Data Science newsletter – September 20, 2021

Notice the red and blue text. That’s where human social / ethical concerns begin. This meta-analysis is worth meditating on.

Make science more reliable by placing the burden of replicability on the community, not on individual laboratories.

Earlier this year, Alexandre Farm became the first dairy in the U.S. to become “certified regenerative”—receiving both the Land to Market verification (EOV) and the Regenerative Organic Certified (ROC) seal for their 100 percent grass-fed milk and yogurt.

It took the Alexandres more than three decades to fine-tune their system of building up soil, restoring wetlands, and bringing a multitude of birds and wildlife to their property, from bald eagles to coho salmon. Stephanie and her husband Blake are both fourth-generation dairy operators who grew up on conventional dairy farms in California. They met at Cal Poly, where, they said, they were taught plenty about farm chemicals and livestock antibiotics but nothing about soil biology. But they were willing to try different approaches, and it turns out that willingness to farm against the grain has been key to their success.

Intel’s AI division is one of the unsung heroes of the modern machine-learning movement. It’s talented researchers have advanced the state of AI chips, neuromorphic computing, and deep learning. And now they’re turning their sights on the unholy grail of AI: the hive mind.

Okay, that might be a tad dramatic. But every great science fiction horror story has to start somewhere.

And Intel’s amazing advances in the area of multiagent evolutionary reinforcement learning (MERL) could make a great origin story for the Borg – a sentient AI that assimilates organic species into its hive mind, from Star Trek.

Area universities are deepening their investment in the region’s growing geospatial industry, a commitment officials with the National Geospatial-Intelligence Agency praised on Tuesday as key for the U.S. to stay ahead in the burgeoning field.

St. Louis University has created new academic programs to recruit and train the next generation of geospatial and data science experts, and Harris-Stowe State University is devising plans to give students of color and other underrepresented groups access to those opportunities, leaders of both universities said on Tuesday at a geospatial conference at SLU.

“If we can actually prepare individuals who live in that community, students who are growing up in that community, for rewarding careers in geospatial intelligence, it can be a gamechanger for them,” Harris-Stowe interim President LaTonia Collins Smith said, “and change the trajectory for not only that individual but for their family and generations to come.”

The goal is to not only build a workforce to support NGA’s new $1.7 billion campus in north St. Louis, but also to signal to the outside world that the region is the nerve center for this sector.

As colleges look for any advantage in the fierce competition to enroll students, hundreds of institutions have turned to algorithms to increase enrollment—especially for using scholarships to convince prospective students to attend. In a series of interviews, representatives from three different algorithmic enrollment vendors independently noted their goal was to “build a better mousetrap.”

It makes sense that so many colleges, over 75%, use data analytics for enrollment—it’s where the money is. Beyond financial stability, enrollment analytics are necessary for institutional planning, such as preparing sufficient student housing and ensuring course availability.

Yet, as I learned more about how these algorithms are used to award financial aid in higher education, I became quite concerned. The webinars, documents, and academic studies I saw all suggest the same deeply troubling problem: these algorithms intentionally reduce scholarships, and in doing so, may contribute to the crises of student loan debt, college dropout, and racial inequities. If it is not already clear, when industry representatives talk about using enrollment algorithms to build a better mousetrap, the students are the mice.

AI is not new. The concept has been around since the 1950s and has been through multiple ups and downs. With traditional AI, experts transfer knowledge to engineers who then encode it into a computer program. The job of the computer is straightforward, relying on simple logical rules. If the air temperature is higher than 27 °C, then open the vents, for example. The rule-based AI can be visualized as a decision tree, such as the one pictured here. It’s for a simple automated supplemental lighting strategy, factoring in global radiation and temperature in the greenhouse.

These rules are manually crafted by in-house programmers and are therefore not easy to maintain, improve, or scale to multiple farms or multiple scenarios. Plus, it is highly dependent on staff who may quit or leave. A robust, maintainable, scalable autonomous grower would be insurance against the operations falling apart.

Modern AI, driven by Machine Learning (ML), fixes most of those setbacks. Unlike traditional AI, the modern AI can learn and figure out its own patterns and rules using data and/or after being shown examples.

