24|AI - the fastest way to produce high-quality translations that harness the collective power of human and machine
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We are already encountering artificial intelligence in many areas of our lives, whether it be the facial recognition feature on our smartphones, self-driving cars, or the voice assistant in our smart home device. Thanks to AI, the frequency of interactions between human and machine is intensifying. Machine learning plays a central role in this: through self-learning algorithms, computers identify highly-complex rules and patterns in training data and can apply these to new, unfamiliar datasets. Deep learning methods and artificial neural networks (ANN) enable high-performance machines to process huge data volumes (Big Data) in a matter of seconds. This has made the artificial reproduction of complex thought, problem-solving and decision-making processes even more efficient.
Machine and plant engineering
Medical technology
Financial reports and statements
Electrical engineering and electromechanics
Medical documents and clinical documentation
Websites, apps, and software interfaces
Automotive and vehicle manufacturing
ISO 17100, ISO 9001 & DIN ISO 27001 ensure process optimisation, reliability, and data security.
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1B+
Words Translated
7,000+
Specialist Translators
10,000+
B2B Clients Worldwide
50+
Languages Covered
98%
Customer satisfaction
Computational linguistics has busied itself with investigating language from a computing perspective for some time now, influencing the development of search engines, chatbots and word processing programmes. Neural machine translation (NMT) was developed using deep learning technology and enables the automated translation of a source text into a target text. The quality of these translations is impressive and the output is considerable: Machines are already translating over a billion words every day - more words than all the human translators in the world translate in a whole year. Despite the technological advances, the human factor remains a key part of the success, however. Peculiarities and nuances such as specialist terminology, grammar, thematic connections and underlying messages spread across multiple sentences can only be adequately addressed by human post-editing. Processing the results of machine generated translation is changing the nature of the industry: established processes must be overhauled and the requirements placed on language experts involved in the process must be re-evaluated.