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The #IoT and #Analytics @ThingsExpo #BigData #BI #AI #DX #MachineLearning

@machinelearnbot

The Internet of Things (IoT) promises to change everything by enabling "smart" environments (homes, cities, hospitals, schools, stores, etc.) and smart products (cars, trucks, airplanes, trains, wind turbines, lawnmowers, etc.). I recently wrote about the importance of moving beyond "connected" to "smart" in a blog titled "Internet of Things: Connected Does Not Equal Smart". The article discusses the importance of moving beyond just collecting the data, to transitioning to leveraging this new wealth of IoT data to improve the decisions that these smart environments and products need to make: to help these environments and products to self-monitor, self-diagnose and eventually, self-direct. But one of the key concepts in enabling this transition from connected to smart is the ability to perform "analytics at the edge." Shawn Rogers, Chief Research Officer at Dell Statistica, had the following quote in an article in Information Management titled "Will the Citizen Data Scientist Inherit the World?": "Organizations are fast coming to the realization that IoT implementations are only going to become more vast and more pervasive, and that as that happens, the traditional analytic model of pulling all data in to a centralized source such as a data warehouse or analytic sandbox is going to make less and less sense.


A 20-Year Community Roadmap for Artificial Intelligence Research in the US

arXiv.org Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.


How to Achieve #DigitalTransformation @ExpoDX @DellEMC #AI #IoT #IIoT #FinTech #SmartCities

#artificialintelligence

Industry after industry is under siege as companies embrace digital transformation (DX) to disrupt existing business models and disintermediate their competitor's customer relationships. But what do we mean by "Digital Transformation"? Digital Transformation The coupling of granular, real-time data (e.g., smartphones, connected devices, smart appliances, wearables, mobile commerce, video surveillance) with modern technologies (e.g., cloud native apps, Big Data architectures, hyper-converged technologies, artificial intelligence, blockchain) to enhance products, processes, and business-decision making with customer, product and operational insights. The digital transformation starts by understanding the organization's business initiatives, and then prioritizing which initiatives are top candidates for enhancement through digital transformation. "Begin with an end in mind" to quote Stephen Covey.


Water Sector Embracing Big Data

#artificialintelligence

The water sector has collected reams of data for decades, but it's only within the last few years that utilities, agencies, consultants and vendors have begun to use that data to improve everything from managing maintenance to predicting water flow to digitally mimicking an entire watershed. The move to leverage digital information in the sector over the last two to three years is "drastic," says Luis Casado, senior vice president of water for Gannett Fleming and one of several people who spoke passionately about the possibilities of water data at Water Environment Federation's annual WEFTEC conference Oct. 1-3 in New Orleans. Firms like Gannett Fleming, Arcadis, Brown and Caldwell, and Jacobs are taking previously underutilized information from supervisory control and data acquisition, or SCADA, systems, and pairing it with historic datasets and additional sensor data to create customized digital dashboards and applications for water agencies and related entities. "It's not a single piece of software, it's an approach of how you look at data and how you merge that information and use it effectively in day-to-day operation," said Kevin Stively, smart utility leader for Brown and Caldwell, in a presentation at the event. He said historical information can be layered on real-time information to help a younger workforce make the operational decisions that older workers relied on their "gut" to make.