In this session, we'll discuss the application of OpenSeq2Seq, an Nvidia Research project directed at speech and text processing, to telephone use cases in Healthcare. We will begin by describing the OpenSeq2Seq project and its goals. We will then cover the speech to text use cases in healthcare, related to member interactions with customer service representatives. Finally, we'll discuss our application of OpenSeq2Seq to the datasets, including normalization and modification to the OpenSeq2Seq code (e.g. in order to enable transfer learning) as well as our results
The need for helping elderly individuals or couples remain in their home is increasing as our global population ages. Cognitive processing offers opportunities to assist the elderly by processing information to identify opportunities for caregivers to offer assistance and support. This project seeks to demonstrate means to improve the elderlys' ability to age at home through understanding of daily activities inferred from passive sensor analysis. This project is an exploration of the IBM Watson Cloud and Edge docker-based Blue Horizon platforms for the use of high-fidelity, low-latency, private sensing and responding at the edge using a RaspberryPi, including deep learning using NVIDIA DIGITS software, K80 GPU servers in the IBM Cloud, and Jetson TX2 edge computing.