In May of last year, after a 13-month slumber, the ground beneath Washington's Puget Sound rumbled to life. The quake began more than 20 miles below the Olympic mountains and, over the course of a few weeks, drifted northwest, reaching Canada's Vancouver Island. It then briefly reversed course, migrating back across the US border before going silent again. All told, the monthlong earthquake likely released enough energy to register as a magnitude 6. By the time it was done, the southern tip of Vancouver Island had been thrust a centimeter or so closer to the Pacific Ocean.

With an aging population and a growing shortage of drivers, Japan is a country where autonomous transportation services would seem to have a bright future. Demand is particularly high for self-driving trucks in regions with few alternatives to hauling freight by road, such as Hokkaido. Among truck manufacturers, UD Trucks Corp., a Japanese unit of Sweden's AB Volvo, has teamed up with an agricultural cooperative in the northern prefecture that is increasingly concerned about the declining number of delivery truck drivers. The company has been testing its autonomous heavy-duty trucks on a 1.5-km-long (about 1 mile) designated route in and around a sugar factory in Shari, eastern Hokkaido. The truck is capable of Level 4 self-driving, meaning it performs all driving tasks without human intervention within a limited area, even in emergencies.

The experts at Greenbone Networks vulnerability analysis and management company discovered 600 unprotected servers exposed online that contained medical radiological images. The research was conducted between mid-July 2019 and early September 2019. The unprotected medical image storage systems were located in 52 countries, the experts discovered that they were affected by 10,000 vulnerabilities, more than 500 of them rated with the highest severity score (CVSS 10 out of 10). Greenbone Networks researchers analyzed about 2,300 Picture Archiving and Communication System (PACS) systems exposed online. PACS servers are used in the healthcare industry to archive images created by radiological processes and to make them available to medical staff for analysis and diagnosis. These systems use the DICOM (Digital Imaging and Communications in Medicine) standard to manage medical imaging data.

A detailed study accumulated to offer Latest insights about acute features of the LegalTech Artificial Intelligence market. The report contains different market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. The report also offers a complete study of the future trends and developments of the market. It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary and SWOT analysis. Legal technology, also known as Legal Tech, refers to the use of technology and software to provide legal services.

Automation and innovation in the work within business is necessary for the reinvention of the system landscapes. The same is possible with the machine learnings together with the help of the artificial intelligence platform. The industries in the recent time are in the tremendous need of the artificial intelligence platform to increase automation, machine interaction and to save time. Furthermore, problem-solving, social intelligence and general intelligence can also be achieved with the help of the artificial intelligence platform. The artificial intelligence platform market is expected to grow during the forecast period due to growth in adoption of cloud based application and services.

The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, where distances are calculated concerning a subset of points called reference points. Its simple formulation has attracted several recent works on extensions and applications. In this paper, we aim to address some open questions related to the MLM. First, we detail theoretical aspects that assure the interpolation and universal approximation capabilities of the MLM, which were previously only empirically verified. Second, we identify the task of selecting reference points as having major importance for the MLM's generalization capability; furthermore, we assess several clustering-based methods in regression scenarios. Based on an extensive empirical evaluation, we conclude that the evaluated methods are both scalable and useful. Specifically, for a small number of reference points, the clustering-based methods outperformed the standard random selection of the original MLM formulation.

How much does someone's living room tell about how they live? Peeking into another person's life might be just part of natural human curiosity, but the answer to this question may provide insights in a wide range of aspects of human behavior. A new study published in EPJ Data Science uses the power of machine learning to explore patterns of home decors--and what they could tell about their owners--in popular accommodation website Airbnb. The Internet has provided the world with more images than can be viewed in a lifetime. Some sites, like Craigslist, Zillow, and Airbnb, specifically let us see the interiors of peoples' homes, nests of revealing human creativity, design, style and culture.

SAN FRANCISCO--Guiding Oracle's development of its latest generation of cloud applications are three main business imperatives: help customers innovate rapidly, create nimble processes, and make the most of their mobile, social, and other communications channels. Speaking at Oracle OpenWorld, Steve Miranda, executive vice president of applications development, emphasized the considerable work the company has done incorporating machine learning algorithms into its comprehensive, tightly integrated suites of cloud applications. "We're ready to run your business in the cloud," Miranda said. At Oracle OpenWorld, Steve Miranda, Oracle's executive vice president for applications development, outlines machine learning capabilities in the company's cloud applications. An intuitive, easy-to-use, voice-enabled user interface that runs on various computing platforms but is especially suited to mobile devices.

G4 instances provide the industry's most cost-effective machine learning inference for applications, like adding metadata to an image, object detection, recommender systems, automated speech recognition, and language translation. G4 instances also provide a very cost-effective platform for building and running graphics-intensive applications, such as remote graphics workstations, video transcoding, photo-realistic design, and game streaming in the cloud. Machine learning involves two processes that require compute – training and inference. Training entails using labeled data to create a model that is capable of making predictions, a compute-intensive task that requires powerful processors and high-speed networking. Inference is the process of using a trained machine learning model to make predictions, which typically requires processing a lot of small compute jobs simultaneously, a task that can be most cost-effectively handled by accelerating computing with energy-efficient NVIDIA GPUs.

Helsinki, Finland - An audience of international peace brokers have gathered inside a room in the historic House of Estates. They have come from South Sudan, the Central African Republic, Ukraine, Colombia and elsewhere to hear a scientist speak. That scientist is Timo Honkela, and his keynote speech on the second day of April's National Dialogues conference is titled Peace from a Different Perspective - a Dialogue of a Million People. But 54-year-old Honkela is working on a machine that he hopes will facilitate world peace. "World peace would be a good goal to work for in my remaining days," he says, smiling over a cup of coffee during a break in the conference.