While adjusting to the unfamiliar pressures of a new school, a new city, and new anxieties, Polly Moser developed anorexia as a freshman at Stanford University in 2018.
By the end of her sophomore year, Moser, 20, was managing her eating disorder with the help of a therapist through Stanford Health Care. But when school administrators shut down campus due to the coronavirus in early March, Moser had to move home to Maryland — nearly 3,000 miles away from her carefully woven health care safety net.
“I really liked my therapist a lot. It was so convenient. It was on campus, so I could bike there,” said Moser, who has not yet found a therapist to work with in Maryland. “Building a rapport and a relationship with a therapist takes a long time. They would have to learn about my whole history, and I’m just not in a mental place where I could do that in an effective way with someone new.”
With sports around the world shut down by the coronavirus pandemic, the World Anti-Doping Agency is looking to artificial intelligence as a new way to detect athletes who cheat.
WADA is funding four projects in Canada and Germany, looking at whether AI could spot signs of drug use which might elude even experienced human investigators. It’s also grappling with the ethical issues around the technology.
Athletes won’t be suspended solely on the word of a machine. Instead, AI is a tool to flag up suspect athletes and make sure they get tested.
Communications of the ACM, Viewpoint, Meredith Ringel Morris
from
According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens; people are disabled to the extent that society creates accessibility barriers. AI technologies offer the possibility of removing many accessibility barriers; for example, computer vision might help people who are blind better sense the visual world, speech recognition and translation technologies might offer real-time captioning for people who are hard of hearing, and new robotic systems might augment the capabilities of people with limited mobility. Considering the needs of users with disabilities can help technologists identify high-impact challenges whose solutions can advance the state of AI for all users; however, ethical challenges such as inclusivity, bias, privacy, error, expectation setting, simulated data, and social acceptability must be considered.
This past week Facebook became one of the largest proponents of remote work. It made headlines for considering allowing people to permanently work from home. In doing so, it joined the likes of Twitter, Square, and Shopify, all of whom are recent, post-pandemic converts. (The idea is not so new to companies like GitLab and Automattic.)
This is the same Zuckerberg who felt compelled to force employees to move closer to Palo Alto. The one who resisted the idea of a San Francisco operation. This guy is suddenly the poster child of “working from home?” Why this abrupt shift in the management philosophy of companies like Facebook?
To answer this question, you need only to abide by the golden rule: follow the money.
Niche, a website that reviews colleges for prospective students, decided to survey those who come to its website about the scenarios. Some of what it found from a survey of 10,000 students — in high school and college — is similar to other surveys. But its findings reinforce the view of many college leaders that getting students to campus is the best way to function … if it can be done safely.
Three scenarios — holding in-person classes like before, offering classes so that some were in person and others online, and having three- to four-week block schedules (in person) — appealed to a majority of undergraduates. One-third of students said they would transfer to another institution if their college only had online options.
When people in the United Kingdom began dying from COVID-19, researchers saw an urgent need to understand all the possible factors contributing to such deaths. So in six weeks, a team of software developers, clinicians, and academics created an open-source platform designed to securely analyze millions of electronic health records while protecting patient privacy.
The new OpenSAFELY analytics platform enabled the largest study yet of hospital deaths related to COVID-19 among more than 17 million adult patients in the U.K.’s National Health Service (NHS). It also shows how large-scale computational power can speedily access and analyze patient information during a public-health emergency, without removing sensitive information from data centers belonging to software companies that maintain electronic health records.
Cornell Tech has announced a $1 million grant from The Atlantic Philanthropies to jump-start its Public Interest Tech (PiTech) initiative, aimed at developing the tools, systems, datasets, research and education needed to address significant public sector concerns.
The gift will help Cornell Tech create the foundational infrastructure for PiTech, with the goal of building a community of researchers and practitioners dedicated to addressing societal challenges that otherwise might not benefit from federal research funding, commercial investment and foundation support.
Nick Bostrom, a 47-year-old Swedish born philosopher and polymath, founded the Future of Humanity Institute (FHI) at the University of Oxford in 2005 to assess how dangerous AI and other potential threats might be to the human species.
