Neural Machine Translation (NMT), one of the most important topics in deep learning, has gained much attention from the industries and academia over the last few years. In order to create simple models out of the complex ones, tech giant Google has been doing a lot of innovations in the domain of human to machine and machine to human translations for quite a few years now. Back in 2017, the tech giant introduced a solution to use a simple Neural Machine Translation (NMT) model to translate between multiple languages where the researchers merged 12 language pairs into a single model. Models into three types which are many-to-one, one-to-many and many-to-many models. Recently, the researchers at Google AI Team built a more enhanced system for neural machine translation (NMT) and published a paper known as "Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges".

The company created a technology that recognises spoken words ever closer to human parity. IBM reached a new AI milestone in speech recognition, achieving an industry record of 5.5% word error rate using the Switchboard linguistic corpus. The company broke the industry record by extending its deep learning technologies and incorporating an acoustic model that learns from positive examples while taking advantage of negative ones. The model gets smarter and performs better when similar speech patterns are repeated. IBM achieved another major AI milestone in conversational speech recognition last year with a computer system that reached a word error rate of 6.9%.