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?
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.
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.
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.
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.
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!