In a briefing on Monday, research leaders across tech, academia and the government joined the White House to announce an open data set full of scientific literature on the novel coronavirus. The COVID-19 Open Research Dataset, known as CORD-19, will also add relevant new research moving forward, compiling it into one centralized hub. The new data set is machine readable, making it easily parsed for machine learning purposes -- a key advantage according to researchers involved in the ambitious project. In a press conference, U.S. CTO Michael Kratsios called the new data set the "most extensive collection of machine readable coronavirus literature to date." Kratsios characterized the project as a "call to action" for the AI community, which can employ machine learning techniques to surface unique insights in the body of data.

PHILADELPHIA--(BUSINESS WIRE)--The Business Intelligence Group today announced that Kneron was named a winner in its Artificial Intelligence Excellence Awards program. Kneron is a leading on-device edge artificial intelligence (AI) company based in San Diego, California. Kneron provides complete end-to-end integrated hardware and software solutions that enable on-device edge AI inferencing in mobile devices, personal computers, and IoT use cases including smart home devices, surveillance, payments, and smart cars. Their solutions augment cloud-based AI to accelerate AI inferencing on any device. As the entire on-device edge AI industry is still emerging, Kneron's early investment and commercialization of its technology have positioned it in a leadership position to enable AI adoption in mass-market devices.

Enterprise service management startup Espressive raised $30 million for its Series B round, the company announced Wednesday. Espressive uses artificial intelligence to automate help desks for employee inquiries. Insight Partners led the Series B round, with participation from previous investors General Catalyst and Wing Venture Capital. The new funding will be used to improve the Santa Clara, California-based company's Natural Language Processing engine and Barista Employee Language Cloud (ELC), according to a statement from Espressive. The ELC learns new phrases, topics, phrase structures and synonyms as employees ask it questions.

AKVIS announces the release of Magnifier AI 10.0! The new version uses artificial neural networks and machine learning groundbreaking image enlargement technologies. The update also offers full compatibility with macOS Catalina and Adobe 2020 and other changes. AKVIS Magnifier AI is efficient image resizing software. It allows blowing up images into supersize prints without loss in quality.

Compared to a few years ago, the artificial intelligence (AI) market is starting to solidify around real-world applications with the pace of change being faster than it ever has been before, as startups and technology providers rush to create platforms and targeted niche solutions for solving specific enterprise problems. According to a new report from Tractica, the rising tide of AI adoption across multiple industries will drive significant growth during the next decade, and the market intelligence firm forecasts that annual worldwide AI software revenue will increase from $3.2 billion in 2016 in 2016 to $89.8 billion by 2025. This forecast is a significant upgrade of Tractica's previous projection for AI market revenue, which was published in 2Q17, due to an improved outlook for a number of specific use cases across multiple industries. "Artificial intelligence is already key to how consumer internet companies operate today, allowing them to roll out hyper-personalized services by following an'AI first' strategy," says research director Aditya Kaul. "The rest of the market in the enterprise and government sectors is still catching up on adopting AI and has yet to fully understand its value, including the breadth and depth of use cases, the technology choices surrounding AI, and the implementation strategies."

Red Hat, Inc., the world's leading provider of open source solutions, today highlighted that more organizations are using Red Hat OpenShift as the foundation for building artificial intelligence (AI) and machine-learning (ML) data science workflows and AI-powered intelligent applications. OpenShift helps to provide agility, flexibility, portability and scalability across the hybrid cloud, from cloud infrastructure to edge computing deployments, a necessity for developing and deploying ML models and intelligent applications into production more quickly and without vendor lock-in. AI/ML represents a top emerging workload for Red Hat OpenShift across hybrid cloud and multicloud deployments for both our customers and for our partners supporting these global organizations. By applying DevOps to AI/ML on the industry's most comprehensive enterprise Kubernetes platform, IT organizations want to pair the agility and flexibility of industry best practices with the promise and power of intelligent workloads. As a production-proven enterprise container and Kubernetes platform, OpenShift delivers integrated DevOps capabilities for independent software vendors (ISVs) via Kubernetes Operators and NVIDIA GPU-powered infrastructure platforms.

Z Advanced Computing, Inc. (ZAC), the pioneer startup on Explainable-AI (Artificial Intelligence) (XAI), is developing its Smart Home product line through a paid-pilot for Smart Appliances for BSH Home Appliances (a subsidiary of the Bosch Group, originally a joint venture between Bosch and Siemens), the largest manufacturer of home appliances in Europe and one of the largest in the world. ZAC just successfully finished its Phase 1 of the pilot program. "Our cognitive-based algorithm is more robust, resilient, consistent, and reproducible, with a higher accuracy, than Convolutional Neural Nets or GANs, which others are using now. It also requires much smaller number of training samples, compared to CNNs, which is a huge advantage," said Dr. Saied Tadayon, CTO of ZAC. "We did the entire work on a regular laptop, for both training and recognition, without any dedicated GPU. So, our computing requirement is much smaller than a typical Neural Net, which requires a dedicated GPU," continued Dr. Bijan Tadayon, CEO of ZAC.

WIRE)--WekaIO (Weka), the innovation leader in high-performance, scalable file storage for data-intensive applications, today announced that Innoviz, a leading manufacturer of high-performance, solid-state Light Detection and Ranging (LiDAR) sensors and Perception Software that enables the mass-production of autonomous vehicles, has selected the Weka File System (WekaFS) to accelerate its Artificial Intelligence (AI) and deep learning workflows. WekaFS has been chosen by Innoviz to improve application performance at scale and deliver high bandwidth I/O to its GPU cluster. Innoviz's solid-state LiDAR sensors are key to the future of autonomous cars. The sensors and Perception Software, which identifies, classifies, segments, and tracks objects to give autonomous vehicles a better understanding of the 3D driving scene, rely heavily on AI. Having recently closed its Series C funding round with $170M secured, Innoviz is choosing and developing the right technologies to empower it to realize its expansion plans and enhance its manufacturing capabilities.

Socionext Inc. has developed a prototype chip that incorporates newly-developed quantized Deep Neural Network (DNN) technology, enabling highly-advanced AI processing for small and low-power edge computing devices. The prototype is a part of a research project on "Updatable and Low Power AI-Edge LSI Technology Development" commissioned by the New Energy and Industrial Technology Development Organization (NEDO) of Japan. The chip features a "quantized DNN engine" optimized for deep learning inference processing at high speeds with low power consumption. Today's edge computing devices are based on conventional, general-purpose GPUs. These processors are not generally capable of supporting the growing demand for AI-based processing requirements, such as image recognition and analysis, which need larger devices at higher cost due to increases in power consumption and heat generation.

"High demand and tight labor markets have made candidates with AI skills highly competitive, but hiring techniques and strategies have not kept up," said Peter Krensky, research director at Gartner. "In the recent Gartner AI and Machine Learning Development Strategies Study, respondents ranked "skills of staff" as the No. 1 challenge or barrier to the adoption of AI and machine learning (ML)." Departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development. These business units are using AI talent for customer churn modeling, customer profitability analysis, customer segmentation, cross-sell and upsell recommendations, demand planning, and risk management. A significant portion of AI use cases are reported from asset-centric industries supporting projects such as predictive maintenance, workflow and production optimization, quality control and supply chain optimization.