A team led by physicists at Washington University in St. Louis received a four-year $1.8 million grant from the National Science Foundation for a collaborative project to identify new quantum materials, which could lead to technological innovations.

The research is titled “Accelerated Discovery of Artificial Multiferroics with Enhanced Magnetoelectric Coupling.” Li Yang, professor of physics in Arts & Sciences, and collaborators will conduct large-scale simulations to identify candidates for artificial multiferroics, a new type of quantum materials. These materials integrate two-dimensional van der Waals magnets with ferroelectrics.

Business Insider reports on how RISELab’s unique research model has sparked a generation of successful startups, featuring interviews with RISELab directors Raluca Ada Popa, Ion Stoica, and other RISE, AMP and RADLab alumni.

Thirty years ago, MIT professor Tim Berners-Lee invented the World Wide Web and altered the course of human history. Now, in the face of misinformation, malicious behavior, and the exploitation of personal data online, he’s determined to slay the beast it has become.

Yan Cui, PhD, associate professor in the UTHSC Department of Genetics, Genomics, and Informatics, recently received a $1.7 million grant from the National Cancer Institute for a study titled “Algorithm-based prevention and reduction of cancer health disparity arising from data inequality.”

Dr. Cui’s project aims to prevent and reduce health disparities caused by ethnically-biased data in cancer-related genomic and clinical omics studies. His objective is to establish a new machine learning paradigm for use with multiethnic clinical omics data.

The worldwide growth of wireless connectivity has provided immense societal benefits and has led to an explosion in usage of the radio spectrum and expansion to higher frequencies. However, the range of available radio frequencies is finite, and as the quantity of users and applications increase the spectrum gets congested. Ubiquitous connectivity must operate in harmony with scientific uses of spectrum — such as Earth observation, astronomy, and geospace and atmospheric sciences — and other vital services such as public safety. Innovations are needed to overcome the challenges of interference and radio spectrum scarcity. This is an interlinked technical and policy challenge.

The U.S. National Science Foundation is announcing a $25 million investment over five years to launch SpectrumX, an NSF Spectrum Innovation Center that will address the growing demand for usage of the radio spectrum. This represents the first federal investment in a national center focused on the transformation of wireless spectrum management. SpectrumX is a coalition of 29 institutions led by the University of Notre Dame.

Medicine is at a watershed moment: decades of technological advances have revealed unprecedented insights into human biology and how various physiological, genetic and lifestyle factors shape health. These findings have laid the groundwork for enduring change in medicine—an opportunity Weill Cornell Medicine is seizing with its June 17 launch of We’re Changing Medicine, a campaign to raise $1.5 billion for an expansive vision of medicine that will make a global impact. The historic investments will serve as the foundation for discovery of new approaches to prevent, detect and cure disease, a vision of care that is equitable and individualized to each patient, and the training of diverse future healthcare leaders. It’s an ambitious pursuit, one befitting the brainpower and achievements of an institution credited with life-changing healthcare innovations.

With more than $750 million raised already, the campaign creates a new synergy between Weill Cornell Medicine’s tri-partite mission to care, discover and teach, with a laser focus on three areas. The institution hopes to revolutionize the rapid acceleration of therapeutics from the bench to the bedside by harnessing the latest scientific approaches—such as translational cellular therapy, regenerative medicine, and artificial intelligence—and reimagining the foundational research landscape to benefit millions of patients around the world. It plans to build a world-class precision health enterprise that uses breakthrough data science to personalize prevention and care, with the long-term goal of changing the trajectory of preventive medicine. And it is poised to transform the medical school experience for a gifted student body through debt-free medical education and the construction of a new residence hall for students to thrive and realize their highest potential.

The National Science Foundation has announced it is providing $2 million to a University of Michigan-led research team in Ann Arbor to enable robots to learn from workers at construction sites. The goal is to make the industry safer and more attractive to workers.

Agriculturalists who are looking to utilize artificial intelligence (AI) on their operations, whether it’s on the farm or within an agribusiness, are in luck – and in a big way.

The U.S. Department of Agriculture’s National Institute of Food and Agriculture (USDA-NIFA) and the U.S. National Science Foundation (NSF) recently announced a $220 million investment into 11 new AI research institutes.

