Data Science newsletter – April 20, 2021

On April 8, representatives from Grand Valley State University met with Fort Valley State University, a historically Black college/university (HBCU) based in Georgia, to sign a new agreement to help support students through a GVSU graduate program in Engineering or Computing, as well as helping diversify the talent for West Michigan employers. According to the University, this is the first of many collaborative agreements that GVSU will have with HBCUs in the future.

FVSU offers many strong programs in the STEM fields of education, but they are not as broad as GVSUs.

The HBCU created a program called Cooperative Developmental Energy Program (CDEP), which identifies high-talent students as early as middle school and mentors them through middle and high school. Then, they complete an accelerated Bachelor’s Degree program in three years. This program is what brought them to the attention of the Padnos College of Engineering and Computing.

The Food and Drug Administration has approved at least 130 AI-powered medical devices, half of them in the last year alone, and the numbers are certain to surge far higher in the next few years.

Several AI devices aim at spotting and alerting doctors to suspected blood clots in the lungs. Some analyze mammograms and ultrasound images for signs of breast cancer, while others examine brain scans for signs of hemorrhage. Cardiac AI devices can now flag a wide range of hidden heart problems.

But how much do either regulators or doctors really know about the accuracy of these tools?

The University of Connecticut (UConn) and AT&T* are working together to advance entrepreneurship, innovation, and data science using AT&T 5G+ millimeter wave and Multi-access Edge Compute (MEC) technology on the Stamford campus. The AT&T 5G+ network will allow the university to advance academic programs that will explore new use cases and expand entrepreneurial activity.

With the support of CTNext and StamfordNext, AT&T’s collaboration with UConn Stamford will bring 5G capabilities to bolster the UConn Stamford Data Science Initiative which includes the Stamford Start-up Studio, the UConn Technology Incubation Program (TIP Digital) in Stamford, and the work of a soon to be hired team of data science research faculty.

The fact that the human body is made up of cells is a basic, well-understood concept. Yet amazingly, scientists are still trying to determine the various types of cells that make up our organs and contribute to our health.

A relatively recent technique called single-cell sequencing is enabling researchers to recognize and categorize cell types by characteristics such as which genes they express. But this type of research generates enormous amounts of data, with datasets of hundreds of thousands to millions of cells.

A new algorithm developed by Joshua Welch, Ph.D., of the Department of Computational Medicine and Bioinformatics, Ph.D. candidate Chao Gao and their team uses online learning, greatly speeding up this process and providing a way for researchers world-wide to analyze large data sets using the amount of memory found on a standard laptop computer.

On a crisp spring morning in 2008, Shane Gero overheard a pair of whales having a chat. Gero, a Canadian biologist, had been tracking sperm whales off the Caribbean island nation of Dominica when two males, babies from the same family, popped up not far from his boat. The animals, nicknamed Drop and Doublebend, nuzzled their enormous boxy heads and began to talk.

Sperm whales “speak” in clicks, which they make in rhythmic series called codas. For three years Gero had been using underwater recorders to capture codas from hundreds of whales. But he’d never heard anything quite like this. The whales clicked back and forth for 40 minutes, sometimes while motionless, sometimes twirling their silver bodies together like strands of rope, rarely going silent for long. Never had Gero so desperately wished he understood what whales were saying. He felt as if he were eavesdropping on brothers wrestling in their room. “They were talking and playing and being siblings,” he says. “There was clearly so much going on.”

Over the next 13 years, Gero, a National Geographic Explorer, would record and get to know hundreds of sperm whales. But he kept coming back to a revelation that struck him as he’d listened to Drop and Doublebend: If humans were ever to decode the language of whales, or even determine if whales possessed something we might truly call language, we’d need to pair their clicks with the context. The key to unlocking whale communication would be knowing who the animals are and what they’re doing as they make their sounds.

As the COVID-19 pandemic surged last summer and contact tracers struggled to identify sources of infections, John Cirrito, PhD, associate professor of neurology at Washington University School of Medicine in St. Louis, and Carla Yuede, PhD, associate professor of psychiatry, began to kick around an idea. Could a biosensor they’d developed years ago for Alzheimer’s disease be converted into a detector for the virus that causes COVID-19?

The biosensor was designed to measure an Alzheimer’s protein in the brain, but there was no reason it couldn’t be repurposed to detect viral particles in the air instead, they thought. Cirrito and Yuede recruited aerosol expert Rajan Chakrabarty, PhD, associate professor of energy, environmental and chemical engineering at the university’s McKelvey School of Engineering to help design a way to rapidly screen for airborne SARS-CoV-2, the virus that causes COVID-19.

Now, with the help of a $900,000 grant from the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health (NIH), the team has two devices in the works.

Fans of the NBA’s Brooklyn Nets are flashing more than game tickets these days when entering Barclays Center.

They’re also required to show a recent negative Covid-19 test, a vaccination card, or their Excelsior Pass—New York’s first-in-the-nation “vaccine passport,” which uses QR codes on a smartphone to prove test results or vaccination against the disease.

The IBM-created Excelsior Pass, which debuted last month, is among a growing number of apps that could help Americans safely return to sporting events, theaters, restaurants, and flights.

But they’re also raising privacy concerns.

“When we first started this, we didn’t have any support whatsoever, and so we thought this is too much of a reach, but we are very fortunate to have gotten support from the Brown Family Foundation, the James Graham Brown Foundation, from Jewish Fund for Excellence, and local support,” Bhatnagar said. “Then, we were fortunate again for the city to support us from October to December with CARES Act funding.”

The future of the project was dependent on a long-ago-requested grant from the Centers for Disease Control and Prevention.

“We had given up the idea that we were going to get support from the CDC,” Bhatnagar tells WLKY. “And then suddenly out of the blue, they called and said we need to talk. So that was very exciting and of course we were very happy.”

