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Software and language, mostly

AI in the language industry: We’re asking the wrong questions

May 8, 2017 Software NLP UX & Design

This text originally appeared as a guest post on Kirty Vashee’s eMpTy Pages blog.

I was honored when Kirti asked me if I would contribute to eMpTy Pages about TMS and intelligent data technologies. I’ve been thinking about this for nearly two months now, until I finally realized what’s been holding me back. I find it difficult to attach to most ongoing discourse about AI, and that’s because I believe the wrong questions are being asked.

Those questions usually revolve around: What part of life can I disrupt through AI? How can my business benefit from AI? Or, if you prefer the fear angle: Will my company be disrupted out of existence if I don’t jump on the AI train in time? Will my job be made obsolete by thinking machines?

My concern is different. But I won’t tell you until the end of this post.

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It’s only as good as your data

I found Kirti’s remark in his recent intro very insightful: “Machine learning” is a fancy way of saying “finding patterns in data.”

That resonates with the way I think about MT, whether it’s the statistical or neural flavor. In simple terms, MT is a tool to extrapolate from an existing corpus to get leverage for new content. If you think about it, that’s what translation memory does, too, but it stops at fuzzy matches, concordance searches, and some amount of subsegment leverage.

Statistical MT goes far beyond that, but at a higher cost: it needs more data and more computation. Neural MT ups the ante yet again: it needs another order of magnitude more computational power and data. The concept has been around for decades; the “deep learning” aka “neural network” explosion of the past few years has one simple reason. It took until now for both the data and the computational capacity to become available and affordable.

The key point is, AI is machine-supported pattern extraction from large bodies of data, and that data has to come from somewhere. Language data comes in the form of human-authored and human-translated content. No MT system learns a language. They process text to extract patterns that were put in there by humans.

And data, when you meet it out in the wild, is always dirty. It’s inconsistent, in the wrong format, polluted with stuff you don’t want in there. Just think of text from a pair of aligned PDFs, with the page numbers interrupting in all the wrong places, OCR errors, extra line breaks, bona fide typos and the rest.

So, even on this elementary level, your system is only as good as your data, not to mention the quality of the translation itself. And this is not specific to the translation industry: the job reality of every data scientist is 95% gathering, cleaning, pruning, formatting and otherwise massaging data before the work can even begin.

Do we have the scale?

AI, MT and machine learning are often used synonymously with automation, but in reality they are far from that. As Kirti explained in another intro, in order to get results with MT you need technical expertise, structure and processes beyond technology per se. All of these involve human effort and skills, and pretty expensive skills too.

So the question is: at what point does an LSP or an enterprise get positive return on such investment? How much content must first be produced by humans; what is the cost of training the MT system; what is the benefit per million words (financial, time or otherwise)? How many million words must be processed before you’re in the black?

No matter how I look at it, this is an expensive, high-value service. It doesn’t scale in a human-less way as software does.

Does the translation industry have the same economy of scale that a global retailer or a global ad broker disguised as a search engine does? Clearly, a number of technology providers, Kilgray among them, are thriving in this market. But I also think it’s delusional to expect the kind of hockey-stick disruption that is the stuff Silicon Valley startup dreams are made of.

Let’s talk about the weather

I have been focusing mostly on MT, but that’s misleading. I do think there are many other ways machine learning will contribute to how we work in the translation industry. Most of these ways are as-yet uncharted, which I think is a consequence of the industry’s market constraints.

I’ll zoom out from our industry now. I checked how many results Google finds if I search for a few similar phrases.

AI in weather forecasting: 1.29M
AI in language processing: 14.2M
AI in police: 69.7M

Of the three, I’d say without hesitation that weather forecasting yields itself best to advanced AI. Huge amounts of data: check. Clear feedback on success: check. Much room for improvement: check. And yet, going by what’s written on the Internets, that’s not what society thinks.

There is a near-universal view that technology is somehow neutral and objective, which I think is blatantly false. Technology is the product of a social and economic context, and it is hugely influenced by society’s shared beliefs, mythologies, fears and desires.

Choose your mythology wisely

I am on odd one: in addition to AdBlock and Privacy Badger, my browser deletes all history when I close it, which is multiple times a day. At first, I just noticed the cookie messages that kept returning. Then I started getting warning emails every time I logged in to Twitter or Google. Finally my password manager screwed me completely, requesting renewed email verification every time I launched the browser.

