In all, a few lines of R or Python code will suffice for a piece of machine intelligence and there's a plethora of resources and tutorials online to train your quasi-neural networks, like all sorts of deepfake networks, manipulating image-video-audio-text, with zero knowledge of the world, as Generative Adversarial Networks, BigGAN, CycleGAN, StyleGAN, GauGAN, Artbreeder, DeOldify, etc. They create and modify faces, landscapes, universal images, etc., with zero understanding what it is all about.

Lately, we've seen many "x-Ops" management practices appear on the scene, all derivatives from DevOps, which seeks to coordinate the output of developers and operations teams into a smooth, consistent and rapid flow of software releases. Another emerging practice, DataOps, seeks to achieve a similarly smooth, consistent and rapid flow of data through enterprises. Like many things these days, DataOps is spilling over from the large Internet companies, who process petabytes and exabytes of information on a daily basis. Such an uninhibited data flow is increasingly vital to enterprises seeking to become more data-driven and scale artificial intelligence and machine learning to the point where these technologies can have strategic impact. Awareness of DataOps is high.

Micron worked with Forrester to put out a white paper on the role of digital storage and memory in artificial intelligence (AI). They make the point that AI will be an important part of modern society and that memory and storage must change to reap the rewards of better AI, and in particular Machine Learning (ML). You can see the report yourself here. The report is based upon an August 2018 survey of 200 IT and business professionals that manage architecture of strategy for complex data sets at large enterprises in the US and China. The chart below gives further details on the industries, company sizes, and IT job functions of the respondents.

In this special guest feature, Greg Council, VP Marketing and Product Management at Parascript, discusses how applied AI can help businesses uncover and use valuable information that they are already storing. In his role at Parascript, Greg is responsible for market vision and product strategy. He oversees all aspects of Parascript software life cycles, leading the successful development and introduction of advanced technology to the marketplace. Greg has over 15 years of experience in marketing, product definition and development, competitive/market analysis, and channel engagement for both on-premise solutions and SaaS. Formerly, he led product management at Evolving Systems and Captaris, now OpenText.