The CDC pledged $8.6 million to sign on as an equal partner over the next six months, which researchers hope will mark the end of the pandemic. Dr. Ted Smith, UofL’s director of the Center for Healthy Air, Water and Soil, leads the wastewater collection effort.

Theme parks have for years been relying on technology to better manage crowds, speed up the purchase of food and drinks, and eliminate gridlock around the most popular rides. Digital tickets have factored into that. So has the practice of tracking guests’ locations within a park via a phone app.

Now, after a yearlong closure due to the COVID-19 pandemic, Southern California’s theme parks are reopening with new safety protocols — many of which lean heavily on such technology. That’s helping the parks lower the risk of spreading the coronavirus and, at the same time, collect more information about their visitors.

A decision by the venerable Carnegie Institution for Science to sell its iconic Washington, D.C., headquarters to the government of Qatar has ignited long-simmering discontent with an imminent restructuring of the 120-year-old organization and with its management.

Last week, after Carnegie President Eric Isaacs announced 2 April that the institute had sold the building to Qatar for an undisclosed amount, more than 140 Carnegie scientists, students, and staff members wrote to Isaacs and the board of trustees, noting what they called Qatar’s dismal human rights record. They urged them to find another buyer for the 1909 beaux-arts building, a Washington, D.C., landmark 1.5 kilometers from the White House. The sale was only the beginning of their complaints. “Qatar was the gas thrown on a fire that already was burning,” one senior Carnegie scientist says.

“I am very, very concerned” about the cultural change at Carnegie and the top-down nature of the reorganization, says Yixian Zheng, director of Carnegie’s embryology department. That plan will consolidate the institute’s three life sciences departments into a new, 12,600-square-meter research building in Pasadena, California. “In my opinion, this is a national problem of corporatization of academic institutions. It is destroying people’s freedom to do science. And it has played out in a very acute way at Carnegie, which is dear to my heart.”

Several U.S. banks have started deploying camera software that can analyze customer preferences, monitor workers and spot people sleeping near ATMs, even as they remain wary about possible backlash over increased surveillance, more than a dozen banking and technology sources told Reuters.

Previously unreported trials at City National Bank of Florida (BCI.SN) and JPMorgan Chase & Co (JPM.N) as well as earlier rollouts at banks such as Wells Fargo & Co (WFC.N) offer a rare view into the potential U.S. financial institutions see in facial recognition and related artificial intelligence systems.

Widespread deployment of such visual AI tools in the heavily regulated banking sector would be a significant step toward their becoming mainstream in corporate America.

Three Arizona State University professors have received a prestigious National Endowment for the Humanities grant for $100,000 to create an undergraduate certificate program in artificial intelligence in digital culture in the School of Arts, Media and Engineering.

The team — Assistant Professor Suren Jayasuriya, Associate Professor Ed Finn and Professor Sha Xin Wei — spent the last year designing the new curriculum with the help of an NEH planning grant they previously received. Outcomes of the project included podcasts produced by the Center for Science and the Imagination with notable figures such as Katherine Bouman, who helped take the first image of a black hole; Moya Bailey, a Black queer feminist scholar, writer and activist; and Regina Kanyu Wang, a science fiction writer, researcher and critic from Shanghai.

“Our planning process last year yielded productive conversations with scientists and engineers, humanities professors and science fiction writers,” Suren said. “We also prototyped new classes such as AME 494 Minds and Machines, where students learned how to prototype AI algorithms while reading and writing about philosophy of mind, cognitive science and their limitations. All these activities gave us insight into developing a new certificate program.”

Del Mar College will be offering the first Artificial Intelligence Certificate program in the country for a community college in the fall of 2021.

The DMC Computer Science, Engineering and Advanced Technology department will offer its first AI course as part of a 14-hour Level I Certificate program that also supports an Associate in Science in Geographical Information Systems degree.

The first course, Introduction to Artificial Intelligence, will cover AI and machine learning and will be taught online by course designer and computer science professor Phillip Davis.

The course is one of five making up the new program.

Protocol spoke with four contract and full-time Google employees in three of the 14 U.S. locations for this story, all of whom were granted anonymity for fear of losing their jobs (except for [Shannon] Wait, whose data center contract recently ended).

Forced unemployment

Google calls workers like Wait temporary, vendor or contract staff — TVCs, for short. Data center TVCs sign up for two-year jobs with Modis Engineering, a branch of the second-largest global contracting company, the Adecco Group. They are called “Level 1” employees, and they usually start with no previous experience or specialty skills. They are trained on the job to perform repair and maintenance tasks on the data center floor, and they work in close partnership with Google employees, many of whom do similar or even identical work. Some contractors even train new Googlers on some of their tasks.

We model social media as collections of users producing and consuming content. Users value consuming content, but doing so uses up their scarce attention, and hence they prefer content produced by more able users. Users also value receiving attention, creating the incentive to attract an audience by producing valuable content, but also through attention bartering—users agree to become each others’ audience. Attention bartering can profoundly affect the patterns of production and consumption on social media, explains key features of social media behavior and platform decision-making, and yields sharp predictions that are consistent with data we collect from #EconTwitter.

The basic idea behind OpenML is that it should be an open platform where datasets are easily available and where you can find algorithms that are relevant to your problem. “An accessible interface to all machine learning research,” summarizes Vanschoren. At the moment, OpenML serves a community of about 150 thousand users worldwide. Understandably, a similar tool wasn’t available. “Commercial parties have little interest in transparency. They rather hold their cards close to their chest. However, they can benefit from having such a platform for internal use – something we realized quickly during the development. Large companies like Amazon have their own tools, of course, but for most companies and organizations, it’s unfeasible to do it themselves.”

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