language technology

11th year of interpreting at Nordic Business Forum

30.4.2024 by Delingua

How generative AI can help elevate your work and boost efficiency

26.4.2024 by Delingua

In the last year, awareness of Generative AI has skyrocketed. The availability of newer and more powerful GPT models has given everyone a chance to benefit from this new technology.

But what exactly are those benefits? How is generative AI really going to change the way you work and why should we be investing our time in understanding these new, powerful tools.

Let’s look at some ways you can use AI to help you work smarter and faster than ever.

Kickstart Creativity

Generative AI is a great starting point for getting your ideas on the page. When working creatively, nothing can be worse than blank page syndrome, where the cursor blinks at you incessantly while you wait for inspiration to strike.

From creating article outlines, to writing first drafts of content, Generative AI can help you jump start your creativity and speed up your workflows. Use it to create first draft outlines or give you heading ideas to get you started. If you’re really stuck it can even create a first draft from scratch to fill that first page.

You can use the new wave of AI tools to help break this cycle and start getting new ideas on the page for you to then finesse with your own style, voice and creativity.

Maintain Consistency & Quality

When working on multilingual content, you want to always maintain a consistent tone of voice and use the right business language. This can be tricky when you have content creators or translators working all around the world and often across different languages.

By interlinking your Generative AI tool with your company termbase and other linguistic assets like translation memories, you can ensure that any content your AI tool generates for you will maintain the correct business tone and language to ensure consistency.

You can train your AI model on your linguistic assets to ensure that it knows your tone of voice, what terms you use and how to spell and write them correctly. This will save you time and effort when validating and boost the overall quality of your content.

Personalise Customer Experiences

It can be difficult to provide always-on multilingual customer service, especially on a global scale. However, AI chatbots can help provide a customer service presence even when your employees are off the clock.

With the latest versions of AI models, these chatbots can be programmed to provide a huge amount of personalised interaction with your customers, in their own language.

Always-on AI chatbots can provide friendly, helpful support to your customers at any time of the day and respond in a conversational manner that seems natural. They can pull data directly from customer databases to personalise the experience and make answering customer queries simple in any language.

Boost Productivity

Data analysis is one of the key areas where AI can help make our lives much easier. AI models are trained on large datasets, and they can easily absorb, review and analyse data in a second before returning the results in an easy-to-read format.

One powerful way this can be used is to summarise long texts. AI can quickly read and create a summary of a longer document by identifying the key points and adding them to a bulleted list. It can also create short-form copy from long-form documents, for example it could build social media posts based on blog articles.

You can input large data sets of any variety into your AI tool and asking them to identify trends, format the content in a variety of ways or to extract key information quickly and easily, cutting down on the manual effort required.

Additionally, you can use an AI tool to help you be more productive in other tools such as Excel by asking you to write a formula that will analyse your data for you in that application.

You do need to be careful about where and what data you upload to open and public AI tools as there is no protection on who can see your data once it is uploaded. Make sure you only upload sensitive data to a company you trust who can provide assurances that nothing will be kept or shared after the task is completed.

Make Generative AI work for you

Generative AI is a powerful tool that you can use to enhance your daily workflows. From creativity to communication, the possibilities are endless. Getting the most out of AI includes understanding how to input the right prompt. If you would like to learn more about AI prompting, read our detailed guide.

Interested to learn more about what LanguageWire AI tools can do for you and your business? Contact us and let’s talk more!

This article was originally published by LanguageWire Company (Verity Hartley).

The future of termbase management

18.4.2024 by Delingua

As we have seen recently, things can change very quickly in business technology. AI has leaped into our lives and is now becoming a major part of most of the daily processes and systems in ways we could never have imagined a year ago.

Termbases, critical business tools for those who seek quality and consistency in the content, and their related science of terminology management will be no different.

Peering into our crystal ball let’s see if we can catch a glimpse of a few of the future trends that will be impacting on our termbase management systems.

Cloud network storage

Long gone are the days when termbases were confined to a single desktop, accessible only by the one who created it. With cloud computing becoming much more ubiquitous in business, we can expect to see termbases becoming not just about storage but about accessibility, availability, and real-time updates. As businesses become increasingly global, the need for language experts to access these linguistic assets from anywhere, be it a bustling office in London or a quiet cafe in Kyoto, becomes imperative.

