This week MapR announced a new solution called Quick Start Solution (QSS), focusing on deep learning applications. MapR touts QSS as a distributed deep learning (DL) product and services offering that enables the training of complex deep learning algorithms at scale. Ted Dunning, MapR chief application architect, explains: "The best approach for pursuing AI/Deep learning is to deploy a scalable converged data platform that supports the latest deep learning technologies with an underlying enterprise data fabric with virtually limitless scale." But being able to run ML or DL on Hadoop does not really make a Hadoop vendor an AI vendor too.

While our ability to utilize machine learning statistical algorithms like regression, SVMs, random forests, and neural nets expanded rapidly starting in roughly the 1990's the application of these handcrafted systems didn't entirely go away. There is very little in common among convolutional neural nets, recurrent neural nets, generative adversarial neural nets, evolutionary neural nets used in reinforcement learning, and all of their variants. They all also need massive parallel processing across extremely large computational arrays, many times requiring specialized chips like GPUs and FPGAs to get anything done on a human time scale. Models That Explain Their Reasoning: Although our deep neural nets are good at classifying things like images they are completely inscrutable in how they do so.

Immersive uses text analytics via machine learning to uncover new insights from unstructured data and has developed a'sentiment index' which allows companies to discern how happy or unhappy customers are based on the content of their emails. Immersive worked with the Victorian Department of Justice to secure text information and allow 10,000 workers in Victoria to find information quickly across multiple systems using text search and indexing technologies. Traditionally an IT department would start with the business requirements, find the data and build an application. "For the last 30 years it was all about converged systems, centralised systems, bringing data in and normalising data… That centralisation or converging of systems was enabling some cost savings and some good analytics," Gnau said.