Mining companies from around the world have begun using artificial intelligence in their operations. From safety and maintenance, to exploration and autonomous vehicles, and drills, AI is being used to navigate efficiencies and speed. With this new technology, however, comes an ever-growing need for a workforce who can navigate these new systems. Thanks to a $1.25 million grant from the National Institute for Occupational Safety and Health, an interdisciplinary team at the University of Nevada, Reno, has committed to graduating six doctoral and four master’s degree students who will address several challenges related to major safety and health issues in mining operations.
“Future mine engineers need to understand emerging technology like AI, drones and big data,” Javad Sattarvand, University College of Science assistant professor of mining engineering and the project’s principal investigator, said. “We claim creating excellence in the workforce is the missing part of the chain, which would make mining engineers more aware of health and safety issues of the future.”
A national, cross-disciplinary team of researchers, led by Xu Liang, professor of civil and environmental engineering at the University of Pittsburgh Swanson School of Engineering, has received a combined $1.3 million from the National Science Foundation to create a new cyberinfrastructure framework that can build such a model, with $437,232 designated for Pitt.
CyberWater, an open framework of cyberinfrastructure, will enable easy integration of diverse data sets and models for investigating water resources and climate-related environmental issues. It will allow users to integrate many different models without the need for coding, and it will enable reproducible computing and seamless, on-demand access to various HPC resources.
Alphabet has tapped former U.S. Food and Drug Administration Commissioner Robert M. Califf to lead the company’s health strategy and policy.
Starting in November, Califf will become the full-time head of strategy and policy for its Verily Life Sciences and Google Health divisions, according to a blog post by Duke University Monday. Alphabet confirmed to CNBC that Califf will be joining the company full-time but declined to comment on specific duties.
University of California-Santa Barbara, The UCSB Current
from
Our world is teeming with data, all of it just waiting to be placed into the appropriate context. Connecting these enormous bodies of information could, according to UC Santa Barbara geographic information scientist Krzyzstof Janowicz, yield a richer, deeper understanding of the world around us.
“In the previous decades, data has typically been stored in what we call ‘data silos,’ ” Janowicz said. “Data gathered by one entity,” he continued, “is often ‘locked away’ and used for specific purposes, for specific ways of thinking. But what if there was a way to store, connect and provide diverse sets of data that could be useful to the many users who need it and could find creative new ways to use or combine it?”
There is such a way, Janowicz has asserted, and with $1 million in initial funding from the National Science Foundation, he and about 20 colleagues from universities, companies and government agencies across the United States are poised to break data out of their silos.
Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements—one of the most common implantable devices—as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8–53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t = 5.14; p < 0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records. [full text]
[Valentina] Di Santo was investigating the motions involved when fish such as skates swim. She filmed individual fish in a tank and manually annotated their body parts frame by frame, an effort that required about a month of full-time work for 72 seconds of footage. Using an open-source application called DLTdv, developed in the computer language MATLAB, she then extracted the coordinates of body parts — the key information needed for her research. That analysis showed, among other things, that when little skates (Leucoraja erinacea) need to swim faster, they create an arch on their fin margin to stiffen its edge1.
ture
But as the focus of Di Santo’s research shifted from individual animals to schools of fish, it was clear a new approach would be required. “It would take me forever to analyse [those data] with the same detail,” says Di Santo, who is now at Stockholm University. So, she turned to DeepLabCut instead.
DeepLabCut is an open-source software package developed by Mackenzie Mathis, a neuroscientist at Harvard University in Cambridge, Massachusetts, and her colleagues, which allows users to train a computational model called a neural network to track animal postures in videos.
After three years of quietly toiling away on a robotic food system, Seattle startup Picnic has emerged from stealth mode with a system that assembles custom pizzas with little human intervention.
Picnic — previously known as Otto Robotics and Vivid Robotics — is the latest entrant in a cohort of startups and industry giants trying to find ways to automate restaurant kitchens in the face of slim margins and labor shortages. And its journey here wasn’t easy.
Picnic CEO Clayton Wood. (Picnic Photo)
“Food is hard. It’s highly variable,” said Picnic CEO Clayton Wood. “We learned a lot about food science in the process of developing the system.”
More than a million New Yorkers could soon willingly agree to carry a government-issued tracking device, whether they realize it or not.
That’s the proposal from Mayor Bill de Blasio, who having recently returned from the cornfield-dotted campaign trail in Iowa, is setting his sights on transforming New York City into something out of a dystopian sci-fi novel. But some critics are urging caution about the move.
“With machine learning, we can take the data that comes from experiments or observations and we can explore the validity of existing theories or hypothesize new ones regarding the interrelationships among urban systems and processes,” explained Beckman, who helps apply data science to urban challenges.
Because cities are so complex, the issues to which Beckman and his Argonne colleagues are applying these techniques span the gamut from combating pollution to improving pedestrian safety, and from predicting crime to understanding the dynamics of the spread of communicable diseases. Maximizing one of these parameters, he said, may impact others, making machine learning an optimal technique for finding relationships in a system too complicated to describe with a theory.
