For anyone who believes that America’s elite institutions of higher learning are taken far too seriously — and I count myself among the believers — the last two years have been bracing. Of course I am referring to Covid policy, in particular the current efforts of Princeton and Yale to restrict the off-campus movements of their students in fairly radical ways.
This week Yale sent out an email laying out requirements for returning students. According to the Yale Daily News, there will be a campus-wide quarantine until Feb. 7, which may be extended. Furthermore, students “may not visit New Haven businesses or eat at local restaurants (even outdoors) except for curbside pickup.”
A lawsuit filed Sunday alleges that 16 major U.S. universities and colleges, including a number of Ivy League schools, have violated antitrust laws by working together to determine students’ financial aid packages.
Driving the news: According to the lawsuit, the schools “participated in a price-fixing cartel that is designed to reduce or eliminate financial aid…and that in fact has artificially inflated the net price of attendance for students receiving financial aid.”
A new research project is underway that will develop new computational methods for analyzing and integrating different types of data available from an existing cohort of young adults, including continuous glucose measurements, images of meals, metabolomics data, gut microbiome samples and more.
As the coronavirus has infected millions of Americans, the news media have become saturated with numbers: new infection cases, hospitalization rates, death tolls, and vaccine trial results. Many Americans have been overwhelmed, and in part because too few of us are comfortable with data, we have been susceptible to a plague of misinformation.
Most Americans don’t have the skills and knowledge to work with data, despite their critical importance to understanding our world and making informed decisions. This data illiteracy must change, and our education system needs to prioritize data-science education for all students.
Technically speaking, data science is nothing new. Scientists, businesses, and governments have long collected and interpreted data and used it as a basis for decisionmaking. But two recent changes have made data science much more relevant to all of us. The first is an explosion in the availability of data, fed by smartphones and the internet. The second is a dramatic improvement in the quality of software tools for analyzing that data.
Here are the top ten universities ranked by research expenditures in each of five social science disciplines plus one category of miscellaneous social sciences (My thanks to Michael Crow, President of Arizona State University and Katie Paquet, Senior Advisor for Strategic Communications at ASU for providing the tabular breakdowns of these data.The full set of data tables and technical information from the HERD survey can be found here.)
What’s particularly funny about AI is that people think that AI success should be evenly distributed. If Tesla can autopilot your car and Google Photos can match your elderly parents’ faces to their baby photos, why can’t your company increase revenue and decrease cost via AI? Heck, AI can’t even figure out how to load your pile of spreadsheets into a data warehouse!
So, what’s causing the disconnect between AI innovation and impact? The issue is twofold. First — all computing challenges are not the same. While some exciting topics like computer vision have made enormous leaps in recent years, most of the classically painful business data processing problems are still well beyond the capabilities of today’s state-of-the-art AI. Second — the engineering tools and practices for successful AI and machine learning are still in their infancy.
Statistical Modeling, Causal Inference, and Social Science; Andrew Gelman
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The recent successes and failures of pre-election polling invite several questions: Why did the polls get it wrong in some high-profile races? Conversely, how is it that the polls sometimes do so well? Should we be concerned about political biases of pollsters who themselves are part of the educated class? And what can we expect from polling in the future? The focus of the present article, however, is how it is that polls can perform so well, even given all the evident challenges of conducting and interpreting them. . . .
Robert Santos was sworn in today as the U.S. Census Bureau’s 26th director, becoming the first Latino person to serve in the role. This appointment follows the U.S. Senate confirmation Nov. 4, 2021, with Santos’ term set to last for five years.
As we mark the one-year anniversary of America’s right-wing temper tantrum, many Americans are probably wondering just how we can prevent such a terrible, violent event from ever happening again. Well, according to the Washington Post, those in the data science community believe they may have a solution.
Many data researchers are currently hard at work on something called “unrest prediction”—an effort to use algorithms to understand when and where violence may break out in a given nation or community. Key to this effort are organizations like CoupCast, a project at the University of Central Florida, which uses a combination of historical data and machine learning to analyze the likelihood that a violent transition of power will take place in one country or another, on any given month. According to Clayton Besaw, who helps run CoupCast, these forecasting models have traditionally been aimed at foreign countries but, unfortunately, America is looking more and more like a reasonable candidate for just such an event.
In 2014, the US Dietary Guidelines Advisory Committee — the group of scientists that makes recommendations about what Americans should eat to be healthy — reached out to me to ask the opposite question: How does diet affect sleep? This was an intriguing question.
It’s also a very important one. About 35 percent of Americans get less than the recommended minimum of seven hours of sleep per night; 10 percent to 30 percent suffer from a sleep disorder like insomnia or sleep apnea. Sleeping too little and sleep disorders have been associated with a host of problems ranging from psychological conditions to chronic diseases such as type 2 diabetes and cardiovascular disease.
But getting more and better sleep isn’t always just a matter of going to bed earlier: It turns out that diet is an under-recognized contributor to good or bad sleep.
“AI algorithms are good at approaching individual tasks, or tasks that include a small degree of variability,” Edward Grefenstette, a research scientist at Meta AI, formerly Facebook AI Research, told CNBC.
“However, the real world encompasses significant potential for change, a dynamic which we are bad at capturing within our training algorithms, yielding brittle intelligence,” he added.
