Google parent Alphabet Inc. is slowing hiring for the remainder of the year, the most drastic action by the web search giant since the Covid-19 pandemic began battering its advertising business several weeks ago.
Chief Executive Officer Sundar Pichai told staff about the decision in an email on Wednesday. He also highlighted other areas of cost cutting, saying the company will be “recalibrating the focus and pace of our investments in areas like data centers and machines, and non business essential marketing and travel.”
Microsoft today announced a series of initiatives aimed at advancing the protection and preservation of biodiversity and ecosystems around the world. It said that it would support the development of a “Planetary Computer” to aggregate environmental data and would leverage AI to develop and deploy technology that helps partners and customers with sustainable decision-making. Microsoft also said that it would advocate for public policy initiatives that measure and manage ecosystems and that it would expand its AI for Earth program to give grantees greater access to machine learning tools.
The first thing you might notice about Michael Snyder is just how many gadgets he has strapped to his hands and wrists on any given day—an Apple Watch, a Fitbit, a Biostrap. The second is his enthusiasm for such devices. For more than a decade, Snyder, a biology researcher at Stanford University, has been using consumer wearables to determine whether these kinds of biosensors—and the data collected from them—can help track the onset of infections or illness.
Now Snyder and his team are launching a new research project. It’s one that he hopes will eventually alert people that they might have viral illnesses, including Covid-19, up to two to three days before symptoms of the virus show up. The team of about a dozen researchers has just started soliciting participants for the study, after what Snyder described as a fast-tracked approval process through Stanford’s Institutional Review Board. They’re using software algorithms that have been trained on health patterns shared during a previous study, and they’re opening this new study up to data from different brands of consumer wearables—Fitbit, Apple Watch, and more.
Ryan Hartman, professor of chemical and biomolecular engineering at NYU Tandon, used a lab reactor, liquefied catalyst, and machine learning for more efficient polymerization design
Harvard Business Review; Yasheng Huang, Meicen Sun and Yuze Sui
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What, then, do the countries that have so far been effectively flattening the curve have in common? Part of the answer is that they tend to be in East Asia — China, South Korea, Taiwan, Singapore and to a lesser extent Japan — where a collectivist spirit may encourage civic-minded embrace of and a more willing compliance with governments’ infection control. In addition, these countries tend to be actively deploying technology to collect data on the virus’s progress and efforts to contain it, including tracking those who are infected and their contacts. These two aspects of East Asian societies do not work independently; they reinforce each other.
Clearly, applying technology in these ways can be an important tool in containing the pandemic. But this use of technology raises sobering policy questions about data sovereignty and privacy, issues that are more contentious in Western democracies than in the more collectivist societies of East Asia. The most effective deployment of technology for tracking individuals’ infection status, movements, and contacts hinges on three critical conditions that might each present difficult dilemmas for Western democracies: The adoption of the needed technologies (whether they are just strongly encouraged or made mandatory); a digital infrastructure enabled and activated by the government; and seamless data sharing between government and business that may afford few privacy protections.
Princeton University, School of Engineering and Applied Science, Office of Engineering Communications
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The National Science Foundation has awarded emergency grants to two teams of Princeton researchers developing ways to better track and contain pandemics including COVID-19.
One effort, led by Kyle Jamieson, an associate professor of computer science, will work to develop a system using cellphones to help public health officials quickly track the contacts of people diagnosed with diseases such as COVID-19, while preserving privacy. That ability could become critical in efforts to contain the disease after the initial surge of cases passes through the population. The second effort, led by researchers at Carnegie Mellon University and by H. Vincent Poor, Princeton’s interim dean of engineering, will expand a mathematical model that allows public officials to measure the effect that mutations and countermeasures have on the spread of the disease. The model, which could be used for any pandemic, would allow governments to gauge the effectiveness of measures such as social distancing and quarantines before implementing them.
Even with the best protocols in place, treating COVID-19 patients is inherently dangerous for health professionals. But what if there was a way to monitor patients from a safe distance?
This week a clinical team in Boston has reported being able to monitor a COVID-19 patient remotely, thanks to a device developed at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) that can monitor a patient’s breathing, movement and sleep patterns using wireless signals.
The federal research and development (R&D) enterprise is a large and complex system,spanning the country,that includes government facilities and employees as well as federally funded work in industry, academia, and the non-profit sector. In FY2019, federal agencies obligated an estimated $141.5 billion for R&D, including $39.6 billion for intramural R&D and $101.9 billion for extramural R&D.1Its work is essential to U.S. economic prosperity, national security, health care, and other national priorities. It also plays a substantial direct role in the U.S. economy.2Today, the operation of the system is being affected profoundly by the Coronavirus Disease 2019 (COVID-19) pandemic and the national response to it.
