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SDL Tados 2021

Localization tech predictions for 2019

AI, Blockchain, Localization Technology, Machine Learning

Happy 2019!

Let’s look over the past year to get a sense of how much progress we have made, and what progress may lie before us. AI has become a norm in our lives and is now part of the vernacular more than ever. People have accepted that their lives interact multiple times a day with AI, and that such technology is becoming ubiquitous. 

What does 2019 hold for us? Well, if we pay attention to the predictions of 16th-century seer Nostradamus, we should brace ourselves for flooding, wars and a strike by a meteor. Nothing all that cheerful but, assuming we survive all of that, what does 2019 hold for technology trends?

AI

We’ve seen an explosion in the use of AI in the delivery of neural machine translation during 2018; expect this to continue in 2019 and beyond. AI is the catch-all term to cover machine learning (ML) and deep learning. Machine learning is an over-arching term for the training of computers, using algorithms, to parse data, learn from it and make informed decisions based on accrued learning. Examples of ML in action is Netflix showing you what you might want to watch next. Or Amazon suggesting books you might want to buy.

Within the localization industry, the use of AI in the form of machine translation (MT) in several forms has significantly improved translation quality outputs, sped up translation of huge quantities of data and reduced the price of translation to make it economically viable.

AI refers to computer systems built to mimic human neural abilities and to perform tasks such as image recognition, parsing speech forms, discerning patterns from complex data sets and informing accurate decision-making. What’s more, AI can do these tasks faster, cheaper and more accurately than humans. Although AI has been around since the 1950s, it can be truly said that it has now come of age. This maturity has been propelled by the ever-increasing computational power now available in the cloud. 

According to Forbes, five out of six people use AI technology each day. These services include such things as navigation apps, streaming services (such as Netflix), smartphone personal assistants, dating apps and even smart home devices (such as remote-activated home security systems). Additionally, AI is used in recommendation engines used by eCommerce sites to schedule trains, to predict maintenance cycles and for other mission-critical business tasks. 

For the localization industry, AI will become a highly-integrated component of MT systems. The role of the human translator will continue evolving to that of an editor of MT texts, rather than translator of raw texts. In addition, pricing models will continue to move from the traditional price per word based on word volumes to pricing on a time-measured rate. MT will become an integral part of the standard workflow. The reality of real-time translation — driven by such technology as the internet of things (IOT) — will see project managers and editors managing workflows of projects required by customers who need a constant flow of updated information. MT will become part of the translation process just as much as other computer-aided translation tools did in the past. And, as ever, evolving technology will bring with it a desire for speedier and cost-effective solutions. 

Machine learning 

localization tech

ML will continue to grow as a tool used by most localization departments as the requirement for the speedy translations of large datasets continues to be a driver in the industry.  

ML is a subset of AI: with ML, computers are automated to learn to do something that they are not initially programmed to do. So, ML is an over-arching term for the training of computers to use smart algorithms to automate actions, to parse complex data and to learn patterns from this learning thus enabling the computer to make informed decisions based on this accrued knowledge. ML can be broadly broken down into two types of learning: supervised and non-supervised learning. 

For supervised machine learning, the training data is pre-labelled and consists of an aligned input data set and desired output data set. For example, an input data set could be a translation memory. An ML algorithm analyses the training data and maps how to convert future inputs to match the learned, desired output data sets. 

Unsupervised ML is like supervised ML; however, the input data sets are not pre-classified or labelled. The goal of unsupervised machine learning is to find hidden structures in the unlabelled data. 

So how does this impact the localization industry? Well, suppose you want to build a translation system to translate from Zulu to French, without any Zulu-French training data. The solution is, you can combine both supervised and unsupervised approaches to achieve this. You can use an English-Zulu data set in combination with an English-French data set, and using unsupervised machine learning, the system can learn how to translate from Zulu into French. 

This approach is commonly referred to as “zero-shot” machine learning — expect to hear more about this in 2019 for MT systems for long-tail languages. 

Blockchain 

While blockchain is most widely known as the technology behind cryptocurrencies, it offers security that is useful in many other ways. 

In simple terms, blockchain can be described as data you can add to, but not take away from or change. These blocks of data can be “chained” together to create incredible secure data repositories. Not being able to change any previous blocks is what makes it so secure. 

This enhanced security is why blockchain is used for cryptocurrencies. It is also why it will play a significant role in localization where it will used to protect information such as a client’s financial details, and to protect and preserve translation memories; especially in translation memories used in distributed translation workflow scenarios. 

Edge computing 

Cloud computing has now become mainstream: most of all global companies now rely on this centralized hosting structure for machine learning and powerful computational power. This cloud market is dominated by just a few gigantic companies such as Amazon, Microsoft, Google and IBM. However, now that we’ve been using cloud computing for some time, companies have realized that accessing all data from a central repository introduces a time-delay latency, which in turn slows down the delivery of services which can, in turn, increase costs. The “round trip” made by cloud-based data is seen by many of today’s companies as a hindrance to their business growth. 

