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Tag: Artificial Intelligence (AI)

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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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MultiLingual creates go-to news and resources for language industry professionals.

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In the future, smart homes will differ from country to country

Localization Technology, Personalization and Design

The mystical world where anything that connects to the internet — including the appliances, devices and machines used in our homes and workplaces — will become ‘intelligent’ enough to preemptively service our needs is fast approaching. Referred to as the Internet of Things (IoT), some designers are expecting to see more developments and changes to the concept of the home in the next ten years than we’ve seen in the last ninety.

Yet the integration of these technologies vastly differs across the world, with each nation taking an approach to hire a programmer for IoT development in their own way. In fact, the use of this technology can even be manifested differently between neighboring cities.

Purchasing power only partly explains this divergence. Cultural preferences and different prioritization for various areas of our lives are all shaping which smart home technologies of the future we’ll be presented with and eventually adopt in our homes. We can see the beginnings of this now, with curated product lines for individual markets.

Consumer priorities for smart technology across countries

In the United States, many of the technological advances for the home are driven by convenience. Technology that takes care of location-specific tasks like kitchen appliances that order, prepare and bring food to the table would be expected to be wildly popular in the American market.

There would, however, be a few localization anomalies appearing in health-conscious states such as California. In these cases, technology focused around supporting an active lifestyle, like automatic climate controls or fridges that prepare healthful drinks after a workout, would be expected to be more successful.

Conversely, in Japan, it is not so much convenience that consumers are looking for, but technology that assists in caring for a growing elderly population. Considering they have one of the highest life expectancies among all developed nations, this shouldn’t come as too much of a surprise.

Here, the IoT is expected to outfit people’s homes with appliances and new technologies such as companion robots that are aimed at caring for elderly relatives. These bots will also be able to provide remote monitoring facilities and complete household chores like cleaning.

Other countries like the UK have a higher uptake of smart energy meters that allow adopters to visualize their consumption and automate the billing process with their energy provider. Spain and Italy, meanwhile, have shown to be more attracted to smart watches.

Integrating smart technologies

One of the biggest challenges to the adoption of smart technologies in homes across the world is not so much the natural disinclination we have to changing our behavior, but more the infrastructure it relies upon. Additionally, the ability for different appliances and devices to “talk” to each other can bring about difficulties as well.

For example, your smart meter needs to be able to talk to your home climate control device to ensure that the device isn’t racking up a massive bill at the end of each month. You could also imagine this situation with your fridge and oven, where the former makes sure the oven has preheated to the right temperature by the time the food is ready to be cooked.

While brands are getting better at allowing for interoperability of products, the infrastructure it relies upon — internet connectivity — is somewhat lacking in varying degrees across nations. While more than 51% of the world has some sort of access to the internet, many developed countries including the US, the UK and Japan still lack 100% broadband coverage. This blocks large parts of their populations from ever being able to access smart home technology.

Once governments in these countries make good on their promises of universal broadband for all, not only will communication and the workplace be completely transformed, but home tech will take off in a number of different areas to service the priorities of each culture.

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Rae Steinbach is a graduate of Tufts University with a combined international relations and Chinese degree. After spending time living and working abroad in China, she returned to New York City to pursue her career and continue curating quality content. Rae is passionate about travel, food and writing.

IUC44

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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Google Translate’s deep AI upgrade represents the future of machine translation

Translation Technology

Artificial intelligence may seem like science fiction, but it’s technically existed for decades. In 1951, students at the University of Manchester created a program for the Ferranti Mark I computer that allowed it to defeat amateurs in checkers and chess. That may not seem too impressive anymore, but it spurred a period of major innovation that continues to this day. Now, that technology can be applied to how we conduct website translations.

Google recently upgraded the AI of Google Translate, making it potentially much more effective than past web translation services. To understand how this new breakthrough works, it’s necessary to get some background on the basic classifications of AI.

Narrow AI

While a computer that can play chess may have been impressive in 1951, now there are plenty of similar (and more sophisticated) programs you can download straight to your phone. These early developments were examples of narrow AI, in which a programmer “teaches” a computer to perform basic, rule-based functions and tasks.

This type of AI can learn how to play checkers, but it can never learn to research the history of checkers. Basically, it can’t develop its own natural curiosity, and wouldn’t know how to apply said knowledge if it could.

Machine learning

Machine learning AI became more prominent in the 1990s. Rather than playing a game with constant rules, machine learning AI represents a shift towards programs that can actually “learn” on their own.

Essentially, the machines leverage specialized algorithms and refer to substantial amounts of data to acquire knowledge. For example, when you ask Siri a question, it sorts through data and breaks it down into subsets, arriving at what is most likely the correct answer. Siri doesn’t technically learn how to research on its own, nor does it retain knowledge or act independently in the same way a human does. What it can do, however, is adapt to learning situations that exist outside of the basic rules it was programmed to follow. Compared to narrow learning AI, which can only do the one particular thing it’s assigned, machine learning AI isn’t nearly as restricted.