This report provides an overview of how the nation’s response to COVID-19 is affecting the federal R&D enterprise, how the federal government and others are addressing those effects, and issues that may arise as the situation develops.The scope of this report is limited to the effects of COVID-19 on federally funded R&D. I
A two-decade-long dry spell that has parched much of the western United States is turning into one of the deepest megadroughts in the region in more than 1,200 years, a new study found.
And about half of this historic drought can be blamed on man-made global warming, according to a study in Thursday’s journal Science.
Scientists looked at a nine-state area from Oregon and Wyoming down through California and New Mexico, plus a sliver of southwestern Montana and parts of northern Mexico. They used thousands of tree rings to compare a drought that started in 2000 and is still going — despite a wet 2019 — to four past megadroughts since the year 800.
The New York State Department of Education has notified Alfred University that it has approved two new degrees programs in analytics. Beginning this fall, the University will offer new Bachelor of Science degree programs in data analytics and business analytics, preparing students for careers in the world’s fastest growing fields.
“These new offerings will provide students with the opportunity to combine analytics with the rich selection of other courses offered at Alfred University,” said Mark Lewis, dean of the Alfred University College of Business. “Our strong alumni and employer networks will provide opportunities for impactful internships, applied experiences and careers after graduation.”
—not making it optional but renouncing it. If they do so they will free themselves of its oppression as a metric, and they will free students from its persecution and inequity.
The pandemic will change the world permanently and profoundly. Even if countries can control the spread of COVID-19 in the coming months, there will be vast political, economic, social, technological, legal and environmental consequences which will last many decades.
In this article, we summarise and synthesise various – often opposing – views about how the world might change. Clearly, these are speculative; no-one knows what the future will look like. But we do know that crises invariably prompt deep and unexpected shifts, so that those anticipating a return to pre-pandemic normality may be shocked to find that many of the previous systems, structures, norms and jobs have disappeared and will not return.
For this reason, adaptation and innovation are more important than ever.
Even as COVID-19 dominates the attention of first responders, scientists, economists and technology companies, far-reaching research is moving ahead in developing quantum computing.
To that end, Intel and QuTech have recently collaborated on research showing they have successfully controlled “hot” qubits at temperatures greater than 1 kelvin. A qubit is the fundamental unit of quantum computing.
Their work is summarized in a new paper published in Nature.
As we struggle to get a grip on exactly how COVID-19 makes us ill and what we can do about it, researchers have created over 50,000 articles. That’s a lot of information! So, how do you make sense of it all? Verizon Media is doing it by using Vespa. This is an open-source, big data processing program to create a coronavirus academic research search engine: CORD-19 Search.
This engine works on top of the COVID-19 Open Research Dataset (CORD-19). This dataset should help medical researchers to find and create new insights in the fight against SARS-CoV-2. The documents within it are updated weekly as new research is published in peer-reviewed publications and archival services like bioRxiv, biological sciences preprints and medRxiv, health science preprints. It also includes document links to PubMed, Microsoft Academic, and the WHO COVID-19 database of publications.
How can data scientists help with the COVID-19 response within their organization and more broadly? While there are many valuable and interesting opportunities to apply your skills, there can be unintended consequences even from your best attempt. So, consider this general advice for data scientists who want to help with this and any disaster response.
If you are feeling like all those online calls and video conferences seem to be leaving you drained by the end of the day, it’s not just you—virtual fatigue is real.
Below are five reasons you may be feeling worn out and tired by virtual communication.
The Networking and Information Technology Research and Development (NITRD) Program
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“To increase the online visibility of important COVID-19 information, Federal Chief Information Officers have been directed to incorporate the new Schema.org vocabulary for tagging websites that contain information about COVID-19 prevention measures, disease spread statistics, etc. These new tags are available in Schema.org Version 7.0, released March 17, 2020 (http://blog.schema.org/2020/03/schema-for-coronavirus-special.html).”
“In addition to the direction to Federal Chief Information Officers, state and local government agencies as well as non-profit and private sector organization are encouraged to utilize these tags for publishing and disseminating information related to COVID-19.”
We understand this is a tough time for businesses as well as consumers. As part of our commitment to sharing data for the greater good, Cuebiq is providing free access to mobility and store visitation patterns during the COVID-19 crisis to help businesses as they look to adjust their strategies to meet this new and uncertain market.
“We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwartz Reisman Institute for Technology and Society, Center for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to improve the verifiability of claims made about AI systems. Developers can use these tools to provide evidence that AI systems are safe, secure, fair, or privacy-preserving. Users, policymakers, and civil society can use these tools to evaluate AI development processes.”
