June 20, 2025
Machine translation is opening opportunities for entrepreneurs to reach target audiences around the globe. Countless industries have already taken advantage of its benefits, most notably tech startups, law firms, medical institutions, and content publishers.
But they’re not the only ones that stand to benefit from the recent developments in machine translation. So, what is machine translation anyway? When did machines start translating languages? Why does it matter to you now? And what about the future?
Machine translation (MT) is the automated process of translating text from one language to another using computer software. It relies on artificial intelligence (AI), specifically natural language processing (NLP), to understand and convert language in real time.
Modern machine translation systems, such as Google Translate and DeepL, use advanced neural networks to produce fast, cost-effective, and increasingly accurate translations. These AI-powered tools help businesses translate large volumes of content across multiple languages without the high cost or delay of manual translation.
As machine translation technology continues to evolve, it has become essential for global communication, enabling companies to reach international markets, localize content, and operate efficiently in multiple languages.
Machine translation systems use different approaches to convert text from one language to another. Here are the four main types:
Rule-based machine translation (RBMT) uses grammar rules and dictionaries to generate translations.
It works best for straightforward text but often needs human post-editing to sound natural or accurate.
Pros:
High control over grammar and terminology.
Suitable for highly predictable or technical content.
Cons:
Requires extensive linguistic resources and manual effort.
Often produces rigid, unnatural translations without post-editing.
Statistical machine translation (SMT) relies on large amounts of bilingual data to find patterns between languages.
It uses statistics to predict the best translation but can struggle with fluency or nuance if the data is limited.
Pros:
Learns from real-world usage, enabling better idiomatic translations than RBMT.
Adaptable to new domains with enough training data.
Cons:
Needs vast aligned corpora to perform well.
May generate grammatically incorrect or disjointed output.
Hybrid machine translation (HMT) combines rule-based and statistical methods.
It typically starts with a statistical translation and then applies rule-based logic to improve the result.
Pros:
Balances fluency (from SMT) with grammatical structure (from RBMT).
Flexible and more accurate than using either method alone.
Cons:
Complexity in integrating both systems effectively.
Still may require post-editing for nuanced texts.
Neural machine translation (NMT) uses deep learning to understand context and produce more natural, fluent translations.
This is the most advanced and widely used method today, powering tools like Google Translate and DeepL.
Pros:
High fluency and contextual accuracy.
Continuously improves with more data and user feedback.
Cons:
Can be unpredictable with low-resource languages.
Less transparent decision-making—harder to trace errors.
Each type has its own strengths, but neural machine translation is now the standard for high-quality, AI-powered translation.
In the past, machine translation was often inaccurate and produced text that was difficult to understand. However, modern machine translation systems are much more accurate and can produce translations that are almost indistinguishable from those produced by human translators.
From just 260 words to billions per day, the inventors of machine translation could have never imagined expansion and commercial use of today’s level in the 1950s. However, it is impossible to recognize the value of these numbers without knowing the history.
Let’s explore the history of machine language translation to get a better understanding of how it works and perhaps, a deeper appreciation for how far we’ve come.
On January 8, 1954, the headline of IBM press release read: “Russian was translated into English by an electronic "brain" today for the first time.” The release went on to detail the first rules-based machine translation system developed by IBM.
Using six linguistic rules, it was able to translate 260 words between Russian and English and caused quite the sensation at the time. However, this computer translation was far from perfect.
The early machines were not viable. Researchers had to come up with extensive dictionaries as well as rules for structure & transformation. Still, it often produced inaccurate translations because it was impossible to come up with every rule.
In the 1980’s, a research team at IBM collected audio recordings and attempted to make developments in the field of speech recognition. However, the results of their efforts were not what they had hoped. So, they began applying statistical methods to machine translation.
Their research spawned more interest in statistical machine translation and formed the foundation for today’s top machine translation systems at Microsoft and Google.
In the 2000s, researchers worked to improve traditional statistical machine translation (SMT). Google Translate began as SMT but evolved into neural machine translation. Though the system has faced criticism for lack of transparency and biased translations, it has made efforts to address those issues while making significant contributions to NMT, which are discussed later in more detail.
From 2017 onward, machine translation evolved rapidly with the rise of transformer-based models like BERT and GPT, enabling more fluent, context-aware translations. Platforms began integrating customizable, domain-specific training to better serve industries like legal, medical, and tech. These systems also started learning from user feedback, allowing real-time improvements and greater accuracy over time.
AI-powered translation agents, such as those from MachineTranslation.com, introduced memory features that retain brand tone, terminology, and user preferences. Enhanced user interfaces with segmented bilingual views made it easier to compare, edit, and validate translations. With support now extended to over 270 languages, modern MT platforms have become essential tools for global communication, localization, and cross-border collaboration.
Machine translation offers a number of advantages for your business, including cost savings, increased efficiency, and the ability to reach new markets. While there are still some challenges to be addressed, the accuracy of machine translation is constantly improving, making it an increasingly viable option for businesses of all sizes.
Machine translation can be much cheaper than human translation, especially when translating large volumes of text. If you partner with the right machine translation company, you can save more in project management costs as well.
For example, we offer testing to ensure your software is ready to launch and provides one year of continued support after delivery.
While human translators understand the nuances of language and bring a higher level of quality, they can take days or even weeks to translate large volumes of text. On the other hand, machine translation can translate thousands of words in minutes or hours.
