps most importantly, should President Trump's interpreter be subpoenaed to testify before the United States Congress.

What does that have to do with interpreting technology? you ask. Fair question. Well, this matter has taken up a lot of my time at the same time I'm due to write this monthly column, with appearances on CNN and MSNBC and fielding calls from media outlets requesting background information on interpreting. So, I don't have a well-researched look at a new technology or commentary on its use in our professional milieu.

Yet the current media flurry points to the ever-pressing need for the interpreting profession to continue formalizing standards and educating the public about the complex and unique skillset required to deliver this essential service.

In that spirit, I'd like to share important new guidance for interpreting technology and an interesting test that has the potential to help us more explicitly articulate why interpreting is likely to resist absorption by the AI tsunami in the near future.

AIIC Publishes Version 1.0 of Its Guidelines for Distance Interpreting

Efforts to take a serious look at remote or distance interpreting within the International Association of Conference Interpreters (AIIC) have gone into high gear in the last six months. Various AIIC regions and, most recently, the Private Market Sector Standing Committee (PriMS) have organized events not just to talk about these disruptive technologies but also test them out and engage in constructive dialog with technology providers.

The PriMS meeting in London this month coincided beautifully with the publication of AIIC Guidelines for Distance Interpreting (Version 1.0). These guidelines have been in development for some time and are comprehensive, to say the very least. They will surely become a reference point for organizations that are looking to expand their multilingual meetings beyond the conference room and are a welcome contribution to the incipient canon of guidelines and best practices for simultaneous interpreting in an increasingly interconnected world.

I would simply add that these guidelines are conference-room centric, which makes sense, after all, AIIC is an association of conference interpreters. But that also means that they do not take into account a new and growing segment of the interpreting market, audio conferencing, video conferencing and webinars-use cases where there is no central conference room, and participants are not in the same physical location. In addition, these new interpreted interactions are shorter in duration and organized with much less lead time.

The Robot Jobs Test

In November 2018, I was contacted by the technology research and analysis firm Gigaom and given an evaluation copy of Byron Reese's The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity. I've read A LOT of books on artificial intelligence over the last two years (at least a dozen). While interesting and informative, none of them have described, let along acknowledged, the disagreement surrounding AI's "inevitable" progression towards human parity and the creation of artificial general intelligence. Reese provides a masterful explanation of why and how AI researchers disagree when it comes to its effects on our economy, employment and future existence. Ironically, as Reese puts it, "these experts disagree not because they know different things, but because they believe different things."

Reese also addresses the much-fretted-about imminent employment apocalypse, the time when AI will purportedly take almost every job on the planet. Or so the mainstream media narrative goes. He includes a "Robot Jobs Test" to help people understand the limitations of AI-based automation. The test is available online at It is a series of 10 questions that you score from 0 to 10. The higher the final composite score, the more likely your job will be lost to automation.

Reese asks questions like: "How many people do your job?" and "How similar are two random days of your job?" One question, "How long does it take to make the hardest decisions on your job?" equates the speed of decision making to threat of automation. The faster the decision must be made; the more likely AI will make it. In the case of simultaneous interpretation, I think this question is not appropriate. Even so, I scored it at a full 10 points because we must make split-second decisions constantly when interpreting, but they entail not just information retrieval, but also creativity, empathy and even charisma. The other nine questions seem well crafted and applicable.

I took the test keeping in mind my last 25 years of experience as a conference interpreter and ended up with a score of 32/100. A score below 70 is "safe for over a decade" and a score below 60 won't be automated "for decades to come," according to Reese. It would be very interesting to have many interpreters take the test and then get a look at the aggregate numbers, which the author is tracking, since he asks you to identify your job at the end of the test. This would allow us to get a glimpse of how replaceable we perceive our own job to be.

Accurate information is the best way to combat misunderstanding. And this book goes a long way at doing just that.

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