These are all well-meaning security measures, with sophisticated fraud detection algorithms in the back. But they work on the assumption that you leave a rich data trail. It is by cutting that trail that you realize how pervasive the big data net already is around you in your digital life. For a different angle on the same issue, read Quinn Norton’s poignant Love in the Time of Cryptography.

Others have written about the way machine learning perpetuates biases that are encoded, often in subtle ways, in their training datasets. In a world where AI in police outscores the weather and language, that’s a scary prospect.

With all of this I mean to say one thing. Machine learning, data mining, AI – whatever you want to call it, in conjunction with today’s abundance of raw digital data, this technology has the potential to be dehumanizing in an unprecedented way. I’m not talking conveyor-belt slavery or machines-will-take-my-job anxiety. This is subtler, but also more far-reachingly insidious.

And we, as engineers and innovators, have an outsized influence on how today’s nascent data-driven technologies will impact the world. The choice of mythology is ours.

UX is the lowest-hanging fruit

After this talk about machine learning and big data on a massive scale, let’s head back to planet Earth. To my translator’s workstation, to be quite precise.

Compared to even a few years ago, there is a marvellous wealth of specialized information available online. There are pages of search results just for terminology sites. There are massive online translation memories to search. There are online dictionaries and very active discussion boards.

Without the need to name names, the user experience I get from 99% of these tools is between cringeworthy and offending. (Linguee being one notable exception.)

Here is one reason why I have a hard time enthusing about cutting-edge AI solutions for the language industry. Almost everywhere you look, there are low-hanging fruits in terms of user experience, and you don’t need clusters of 10-kilowatt GPUs to pluck them. I think it’s misguided to go messianistic until we get the simple things right.

Two corollaries here. One, I myself am guilty as charged. Kilgray software is no exception. We pride ourselves that our products are way better than the industry average, but they, too, have a ways to go still. Rest assured, we are working on it.

Two, user experience also happens in the context of market constraints. All of the dismal sites I just checked operate on one of two models: ad revenues, or no revenues. I have bad news for you. These models make you the product, not the customer. This is not specific to the translation industry. The world at large has yet to figure out a non-pathological way to monetize online content.

Value in human relationships

I’ve been talking to a lot of folks recently whose job is to make complex translation projects happen on time and in good quality. Now it may be that my sample is skewed, but I saw one clear pattern emerging in these conversations.

I wasn’t told about standardized workflows. I didn’t hear about machine learning to pick the best vendor from a global pool of X hundred thousand translators. I didn’t perceive the wish to shave another few percent off the price by enhanced TM leverage.

The focus, invariably, was human relationships. How do I build a long-term working relationship based on trust with my vendors? How do I do the same with my own clients? How do I formulate the value that I add as a professional, which is not churning through 10% more words per day, but enabling millions of additional revenue from a market that was hitherto not accessible?

Those are not yet the questions I’m asking about AI, but they are closing in on my point. In a narrow sense, I see technology as an enabler: a way to reduce the drudge so humans have more time left for the meaningful stuff that only humans can do.

Fewer clicks to get a project on track? Great. More relevant information at the fingertips of translators and project managers? Awesome. Less time wasted labeling and organizing data, finding the right resources, finding the right person to answer your questions? Absolutely. Finding the right problems to work on, where your effort has the greatest impact? Prima.

AI has its place in that toolset. But let’s not forget to get the basics right, like building software with empathy for the end user.

The right question

Whether or not AI will be part of our lives is not a question. Humans have a very elastic brain, and whatever invention you give us, we will figure out a use for it and even improve on it.

I argued that technology is not a Platonic thing of its own, but the product of a specific social and economic context. I also argued that if you instrumentalize big data and machine learning within the wrong mythology, it has a disturbing potential to dehumanize.

But these are not unescapable forces of nature. The mythology we write for AI is a matter of choice, and the responsibility lies with us, engineers and innovators.

The right question is: How do I use AI responsibly? Is empathy at the center of my own engineering work?

No touchy-feely idealism here; let’s talk enlightened self-interest.

As a technology provider I can create products with the potential to dehumanize work and encroach on privacy. That may give me a short-term advantage in a race to the bottom, but it will not lead to a sustainable market for my company. Or I can create products that help my customers differentiate themselves through stronger relationships, less drudge, and added value to their clients. Because I’m convinced that these customers are the ones who will be successful in the long run, I am betting on building technology for them.

That means engaging with customers (then engaging some more) to learn what problems they face every day, instead of worrying about the AI train. If the solution involves AI, great. But more likely it’ll be something almost embarassingly simple.