Argonne’s work at the intersection of machine learning and the urban environment leverages the laboratory’s deep and broad multidisciplinary teams and powerful scientific tools to solve some of society’s most complex problems. This can be seen most directly in the National Science Foundation-funded Array of Things (AoT), a partnership between Argonne, the University of Chicago, and the City of Chicago. AoT is a network of over 100 programmable, multisensor devices (nodes) deployed throughout Chicago, on track to grow to 200 by late 2019.
Implementing machine learning in the real world isn’t easy. The tools are available and the road is well-marked—but the speed bumps are many.
That was the conclusion of panelists wrapping up a day of discussions at the IEEE AI Symposium 2019, held at Cisco’s San Jose, Calif., campus last week.
The toughest problem, says Ben Irving, senior manager of Cisco’s strategy innovations group, is people.
It’s tough to find data scientist expertise, he indicated, so companies are looking into non-traditional sources of personnel, like political science. “There are some untapped areas with a lot of untapped data science expertise,” Irving says.
Melvin Greer, chief data scientist at Intel (Nasdaq: INTC), has been appointed fellow-in-residence and senior adviser to the FBI’s information technology applications and data division.
“Melvin is the first executive to come to the Information and Technology Branch under the Special Government Employee Program, which allows him to remain at Intel and serve his country,” Michael Gavin, assistant director of the bureau’s ITADD, said in a statement published Friday.
The goal of bringing the Knight Campus to UO is to have a major research-oriented center with scientific programs and innovation, particularly in areas such as origins of diseases and developing new technologies to improve medicine.
Here’s a breakdown of what’s still left to be done and what students and faculty can expect.
I’m happy to announce PLOS’ participation in a new service, Review Commons, that will provide a platform for rapid, objective, journal-independent peer reviews for manuscripts and preprints. We are excited to be part of this initiative and to learn from our community’s response how we can rethink peer review to save authors’, reviewers’, and editors’ time and enhance transparency and objectiveness.
What it is
Created by ASAPbio and EMBO Press, Review Commons will organize a single round of journal-agnostic review for manuscripts in the life sciences submitted to the service.
Harvard University, Institute for Quantitative Social Science
from
Gary King, Albert J. Weatherhead III University Professor and director of Harvard University’s Institute for Quantitative Social Sciences (IQSS) will be leading our collaboration with Microsoft to build a platform to ensure data is kept private, while enabling researchers from academia, government, nonprofits and the private sector to gain new and novel insights that can rapidly advance human knowledge. This project is intended to provide both mathematical guarantees of privacy for individuals that may be represented in the data and statistical guarantees for researchers who will be analyzing the data.
A new era for Boston University, in which the burgeoning fields of computing and data sciences are ingrained throughout the University’s interdisciplinary curricula, took two big steps forward on Friday, September 20: BU’s Board of Trustees, in town for a meeting and the culmination of the University’s first comprehensive fundraising campaign (which raised more than $1.85 billion), approved both the construction of a building to house the Center for Computing & Data Sciences and the formation of a new faculty unit to lead the discipline.
New York, NY October 4, starting at 6:30 p.m., School for Poetic Computation (155 Bank Street). “We’re happy to host a salon for SFPC’s Fall 2019 session with a variety of speakers including leading practitioners in the field, distinguished alumni, and friends of the school.” [free, registration required]
New York, NY October 15-16. “The nation’s thought leaders in Artificial Intelligence (AI) will discuss innovation and share insight into the application of AI in all aspects of life, society and medicine at the eighth annual SINAInnovations festival.” [registration required]
“When responding to a natural disaster, it’s helpful to know the scale of the damage up front. The Pentagon’s Defense Innovation Unit (DIU) thinks computer vision technology can help deliver this kind of information — and that’s the focus of xView2, a new challenge organized by DIU with partners at NASA, the Federal Emergency Management Agency, the Joint Artificial Intelligence Center and more.” Deadline for submissions is November 22.
“For the Rule 30 Prize Problems, I’m concentrating on a particularly dramatic feature of rule 30: the apparent randomness of its center column of cells. Start from a single black cell, then just look down the sequence of values of this cell—and it seems random” … “But in what sense is it really random? And can one prove it? Each of the Prize Problems in effect uses a different criterion for randomness, then asks whether the sequence is random according to that criterion.”
“Apache Kafka and its accompanying key-value store are being used to provide persistent storage for a growing list of relational databases. Most used a key-value store as a foundation.”
“Among the latest to emerge is KarelDB, a relational database built almost entirely on open source components, including Apache Calcite for the SQL engine along with Apache Omid for transactions and control features. The open-source database so far only supports a single node, but database watchers consider it sufficiently promising to track for future scaling.”
“In most disciplines, the supervisor/PhD student relationship is established through the bonding process that occurs during the development of a doctoral thesis, where the student is supposed to be guided by the professor. This relationship, during a specific and limited period of time, can generate links that endure over the time, far beyond an employment relationship that is established for the fulfillment of the objectives of a project.”
“TensorFlow 2.0 is driven by the community telling us they want an easy-to-use platform that is both flexible and powerful, and which supports deployment to any platform. TensorFlow 2.0 provides a comprehensive ecosystem of tools for developers, enterprises, and researchers who want to push the state-of-the-art in machine learning and build scalable ML-powered applications.”