AI researchers have started to show that there are ways to efficiently adapt AI training methods to changing environments or tasks, resulting in more robust agents, Grefenstette said. He believes there will be more industrial and scientific applications of such methods this year that will produce “noticeable leaps.”
Contrast how science is increasingly done today. Facebook’s machine learning tools predict your preferences better than any psychologist. AlphaFold, a program built by DeepMind, has produced the most accurate predictions yet of protein structures based on the amino acids they contain. Both are completely silent on why they work: why you prefer this or that information; why this sequence generates that structure.
You can’t lift a curtain and peer into the mechanism. They offer up no explanation, no set of rules for converting this into that – no theory, in a word. They just work and do so well. We witness the social effects of Facebook’s predictions daily. AlphaFold has yet to make its impact felt, but many are convinced it will change medicine.
Somewhere between Newton and Mark Zuckerberg, theory took a back seat. In 2008, Chris Anderson, the then editor-in-chief of Wired magazine, predicted its demise. So much data had accumulated, he argued, and computers were already so much better than us at finding relationships within it, that our theories were being exposed for what they were – oversimplifications of reality. Soon, the old scientific method – hypothesise, predict, test – would be relegated to the dustbin of history. We’d stop looking for the causes of things and be satisfied with correlations.
Computing Community Consortium, The CCC Blog, Maddy Hunter
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Announced on December 9th, 2021, Moderna Inc., a biotechnology company and a key player in mRNA vaccines, is partnering with Carnegie Mellon University (CMU) to launch an Artificial Intelligence Academy. The academy aims to teach Moderna employees to identify and integrate AI and machine learning solutions into the company ecosystem and into the vaccine distribution pipeline.
The data are now clear on the environmental benefits of plant-based diets. But the authors of the analysis say that so far, research has been less clear about the global affordability of eating vegan and vegetarian food. What’s more, where studies have explored the costs of greener diets, they have typically done so in wealthier western countries, limiting the relevance of their findings.
All this suggests we need a clearer picture of how cost factors into sustainable eating, and whether it could be an impediment to adopting greener diets.
To provide a more global view, the authors set about comparing the cost of eating four sustainable diets—flexitarian, pescatarian, vegetarian, and vegan—across 150 countries. They zoomed in especially on 463 primary food items that reflect the broad spectrum of consumption patterns across regions for these four diet types. This then allowed them to compare the national differences in the costs of eating a planet-conscious diet at a granular level.
New reports from the National Student Clearinghouse Research Center and some states show an “unprecedented” decline in college enrollment among high school graduates—especially the most underserved.
“MSML2022 is the third edition of a newly established conference, with emphasis on promoting the study of mathematical theory and algorithms of machine learning, as well as applications of machine learning in scientific computing and engineering disciplines. This conference aims to bring together the communities of machine learning, applied mathematics, and computational science and engineering, to exchange ideas and progress in this fast growing field.” Deadline for submissions is February 28.
“JSMF is encouraging researchers to pursue new questions using conceptual and methodological approaches that take seriously the trajectories, biological and experiential, and contribute to the ongoing development of cognition and behavior occurring across the lifespan. Individual projects need not cover the full human life span but the reasons for focusing on specific age ranges for study should be fully articulated. Research plans that only propose to document task performance of subjects at different ages (e.g., comparing 15-year-old subjects to 60-year-old subjects) are not responsive to the call for proposals.” Deadline for submissions is April 1.
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The eScience Institute’s Data Science for Social Good program is now accepting applications for student fellows and project leads for the 2021 summer session. Fellows will work with academic researchers, data scientists and public stakeholder groups on data-intensive research projects that will leverage data science approaches to address societal challenges in areas such as public policy, environmental impacts and more. Student applications due 2/15 – learn more and apply here. DSSG is also soliciting project proposals from academic researchers, public agencies, nonprofit entities and industry who are looking for an opportunity to work closely with data science professionals and students on focused, collaborative projects to make better use of their data. Proposal submissions are due 2/22.
arXiv, Computer Science > Human-Computer Interaction; Ildar Rakhmatulin, Sebastian Volkl
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This paper presents an inexpensive, high-precision, but at the same time, easy-to-maintain PIEEG board to convert a RaspberryPI to a Brain-computer interface. This shield allows measuring and processing eight real-time EEG (Electroencephalography) signals. We used the most popular programming languages – C, C++ and Python to read the signals, recorded by the device . The process of reading EEG signals was demonstrated as completely and clearly as possible. This device can be easily used for machine learning enthusiasts to create projects for controlling robots and mechanical limbs using the power of thought. We will post use cases on GitHub (this https URL) for controlling a robotic machine, unmanned aerial vehicle, and more just using the power of thought.
Scientists often struggle to explain their research in lay terms — whether to funding agencies and tenure and promotion committees, or to friends and family.
I study tissue regeneration in worm models of injury and tissue repair. I also study how health-care interruptions due to the pandemic have affected people with cancer. Like all researchers, I publish my findings in peer-reviewed journals. But those are for other researchers. As Steven Pinker, a cognitive scientist at Harvard University in Cambridge, Massachusetts, wrote in a 2014 essay in The Chronicle of Higher Education, academic writing is often “bloated, obscure, clumsy and unpleasant to read”. Not to mention that many good articles are hidden behind paywalls.
So, to make my research more accessible, I decided to take advantage of a resource that much of the world already uses: YouTube.
