The term "real time" has been infused throughout tech, from real-time stock picks to real-time pizza tracking. As everyday enterprises begin incorporating data tools and tactics used inside the biggest of big tech companies, a sector of data services providers has emerged to help them take advantage of the truly real-time analytics and machine learning approaches only giant companies with far larger database teams and resources could have afforded in the past. Companies like Hazelcast, Rockset, Tecton and others enable split-second analytics and machine learning for things like financial fraud prevention, dynamic pricing or product recommendations that respond to what you just clicked. These companies promise to leave plodding batch-data processing for old-school business intelligence analysis in the dust. But whether every enterprise needs, wants or is ready to operate at a clip as fast paced as a Citibank, Uber or Amazon remains to be seen. Updating data every few days, every night or even every hour or so for business analysis using a typical batch processing approach "is like playing Monday morning quarterback," said Venkat Venkataramani, CEO and co-founder of Rockset, a company that provides a database for building applications for real-time data, analytics and queries.

Businesses that trust AI to operate will leverage different kinds of data input and infuse automation into how they extract insights. The year began with an ambitious data mandate for organizations: leverage data analytics and AI techniques to keep up with the competition and increase efficiency. Pressed by the challenges of a redrawn business landscape, leaders searched for guidance in their data and analytics toolkit. In the pivot to distributed work, AI helped field rising help desk requests from a mobile workforce. Data analytics informed leaders in near-real time how consumption patterns shifted, helping manage supply chain constraints.

The Pentagon's clearinghouse on artificial intelligence is set to deliver a new tool to special operators to conduct psychological operations in near-real time in the information environment. The tool, called Entropy, is meant to reduce the cognitive burden on personnel performing military information support operations, an official said during a virtual presentation on Sept. 10 during the Department of Defense AI Symposium. In its simplest form, Entropy, under development by the Joint AI Center, will ingest data streams consisting of both text and video from what the military calls the information environment, or more commonly the internet, and provide the operators with summaries of trends based on the information. This is what officials described as the passive portion. An active portion, which is still under consideration, would build upon the passive component by taking the topics identified, feeding them into a language model, and downselecting messages for the team to review and score.

Fueled by improvements in speech recognition, machine learning, better algorithms, cloud processing, and more powerful computing devices, the quality of machine translations is improving. Learning another language has never been a simple proposition. It can take months of study to absorb the basics and years to become fluent. Of course, there's the added headache that learning a language doesn't help if a person encounters one of the world's other 7,000 or so languages. "There has always been a need for human translators and interpreters," says Andrew Ochoa, CEO of translation technology firm Waverly Labs.

Welcome to a new series of short articles I am presenting about Artificial Intelligence specifically in the Azure AI stack. The objective is that you will learn about an Azure based AI service in no more than one minute and thus quickly get familiar with the entire stack over a short period of time. These are going short, easily digestible articles so let's get started! What is Azure Databricks Overview?