Technology stands still for no man, and so, for many, the cloud has reached its peak as a service for some technologies. The cloud will continue to be used to analyze and process huge swathes of data, but the advent of the IoT (connected security systems, electronic appliances, vending machines, automated lighting), where data processing needs to be high speed, if not real time, demands a different model. So the logical and necessary next move is to move this data processing to the edge. The edge simply means that data processing is moving from a far-away storage location to a geographical site closer to the data source. The advent of powerful computer chips that allows such processing to be done locally has expedited this move to the edge. Indeed, many of today’s cloud setups automatically look to place the processing of data at the optimum edge site for that data’s requirements. 

So, edge computing solves the latency problem by simply moving the data processing closer to home. Closer to home means less time spent uploading and downloading data. Instead of the centralized storage model, which has hitherto driven AI, companies are moving their data into the “local community” to be processed. This move will undoubtedly make data access much faster and facilitate the growing demand for real-time computing. 

How will this impact localization? Well, in 2019 we can expect to see the edge model used in domain-adapted MT systems, and distributed translation workflows that are designed to meet the increasing demand for data distribution in real-time. 

Conclusion 

We are on the verge of an explosion in the use of AI. The inevitable growth of AI will fundamentally re-shape how companies manage translation workflows; the very engine of their work process. Real-time translations will often become the norm. 

I also predict that changes will happen at a human level; for example, the role of the translator will change from that of translator of raw text to that of editor of huge volumes of high-quality MT-produced text. This will be a beneficial change, allowing translators to increase their capacity and so increase their income. In 2019, we predict that the overall transformation effected by the advent of AI at all levels of the industry will bring with it an increased velocity of production, an improved efficiency in the delivery of translations, and a reduction in the cost of translating huge volumes of data. 

We hope you all have a very successful 2019! 

 
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Tony O’Dowd is the founder and chief architect of KantanMT.com, a cloud-based custom machine translation solutions provider. He is a serial entrepreneur and localization veteran, with almost 30 years’ experience working in the localization industry.

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Haven’t an Iota About Fintech Localization? Try Cryptocurrencies

Localization, Localization Technology

Money, Money, Money Meets Its Waterloo

Apologies to ABBA fans about the cheesy introduction. But, mamma mia we need to talk about cryptocurrencies!

Lattés with your Litecoin? Crypto Café in Dublin, accepts cryptocurrencies and hard cash. (Image: Ultan Ó Broin)

Lattés with your Litecoin? Crypto Café in Dublin, Ireland accepts cryptocurrencies and hard cash. (Image: Ultan Ó Broin)

The Chips Are Down For Fintech

I enjoyed a must-read Medium article from Graham Rigby of Iota Localisation Services about the challenges of Fintech localization. Graham talks about how Fintech localization is different from ERP financial or vertical banking localization. He also tells us how a changing business environment means localization providers need to be agile, collaborative, and flexible:

“The way financial products are sold, communicated, and presented in the current market mean that linguists who have spent 20 years translating mortgage terms might not be best equipped to deal with the style and nuance of the text in a money transfer app.”

Indeed, the very notion of a “bank” itself has changed: Deutsche Bank in Berlin is now into Kaffee und Kuchen for the hip and happening people of the Hauptstadt.  ImaginBank from Spain is aimed at snombies, sorry, I mean the mobile generation.

And now, cryptocurrency localization is upon us, and that requires linguistic domain expertise too. Ironically, there is even a cryptocurrency called … Iota (designed for use with the Internet of Things [IOT]).

Oh No, It’s ONO!

I’ve changed career in the last few months, now offering digital transformation consultancy to established and startup ventures seeking to design the right digital thing the right way and to be ready to go global. I’ve been diving into the cryptocurrency space and grappling with the new ideas, concepts, and a new strange language that comes with it.

Cryptocurrency word cloud: Has language itself been disrupted by innovation? (Wordle by Ultan Ó Broin)

Cryptocurrency word cloud: Has language itself been disrupted by innovation? (Wordle by Ultan Ó Broin)

This is about much more than the Bitcoin and blockchain buzzwords du jour that people throw about without actually having an iota what these mean or indeed possible uses (blockchain, for example is behind the Chinese social media platform, ONO).

Mental “Block” About Cryptocurrencies?

If you want to explore this decentralised space further, there’s a blog series worth reading from Genson C. Glier on blockchain, Bitcoin, Ethereum, and cryptocurrency. I also recommend  this podcast from Tim Ferriss that covers all you were afraid to ask about, although some of terms and concepts will make your head spin (cheat list: jump to the “Show Notes” on the podcast). Try understanding these terms: Miner, Smart Contract, Daap, Truffle, Ganache, Hashcash, “Wet” Code, “Dry” Code, ICO, Metamask, and Gas.

Advertisement for eToro cyrptocurrency platform on Dublin public transport. Interest in cryptocurrencies has increased greatly in Ireland.

Advertisement for the eToro cryptocurrency platform on Dublin public transport. Interest in cryptocurrencies has increased greatly in Ireland. (Image: Ultan Ó Broin)

Although many people and institutions are rightly cautious about cryptocurrencies, they are a “thing” now and attitudes are shifting from suspicion to curiosity Providing localization of the conversation around cryptocurrencies and non-developer facing terms would be a great starting point to increase familiarity and adoption

Providing localization of the conversation around cryptocurrencies and non-developer facing terms would be a great starting point to increase familiarity and adoption Click To Tweet.

