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Tag: machine learning

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AI Data Training Company Appen Sees Increasing Rise in Stock Price

AI

This week, Appen LTD, a global leader in machine learning and artificial intelligence (AI) data, reported a 6.9% increase in share value — the most in 11 weeks. Appen is of note to the localization industry primarily as a seller of training data that can be used in applications such as machine translation.

The Motley Fool reported last month that Appen has seen a steady increase in share prices since March. The news comes as a boon to the company’s shareholders, who saw a significant drop in prices in mid-February as countries began responding to COVID-19. In spite of this year’s challenges, the company is in the middle of a strong upswing.

Indeed, during one of the price peaks, the founder and the CEO of the company cashed in their shares for a combined $61 million. Since March, the price has increased 99% from its $17.14 low to a high of $34.17. Following a year of immense success in 2019, with a 47% increase in revenue to $536 million, Appen has proven its resiliency during the pandemic.

With over 1 million contractors in over 130 countries and 180 languages, the company studies human speech and interactions with each other and with AI to collect training data that teaches AI models and machine learning algorithms to (theoretically) make good decisions. Its worldwide crowd of contributors paired with its innovative data collection platform ensures premium localization of text, images, audio, video, and sensory content, which has built it a strong reputation in a variety of industries.

Appen claims to be the data industry’s “most advanced AI-assisted data annotation platform.” Working with tech companies such as Apple, Google, Microsoft, and Adobe, Appen sells data sets to assist with machine translation, proofing tools, automatic speech recognition, computer vision, semantic search, text-to-speech, virtual assistants and chatbots.

 

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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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Humor and AI: Does it travel?

Localization Technology, Personalization and Design

Conversational interfaces such as chatbots and voice assistants present many localization challenges — humor, for example. And that’s not even considering if the original content was all that funny to begin with.The secret to AI comedy must be in the data Click To Tweet

Humor: The final frontier

“Are there any Scottish people in the audience?”

Always a great start to a presentation at a conference. The response I received was, “You’re going to show that Scottish Elevator Voice UI video, right?”

I wasn’t.

Instead, I used the top jokes from the 2018 Edinburgh Festival Fringe as my opener to a workshop at ConverCon 18 on the subject of artificial intelligence (AI), personality, and conversational UI.

Of course, humor is an integral dimension of human personality and therefore part of that natural, conversational human-machine dialog. But humor has been called the final barrier for AI for good reason. There are many challenges.

I began my ConverCon workshop by telling the best joke from the Fringe.

“Working at the Jobcentre has to be a tense job — knowing that if you get fired, you still have to come in the next day.”

As soon as I recited the joke, I realized that it may not have been that funny to my global audience. Had they any idea what a Jobcentre is? It’s a British public service. In Ireland, the equivalent, an Intreo Centre, is offered by the Department of Work Affairs and Social Protection. In the United States, it might be called a WorkForce Center or One-Stop Center.

Conversational UI and the secret to comedy

Real US English examples of conversational interfaces, chatbots and AI can be tricky when it comes to humor.

Take this processing message from the Meekan scheduling robot on Slack. It makes a “witty” reference to hacking into TSA servers and No Fly Lists. I really winced at that one. I know what the TSA and No Fly Lists are, and I still didn’t get the joke.

Meekan scheduling robot on Slack (Image by Ultan O'Broin)

Meekan scheduling robot on Slack (Image by Ultan O’Broin)

This got me thinking about the challenges of humor and AI. If the secret to human comedy is timing, then the secret to AI comedy must be in the data, as well as the context.

Humor does have a place in conversational interaction, even in the most seemingly unlikely interactions, for example, Woebot. But humor needs to be done right.

Humor is not only the final frontier for AI, it’s a human personality trait that is easily lost in translation. Worse still, even in the original language, humor is not always that funny to everyone in a native audience. Of course, you don’t have to be Geert Hofstede to realize that humor doesn’t travel across cultures, but machines don’t get that. Yet.

So, as the localization industry rises to the challenge of dealing with AI, personality, humor, and the realization that no UI is the best UI of all, we can expect new talents will flourish to ensure that the conversational user experience resonates with the target audience. Do today’s translators need to have performing arts backgrounds or be comedians to enhance that local conversational interaction? I think storytelling skills are about to become hot property in every language.

Do today's translators need to have performing arts backgrounds or be comedians to enhance that local conversational interaction? Click To Tweet

Your punchline?

You may have other examples of humor and localization challenges from the world of technology. If so, share them in the comments!

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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.

Conversational UI Language Design at LocWorld35

Language in Business, Language in the News, Personalization and Design, Translation Technology

Oracle Applications User Experience (OAUX) team member (and Microsoft Alum) Karen Scipi (@karenscipi) presented on the subject of Conversational UI in the Enterprise at #LocWorld35 Silicon Valley. Karen covered the central importance of  language design for chatbots and other conversational user interfaces (CUIs) for global work use cases.

