Machine learning

How Explainable Artificial Intelligence Can Propel the Growth of Industry 4.0

With the advent of industry 4.0, advancements in artificial intelligence (AI) have become vital to helping with industry efficiency and performance. Recently, a group of researchers surveyed the existing AI and explainable AI (XAI) based methods used in Industry 4.0 highlighting the need for XAI-based methods to help build efficient smart cities, factories, healthcare, and human-computer interactions. The very first industrial revolution historically kicked off with the introduction of steam- and water-powered technology.

NLP Logix Implements 3xLOGIC Integrated Video And Access Security

3xLOGIC announced NLP Logix has deployed 3xLOGIC integrated video and access control at their Jacksonville, FL headquarters. Bates Security, with offices in Lexington, KY and Jacksonville, specified the system and completed the installation. NLP Logix automates repetitive tasks currently being done by humans. Using its suite of algorithms, and proprietary technology, NLP Logix automates such things as interpreting imagery, matching candiates to job requirements, extracting text from documents, or as CEO Ted Willich said, “We do AI, we teach machines to do things humans do.”

Global Net Solutions Unveils Its IoT S-Badge

Global Net Solutions (GNS), an innovator in facilities-based smart security and business intelligence solutions, unveiled its S-Badge, a revolutionary IoT-based security solution designed to improve safety and tackle insider threats and breaches in high security environments, such as airports, hospitals, school districts and college campuses, government agencies and more.

Stanford Team Creates Computer Vision Algorithm to Describe Photos

Computer software only recently became smart enough to recognize objects in photographs. Now, Stanford researchers using machine learning have created a system that takes the next step, writing a simple story of what’s happening in any digital image. At the heart of the Stanford system are algorithms that enable the system to improve its accuracy by scanning scene after scene, looking for patterns, then using the accumulation of previously described scenes to extrapolate what is being depicted in the next unknown image.