Many organisations are sitting on a wealth of untapped data in the form of call centre recordings and customer emails that contain valuable insights into the customer experience, while e-commerce sites have a lot of product image data that is ripe for classification and analysis.
These are examples of unstructured data sources that can be fed into deep learning algorithms to improve customer insights.
http://www.computerweekly.com/feature/Machines-delve-deep-into-customer-minds/
Researchers at IBM recently announced that the firm's distributed visual data recognition models can now be trained faster than competing models from Facebook, according to TechCrunch. Distributed visual data processing is a subset of deep learning, which is a branch of machine learning, that uses several graphics processing units (GPUs) to process and analyze massive data sets. It's ideal for very large deep learning projects where the data is too large to be processed on a single GPU. While not specifically designed for the IoT, distributed visual data recognition could aid larger IoT ecosystems in particular.
http://www.businessinsider.com/visual-data-recording-iot-2017-8/
