You may know the Fitbits as handy little doo-dads strapped onto wrists that help people chart their fitness, but now a Fitbit-style device (an accelerometer) is being used to unveil the secrets of group cohesion in baboons.
Researchers from the Max Planck institute of Animal Behaviour, Germany, sought to understand the costs and benefits of moving in groups. To do this, they attached accelerometers to a troop of baboons moving through the wild, as detailed in a new study published in Proceedings of the Royal Society B.
Group living is a common feature among all sorts of animals, including many of our primate cousins. The benefits of group living include protection from predators and the teaching of skills, but there can be associated physical costs, too.
As a professor and a researcher in the field of operations management, my current research focuses on improving the efficiency and effectiveness of health care delivery. My colleagues and I recently published a multiyear study showing that integrating a mobile health app with ongoing medical care can significantly improve the health of patients with hypertension – a widespread, serious and potentially deadly chronic medical condition.
But it’s not easy to use health apps this way as a regular part of medical care in the U.S.
Stanford University, Stanford Institute for Human-Centered Artificial Intelligence, Mike Wu, Chris Piech, and Chelsea Finn
from
tl;dr. With the rise of large online computer science courses, there is an abundance of high-quality content. At the same time, the sheer size of these courses makes high-quality feedback to student work more and more difficult. Talk to any educator, and they will tell you how instrumental instructor feedback is to a student’s learning process. Unfortunately, giving personalized feedback isn’t cheap: for a large online coding course, this could take months of labor. Today, large online courses either don’t offer feedback at all or take shortcuts that sacrifice the quality of the feedback given.
Today’s digital world has created a booming demand for new skills, including the technical knowledge to develop artificial intelligence (AI) tools as well as the aptitude to apply and use AI in the workplace. But a new survey of higher education officials suggests that demand for AI training is outpacing supply and the current ability of higher education institutions to meet that demand.
The study, which polled 246 prequalified higher education administrators, educators and IT decision makers from a mix of community colleges, four-year colleges and vocational schools, also suggests that while higher education officials recognize the growing demand for AI instruction, 52% of them say they are struggling to attract instructors to teach AI courses.
One reason is that the demand for AI subject matter experts — and what companies are willing to pay them — is so high in the commercial sector that schools are having a hard time competing for talent.
“As more American companies embrace long-term remote or hybrid work, they have their pick of top tech talent across several U.S. regions–and beyond schools with computer science programs that often top academic rankings, like Carnegie Mellon University or MIT,” wrote the report’s author, HackerRank CEO Vivek Ravisankar.
For example, students at the New Jersey Institute of Technology topped the charts in four out of five languages, according to the report. Outside the New York metropolitan area, “there are pockets of great talent” at the State University of New York at Buffalo and Rochester.
“Moving west, top talent emerges at University of Texas at Arlington and Dallas–notable as some companies open new office spaces in Texas. Oregon State University is another standout,” Ravisankar wrote.
A new technique, developed by [Polly] Fordyce and her colleagues at Stanford and detailed this week in the journal Science, could help change that. Dubbed HT-MEK — short for High-Throughput Microfluidic Enzyme Kinetics — the technique can compress years of work into just a few weeks by enabling thousands of enzyme experiments to be performed simultaneously. “Limits in our ability to do enough experiments have prevented us from truly dissecting and understanding enzymes,” said study co-leader Dan Herschlag, a professor of biochemistry at Stanford’s School of Medicine.
By allowing scientists to deeply probe beyond the small “active site” of an enzyme where substrate binding occurs, HT-MEK could reveal clues about how even the most distant parts of enzymes work together to achieve their remarkable reactivity.
Many scientists are transitioning to a new way of working, known as open science, which will require new ways of evaluating researchers’ work. At Utrecht University we are adapting the reward system so it will incentivise this shift.
The change that has received the most public attention, ditching the publishing metric known as the journal impact factor, is important, but it’s just one step in a much larger transformation.
Through open science, researchers and research administrators seek to improve the quality, reproducibility and social impact of research. Open science includes open access publishing, so citizens and peers can access the fruits of publicly-funded research without paying for the privilege, and moving to a system of FAIR data, making information easy for researchers to find, access, and reuse. Open science also includes software sharing.
The new spirit to back this is, ‘open, if possible, closed if necessary’.
An Austin-based competitor to Elon Musk’s Neuralink raised $20 million in seed funding for its brain-computer interface technologies with the help of Dallas-based Westcott Investment Group.
Paradromics Inc., founded in 2015, is a leading developer of the high data rate technology that aims to help people with neurological disorders. The company says its first commercial product, the Connexus Communication Device, will restore communication for those who have lost the ability to speak because of severe paralysis.
