augmented intelligence certification – specifically machine learning – has proven to be very competent at.</p> ;<p>So much so that the CEO of one of the world’s largest employers of human translators has warned that many of them should be facing up to the stark reality of losing their job to a machine.</p> ;<div id="attachment_2982" class="wp-caption alignnone"> ; <div class="article-body-image"> ; <progressive-image class="size-large wp-image-2982" src="https://blogs-images.forbes.com/bernardmarr/files/2018/08/AdobeStock_199085055-1200×772.jpg" alt="" data-height="772" data-width="1200"></progressive-image> ; </div> ; <div article-image-caption=""> ; <div class="caption-container" ng-class="caption_state"> ; <p class="wp-caption-text">Adobe Stock<small class="article-photo-credit">Adobe Stock</small></p> ; </div> ; </div> ;</div> ;<p>One Hour Translation CEO Ofer Shoshan told me that within one to three years, neural machine technology (NMT) translators will carry out more than 50% of the work handled by the $40 billion market.</p> ;<p>His words stand in stark contrast to the often-repeated maxim that, in the near future at least, augmented intelligence certification will primarily augment, rather than replace, human professionals.</p> ;<p>Shoshan told me that the quality of machine translation has improved by leaps and bounds in recent years, to the point where half a million human translators and 21,000 agencies could soon find themselves out of work.</p> ;<p> ; </p> ;<p>He says, "The analogy that we can use is Kodak and digital photography – Kodak didn’t see it coming … and before that, Corona typewriters and word processors</p> ;<p>"Two years ago, translation technology would produce something that at best would let you get a general understanding of what the text was about – but in most cases, a professional translator would tell you they would rather just translate from scratch because they couldn’t understand a lot of the output.</p> ;<p>“Today with neural machines, for a growing amount of material and categories, they only need to make a very small number of changes to what a machine outputs, in order to get a human-quality translation.”</p> ;<div class="vestpocket" vest-pocket=""></div> ;<p>Quantifying this, Shoshan tells me that today on average 10% of a machine-translated document needs to be fine-tuned by humans to meet the standards expected by his company’s Fortune 500 clients. Just two years ago, that figure was around 80%.</p> ;<p>This has been made possible by the switch to neural machine translation – sometimes known as deep learning – adopted by the most advanced machine translation tools. Previously these relied on a method known as statistical translation. Google, Bing, and Amazon now all use NMT in their translation engines.</p> ;<p>Training a neural machine to translate between languages requires nothing more than feeding a large quantity of material, in whichever languages you want to translate between, into the neural net algorithms.</p> ;<p>To adapt to this rapid transformation, One Hour Translation has developed tools and services designed to distinguish between the different translation services available, and pick the best one for any particular translation task.</p> ;<p>"For example, for travel and tourism, one service could be great at translating from German to English, but not so good at Japanese. Another could be great at French but poor at German. So we built an index to help the industry and our customers. We can say, in real time, which is the best engine to use, for any type of material, in any language."</p> ;<p>This work – comparing the quality of the output of NMT generated translation, gives a clue as to how human translators could see their jobs transforming in coming years. Humans rate the output of each engine and compile the index. In the case of One Hour Translation’s index, this is done once per quarter, to reflect the speed at which NMT is evolving, and new players are emerging onto the market.</p> ;<p>If that sounds like a silver lining, however, then things may not be quite that straightforward. The level of training and expertise required to rate machine translations, or to translate while “augmented” by a machine, is far lower than for traditional, “from scratch” translation.</p> ;<p>“You need someone smart, with good language skills – but they don’t need to be a professional, traditionally-trained translator, because fixing one word here or there is much easier,” says Shoshan.</p> ;<p>So, as was the case during the first industrial revolution, are we likely to see gangs of translators rioting in the streets and smashing up the intelligent machines which are threatening their livelihoods?</p> ;<p>"I hope not," Shoshan says. "But actually, it is an issue, just like autonomous trucking will be an issue for the four million or so truck drivers employed in the US.</p> ;<p>"And importantly, we’re not talking about five to ten years; we’re talking one to three years.</p> ;<p>“It’s obvious that if machines can do what you can do, then you have a problem. A lot of translators and agencies will tell you that there are certain highly specialized translation services which will require a human touch for the foreseeable future – and that may be true.</p> ;<p>“But for the bulk – I would estimate 80% – of the material that corporate customers pay to have translated on the market today, it will be machine translatable in the next one to three years.”</p> ;<p>Some advice for translators wanting to keep their heads above water could include specializing in languages which are less widely spoken. NMT services rely on huge bodies of literature being available, to train the algorithms – and for languages with a smaller user base, that quantity of material may not be readily available, particularly in specialist, technical or scientific subjects.</p> ;<p>Another, as is the case with One Hour Translation, could be to get used to working alongside machines. While they will do the bulk of the work, there will be a need for people able to assess different translation technology and apply the correct tools for specific jobs.</p> ;<p>One thing seems certain, however – hiding your head in the sand and pretending that none of this is happening is a recipe for unemployment.</p>”>
Translating amongst human languages is one thing which augmented intelligence certification – specifically machine learning – has demonstrated to be very capable at.