In the main foyer of the institute, complex equations beyond most people’s comprehension are scribbled on whiteboards next to words like “AI safety” and “AI governance.” Pensive students from other departments pop in and out as they go about daily routines.
The National Science Foundation (NSF) would get a sweeping remake—including a new name, a huge infusion of cash, and responsibility for maintaining U.S. global leadership in innovation—under bipartisan bills that have just been introduced in both houses of Congress.
Many scientific leaders are thrilled that the bills call for giving NSF an additional $100 billion over 5 years to carry out its new duties. But some worry the legislation, if enacted, could compromise NSF’s historical mission to explore the frontiers of knowledge without regard to possible commercial applications.
The Endless Frontiers Act (S. 3832) proposes a major reorganization of NSF, creating a technology directorate that, within 4 years, would grow to more than four times the size of the entire agency’s existing $8 billion budget.
University of Pennsylvania, The Daily Pennsylvanian student newspaper, Ericka Pica
from
The Wharton School announced in a May 7 press release the launch of the Wharton Artificial Intelligence for Business initiative after receiving a $5 million donation for its creation by two Penn graduates.
The program, which aims to inspire teaching and research in the field of artificial intelligence, will be led by John C. Hower Professor of Technology and Digital Business Kartik Hosanagar, and will be a part of the Analytics at Wharton initiative and the school’s More Than Ever campaign. Tao Zhang and Selina Chin, 2002 Wharton MBA graduates, contributed the $5 million donation for the program, announced in the press release.
Amazon is looking to invest in localized podcast content, like news and sports, sources tell Axios. Sports content is top of mind as the company plans to buy up more TV rights and have adjacent audio content for users.
Why it matters: Amazon sees a strategic advantage in podcasts by leveraging Alexa voice tech to help users discover personalized content.
A University of Manitoba professor is leading a global effort to study the impact scaling back human activity due to COVID-19 has had on wildlife.
Fewer cars may decrease traffic mortality and quieter streets may allow animals, such as birds, to better communicate and find mates.
However, decreased traffic also may lead to increased activity of species such as rats and domestic cats, which could harm native wildlife, said Nicola Koper, a professor of conservation biology at the U of M’s Natural Resources Institute.
To make sense of what’s actually happening, she’s launched an initiative to co-ordinate biologists across the world. The C19-Wild Research Group brings together ecological teams to share knowledge, ideas and research progress.
The Conversation, Richard B. Primack and Casey Setash
from
For the first time in 50 years, ornithologists at the Manomet nature observatory in Plymouth, Massachusetts are not opening their mist nets every weekday at dawn to catch, measure and band migrating songbirds. Due to the coronavirus pandemic, the center has essentially canceled its spring field season and will be doing only very limited sampling. Going forward, its long-term banding data will contain only a fraction of the usual information on songbird migrations during the spring of 2020.
Across the world, field stations, nature centers and universities have shut down long-term research to protect scientists, staff, students and volunteers from COVID-19. There’s good reason for this step, but it comes at a cost.
Online June 29-July 31. “A FREE five week virtual Summer program for any underrepresented student (including women, underrepresented minorities – African Americans, American Indians including Native Alaskans, Latinxs/Hispanics and Native Pacific Islanders – and persons with disabilities) who will be enrolled in a mathematical/statistical graduate program in Fall 2020.” Application required.
“The COVID-19 EHR DREAM Challenge is now OPEN for Submissions! The results of the highest performing models will be used in the Fred Hutch CovidWatch study to prioritize study recruitment.” This challenge does not have a planned end date.
Communications of the ACM, Rishi Gupta and Tim Roughgarden
from
We model the problem of identifying a good algorithm from data as a statistical learning problem. Our framework captures several state-of-the-art empirical and theoretical approaches to the problem, and our results identify conditions under which these approaches are guaranteed to perform well. We interpret our results in the contexts of learning greedy heuristics, instance feature-based algorithm selection, and parameter tuning in machine learning.