For today’s amateur naturalists, the iNaturalist online platform has greatly reduced the effort and time needed to obtain an accurate taxonomic identification. Rather than capturing, preserving and shipping a specimen to an expert, iNaturalist users just need to snap a photo with their camera or smartphone and upload it. From there, computer vision technology takes over to suggest an identification within a few seconds.
iNaturalist was launched in 2008 as a Master’s project by students from the University of California, Berkeley School of Information and has since grown into a community of nearly 500,000 users who, collectively, have captured more than 6.5 million observations of over 120,000 species of plants, animals, insects and fungi.
Horizon 2020 is a whopping €79bn pot of cash that’s being invested across European science and research, with around €1.3bn received by UK institutions each year.
It’s supported some of the UK’s innovation leaders like Steve Lindsey, dubbed “the next Dyson”, and Britain’s leadership in graphene research.
And it’s not just funding that Horizon 2020 is important for, it also supports researchers cooperating and coordinating across the continent, critical in making new discoveries.
The University Library System at the University of Pittsburgh, in partnership with the Carnegie Library of Pittsburgh, the Western Pennsylvania Regional Data Center, and the Urban Institute, which supports and coordinates the National Neighborhood Indicators Partnership, will develop the capacity of public and academic libraries to serve as key partners in local open civic data ecosystems. The project’s primary output will be a guide and toolkit to help public and academic libraries: identify local needs and contexts around open civic data; consider roles, opportunities, practices, and governance in the civic data ecosystem; anticipate and address common challenges; measure local civic open data health and capacity; and learn from examples of successful civic data partnerships.
Engineers at CTA.ai, an imaging-technology start-up in Poland, are trying to popularize a more comfortable alternative to the colonoscopy. To do so, they are using computer chips that are best known to video game fans.
The chips are made by the Silicon Valley company Nvidia. Its technology can help sift speedily through images taken by pill-size sensors that patients swallow, allowing doctors to detect intestinal disorders 70 percent faster than if they pored over videos. As a result, procedures cost less and diagnoses are more accurate, said Mateusz Marmolowski, CTA’s chief executive.
Health care applications like the one CTA is pioneering are among Nvidia’s many new targets. The company’s chips — known as graphics processing units, or GPUs — are finding homes in drones, robots, self-driving cars, servers, supercomputers and virtual-reality gear. A key reason for their spread is how rapidly the chips can handle complex artificial-intelligence tasks like image, facial and speech recognition.
Under Trump, though, there’s no one on the council, known as PCAST. It’s one of many science-and-tech advisory arms at the White House that’s still severely depleted in staff, a series of vacancies made all the more striking by the president’s previous push to cut federal research spending.
In the meantime, PCAST’s charter, technically, is set to run out: Obama’s executive order authorizing the council expires at the end of September.
At the moment, a spokesman for Trump’s tech team told Recode the president is on track to sign his own executive order re-establishing PCAST this month.
Google operates a “Computer Science in Media Team” that stages “interventions” in Hollywood to steer film-makers towards realistic and accurate depictions of what it’s like to work in IT. … The efforts of that team have now been detailed in a study [PDF], Cracking the Code: The Prevalence and Nature of Computer Science Depictions in Media.
A new report from Goldman Sachs indicates that China now has the resources, ambitious plans, and high-level government support to potentially create an artificial intelligence (AI) and machine learning powered intelligent economy over the next few years. “China’s Rise in Artificial Intelligence,” explains that the country is using AI to drive economic progress, and is therefore emerging as a major global contender in the field. Part of the reason for this is because both private industry and the government have identified machine learning and AI as the next major sources of innovation.
“We believe AI technology will become a priority on the government’s agenda, and we expect further national/regional policy and funding support on AI to follow,” the report said.
University of California-Santa Barbara, The UCSB Current
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Ranking universities is big business. Numerous organizations rate everything from the overall “best” in the world to individual programs like engineering or sociology. Indeed, metrics are baked into the culture of higher education. But what happens to research and learning when they are rated, ranked and judged by that number crunching? What happens to universities?
Christopher Newfield, a professor of literature and American studies in UC Santa Barbara’s Department of English, and a team of collaborators intend to find out. To support their work, Newfield and his project co-director, Laura Mandell, a professor of English at Texas A&M University, have been awarded a $175,000 research grant by the National Endowment for the Humanities.
“I’m thrilled that we’ve received this award — it’s one of only 12 in the country for the NEH’s Collaborative Research program,” Newfield said. “I hope our findings can help make the case for more funding for humanities research.”
New York, NY Looking for data sets to use in your research? Come learn about ICPSR and the Roper Center, two of the large collections of data sets available through NYU Libraries, covering a wide range of topics in social research including public opinion, politics, education, demographics, criminal justice, and more. Tuesday, September 12, in Bobst Library 617. [registration required, NYU only]
Snowbird, UT AIIDE is the definitive point of interaction between entertainment software developers interested in AI and academic and industrial AI researchers. October 5-9. [$$$]
International Foundation of Robotics Research, Journal of Machine Learning
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Mountain View, CA The Conference on Robot Learning (CoRL) is a new annual international conference focusing on the intersection of robotics and machine learning. The first meeting will be held in Mountain View, California, November 13-15. [waitlist available, $$$]
Melody Goodman from NYU College of Global Public Health is interested in community partners/community health stakeholders who have participated in previous community-engaged research studies. Each participant will be asked to participate in 4 web-based surveys. Each web-survey will take approximately 30-60 minutes to complete.
“For this tutorial, we will build a very simple service that takes in a text string and returns the average number of characters per word. The stringr package provides some very useful functions that will allow us to do this quickly and easily”
arXiv, Computer Science > Digital Libraries; Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg
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Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning. The 2016 edition of the conference comprised more than 2,400 paper submissions, 3,000 reviewers, and 8,000 attendees, representing a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and over 100% in terms of attendees as compared to the previous year. In this report, we analyze several aspects of the data collected during the review process, including an experiment investigating the efficacy of collecting ordinal rankings from reviewers (vs. usual scores aka cardinal rankings). Our goal is to check the soundness of the review process we implemented and, in going so, provide insights that may be useful in the design of the review process of subsequent conferences. We introduce a number of metrics that could be used for monitoring improvements when new ideas are introduced.