Although you would think the term "Generative AI" is a relatively new concept, this is not necessarily the case. We have been surrounded by artificial intelligence for quite some time, perhaps since MIT created ELIZA in 1966. ELIZA was a...
Back translation and synthetic data are two popular and common techniques used to augment training datasets for Machine Translation (MT) systems and to enhance their performance.
It is a rare occurrence to find a spare 30 minutes in Manuel Herranz's busy schedule as Pangeanic's CEO. However, the topic of today's interview holds significant value for audiences who have been exploring Large Language Models, GenAI, and AI in...
A Transformer is a type of deep learning architecture that uses an attention mechanism to process text sequences. Unlike traditional models based on recurrent neural networks, Transformers do not rely on sequential connections and are able to...
Data augmentation is a technique commonly used in machine learning and computer visionto artificially increase the size and diversity of a training data set. It consists of applying several transformations or modifications to existing data samples,...
Human translation remains superior to machine translation (MT) because only human translation can achieve the quality necessary to preserve the style, tone and intent of the text to be translated. Machines are not capable of understanding at the...
The proofreading phase in translation is an opportunity to perfect translated content and transcend boundaries, conveying the same message and intent to the target audience.
Rapidly advancing technology and the growing need for accurate and efficient data analysis have led organizations to seek customized data sets tailored to their specific needs.
The technological advances that have occurred over the course of the last few decades have made it possible to optimize and streamline the work of human translators. One of these advances is machine translation (MT).
Synthetic data is data that has been artificially generated from a model trained to reproduce the characteristics and structure of the original data.
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