The industry is majorly embracing AI. For instance, Match has an AI-enabled chatbot named “Lara” who guides people through the process of romance, offering suggestions based on up to 50 personal factors.
Tinder co-founder and CEO Sean Rad outlines his vision of AI being a simplifier: a smart filter that serves up what it knows a person is interested in.
Dating website eHarmony has used AI that analyses people’s chat and sends suggestions about how to make the next move. Happn uses AI to “rank” profiles and show those it predicts a user might prefer.
“It is true that some things cause allergies more often than others, but the biggest factor is the frequency of the interaction with the study organism,” said John Carlson, a physician and researcher at Tulane University who specializes in insect and dust mite allergies. “You probably have about a 30 percent chance of developing an allergy to whatever it is that you study.” While data is limited, that estimate is in line with research on occupational allergies, which studies suggest occur in as many as 44 percent of people who work with laboratory rodents, around 40 percent of veterinarians, and 25 to 60 percent of people who work with insects.
Federal guidelines suggest that laboratories have “well-designed air-handling systems” and that workers don appropriate personal protective equipment, or PPE, to reduce the risk of developing an allergy. However, interviews with researchers and experts suggest that there may be little awareness of—or adherence to—guidelines like these. For scientists working with less common species and those engaged in fieldwork, information on what exactly constitutes appropriate PPE may be very limited.
Many researchers, perhaps especially those who do fieldwork, are used to being uncomfortable in service of their work, Carlson points out. “I think that a lot of researchers are so interested in the process of the research,” he said, “that they aren’t really considering the long-term effects that it could have on them.”
… I’ll be the first to admit that I can’t manage the health care system at times. Even the most well-trained health policy experts and doctors say they can’t either, particularly when faced with a complex billing issue.
Enter a crop of companies that have emerged in the past few decades to help us find the right doctors, manage health plan complexities and get second opinions about treatment. Some of these businesses have recently exited into the public markets, including Accolade. Others, like Grand Rounds, are not likely too far behind. And an emerging bunch like Included Health are just getting started.
As Grand Rounds’ CEO Owen Tripp wrote me this week, his company exists because: “Every single person in America — even the most healthcare savvy — feels outmatched and overwhelmed by its complexity.”
For this science-writing basketball fan, one of the few good things to come out of the pandemic has been the collaboration between the NBA and epidemiologists. Last summer, the league had the money and motivation to enshrine players, coaches, and staff in a “bubble” for four months — a fascinating premise for any science experiment.
The NBA tested its players and staff every day, at a time when there wasn’t a lot of testing going on in the United States. This setup enabled scientists at Yale University to pilot a new type of diagnostic test that relied on spit instead of a nasal swab. Thanks to the perfectly named SWISH study (Surveillance With Improved Screening and Health), the researchers were able to validate their saliva test for SARS-CoV-2, which was authorized by the U.S. Food and Drug Administration in August.
Scientific American, E&E News, Benjamin Storrow and Chelsea Harvey
from
A growing body of scientific research suggests that bouts of extreme cold may be a function of a warming planet. The science is hardly settled, but the initial theory holds that as temperatures in the Arctic rise, cold air is pushed into lower latitudes. The result is a strange dichotomy where winters become warmer, on average, but can be punctuated by frigid blasts of polar air.
Judah Cohen, a climate scientist and director of seasonal forecasting at Atmospheric and Environmental Research, an environmental analytics firm, is a leading advocate of the idea. His own research suggests a statistical correlation between warm spells in the Arctic and extreme winter weather in the United States—when warming happens, winter storms are often not far behind.
University of California-Santa Cruz, UC Santa Cruz Newsroom
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Yang Liu, assistant professor of computer science and engineering in the Baskin School of Engineering at UC Santa Cruz, has received $1 million in funding from the National Science Foundation (NSF) and Amazon for research on the long-term effects of human interactions with artificial intelligence (AI) systems used to support decision-making.
Liu’s project, called “Fairness in Machine Learning with Human in the Loop,” is funded through the NSF’s Program on Fairness in AI in Collaboration with Amazon. Machine learning is a powerful data-driven approach that is now being used in many ways that affect people’s lives, prompting growing concerns about how to ensure that the technology is fairly and responsibly deployed and leads to equitable outcomes.
It’s not just that “brains are able to generalize and learn better and faster than the state-of-the-art AI systems,” said Yoshua Bengio, a computer scientist at the University of Montreal, the scientific director of the Quebec Artificial Intelligence Institute and one of the organizers of the 2007 workshop. For a variety of reasons, backpropagation isn’t compatible with the brain’s anatomy and physiology, particularly in the cortex.
Photo of Geoffrey Hinton of the University of Toronto in his computer science laboratory.
Bengio and many others inspired by Hinton have been thinking about more biologically plausible learning mechanisms that might at least match the success of backpropagation. Three of them — feedback alignment, equilibrium propagation and predictive coding — have shown particular promise. Some researchers are also incorporating the properties of certain types of cortical neurons and processes such as attention into their models. All these efforts are bringing us closer to understanding the algorithms that may be at work in the brain.
