“Tell me, do you still have a job?” Questions like this are now part of everyday life for some people.
The world was confronted with version 3.5 of ChatGPT in late 2022 and, since then, everyday working life has changed significantly in some areas. In addition to copywriting and programming, this also includes language services. Although many have “prophesied” the demise of entire occupational groups, this still a long way off as we approach the end of 2024.
In this article, we will take a look into the future. Will AI eliminate human translators? Is translation becoming (even more) of a commodity?
Let’s begin by taking a look at the current state of affairs. Here are some topics currently being debated:
Technological innovations
“Artificial intelligence is revolutionizing translation processes by better understanding and imitating human speech patterns.” It is a sentence that you hear time and time again. But is that new? Not at all! The revolution in the translation process has been promised – or threatened, as the case may be – for 60 years.
Integration into everyday life
Companies are increasingly relying on AI solutions to efficiently manage and provide multilingual content.
The importance of AI in the language mediation process should not be underestimated. The technology facilitates:
- Real-time translations: Advanced algorithms break down language barriers.
- Cost efficiency: Automation reduces costs and time spent on translation projects.
These developments are creating a dynamic landscape in which companies such as Reddit and YouTube are playing a pioneering role. The integration of AI is becoming the norm, paving the way for new standards and quality controls. In a previous article, we tested how suitable these AI translation systems actually are for everyday use compared to established systems such as DeepL.
Current trends in AI translation
The localization industry has shown remarkable growth for decades and is estimated to be worth around $30 billion by 2025. Companies such as Reddit and YouTube are playing a crucial role in the development of language AI by taking innovative approaches to improving machine translation. These platforms integrate advanced technologies to enable their users to communicate seamlessly in different languages.
Another significant trend in this context is the introduction of translation as a feature (TaaF) in content management systems (CMS) such as Contentful. Apple has been working on integrating AI into its iOS operating system for some time. A translation AI will also be used. Translation is therefore becoming more and more of a commodity.
By integrating AI-supported translation functions, companies can adapt content more efficiently for international markets. This not only improves access to information, but also promotes global cooperation and interaction.
Paradoxically, the human translation service is becoming increasingly important in this context. By dealing with less than optimal results on a daily basis, companies recognize the value of professional specialist translations to localize their content accurately and culturally appropriately.
Neural machine translation vs. large language models: a look into the future
Neural machine translation (NMT) and large language models (LLMs) such as GPT-4 or comparable products from Anthropic, Google or Facebook represent two different approaches in translation technology.
Differences and advantages
NMT:
- Online translation tools such as DeepL use neural networks to analyze texts.
- They were specially developed for the translation of content.
- They often offer greater accuracy for specific translations, but have difficulties with context.
LLMs:
- LLMs are based on extensive data sets and allow more flexible adaptation to different language contexts.
- They were never developed specifically for translation.
- They are stronger in the processing of complex sentence structures, but can vary in quality.
Challenges
Both approaches have their challenges. Both have difficulties with stylistic devices such as irony or word play. NMT can be flawed in ambiguous contexts, while LLMs are prone to inconsistent results. And ambiguity is a problem even for the best AI.
The forecasts for 2025 point to an increasing dominance of LLMs. These could change the translation landscape by not only increasing accuracy but also improving user-friendliness.
Advances in AI-powered translation tools: a new era of real-time translation
The world of AI-supported translation tools has rapidly evolved and now offers solutions that facilitate real-time translations. This will significantly reduce language barriers and promote global communication.
Leading tools such as:
- ClickUp Brain
- QuillBot Translator
- DeepL
are setting standards with innovative functions to improve translation quality. These functions include:
- Context-sensitive understanding: This improves the accuracy and relevance of translations.
- User-friendly interfaces: These enable easy handling for users of all levels of experience.
- Integration into existing systems: This promotes seamless use in content management systems (CMS).
This development of AI translation by 2025 is expected to further break down language barriers and facilitate interaction between different cultures.