Deep learning

Deep learning AI has been on the rise in the past decade. Structurally, the algorithms are based on the human brain. Although tech visionaries understood the value of this approach, until the right hardware and technologies were available, it was impossible to design such a complex system.

Unlike machine learning AI, which can mimic some form of actual thought, deduction, or reasoning, deep learning is the first type of AI which can use knowledge of past behavior and apply it to new problems outside of its programming. For example, a deep learning program from Google was able, after being exposed to 10 million images, to recognize specific objects (like cats) twice as accurately as previous image recognition programs.

In all likelihood, general AI — the kind featured in sci-fi movies with independent, thinking robots — will be a part of the everyday reality in the near future. This phenomenon will likely have a major impact on translating language.AI will likely have a major impact on translating language. Click To Tweet

Most online translation services work by dividing a sentence or phrase into smaller parts, referring to dictionaries to identify the equivalent words, and relying on post-processing to adjust the sentence structure according to the language’s specific grammatical rules. Anyone who has used one of these tools before knows the results are far from perfect.

That’s why Google Translate is shifting to a new method. Previously, the service worked by using hundreds of narrow AI programs to translate text. Now that Google has begun implementing deep learning AI in its translation service, the program will actually be able to learn from past experience. This allows it, in theory, to avoid the kinds of errors that are commonplace in online, machine translations.

The upgrade also allows it to bridge the gap between language problems it may not have encountered before. Perhaps it translates a Japanese text into English, then a Korean text to English. Early results indicate it will then translate Japanese to Korean with decent-to-remarkable accuracy.

Researchers call this breakthrough the “zero-shot translation,” and it represents the breakthrough fact that Google neural machine translation — the current moniker for the new translation program — basically “learned” how to achieve it independently. In fact, Google experts only have theories regarding how it accomplished this feat; they’re not entirely sure.

While this absolutely marks a major leap forward in online translation services, it doesn’t mean human translators will be replaced anytime soon. Effective translation requires not only an understanding of the nuances of language, but also an understanding of a given culture and how cultural attitudes impact the effectiveness of language. Until a machine can do that, it’s not quite human just yet.

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​Sirena Rubinoff is the content manager at Morningside Translations. She earned her B.A. and Master’s Degree from the Medill School of Journalism at Northwestern. After completing her graduate degree, she won an international fellowship as a Rotary Cultural Ambassador to Jerusalem. She covers topics related to software and website localization, global business solutions, and the translation industry as a whole.

Language at the ❤ of Conversational Interfaces

Personalization and Design, Translation Technology

A Chat About Language and UI

Robotspeak in San Francisco. A great store, but it’s also exactly how conversational interfaces should NOT sound: like a robot. Conversational interfaces offer a natural way to deal with a multitude of digital asks and tasks and the crafting of language is critical to that intent. (Image by Ultan Ó Broin)

Robotspeak in San Francisco. A great store, but it’s also exactly how conversational interfaces should NOT sound: like a robot. Conversational interfaces offer a natural way to deal with a multitude of digital asks and tasks and the crafting of language is critical to that intent. (Image by Ultan Ó Broin)

Chatbots and conversational interfaces are all the rage right with startups, VCs, innovators and users alike. Messenger apps have surpassed social media in terms of popularity and we’re witnessing the awesome agency of chatbots such as KLM Messenger as a natural way for users to perform a huge range of digital asks and tasks without the need for special devices or apps.

Going Global With Conversational Interfaces

But what are the localization and translation aspects to chatbots and conversational computing?

To a large extent, the natural language processing (NLP) backend capabilities of the bot or messaging platform determine much of the linguistic side of the user experience (UX). However, there are plenty of other considerations for internationalization and localization people to concern themselves with, not least educating designers and developers in globalization best practices.

Check out this super article “Do you want your chatbot converse in foreign languages? My learnings from bot devs” by Artem Nedrya for a start.

It is also very clear that there is a huge role for the conversational UI writer in the design and creation of conversational interfaces. An understanding of language, its style, tone, grammar, and so on, is central to making or breaking a conversational interface UX but also to ensuring that any content created is localizable and makes sense to a local user.

Here’s an article I wrote for Chatbots Magazine that covers the topic of language and chatbot UX that also touches the translation space. I hope you find my thoughts in “Writing Skills: At the ❤️ Of Chatbot UX Design” useful.

Conversational UI is dependent on bot and messenger platform NLP capability but human language skills are still definitely at the core of conversational UI design. (Image by Ultan Ó Broin)

Conversational UI is dependent on bot and messenger platform NLP capability. But human language skills are still definitely at the core of conversational UI design. (Image by Ultan Ó Broin)

Don’t be surprised if you see the topics of chatbots and conversational interfaces coming up on the agendas of localization conferences and in publications a lot more!

As ever, for a conversation on this blog post, find the comments box!

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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.

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.