Cloud platforms don’t just facilitate this global reach; they also ensure that backup, security, and updates are managed centrally, reducing the nitty-gritty tasks for businesses, and freeing them to focus on their core operations. It also allows for greater levels of integration with more applications ensuring that you can apply your business language to your content, at anytime and anywhere.

Automation

Imagine a world where the process of updating a termbase doesn’t require manual intervention but is instead a seamless, automatic process. That’s where automation steps in.

Instead of combing vast amounts of content to identify and add new terms, automation tools, driven by AI, can detect, classify, and even suggest terms for inclusion. This not only streamlines the process saving you time and money, but also reduces the chances of error that manual processes can introduce therefore producing a much higher-quality final product.

AI data enhancement

Large Language Models thrive on data. The richer and more diverse the data, the more adept these models become by training the model with your specific business language. Termbases, teeming with linguistic intricacies, provide a golden opportunity here. By integrating termbases into AI training data, businesses can ensure that their AI tools are not just linguistically aware but are also culturally and contextually astute.

For businesses aiming for global resonance, this is a game-changer. A chatbot trained with a comprehensive termbase, for instance, would not just communicate; it would connect, understand nuances, and deliver a richer customer experience. Generative AI models would be able to produce content in your business voice using correct product names and ensuring consistency across the board.

Collaborative platforms

The beauty of collaboration is that it captures a myriad of perspectives. In the context of termbases, this means a more robust and diverse linguistic database. Collaborative platforms are set to revolutionise termbase management by enabling teams, irrespective of their geographical location, to contribute to, refine, and leverage a centralised termbase.

This collective approach ensures that the linguistic spectrum captured is broad, nuanced, and immensely valuable and specific to your business needs.

Looking forward in termbase management

The great news is that many of these exciting changes to greater improve and simplify termbase management are already here and improving day by day. You just need to understand and take advantage of them.

This article was originally published by LanguageWire Company (Verity Hartley)

Validation – high-quality translations are a joint effort

27.3.2024 by Jari Herrgård

Successful translations require teamwork. Although the key person in the translation process is the translator and their tools, they alone are not able to achieve ideal quality. In the translation process, the best possible result always requires input from the translation agency’s other employees as well as the customer. Validation is perhaps the most important tool for assessing the linguistic quality of translations.

If you are a customer, you may be asking why you should contribute to the process – it’s our job, after all. You are correct and, as a customer, your involvement is not obligatory. But if you want the best result, it is in your best interest to participate in the process – especially through feedback.

To some extent, language is a matter of opinion. We are all fluent in at least our own native tongue, and many of us also know several other languages. Therefore, we all have a lot of opinions about language. This is especially true in work contexts if language plays a central role in our work and we put a lot of thought into how a message should be conveyed. We know how we want to address the reader, and perhaps the organisation we work in has even defined common rules for what kinds of images we should evoke.

Even if these rules are self-explanatory within the organisation and come naturally to you, they may not be self-evident to the translator. The translator is always a bit of an outsider and depends on what they are told about your preferred language and its nuances. This is where teamwork comes into play: the ideal translation process involves cooperation to ensure that the translation matches the customer’s communication needs as closely as possible. By providing feedback on the translations, the customer can ensure that each translation is better than the one before and closer to the customer’s ideal.

Validation as a tool

One of the most important tools for the process described above is validation. This means a review by the customer after the translation has been completed. It may often be carried out by an expert whose main work tasks do not include translation. The expert is responsible for checking, in particular, the terminology and style, and assessing whether they are in line with the customer’s brand. Other review work can also be done at the same time.

Validation in itself is a simple step, and there is no doubt about its usefulness, but it is still often skipped altogether. Typically, validation is carried out by sending PDF or DOCX files to vendors or technical experts, for example. The expert will use the comment or track changes functions to add their own comments and corrections to the document. These will be included in the final version and imported into the translation memory by the translation agency. This process is quite laborious and may complicate version control, which are the main reasons why it is often skipped.

Validation with system support

At LanguageWire, workflow and translation work are based on a shared platform used by customers, translators and internal project managers. Using a single platform has many benefits, especially in terms of translation project communications and transparency.

