From Mainframes to Deep Learning Clusters: IBM's Speech Journey

Here at The Next Platform, we tend to focus on deep learning as it relates to hardware and systems versus algorithmic innovation, but at times, it is useful to look at the co-evolution of both code and machines over time to see what might be around the next corner. One segment of the deep learning applications area that has generated a great deal of work is in speech recognition and translation--something we've described in detail via efforts from Baidu, Google, Tencent, among others. While the application itself is interesting, what is most notable is how codes and systems have shifted to meet the needs of new ways of thinking about some of the hardest machine learning problems. And when we stretch back to the underpinnings of machine translation and speech recognition, IBM has some of the longest history--even if that history doesn't have a true deep learning element in relatively recently. In his 36 years at IBM focusing on speech and language algorithms, Michael Picheny, senior manager for IBM's Watson Multimodal division (an area that focuses on language and image recognition, among other areas), much has changed for both code and the systems required to push speech recognition.