The increased translation efficiency means corporations can get real value out of big data by implementing machine translation and post-editing services.
Even the best human translators make errors. Machine translators can improve the accuracy of human translation and vice versa, as they work together, the strengths of each complementing the other’s weaknesses.
While global connectivity continues to expand, consumers in 2024–2025 increasingly favor locally relevant and culturally attuned experiences. A recent McKinsey report found that 47% of consumers now prefer locally owned brands, and 90% of companies agree that localization—tailoring products, language, and marketing to specific markets—is essential for global growth.
This highlights the importance of modern machine translation tools that incorporate AI-powered glossaries, context-aware translation memory, and brand-specific terminology. By adapting content more precisely to local audiences, businesses can expand internationally with both speed and cultural resonance.
Unless you have built a machine translation system of your own, you’ll need to choose a machine translation company to oversee the localization process. This decision is crucial to your success. To dive a bit deeper into localization, we spoke with a variety of MT experts at Tomedes, a leading provider of translation services.
If you’re a tech startup or business, whose aim is to globalize, these are some of the factors to look for in a company.
Make sure the provider offers high-quality translations. This is arguably the most important factor in choosing a machine translator. There's no point in using a provider that produces poor-quality translations – it will only reflect negatively on your business.
Does the provider have experience translating in your industry or domain? This is important to ensure that they are familiar with the terminology and concepts used in your content, especially if you’re in the tech, legal, or medical industries.
Check whether the provider offers human post-editing cases, it may be necessary to have a human edit your machine-translated content before it's published. This is particularly important for critical content (e.g., marketing materials, legal documents, etc.).
See if the provider offers any additional features that could be helpful for localization. For example, MachineTranslation.com offers key term glossaries and style guides that can be used to ensure your translations are consistent with your brand's voice.
Make sure the provider offers competitive pricing. While quality is important, you also need to conserve your resources for a successful localization. Be sure to compare prices from multiple providers before making your final decision.
Check the provider's customer reviews. This is a good way to get a sense of what others have thought about the quality of the provider's translations.
Does the provider offer any formal assurances for quality or delivery time, such as Tomedes 100% Quality Guarantee? This can give you peace of mind knowing that you'll be satisfied with their services.
Recent advancements in neural machine translation (NMT) are driving major improvements in translation quality. A 2024 AMTA conference study showed that integrating large language models (LLMs) into NMT pipelines enhances fluency, consistency, and contextual awareness . Meanwhile, Meta’s NLLB-200 model now supports over 200 languages with zero-shot capabilities, enabling accurate translation even for low-resource languages .
These innovations are reducing the need for manual correction and enabling real-time, multilingual communication at scale.
AI models are now trained using domain-specific data to improve accuracy in technical fields such as legal, medical, and scientific translation. Research published in 2024 confirms that industry-tuned models deliver significantly better results than general-purpose engines, particularly when using structured content like contracts, research papers, and user manuals . This shift supports faster, more reliable translation workflows across sectors.
In 2025, zero-shot translation technologies are evolving to automate translation between languages with little to no training data. Meta’s work on multilingual transformers and projects from Google and OpenAI's ChatGPT point toward near-real-time, automated translation systems that don't rely on traditional bilingual corpora . These platforms are particularly valuable for rapidly localizing emerging or low-resource language content.
Leading platforms in 2025 now include segmented bilingual views, context-aware glossaries, and predictive suggestions for editors. Research on translator behavior shows that such tools reduce cognitive load, editing time, and improve overall translation quality . Usability has become a key driver in the design of modern MT platforms.
With the rise of neural and AI-driven systems, automated quality assessment is becoming essential. A 2024 study from the European Association for Machine Translation (EAMT) found that AI-based QA tools can detect fluency errors and contextual mismatches more effectively than rule-based systems .
These tools are critical for high-stakes content such as legal documents, healthcare communication, and financial reports.
Machine translation is not perfect, so we must address and acknowledge its limitations. One of the biggest challenges is dealing with idioms and colloquialisms, which can often be difficult for machines to understand. Then, there is always the issue of bias, which has been frequently cited in AI research. The solutions to these limitations, or rather how we respond to them will undoubtedly shape the future of machine translation and our global culture.
While many engage in the debate of humans versus machines, innovators have embraced technology as our future. They bring together the skills of translators and state-of-the-art technology to enhance their services. Here’s what they have to say about machines and translation:
“The speed of machine translation is complemented by the unique touch that human translators bring to the table. Rather than choosing one over the other, MachineTranslation.com blends the strengths of both to deliver accurate, efficient, and culturally attuned results.”
OFER TIROSH, CEO of MachineTranslation.com by Tomedes
"Lilt [aims to] build a solution that [will] combine the best of human ingenuity with machine efficiency."
SPENCE GREEN, CEO of Lilt.
“The world is evolving before our eyes and we must evolve with it. Like all conscientious businesses, we are using this moment in history to reflect on how we can grow.”
SCOTT W. KLEIN, CEO of LanguageLine Solutions
Without a doubt, machine translation will continue to grow in popularity and accuracy in the coming years. We move forward hoping to reach the dreams of the earliest researchers who hoped not just for advanced technology but for something more—“for the constructive and peaceful future of the planet."
Ready to experience the power of the world’s top LLMs and MT engines—all in one place? Subscribe today at MachineTranslation.com and unlock smarter, faster, and more accurate translations built for your workflow.