The validation step also takes place entirely on this platform, which makes the process quick and efficient. Usually, the validation step is added as soon as the project is created.

Validation takes place after the translators have done their work. The experts assigned to the project will receive an automatic message from the system and, by clicking on the link, they will be forwarded to the right project. There is no need to search for the project or upload any files, and the expert can start working in their browser right away.

Validation is done in the same tool that the translators use, and the validator has access to all the same information. This means, for example, that they will automatically be able to view the correct terminology if there is a glossary attached. This helps to avoid situations where outdated or otherwise incorrect terminology is used in the text during the validation phase. The validator also has access to other tools, such as proofreading and translation tools. Our user-friendly interface ensures that you do not need to be a professional translator to benefit from the translation platform. Everything you need can be done with basic browser skills.

In addition to possible corrections, the validator can also communicate with the translator, which helps to quickly solve potential dilemmas. Since everyone works in the same digital environment, all corrections are also saved directly in the translation memory, and they do not need to be separately exported at the end of the project.

In order to ensure the best possible translation quality and that the translator and proofreader learn as much as possible, a language specialist will check the changes made by the validator at the end. This ensures that all changes follow the customer’s own standards and, of course, the general rules of the language.

Would you like to discuss your organisation’s language needs? Contact us via email at contact-fi@languagewire.com for further information!

Artificial intelligence – the latest localisation superpower

22.3.2024 by Delingua

What is generative AI?

29.1.2024 by Delingua

We’re on the cusp of a technological revolution. One that promises to change how we research, work, and even live. 2023 was the year of Generative AI.

Generative AI isn’t just a buzzword; it’s a powerful force that has the potential to transform the way we communicate with each other and create content. It’s like having a creative genius, a tireless assistant, and a subject matter expert at your fingertips, ready to help your business thrive in an era where innovation is the name of the game.

So, whether you’re an industry leader looking to revolutionize your business or simply someone curious about the many possibilities of technology, get ready to discover just what exactly Generative AI has to offer.

Let’s dive in.

What is Generative AI?

Generative AI, short for Generative Artificial Intelligence, is a cutting-edge technology that empowers machines to autonomously create content across various domains. Unlike traditional computer programs that follow predefined rules, Generative AI models are designed to simulate human-like creativity, decision-making, and problem-solving processes.

At the heart of Generative AI lies the concept of machine learning, particularly deep learning, a subset of artificial intelligence. These AI models are capable of not only processing and analysing vast amounts of data but also generating new content based on the patterns and knowledge they’ve gleaned from the data they are trained on.

Versatility of Generative AI

Generative AI is incredibly versatile and can be applied in a variety of different ways to aid content creation and research. For example:

Content Generation: Generative AI can create written content, including articles, blog posts, product descriptions, and marketing materials. It can generate text in different styles, tones, or languages, making it a valuable tool for content marketers and copywriters.

Art and Design: In the creative world, Generative AI can produce art, design concepts, and even music compositions. Artists and designers can use AI-generated content as a source of inspiration or to augment their existing creative processes.

Simulation and Prediction: In industries such as finance and logistics, Generative AI can simulate scenarios, predict outcomes, and provide recommendations. This assists businesses in making data-driven decisions and allows data analysts to optimise their workflows.

Personalisation: Generative AI analyses user data to tailor content and recommendations. For instance, e-commerce platforms use it to suggest products based on a customer’s browsing and purchase history, enhancing the shopping experience for a user.

Language Translation: Language models like GPT-3 can translate text from one language to another with a high level of accuracy in many language pairs and can be further personalised with business-relevant language using termbases and translation memories as training data.

How Does Generative AI Work?

At its core, Generative AI relies on the power of large datasets and complex algorithms. These AI models are trained on massive amounts of data, learning to mimic human creativity and decision-making processes. For instance, OpenAI’s ChatGPT, currently the most well-known Generative AI model, has been trained on a diverse range of internet text, making it capable of producing high-quality content across various domains.

Generative AI operates on a blend of advanced algorithms, vast datasets, and deep learning techniques. Let’s delve a little deeper into the technicalities of Generative AI to help you understand its fundamental mechanisms.

