Google is consistently at the head of the pack when it comes to A.I. and algorithm-based learning, and Translate's no exception. The program generates translations using patterns found in huge amounts of text, discovered through millions of documents that have already been translated by humans. As time goes on, the program recognizes more and more patterns, receives input from real people, and continues to refine its translations.
In September, Google switched from Phrase-Based Machine Translation (PBMT) to Google Neural Machine Translation (GNMT) for handling translations between Chinese and English. The Chinese and English language pair has historically been difficult for machines to translate, and Google managed to get its system close to human levels of translation by using bilingual people to train the system ... Google planned to add GNMT for all 103 languages in Google Translate. That would mean feeding in data for 103^2 language pairs, and the artificial intelligence would have to handle 10,609 models.
Google tackled this problem by allowing a single system to translate between multiple languages
source: https://www.inc.com/justin-bariso/the-ai-behind-google-translate-recently-did-something-extraordinary.html
Not all languages are as well documented and understood as English and Chinese. For machine translation to work for any pair of languages or for all languages, we will need as much documentation for each language as their is for English and Chinese. You mention that there are 103 languages in Google Translate. There are still over 7,000 languages spoken in the world so Google Translate is not yet touching upon even 1.5% of the World's languages, let alone recognizing the documentation for those languages that has already been done, and the vast amount still needed.
While machine translation has made significant progress in recent years, simultaneous translation of any language remains a difficult task for AI. One of the main challenges is the complexity of natural language, which includes nuances in grammar, syntax, and vocabulary that can be difficult for machines to understand and translate accurately.
Another challenge is the lack of large amounts of training data for certain language pairs, especially for less common languages. Machine translation models rely heavily on large datasets of parallel texts (i.e., texts in two languages that are translations of each other), and the availability of such data can be limited for some language pairs.
Furthermore, simultaneous translation requires real-time processing, which puts additional constraints on the speed and accuracy of the translation system. This means that the system must be able to process and translate speech or text quickly and accurately, while also taking into account the context of the conversation.
To improve simultaneous translation, researchers are exploring new approaches that combine different AI techniques, such as neural machine translation, speech recognition, and natural language understanding. They are also working on developing more sophisticated algorithms that can take into account the context of the conversation, as well as the cultural and linguistic differences between the languages being translated.
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