AiThority Interview with Jonas Ryberg, VP of Digital Globalization Services at Pactera Technology International

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How are AI and language services converging?

The global on-demand economy is accelerating at an astounding rate, as geographic borders simply fade away when it comes to delivering products and services. In fact, to a joint report by Business of Fashion and McKinsey & Company, by 2020, consumers will spend $1 trillion on cross-border e-commerce. Companies wanting to leverage this opportunity need to translate, localize and personalize their products and services for multiple markets, quickly and accurately. That’s where AI comes in. AI’s recent growth spurt, combined with a seemingly insatiable appetite for global data to train the AI, is shaping a global demand for offerings that combine AI and language services.

How have AI-Infused Language Services evolved from Neural Machine Learning?

Neural Machine Translation (NMT) has become a go-to AI application for translators and localization platforms, and will adoption will continue to grow. There are multiple other areas where AI can be leveraged for Language Services. For example, Text-to-Speech and Speech-to-Text, predictive tools for resourcing, and smart quality assurance.

How is Neural Language Processing different than Neural Machine Learning?

Natural Language Processing (NLP) is a subfield of Artificial Intelligence that covers interactions between computers and human languages, in particular how computers to process, parse and understand natural language. NLP comprises speech recognition, Natural Language Understanding, Natural Language Generation, and machine translation. Combined with AI technologies such as information retrieval, Machine Learning, and sentiment analysis, NLP lays the foundation to create breakthrough opportunities in such areas as customer interaction, information monitoring, data mining, and expert systems.

What did Processing present by quick and accurate Localization?

Many business solutions, including those that are AI developed, are English-centric. Everyone from the CIO to Project Manager to the coder who develops AI solutions typically operates in a monolingual environment. Marketing, customer support, and product specialists all work in one language. Localization is, as usual, an afterthought. But the business reality is, customers and their data speak many, many, different languages, and there is tremendous opportunity in leveraging local content at an early stage in AI development.

This allows a company’s online chatbot to become a tool versed in multiple languages, servicing ANY customer who accesses the site. Another example, social media, few companies monitor what is being said about them in other countries. And the marketing content they so carefully develop is not translated to markets where it might have an impact. Smart, fast, turn-key translations services could localize all these processes and more.

What part has Pactera played in Localization services and how is AI changing the process?

At Pactera, we envisioned how traditional language services could be transformed by catering to the emerging hunger for data to fuel AI/Machine Learning models on a global scale. Over the past decade, that trend was amplified and created cross-pollination between traditional language services and AI-related data services.

Now, the two service lines have finally converged, driven by similarities in not only the delivery framework but enhanced by learning from different best practices as well. Today and in the future, Pactera is positioned with the skills of new talents, next-generation AI, and a foundation of many years of experience refining a global program and quality management framework.

How does Pactera’s OneForma platform work?

OneForma is a web-based platform of intelligent AI Data Project Management tools that enables large-scale, high volume AI Data and localization project execution. With OneForma, Pactera has developed a set of tools on a unified framework to give AI model developers the right set of training data at scale and in multiple locales. Essentially, it’s a single-source solution that seamlessly converges all separate functions in the life cycle of language service and AI data programs.

The result is one connected framework, which effectively removes the common challenges that traditionally plague large scales de-centralized teams challenges like lack of visibility, interoperability, scalability and quality control. OneForma has the flexibility and versatility to take on virtually any localization or globalization project, making use of Pactera’s leading AI Enablement Service, managed by its pool of over 100,000 worldwide talents. We consider it the premier business solution for complex AI data and Language Services campaigns.

How is your platform different than other available solutions?

Most AI projects suffer from poor quality, unclean data. It all comes back to the classic garbage in/garbage out tech scenario. AI model accuracy and output quality, its ability to outperform humans and meet business objectives depends on the size and cleanliness of the incoming dataset. At project launch, there is typically too much general data from public sources, from parsing websites on the internet, from company record-keeping, but there is no guarantee that inside the corpus there aren’t any patches of information that can contaminate the dataset. One of the key advantages of OneForma is its Data Analytics component, which provides advanced data analytics on a granular level, in order to help managers and clients extract meaningful insights from project data in real-time.

What start-ups are you following?

We are tracking various AI and Language Services related startups, such as Cloudwords, Lilt, Iconic and Memsource, and partnering with some of these.

Which specific spheres in AI are you particularly interested in?

The obvious area is Neural Machine Translation, but this was just a starting point. We are very excited about the work we do in Speech related AI, predictive analytics, and are increasingly leveraging Computer Vision.

How do you seek information on Artificial Intelligence related topics?

We attend industry events, local meet-ups and spend quite a bit of time reading online and offline.

Thank you, Jonas! That was fun and hope to see you back on AiThority soon.