“In the tradition of USDA-NIFA investments, these new institutes leverage the scientific power of U.S. land-grant universities informed by close partnership with farmers, producers, educators, and innovators to provide sustainable crop production solutions and address these pressing societal challenges,” says Dr. Carrie Castille, USDA-NIFA Director. “These innovation centers will speed our ability to meet critical needs in the future agricultural workforce, providing equitable and fair market access, increasing nutrition security and providing tools for climate-smart agriculture.”

A new report from the Metropolitan Policy Program at the Brookings Institution ranked more than 360 cities based on their AI economic prowess.

Bay Area metros — San Francisco and San Jose—- topped the list, according to Brookings, a public policy think tank based in Washington, D.C. They were followed by 13 “earlier adopter” cities that managed to claw out a toehold in AI, including San Diego.

“Not everywhere should be looking to artificial intelligence for a major change in its economy, but places like San Diego really need to,” said Mark Muro, a Brookings fellow and co-author of the report. “I think the costs of being out of position on it are pretty high for San Diego, and the benefits of leveraging it fully are really high.”

The U.S. Department of Energy (DOE) announced $1 million for a one-year collaborative research project to develop artificial intelligence (AI) and machine learning (ML) algorithms for biomedical, personal healthcare, or other privacy-sensitive datasets. This funding is in response to congressional direction for DOE to expand its successful collaborative research efforts with the National Institutes of Health (NIH) in the areas of data and computation. Privacy-preserving AI research is a topic of mutual interest to DOE and NIH and joint research will encourage their respective research communities to work more closely on common scientific challenges.

The project is led by Argonne National Laboratory in collaboration with Lawrence Livermore National Laboratory, the University of Chicago, the Broad Institute, and Massachusetts General Hospital. The project is entitled “PALISADE-X: Privacy Preserving Analysis and Learning in Secure and Distributed Enclaves and Exascale Systems.” This project pursues innovative research to explore the development and use of privacy-preserving artificial intelligence and machine learning for key, grand challenge datasets such as those that are the focus of the NIH Bridge2AI program. A potential demonstration of AI capabilities includes predicting the severity of COVID-19 using radiological datasets from multiple organizations.

“Significant money is flowing into the region to support almost exclusive contracts or research and development initiatives,” explained Mark Muro, Brookings senior fellow and report co-author. “That’s very important in itself. At the same time, because federal research done at UW-Madison is also building a talent base of skilled researchers and graduate students, there’s a pipeline for future AI expansion.”

“(AI) technologies can be applied in a myriad of industries, and are widely presumed to likely drive major productivity gains,” Muro added.

For the report, Muro and his team used seven metrics to assess the research capabilities and commercialization activities of 385 metropolitan areas in the United States. The metrics sorted each area into one of five categories.

Stanford was ranked the top U.S. business school in the Bloomberg Businessweek 2021-22 Best B-Schools MBA ranking. A repeat winner, Stanford scored highest in compensation, networking, and entrepreneurship. Dartmouth’s Tuck school came in second, with Harvard third. For the first time, Bloomberg Businessweek’s MBA ranking included a Diversity Index that measures U.S. schools on race, ethnicity, and gender in their classes.

Bloomberg Businessweek ranked 119 MBA programs around the world. IMD once again was tops in Europe, and CEIBS was a repeat winner in Asia-Pacific. Queen’s Smith school climbed to No. 1 in Canada. Three schools were ranked for the first time, and 102 schools in the ranking moved up or down.

A $225 million facility for the new School of Computer, Data & Information Sciences is coming soon to the University of Wisconsin-Madison, the university announced Friday in a press conference.

Two buildings will be torn down to clear room for the 300,000 square-foot development, which will be located next to the Discovery Building at the corner of Charter and University Avenue. Designed to be the most sustainable campus building, with seven stories divided into research, learning and student “ecosystems,” CDIS is set to open in 2024.

Chancellor Rebecca Blank unveiled plans for the privately funded initiative, urging alumni and stakeholders to make an investment. The Morgridge family, longtime benefactors of UW-Madison, has committed $75 million. An additional $50 million will come from the Wisconsin Alumni Research Foundation. The university is seeking to raise another $50 million, which the Morgridges have pledged to match.

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