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Information Management


From Digitized Images to Online Catalogs Data Mining a Sky Survey

AI Magazine

The value of scientific digital-image libraries seldom lies in the pixels of images. For large collections of images, such as those resulting from astronomy sky surveys, the typical useful product is an online database cataloging entries of interest. We focus on the automation of the cataloging effort of a major sky survey and the availability of digital libraries in general. The SKICAT system automates the reduction and analysis of the three terabytes worth of images, expected to contain on the order of 2 billion sky objects. For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object.


Learning Compact Visual Descriptors for Low Bit Rate Mobile Landmark Search

AI Magazine

Coming with the ever growing computational power of mobile devices, mobile visual search have undergone an evolution in techniques and applications. A significant trend is low bit rate visual search, where compact visual descriptors are extracted directly over a mobile and delivered as queries rather than raw images to reduce the query transmission latency. In this article, we introduce our work on low bit rate mobile landmark search, in which a compact yet discriminative landmark image descriptor is extracted by using location context such as GPS, crowd-sourced hotspot WLAN, and cell tower locations. The compactness originates from the bag-of-words image representation, with an offline learning from geotagged photos from online photo sharing websites including Flickr and Panoramio. The learning process involves segmenting the landmark photo collection by discrete geographical regions using Gaussian mixture model, and then boosting a ranking sensitive vocabulary within each region, with an "entropy" based descriptor compactness feedback to refine both phases iteratively.


Artificial Intelligence (AI) vs WordPress plugins - BrightMinded

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It's no surprise that Alexa will think that, as do many articles on artificial intelligence in business. However, is that sound advice? Is it better to look at what AI is capable of and then try to implement AI into your business? Or should you identify your core business needs first, and then address them in the simplest, fastest and most cost-effective way possible? We think it is the latter, but understand it's easy to get carried away in the AI hype.


The 'Invisible' Materiality of Information Technology

Communications of the ACM

Such a disappearance is a fundamental consequence not of technology but of human psychology. Whenever people learn something sufficiently well, they cease to be aware of it. Thus, Weiser's vision is even broader: as this technology becomes truly embedded in human activity we won't be aware of it at all. As the field of ubiquitous computing has evolved, with computation embedded in walls, clothes, and so forth, the materiality to support it is often physically and intentionally hidden from the user. Indeed, this material disappearance is often considered evidence of good design. The "agent" metaphor, in particular in its early presentations such as the Knowledge Navigator and Starfire, is also another utopian vision. These virtual agents are typically accessible via peripherals such as screens or phones, doing the bidding of those they serve.


What Do DDT and Computing Have in Common?

Communications of the ACM

Writing on the 50th Earth Day brings to mind the origins of U.S. environmental movement. DDT is, of course, Bis(4-chlorophenyl)- 1,1,1-trichloroethane, perhaps the most effective insecticide ever invented. DDT was used widely with remarkable effectiveness in the 1940s and 1950s to combat malaria, typhus, and the other insect-borne human diseases. Its efficacy was unsurpassed in insect control for crop and livestock production, and even villages and homes. In short, it was a wonder chemical.7


DSC Data Science Search Engine

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Embracing Responsible AI from Pilot to Production - May 27 On average, 80% of AI projects fail to make it to production. But it IS possible to successfully launch AI, at scale, that is built responsibly and works for everyone. How you scale from pilot to production is critical to ensuring AI success, while continuing to be a good corporate citizen through responsible productization.


Massive Growth Of Global Lab Automation Industry 2020:Booming Worldwide Top Key Players Perkinelmer, Inc., Danaher Corporation, Thermo Fisher Scientific, Inc., Agilent Technologies, Inc – 3w Market News Reports

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By Equipment the market for lab automation is segmented into automated liquid handlers, automated plate handlers, robotic arm, automated storage and retrieval systems. By software the lab automation market is segmented into laboratory information management system, laboratory information system, chromatography data system, electronic lab notebook, scientific data management system. On the basis of analyzer the market is segmented into biochemistry analyzers, immuno-based analyzers, hematology analyzers segments. By application the segmentation of the market is drug discovery, genomics, proteomics, protein engineering, bio analysis, analytical chemistry, system biology, clinical diagnostics, lyophilization. Based on end user the lab automation market is segmented into biotechnology & pharmaceuticals, hospitals, research institutions, academics, private labs. On the basis of geography, lab automation market report covers data points for 28 countries across multiple geographies such as North America & South America, Europe, Asia-Pacific, and Middle East & Africa. Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others. In 2017, North America is expected to dominate the market.


We built an AI-powered search tool for 60,000 COVID-19 research papers

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We estimate that there are as many as 500,000 papers relevant to COVID-19 that were published before the outbreak, including papers related to the outbreaks of SARS in 2002 and MERS in 2012. Any one of these might contain the key information that leads to effective treatment or a vaccine for COVID-19. Traditional methods of searching through the research literature just don't cut it anymore. This is why we and our colleagues at Lawrence Berkeley National Lab are using the latest artificial intelligence techniques to build COVIDScholar, a search engine dedicated to COVID-19. COVIDScholar includes tools that pick up subtle clues like similar drugs or research methodologies to recommend relevant research to scientists.


Artificial intelligence systems aim to sniff out signs of COVID-19 outbreaks

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HealthMap uses artificial intelligence and data mining to spot disease outbreaks and issue location-specific alerts (colored dots) on COVID-19 and other diseases. It sounded an early alarm on the pandemic. Science's COVID-19 reporting is supported by the Pulitzer Center. The international alarm about the COVID-19 pandemic was sounded first not by a human, but by a computer. HealthMap, a website run by Boston Children's Hospital, uses artificial intelligence (AI) to scan social media, news reports, internet search queries, and other information streams for signs of disease outbreaks.


Nvidia, Digital Realty Team Up on Enterprise AI - SDxCentral

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Colocation giant Digital Realty deepened its ties to Nvidia with a service that allows enterprises to deploy Nvidia-powered artificial intelligence (AI) and machine learning workloads on Digital Realty's data center platform. Nvidia launched its DGX-Ready Data Center program last year with 19 data center partners including Digital Realty. The AI partner program gives customers access to Nvidia's AI infrastructure inside the colocation providers' facilities. Meanwhile, Digital Realty in November announced PlatformDigital. At launch the data center platform offered customers four new services that they could deploy on top of PlatformDigital.