An interdisciplinary team of biologists and mathematicians at the University of California, Irvine has developed a new tool to help decipher the language cells use to communicate with one another.
In a paper published today in Nature Communications, the researchers introduce CellChat, a computational platform that enables the decoding of signaling molecules that transmit information and commands between the cells that come together to form biological tissues and even entire organs.
“To properly understand why cells do certain things, and to predict their future actions, we need to be able to listen to what they are saying to one another; mathematical and machine learning tools enable the translation of such messages,” said co-senior author Qing Nie, UCI Chancellor’s Professor of mathematics and developmental & cell biology.
An imperfect understanding of the ongoing health crisis has continually confounded the efforts of the world’s public health systems and governments to rally an effective response to the COVID-19 pandemic.
A digital landscape flooded with misinformation and disinformation has sown distrust among the public, further complicating matters for governments — especially states — that depend on widespread cooperation to accomplish their public health goals. According to a Pew Research Center survey last July, one-quarter of Americans believed in an unsubstantiated conspiracy theory that the coronavirus outbreak had been planned by a group of powerful elites. In more recent surveys, between 15% and 29% of health care workers said they would refuse a COVID-19 vaccine, citing concerns with efficacy or potential side effects.
But even a thorough and reliable data set on basic metrics, such as who is becoming infected and who is receiving vaccinations, is a requisite baseline for understanding the disease that continues to escape capture. And governments’ mistakes during the pandemic, though often legitimate, risk eroding even further the trust of an already skeptical public worn down by political rhetoric, clashing public health directives and fatigue one year into the crisis.
Science; Jan M. Brauner, Sören Mindermann, Mrinank Sharma
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Early in 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission was curbed in many countries by imposing combinations of nonpharmaceutical interventions. Sufficient data on transmission have now accumulated to discern the effectiveness of individual interventions. Brauner et al. amassed and curated data from 41 countries as input to a model to identify the individual nonpharmaceutical interventions that were the most effective at curtailing transmission during the early pandemic. Limiting gatherings to fewer than 10 people, closing high-exposure businesses, and closing schools and universities were each more effective than stay-at-home orders, which were of modest effect in slowing transmission.
The Gradient; Wessel Stoop, Florian Kunneman, Antal van den Bosch, Ben Miller
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Digital harassment is a problem in many corners of the internet, like internet forums, comment sections and game chat. In this article you can play with techniques to automatically detect users that misbehave, preferably as early in the conversation as possible. What you will see is that while neural networks do a better job than simple lists of words, they are also black boxes; one of our goals is to help show how these networks come to their decisions.
On February 16th, I wrote that Australia’s News Media Bargaining Code threatened to splinter the internet. On February 17th, the splintering arrived: Google cut a deal with News Corp. that will ensure its services continue to be provided in Australia, and Facebook walked away from the bargaining table and began preventing people from sharing news links from Australian publishers around the world.
I think Facebook basically did the right thing, and Google basically did the wrong thing, even though Google had a much tougher call to make. Today, let’s talk about why the tech giants made the decisions that they did, why Australia’s shakedown is rotten, and what’s likely to happen next. (If you didn’t read my piece on the subject, it offers a lot of useful context for what follows.)
In development for three years, the bargaining code is intended to give Australia’s heavily concentrated media industry more leverage as publishers seek direct payment from Google and Facebook for the right to display links to their work. It does this by forcing the platforms into binding arbitration with publishers who bring cases, and puts the decision for how much the platform has to pay the publishers into the hands of the arbiter. Each side throws out a number, and the arbiter picks the one they think is most fair.
While the Tesla gigafactory goes up near Austin and Miami Mayor Francis Suarez lures lone venture capitalists to the beach, Airbnb will build a new tech hub in a less-hyped city: Atlanta.
The company chose Atlanta for its future growth for one explicit reason: In order to meet its hiring goals for technical teams with a diverse range of perspectives, the company needed a location that produces diverse and creative talent and would continue to attract more. No other city could surpass Atlanta’s potential to do that, Chris Lehane, Airbnb’s senior vice president for global policy and communications, told Protocol.
The National Science Foundation and Amazon teamed up to fund a second round of research projects aimed at promoting trustworthy artificial intelligence and mitigating bias in systems.
The latest cohort selected to participate in the Program on Fairness in AI include multi-university projects to confront structural bias in hiring, algorithms to help ensure fair AI use in medicine, principles to guide how humans interact with AI systems, and others that focus on education, criminal justice and human services applications.
“With increasingly widespread deployments, AI has a huge impact on people’s lives,” Henry Kautz, NSF division director for Information and Intelligent Systems, said. “As such, it is important to ensure AI systems are designed to avoid adverse biases and make certain that all people are treated fairly and have equal opportunity to positively benefit from its power.”
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.
based on @gvwilson
@dhavidearuliah
@ctitusbrown
et al (2014) paper (https://doi.org/10.1371/journal.pbio.1001745)
here’s my April 2020 update on academic software development, based on many years of lived experience — http://ivory.idyll.org/blog/2020-software-and-workflow-dev-practices.html – feedback and questions always welcome!