Karen Scipi presenting on Conversational UIs in the Enterprise at Localization World in Silicon Valley 2017 (Image credit: Olga)

Karen Scipi presenting on Conversational UIs in the Enterprise at Localization World in Silicon Valley 2017 (Image credit: Olga)

Karen even developed two chatbot integrations for Slack introducing her topic. One was in English, the other was in Italian.

Italian LocWorld Chatbot Conversation Example

Italian LocWorld Chatbot Conversation Example (Source: Karen Scipi)

What’s a Conversational UI?

Chatbots and the alike are a very hot topic, wrapped up in the artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and robotics part of technology’s evolution. However, user experience design insight and an empathy for how people interact with each other through technology in work, at play, or in everyday life makes the difference when creating a great user experience in any language.What could be more 'natural' than talking to a computer? Click To Tweet

CUI means we moved from a “user”-centric concept of design to a human-centric one. After all, what could be more “natural” that talking to a computer? Both humans and computers “converse” in dialog, and it’s the language design knowledge for such a conversation that’s critical to delivering a natural, human-like interaction between the two.

Examples of CUIs include Facebook Messenger, Slack bots, TelegramAmazon Echo and Alexa devices, and so on. Interaction can be via voice, SMS messaging, typing text on a keyboard, and so on.

In the enterprise there are a broad range of considerations and stakeholders that localization and UX pros must to consider. Fundamentally though, enterprise CUIs are about increasing participation in the user experience of work, making things simpler.

 

Oracle Conversational UI image showing the interaction and participation of humans and the cloud - in any language! (Source: OAUX)

Oracle Conversational UI image showing the interaction and participation of humans and the cloud – in any language! (Source: OAUX)

Localization of Conversational UIs

To an extent, the localization or language part of the CUI interaction is determined by the NLP support of the chatbot or other platform used: what languages it supports, how good the AI and ML parts are, and so on. However, language skills are at the heart of the conversational UI design, whether it’s composing that  user storyline for design flows or creating the prompts and messages seen by the human involved.

This kind of communication skill is much in-demand: It is a special type of talent: a mix of technical writing, film script or creative writing, transcreation, and interpreting. It’s a domain insight that gets right down to the nitty-gritty of replicating and handling how humans really speak and write: slang, errors, typos, warts and all. CUI language designers must even decide how emoji and personality can or should be localized in different versions of a chatbot.

Where’s the Conversation Headed?

The conversational UI market is growing globally as messenger apps take over. Localization and language pros cannot ignore the conversational UI space.

Karen will be speaking next at the Seattle Localization User Group (SLUG) in December (2017) about Conversational UIs in the Enterprise.Localization and language pros cannot ignore the conversational UI space. Click To Tweet

 

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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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Boaty McBoatface: Man versus Machine at Localization World

Language in Business, Translation Technology

Yes, the whole Boaty McBoatface thing has now entered the language space too.

Boaty McBoatface: Your future of translation may lie in machine learning and related technology

Boaty McBoatface: Your future of translation may lie in machine learning and related technology.

Parsey McParseface, part of Google’s SyntaxNet, an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems is out there:

Parsey McParseface is built on powerful machine learning algorithms that learn to analyze the linguistic structure of language, and that can explain the functional role of each word in a given sentence. Because Parsey McParseface is the most accurate such model in the world, we hope that it will be useful to developers and researchers interested in automatic extraction of information, translation, and other core applications of NLU.

I wonder could Parsey McParseface have a role in determining if a translation was correct or incorrect, given the context (or as the UK’s Daily Telegraph newspaper would so earthily have it, act as a “bolloxometer“)? Whither the QA or real-time interpretation possibilities.

This is all fascinating stuff sure, and definitely machine learning is a driver of smart user experiences, along with other areas. The Globalization, Internationalization, Localization, and Translation (or GILT) industry needs to be onboard with these emerging technologies and explore their possible application.

It’s the kind of thing I had intended to talk about at Localization World 31 in Dublin (yes, I even included Parsey McParseface). Alas, personal circumstances intervened and I did not speak. Some other time perhaps.

In the meantime, I am sharing the slides I had intended as a backdrop to the discussion. Perhaps they will help you orient yourself to the differences between machine learning, artificial intelligence, NLP, Big Data, robots, and more. They may even help you figure out if you have a future in the GILT industry and what that might look like.

Enjoy:

Smart UX in the World of Work

Context is King: Smart UX in the World of Work

Smart User Experiences and the World of Work: Context is King from Ultan O’Broin

Comments welcome.

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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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