In response to what it says is customer demand for "relentless" automation, Oracle plans to release in November a series of updates to its Fusion Cloud ERP and EPM suite that add features designed to streamline the process of logging and tracking transactions, while offering enhanced, AI-based analytics meant to optimize business processes. "Organizations at large are really looking to us to help them to improve the speed, the accuracy of the business processes, and really weeding out those mundane, really non-value add tasks as much as possible," said Juergen Lindner, Oracle's senior vice president of SaaS marketing. See "The best ERP systems:10 enterprise resource planning systems compared," with evaluations and user reviews. Learn why companies are increasingly moving to cloud ERP and how to spot the 10 early warning signs of ERP disaster. Get weekly insights by signing up for our CIO Leader newsletter.

Despite my incessant buzzword bashing, I'll concede this much: it's important to grapple with next-gen tech via experts who actually know what they are talking about. We got an earful on day one of the Constellation Research Connected Enterprise 2019 event. How quantum computing could (someday) break 2048-bit RSA encryption https://t.co/o5EfaqgcZN "New study shows quantum tech will catch up with today's encryption standards sooner than expected" pic.twitter.com/yfOgi9lXoj Still, next-gen tech needs to be held to the fire of project results.

AI for BI is a key tenet of the third generation of analytics. Sometime in the middle of the current decade, features such as augmented intelligence, machine learning and natural language processing started to become key parts of business intelligence platforms. In the years since, although analytics platforms have progressed, AI for BI still hasn't matured to the point where analytics tools can truly free up humans from the mundane tasks associated with data analysis, to the point where data analysis is part of everyday applications rather than a stand-alone application unto itself, or to the point at which BI platforms can predict for humans a likely outcome before they even request it. And it hasn't gotten to the point where it's accessible to everyone. In September, Constellation Research released a report entitled "Augmented Analytics: How Smart Features Are Changing Business Intelligence."

A group of data, technology, digital services and other organizations on Tuesday launched the AI Global Marketplace. The marketplace is, in essence, an artificial intelligence app store. It will host more than 2,000 high-value AI assets focused on customer engagement and process intelligence problems for the banking, insurance, healthcare and digital commerce markets. The launch group includes CognitiveScale, HyperGiant, the IEEE, USAA, the Saxena Foundation, the Honourable Society of the Inner Temple, and the University of Texas at Austin. Software assets in the AI Global Marketplace will be based on the beta version of an open interface: the Cognitive Agent Modeling and Execution Language, or CAMEL.

According to Salesforce, there are three main adoption hurdles for AI in business: identifying a viable use case and having data to support it; addressing change within employee workflows and embracing new technology; and trusting whether the AI is correct and aligns with company values. To address those issues, Salesforce recently launched Einstein's Guide to AI Use Cases, an interactive website aimed at helping organizations interested in Salesforce AI identify the use case that best fits a user's specific industry. Users are asked what group best represents their departments, which key performance indicators (KPIs) matter most to their businesses, which use cases positively affect those KPIs and what other companies have been successful with that use case. Once a user has chosen a use case, the site provides a description and a "What You'll Need" section, which advertises the best Salesforce AI product for that use case. Users can also see an example of a customer using that technology.

The executives at Airbnb Inc. are strong believers in the power of data -- so much so, that they want every single employee to be able to use it. This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

Google researchers have come up with a method for machine learning that tackles two of the technology's biggest pain points to date: End-user privacy and device and network resource consumption. It's called Federated Learning, and while still in the labs, could have a profound influence going forward. Here are the details from Google's official blog post: Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. It works like this: your device downloads the current model, improves it by learning from data on your phone, and then summarizes the changes as a small focused update. Only this update to the model is sent to the cloud, using encrypted communication, where it is immediately averaged with other user updates to improve the shared model.

What do you do when you're reading an article or paper, one that it's very important you understand, and get stumped by a particular passage? More often than not, you'll head over to Google--or whatever your favorite search engine is--start surfing the Web, and won't stop until you find a satisfactory answer to the puzzle. Researchers at MIT have developed a machine learning system that behaves much the same way in the course of performing information extraction, the process of creating structured data from unstructured formats such as plain text. Here are the key details from MIT's newsroom: Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators. For instance, humans might label parts of speech in a set of texts, and the machine-learning system will try to identify patterns that resolve ambiguities -- for instance, when "her" is a direct object and when it's an adjective.