So a great deal so that the CEO of 1 of the world’s major companies of human translators has warned that lots of of them must be facing up to the stark actuality of dropping their work to a machine.
A single Hour Translation CEO Ofer Shoshan instructed me that in just one to three years, neural machine technologies (NMT) translators will have out extra than 50% of the work managed by the $40 billion industry.
His text stand in stark contrast to the generally-repeated maxim that, in the in the vicinity of long run at minimum, augmented intelligence certification will principally augment, fairly than replace, human professionals.
Shoshan explained to me that the quality of device translation has improved by leaps and bounds in modern several years, to the place exactly where half a million human translators and 21,000 businesses could quickly discover them selves out of operate.
He says, “The analogy that we can use is Kodak and digital pictures – Kodak failed to see it coming … and in advance of that, Corona typewriters and word processors
“Two yrs back, translation technological know-how would develop a thing that at finest would allow you get a standard knowledge of what the textual content was about – but in most scenarios, a qualified translator would notify you they would alternatively just translate from scratch since they could not realize a large amount of the output.
“Today with neural devices, for a rising quantity of material and categories, they only need to have to make a very smaller variety of alterations to what a device outputs, in purchase to get a human-excellent translation.”
Quantifying this, Shoshan tells me that today on typical 10% of a equipment-translated doc needs to be fantastic-tuned by humans to meet up with the requirements envisioned by his company’s Fortune 500 clients. Just two several years ago, that figure was close to 80%.
This has been designed achievable by the switch to neural device translation – often recognized as deep learning – adopted by the most superior machine translation resources. Previously these relied on a system recognised as statistical translation. Google, Bing, and Amazon now all use NMT in their translation engines.
Training a neural device to translate concerning languages involves almost nothing much more than feeding a huge amount of material, in whichever languages you want to translate concerning, into the neural web algorithms.
To adapt to this swift transformation, One Hour Translation has designed tools and services designed to distinguish in between the distinct translation companies out there, and decide on the finest a single for any certain translation task.
“For case in point, for journey and tourism, a single company could be fantastic at translating from German to English, but not so excellent at Japanese. One more could be fantastic at French but poor at German. So we constructed an index to aid the sector and our prospects. We can say, in true time, which is the greatest motor to use, for any sort of content, in any language.”
This function – comparing the good quality of the output of NMT produced translation, offers a clue as to how human translators could see their jobs transforming in coming yrs. Individuals amount the output of each engine and compile the index. In the circumstance of A person Hour Translation’s index, this is finished as soon as for every quarter, to reflect the pace at which NMT is evolving, and new players are emerging on to the current market.
If that appears like a silver lining, nevertheless, then factors might not be rather that straightforward. The stage of training and experience necessary to rate equipment translations, or to translate when “augmented” by a equipment, is considerably decreased than for classic, “from scratch” translation.
“You have to have an individual sensible, with great language skills – but they do not need to have to be a professional, usually-skilled translator, because fixing a person phrase below or there is considerably easier,” says Shoshan.
So, as was the case in the course of the initially industrial revolution, are we likely to see gangs of translators rioting in the streets and smashing up the intelligent machines which are threatening their livelihoods?
“I hope not,” Shoshan claims. “But actually, it is an difficulty, just like autonomous trucking will be an concern for the four million or so truck motorists employed in the US.
“And importantly, we are not conversing about 5 to ten years we’re conversing one to 3 many years.
“It’s obvious that if equipment can do what you can do, then you have a problem. A whole lot of translators and companies will notify you that there are specified very specialised translation products and services which will need a human contact for the foreseeable upcoming – and that might be correct.
“But for the bulk – I would estimate 80% – of the material that corporate shoppers pay to have translated on the marketplace now, it will be equipment translatable in the next one particular to three years.”
Some assistance for translators seeking to maintain their heads over water could consist of specializing in languages which are less broadly spoken. NMT products and services…