1. Data and Training

As we said above, the core of Generative AI is the process of training. Imagine it as teaching a machine to understand and generate content by exposing it to a massive amount of data. For instance, in the case of language generation, an AI model is trained on vast collections of text, encompassing everything from books and articles to websites and social media posts.

2. Neural Networks

The central component of Generative AI is a neural network, which mimics the structure and function of the human brain. Neural networks are composed of interconnected nodes, or artificial neurons, organised in layers. There are typically three types of layers in a neural network:

Input Layer: This is where data is initially fed into the network.
Hidden Layers: These intermediate layers process and transform the data.
Output Layer: The final layer produces the desired output, which, in the case of Generative AI, is the generated content.

3. Learning and Weights

During training, the AI model learns the relationships and patterns within the data by adjusting the strengths of connections between artificial neurons. These connection strengths are referred to as “weights.”. The learning process involves iteratively adjusting these weights to minimise errors and make the model better at predicting or generating content.

4. Generating Content

Once the AI model is sufficiently trained, it can generate content by taking an initial input, often referred to as an “input” or “prompt,” and processing it through its neural network. The model then leverages the patterns and associations it has learned from the training data to produce coherent and contextually relevant output.

5. Fine-Tuning

To make the generated content even more accurate and tailored to specific tasks, businesses often engage in a process called “fine-tuning.” This involves training the model on a narrower dataset that aligns more closely with the desired application. For example, a company might fine-tune a Generative AI model on its product descriptions to ensure they match its brand style and tone.

How Businesses Can Leverage Generative AI

Generative AI’s ability to create content autonomously is a game-changer for businesses. It can automate content generation, reduce operational costs, enhance personalisation, and accelerate innovation.

The business advantages of Generative AI are multifaceted. Firstly, it offers cost-effective content generation. Imagine automating the creation of product descriptions, social media posts, or even legal documents. This not only saves time but also reduces operational costs.

Moreover, Generative AI enhances personalization and customer engagement. By analysing user data, it can generate tailored recommendations, advertisements, and even chatbot responses, leading to higher customer satisfaction and loyalty.

Additionally, Generative AI accelerates product development. It can assist in generating design concepts, and prototypes, and even simulate product performance under different conditions, helping businesses bring new products to market more quickly.

The benefits and use cases for specific industries are still being explored but the potential of this new technology seems limitless. For example, the healthcare industry has already started utilising Generative AI for drug discovery, predicting disease outcomes, and even generating medical reports.

Concerns and Challenges Surrounding Generative AI

While Generative AI offers tremendous potential for businesses, it’s essential to address certain concerns and challenges to ensure responsible and ethical usage rather than blindly embracing new technology without proper scrutiny. There are some issues you need to consider before implementing Generative AI into your processes.

Ethical Considerations

Generative AI can sometimes produce content that is misleading, offensive, or biased. This raises ethical concerns, especially when businesses use AI-generated content in customer-facing communications or decision-making processes.

Privacy and Data Security

Generative AI relies on extensive datasets, and using this data responsibly is critical. Mishandling data can lead to privacy breaches and regulatory violations, potentially damaging your business’s reputation.

Regulatory Compliance

AI, especially in highly regulated industries like finance and healthcare, must comply with a myriad of regulations. Ensuring that AI-generated content meets these standards can be challenging.

Training Data Quality

The quality and representativeness of training data can influence the effectiveness of Generative AI. Biased or incomplete data can lead to biased or inaccurate AI outputs.

Generative AI holds immense promise for businesses, but it also presents challenges that must be addressed responsibly. By implementing ethical guidelines, prioritising data security, defining accountability, and remaining vigilant, businesses can harness the power of Generative AI while minimising risks. The key is to strike a balance between innovation and responsibility, ensuring that AI serves as an asset to your organisation without compromising ethics and integrity.

The Future of Generative AI and Language Service Providers

At LanguageWire we have already talked in depth about how we foresee Large Language Models being the future of not only content generation but also machine translation.

Embracing this new technology will be the next step in innovation in this field. The field of Generative AI is constantly evolving, and we anticipate even more remarkable developments in the future.

This article was originally published by LanguageWire (Verity Hartley)