Google, Baidu, and Microsoft have the resources to build dedicated deep learning clusters that give the deep learning algorithms a level of processing power that both accelerates training time as well as increases their model's accuracy. Yahoo, however, has taken a slightly different approach, by moving away from a dedicated deep learning cluster and combining Caffe with Spark. The ML Big Data team's CaffeOnSpark software has allowed them to run the entire process of building and deploying a deep learning model onto a single cluster. The MapR Converged Data Platform is the ideal platform for this project, giving you all the power of distributed Caffe on a cluster with enterprise-grade robustness, enabling you to take advantage of the MapR high performance file system.
More importantly, however, Google and its competitors are moving towards keying their search algorithms to understand natural speech as well, in anticipation of more and more voice search. But new machine learning algorithms are making more accurate, real-time translations possible. You might also be interested in my new big data case study collection, which you can download for free from here: Big Data Case Study Collection: 7 Amazing Companies That Really Get Big Data. My current book is Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance' and my new books (available to pre-order now) are Key Business Analytics: The 60 Business Analysis Tools Every Manager Needs To Know and Big Data in Practice.