Quality control in machine translation: the path to DIN ISO 5060
The quality of machine translation is crucial for success in the localization industry. Although AI translation tools are becoming increasingly accurate, the need for quality control remains undisputed. DIN ISO 5060 was introduced as an important standard to enable objective and systematic assessments of translations.
Important aspects of DIN ISO 5060:
- Terminology: Ensuring consistent use of subject-specific terms.
- Content accuracy: Verification of conformity with the original text.
- Language conventions: Consideration of regional differences and linguistic peculiarities.
- Style: Maintaining the writing style and tone of the source material.
- Target group orientation: Adaptation of content to the needs of the target group.
The introduction of this standard promotes not only transparency, but also trust in machine translations. A clear evaluation matrix makes it possible to continuously improve quality and respond to specific requirements.
Future developments in 2025: imitating human language conventions and promoting linguistic diversity
Progress in the development of artificial intelligence will be decisive for the understanding of human language conventions. Forecasts indicate that AI systems will increasingly be able to grasp nuances and cultural contexts. This could significantly improve translation quality.
Important developments include:
- Improved contextualization: AI will be able to better understand the context of conversations, leading to a reduction in translation errors.
- Increase in supported languages: The number of supported languages will grow to enable broader global communication. Technologies such as deep learning and neural networks contribute to the efficient integration of new languages.
These trends promote not only linguistic diversity, but also interaction between cultures by breaking down barriers and creating a deeper understanding of different modes of expression.
Challenges for the future of AI translation: NLP and its unpredictability
The development of AI translation in 2025 faces significant challenges, which are characterized by the unpredictability of natural languages. Since the dawn of machine translation in the 1960s, the fallibility of authors has been a problem that translation tools have struggled to cope with. However, as people become less and less involved in the content creation process, this potential source of error may slowly disappear.
Ongoing challenges
- Contextual understanding: Despite advances, AI systems can struggle to fully grasp context, which can lead to misunderstandings. However, it seems to be only a matter of time before this problem is solved.
- Cultural nuances: Different language cultures produce specific meanings and expressions that are difficult for a machine to interpret.
- Multiplicity: Many words have multiple meanings, which can affect translation accuracy.
Possible solutions
- Improvement of the algorithms: By using more advanced algorithms and machine learning techniques, the understanding of context and meaning can be improved.
- Integration of human expertise: The combination of machine translation and human proofreading offers an opportunity to increase translation quality.
These challenges require continuous innovation in AI translation technology.
Conclusion: The future of AI translation is promising but challenging
Contrary to some opinions, we believe that we are still a long way from the predicted doomsday of our industry. On the contrary: Developments in AI translation tools show promising progress and challenges. The technological innovations up to 2025 suggest the following trends:
- Increased integration of AI into content management systems (CMS) with a focus on translation as a feature (TaaF).
- Growth of the localization industry, estimated at around $30 billion, driven by such companies as Reddit and YouTube.
- Progress in neural machine translation (NMT) and large language models (LLMs), which offer different approaches for high-quality translations.
- Increasing number of supported languages and improved imitation of human language conventions by AI.
Despite these positive developments, the need for quality controls and standards such as DIN ISO 5060 remains. The challenge of understanding natural language will continue to play a central role.
The translation market will adapt to these trends while remaining true to its core values.
At Leinhäuser, technology has always played an important role in the implementation of our translation services. In the past, this included CAT tools or classic machine translation systems such as Google Translate and later DeepL. In the future, we will increasingly work with AI translation software, deep learning tools, natural language processing tools or other forms of artificial intelligence.
We are completely tool-agnostic when it comes to transferring content from a source language into one or more target languages. Our aim is always to do this as efficiently and authentically as possible. Our customers have expected this from us for over 25 years, and we will continue to do so.
Contact us if you need to internationalize products or services. We can help you in the coming year and beyond with human and artificial intelligence.
Editorial Team Leinhäuser
Languages are our passion.
That's why we regularly take a close look at the latest developments and new tools that are impacting the world of communication.
In various blog posts, our in-house experts share their knowledge and insights on specific areas of our portfolio and shed light on important future trends for our industry.
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