Decoding language with AI

Machine translation has come a long way since its early days in the 17th century when Arabic scholars believed language was coded. The field saw significant growth after Georgetown University and IBM's public demonstration of machine translation in 1954, which attracted government funding for further research. However, the Automatic Language Processing Advisory Committee (ALPAC) report in 1966 claimed that machine translation was less accurate and more expensive than human translators, leading to a decline in interest. In the 1980s, interest in machine translation research resurged with the development of commercial systems like Systran and Logos. The use of neural networks for machine translation emerged in the early 2000s, offering more efficient and reliable translations than previous methods. Google's announcement of its zero-shot translation system in 2016 marked a significant milestone as it allowed transfer learning between language pairs not previously fed to the system. Today, machine translation systems like Google Translate, Amazon Translate, and DeepL provide relatively high-quality translations thanks to advancements in the field. Future research will likely focus on improving localization for languages with limited resources or data, as well as decreasing the edit distance score to reduce post-editing requirements.