Given what’s happened over the last year and a half, you’d think the Department of Health and Human Services would have something which the departments of Defense and Energy, as well as the intelligence community have — namely, an advanced research projects agency. Now the Biden administration has proposed just that: a $6.5 billion ARPA Health as part of the National Institutes of Health. With how an ARPA Health might work, Federal Drive with Tom Temin spoke with NIH Director Dr. Francis Collins, starting with the question, isn’t the NIH itself a sort of ARPA already? [transcript]
As summer 2020 passed, the thought of living in tight dorm quarters looked less and less appealing, so we began apartment hunting. We found that although the rental market in the small college town of Boone, North Carolina, is competitive, she could get into an apartment for less than the cost of a dorm.
Student housing is a boon to universities and colleges across the country, and dorm costs have skyrocketed 111% at public four-year institutions over the past 30 years, far faster than rents. In many markets, first-year students could rent apartments nearby for less than they pay on campus, particularly if they’re sharing the costs with roommates. But they’re not always given that option.
Epic Systems has mounted an aggressive push in recent years to spur the adoption of algorithms to predict everything from the onset of sepsis to who won’t show up for medical appointments. But in a new investigation, Casey found that health systems are finding these products often provide irrelevant or inaccurate information on seriously ill patients. In the case of sepsis, some hospitals reported favorable outcomes after a lengthy effort to customize the algorithm. But others said the company’s user guides omit crucial information needed to assess reliability and fairness, and that an Epic program that ties financial rewards in part to algorithm adoption is creating the wrong incentives in a context of largely unproven technology. Read the full story here.
Health systems looking for more information on algorithms developed by Epic and other companies or researchers may run into a gaping hole in the scientific literature. In a recent review of more than 4,000 studies of clinical decision support systems, researchers found only 12 had attempted to replicate a previous experiment. “We basically don’t replicate,” Enrico Coiera, director of the Center for Health Informatics at Macquarie University in Sydney and author of the review, told Katie. “Three in 1,000 papers is extremely low, even by the benchmarks of other disciplines that said they were in crisis,” like psychology. “So we have a problem.”
Becoming Human: Artificial Intelligence Magazine, Jonathan Hvithamar Rystrøm
from
I recently fell in love with Explainable AI (XAI). XAI is a set of methods aimed at making increasingly complex machine learning (ML) models understandable by humans. XAI could help bridge the gap between AI and humans. That is very much needed as the gap is widening. Machine learning is proving incredibly successful in tackling problems from cancer diagnostics to fraud detection. However, the human users are left staring at a black box. Even us Data Scientist can have a hard — if not impossible — time explaining why our neural network behaves as it does.
Here XAI comes to the rescue. With clever techniques like SHAP, semantic dictionaries and dimensionality reduction we can explain and visualize the models in a more human friendly manner. No matter whether you’re an experienced data scientist debugging a convolutional neural network or a social worker trying to help a citizen with the help of a model, you’ll have an easier time understanding and trusting it. This can hopefully help making AI empowering instead of baffling. Therefore I believe XAI will be a key piece in making AI beneficial.
At UChicago, Szostak will lead a new interdisciplinary program called the Origins of Life Initiative, which will seek to understand the earliest processes governing the origin of life on Earth and elsewhere in the universe. Studies on the origins of life have a long history at UChicago, including the famous 1952 Miller-Urey experiment, which examined the abiotic synthesis of amino acids essential to life.
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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.
We’re releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce. Triton makes it possible to reach peak hardware performance with relatively little effort; for example, it can be used to write FP16 matrix multiplication kernels that match the performance of cuBLAS—something that many GPU programmers can’t do—in under 25 lines of code. Our researchers have already used it to produce kernels that are up to 2x more efficient than equivalent Torch implementations, and we’re excited to work with the community to make GPU programming more accessible to everyone.
googlesheets4 is a package to work with Google Sheets from R. It wraps v4 of the Sheets API. googlesheets4 is focused on spreadsheet-y tasks that require a notion of worksheets, cells, and ranges, while the companion package googledrive handles more general file operations, such as renaming, sharing, or moving.
This week, the sixth edition of Conservation Evidence’s flagship publication, What Works in Conservation, is published. What Works provides a freely-available, comprehensive overview of the expert assessment of evidence for the effectiveness (or not) of management actions collated within Conservation Evidence synopses. It is a freely-available resource for conservation managers, practitioners and policy-makers who want to incorporate evidence into their management decisions.
The exciting addition to What Works in Conservation 2021 is the inclusion of evidence for all mammals, with the addition of the Terrestrial Mammal Conservation and Marine and Freshwater Mammal Conservation synopses, as well as the 2021 update of the Bat Conservation synopsis (the Primate Conservation synopsis was added in 2017). This means that decision-makers working in mammal conservation across the world now have access to a free resource to help inform their work to conserve threatened species.
Check out our paper Governing AI Safety through Independent Audits published in @NatMachIntell
with an amazing set of international coauthors. FREE ACCESS https://rdcu.be/cpgDK
