machine translation

8 reading tips for summer holidays

17.6.2024 by Delingua

“We are going on a summer holiday…” For some, the summer holidays have already started or are fast approaching. In this article, you will find a list of interesting things to read concerning the language service industry. Get comfortable, enjoy the sun and dive into the world of language services.

And don’t forget we are open all summer! We have arranged our holidays so that we can take care of your translation and interpreting assignments even during the summer while you take some well-earned time off.

1. LanguageWire Translate – AI-based machine translation service

Machine translation engines are used daily in both professional and private contexts – often without putting too much thought into how the translation itself is produced. Our AI-based machine translation service, LanguageWire Translate, is our vision of what kind of requirements a machine translation system for corporate use should meet. Read our article and learn more about the features of LanguageWire Translate.

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2. Five reasons why integration is a good idea

LanguageWire’s APIs and connectors are a time- and effort-saving solution, helping you avoid copying text unnecessarily from one place to another and allowing you to import any changes, no matter how big or small, to all language versions of your website in almost real time. A connector is a kind of system integration that allows the customer to order a translation of the content through an existing publishing system. We have listed five key reasons why integration is a good idea.

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3. Boost your efficiency with generative AI

Growing awareness of the potential of generative AI and the availability of newer and more powerful GPT models have given everyone a chance to benefit from new AI technology. But what exactly are these benefits? Read our article and learn more about how AI can help you work smarter and faster than before.

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4. Simultaneous interpreting – what is it?

What is simultaneous interpreting and why should it be offered? Simultaneous interpreting is an interpreting method in which the interpreter interprets into another language at the same time as the speaker is speaking. When participants have the opportunity to follow a meeting or a conference in their own language, they get the feeling that they and their language needs have been taken into account.

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5. Validation – high-quality translations are a joint effort

In the translation process, in addition to the translator, the best possible result requires the input of other employees of the translation agency as well as the customer. One of the most important tools for assessing the linguistic quality of translations is validation. Validation means a review by the customer after the translation has been completed, in which the terminology and style of the text are evaluated in particular. Read our article to learn more about validation and the related technology.

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6. Large language models – what are they?

Over the past year, it has been difficult not to come across content created by generative AI. Generative AI is developing at a tremendous pace and language technology is also benefiting from these breakthroughs. From the perspective of language technology, the most important role is played by Large Language Models (LLMs). Read our article to learn how large language models are revolutionizing the language services industry.

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7. What are the differences between a termbase and a translation memory?

What are the differences between a termbase and a translation memory, and how do they work together? And how can they benefit your company? If your company translates texts regularly into several languages, you have probably already heard about termbases and translation memories. Both are important tools for the translator, but they are used for different purposes.

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8. Tips for linguistic accessibility

Accessibility means that everyone has equal access to services. Although the Digital Services Act mainly applies to public online services, accessibility is also beneficial for other digital service providers. Read our tips on how you can create linguistically accessible content and how a translation agency can help you.

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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)

Artificial intelligence – the latest localisation superpower

22.3.2024 by Delingua

Large language models – what are they?

13.3.2024 by Sohvi Kähärä

Over the past year, it has been difficult not to come across content created by generative AI.You may have seen a LinkedIn post where OpenAI’s AI-based ChatGPT has been asked to give good tips on how to get a salary raise. Perhaps you have encountered a chatbot when you have needed help with booking an appointment online. You may have even laughed at images created by DALL·E, where the faces of familiar celebrities have been digitally added to classic works of art.

Generative AI is developing at a tremendous pace and language technology is also benefiting from these leaps of development. From the perspective of language technology, the most important role is played by large language models (LLMs).

Large language models need massive amounts of data

Generative AI solutions created for producing and processing language are called large language models. They are statistical models that calculate the probabilities of the occurrence of words or parts of words. The simplest language models calculate the probability simply by calculating how many times the word appears in the given text materials, without taking the surrounding words and their meanings into account.

Large language models, on the other hand, are advanced language models that are based on neural networks and, as their name suggests, are trained with large amounts of data. The training of the language models of previous neural machine translation engines often requires a dataset of hundreds of millions of variables but large language models require billions of them.

Current neural network-based language models, including large language models, are most often based on transformers. Transformers are neural network architectures that consist of many overlapping layers. Previous machine learning models focused on individual words in their training data, while transformers are capable of simultaneously processing and taking into account words within a sentence or a longer text and analysing, among other things, broader relationships within the sentence.

Large language models use huge text materials to learn which words appear in which context and which words often follow each other. This allows large language models to predict what words and phrases would appear in an answer suitable to a certain type of question, how a beginning of a poem could continue or how a certain matter would be expressed in another language in the context in question.

A one-size-fits-all solution is not suitable for all cases

Since the amount of data needed for training large language models is enormous, a lot of generic content will inevitably have to be used. Large language models can also be customised, or personalised, to make them more suitable for different tasks and needs. This can be done by adding additional training data in a more limited subject area and modifying the model’s weightings. It is also important to choose a suitable customisation strategy, such as building a ready-to-use model or learning through use with the aid of prompt engineering.

Especially when large language models are used for translation, it is useful to personalise language model with high-quality, fully matching bilingual materials. It is especially good if the texts used in personalisation and the new texts to be translated are from as similar subject areas as possible. For this purpose, training language models with existing translation memories is a good idea: all of the customer’s translations are saved in translation memories by language pair so these texts are always bilingual, are good matches and use the company’s desired style and terminology.

In this way, the language model learns what the customer’s texts are like and is able to imitate their style better in the future. This improves, among other things, how machine translation produced by large language models is aligned with the company’s style. This, in turn, speeds up translating as higher-quality machine translation that is customised according to the customer’s needs reduces the time involved in any post-editing required.

The downsides of AI

Although continuous learning and adaptability associated with large language models are in many respects good, they also have their downsides. Language model-based solutions in general use learn as they are running so they can also be intentionally misled and taught false information. If you repeatedly teach a language model that a pike is a bird instead of a fish, it will start believing this to be true. Large language models also suffer from so-called hallucinations, which you can read more about in our article on getting the most out of machine translation.

In addition to invented facts, another risk associated with language models trained with a large amount of data is that confidential information ends up in the wrong place. Large language models in general use are open to everyone and, when using them, it is not possible to be sure whether your data will be made available to others. Therefore, it is important to choose a reliable partner for whom data security issues are a priority.

When training large language models with translation memories, it is necessary to take care of data security and ensure that customer-specific data is used only in each customer’s own model. This way, you can avoid the situation in which the language model would introduce another customer’s confidential information into the translation. We can build a customer-specific language model directly as part of LanguageWire’s highly secure ecosystem. Read more about LanguageWire’s insights into the data security in large language models here.

Large language models as part of the future

Training large language models for translation needs is a giant leap forward as they can provide more accurate and natural translations into multiple languages. These models make it possible to produce translations quickly while taking into account the desired style and terminology. In the future, this development can break down language barriers and foster interaction between people around the world by strengthening understanding and cooperation on a global scale.

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)