Read the small print. Consumer warning about cryptocurrencies lack regulation and protection on an eToro advertisement in Dublin, Ireland. (Image: Ultan Ó Broin)

Read the fine print. Consumer warning about cryptocurrency lack of regulation and protection on an eToro advertisement in Dublin, Ireland. (Image: Ultan Ó Broin)

Cryptocurrency Localization Needed

Generally, cryptocurrencies are for most adopters a form of value storage. However, cryptocurrencies are rapidly becoming a medium of value exchange, too (“digital money”). Bitcoin ATMs are appearing globally, for example. In Ireland, about 120,000 people in Ireland now own a cryptocurrency, a 300 per cent increase in the last four years. And yet, that basic usability heuristic of using plain language to communicate a concept even to experts to enable ease of use and adoption has already gone out the window.

The list of Bitcoin-friendly countries contains some surprises (Estonia is number one), and includes locations where English is very often not a mother tongue (although development tools and coding platforms are in English). We cannot be dismissive of the significant regulatory and security aspects of cryptocurrencies for now. But localization challenges are worth planning for now if cryptocurrencies are to move to the mainstream beyond those Silicon Valley types and their friends.

It’s likely, of course, that we will also see traditional finance, banking, Fintech, and cryptocurrencies all interact with more solidity in the future, adding to the need for more localization creativity.

Cryptocurrency Disruption Includes Language

At times, it’s hard to accept that the localization maxim English Is Just Another Language could apply in a cryptocurrency space that seems to have disrupted the notion of the English language itself. James Joyce might be proud of this kind of word invention, and of course it’s all a matter of context. But I remain gobsmacked by some of the terms I come across. It’s clear that lack of localization is a serious barrier to cryptocurrency adoption when even someone who has  worked in digital tech for three decades is struggling.

I need to learn that lingo though, as Dublin seems to be place it’s all happening for those cryptocurrency and blockchain ambitions.

Ah, the irony of that word, block, when it comes to getting your head around cryptocurrencies.

More About Cryptocurrencies?

 

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Ultan Ó Broin (@localization), is an independent UX consultant. With three decades of UX and L10n experience and outreach, he specializes in helping people ensure their global digital transformation makes sense culturally and also reflects how users behave locally.

Any views expressed are his own. Especially the ones you agree with.

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The Irish Language: A Cereal Troublemaker Hits the Gaeltacht

Language, Localization Culture

The semantics of selfies in Irish

I was reminded of the whole Dara Ó Briain (@daraobriain“Sé-Mé” #selfie uproar (a classic case of urban Irish — or Gaeilge, and not “Gaelic” — usage versus the “official” Irish (where “selfie” is “féinín”) when I visited my son in the Gaeltacht (or primarily Irish-speaking area) in Ireland recently.

Dara Ó Briain discovers "Sé-Mé". And the sky fell.

Dara Ó Briain discovers “Sé-Mé“. And the sky fell.

Flaky terminology

I joined my son (aged 13) for breakfast and asked him if he knew the Irish for “cereal.” Officially, the term would be “gránach bricfeasta” or similar, but he simply said, “calóga” (which basically means “flakes”).

Kellog's Special K in France

Kellog’s Special K on sale in France (Carrefour, Paris). Image by Ultan O’Broin.

But I thought he’d said “Cellógga,” my Dublin urban Irish ear already tuned into expecting to hear brand names and slang as terminology. That’s the Irish language for you today in Ireland: more people than ever (claim to) speak it, but we just can’t understand each other.

That's the Irish language for you today in Ireland: more people than ever (claim to) speak it, but we just can't understand each other. Click To Tweet This issue of an evolving Irish language demographic was covered by Brian Ó Broin (no relation) a few years back in a previous issue of MultiLingual and he has also written about emerging Schism fears for Gaeilgeoirí (or Irish language speakers) elsewhere.

Whereas I could natter along in my pidgin Dublin Irish about “blockchain” or “chatbots” to other Dubliners, when weather announcements are made on Ireland’s official Irish broadcasting network in Irish, I haven’t a clue what they’re talking about.

Language wars not worth fighting

I am sure other languages (French, for example) face these kind of issues. But does it really matter as long as people can communicate, and use the context to figure out the differences?

And I don’t think the official Irish versus everyday street version delineation is as clear-cut as many would like to think.

It was remarkable that many people in the Gaeltacht that I met switched between the urban “pidgin” Gaeilge, official Gaeilge, and even interspersed the conversation with English terms, depending on their innate human sense of what the listener would get.

As for that Kelloggs Special K, ironically there is no letter “K” in the Gaeilge alphabet.

If you’ve found yourself in similar situations or come across terminology conflicts in the digital age, then let us know in the comments!

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+ posts

Ultan Ó Broin (@localization), is an independent UX consultant. With three decades of UX and L10n experience and outreach, he specializes in helping people ensure their global digital transformation makes sense culturally and also reflects how users behave locally.

Any views expressed are his own. Especially the ones you agree with.

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