Research on intelligent software systems and machine learning made great strides in 2016, and some of the credit for that goes to the Google Brain team. Going forward, the team said it will continue to conduct machine learning research, especially in areas like healthcare, AI safety, and natural language understanding.
The Google Brain team doesn’t have many restrictions when it comes to its research projects and portfolios, which is why its papers cover everything from deep learning games to neural networks. Not only did these papers make it into top-tier machine learning conferences like NIPS, ICML, and ICLR in 2016, they demonstrated new approaches to improving people’s lives with advanced software systems, according to Jeff Dean, Google senior fellow and member of the the Google Brain team.
NLU: Getting computers to understand our language In 2016, the Google Brain team took previous research from a paper called “Sequence to Sequence Learning with Neural Networks,” which demonstrated that the approach could be used for machine translation, and replaced the translation algorithms powering Google Translate with a new end-to-end learned system. The new system “closed the gap between the old system and human quality translations by up to 85% for some language pairs,” wrote Dean.
“A few weeks later, we showed how the system could do ‘zero-shot translation,’ learning to translate between languages for which it had never seen example sentence pairs,” wrote Dean.
