Loyola University Chicago has landed a $20 million donation for a new health care school as it seeks to meet rising demand in the field.
The gift from former Baxter International CEO Robert Parkinson and his wife, Elizabeth, both Loyola alumni, will fund the new Parkinson School of Health Sciences & Public Health. Degree programs will start this fall, with a new building likely to follow on Loyola’s west suburban Maywood campus, says Loyola President Jo Ann Rooney.
UNC Greensboro recently announced that it will launch the University’s first-ever master of science in informatics and analytics in fall of 2019.
The new, interdisciplinary graduate program is designed to develop leaders and problem-solvers who possess the knowledge and skills to be successful in the rapidly growing industry of data science.
The Conversation; Cristina Sanza, Brittney Borowiec, David Secko, Farah Qaiser, Fernanda de Araujo Ferreira, Heather MacGregor, Michael Bramadat-Willcock, Pouria Nazemi
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The future of science journalism is both exciting and perilous. Those wanting to enter the field can follow tradition such as transmitting information through a single platform or reshape how science stories are told. It’s a choice we can no longer ignore.
Last summer, graduate students from around the world took part in Projected Futures, an intensive summer school that seeks to rethink how science is communicated with society. We came up with some key ways to create better science stories — and boost interest and trust in science.
Enhancing model-risk management to address the risks of machine-learning models will require policy decisions on what to include in a model inventory, as well as determining risk appetite, risk tiering, roles and responsibilities, and model life-cycle controls, not to mention the associated model-validation practices. The good news is that many banks will not need entirely new model-validation frameworks. Existing ones can be fitted for purpose with some well-targeted enhancements.
The massive, sustained computational power required by machine learning workloads is anything but business as usual. Most data center operators can grapple with gradual increases in IT footprint, but high-density GPU clusters for machine learning raise the stakes, particularly where cooling is concerned.
Perhaps newer data centers, especially those using containment strategies, have the infrastructure to adequately cool what, in some cases, amounts to 30 kW per rack or more. Most older data centers, though, aren’t ready to sustain these requirements.
arXiv, Astrophysics > Instrumentation and Methods for Astrophysics; Michelle Ntampaka et al.
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In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors.
In interviews, Hinton, Bengio, LeCun and several other people involved at the time all mentioned Canadian Institute For Advanced Research grants as a key reason why neural network expertise stayed in Canada during the decades when nearly nobody believed in the technology.
“It enabled Yoshua and Yann to do their thing, despite the headwind they were getting. This clearly adds to the Canadianness of AI,” Memisevic said. “There was this agency that just gave them the funds they needed so they could do their weird stuff that nobody else believed in.”
LeCun fondly remembers little gatherings where a small clutch of researchers could bounce ideas off each other and refine their thinking.
The only place in San Francisco still pricing real estate like it’s the 1980s is the city assessor’s office. Its property tax system dates back to the dawn of the floppy disk. City employees appraising the market work with software that runs on a dead programming language and can’t be used with a mouse. Assessors are prone to make mistakes when using the vintage software because it can’t display all the basic information for a given property on one screen. The staffers have to open and exit several menus to input stuff as simple as addresses. To put it mildly, the setup “doesn’t reflect business needs now,” says the city’s assessor, Carmen Chu.
As a leader in the global movement toward open access to publicly funded research, the University of California is taking a firm stand by deciding not to renew its subscriptions with Elsevier. Despite months of contract negotiations, Elsevier was unwilling to meet UC’s key goal: securing universal open access to UC research while containing the rapidly escalating costs associated with for-profit journals.
In negotiating with Elsevier, UC aimed to accelerate the pace of scientific discovery by ensuring that research produced by UC’s 10 campuses — which accounts for nearly 10 percent of all U.S. publishing output — would be immediately available to the world, without cost to the reader. Under Elsevier’s proposed terms, the publisher would have charged UC authors large publishing fees on top of the university’s multi-million dollar subscription, resulting in much greater cost to the university and much higher profits for Elsevier.
North Carolina’s public community colleges had a problem. Although the system had a wealth of learning resources faculty members could use to develop their courses, there was no simple way to share and organize those materials across all 58 campuses.
That will soon change, however, with the help of artificial intelligence (AI). Through the machine learning company Tanjo, the community college system is rolling out a custom AI “brain” in the coming months that will map and organize its digital content.
The new tool will be critical to linking faculty to relevant resources, said Richard Boyd, Tanjo’s CEO. Rather than wade through thousands of files in disparate places, faculty members will be able to use the AI system to source documents of interest to them from a central location.
One of the big challenges of developing a machine learning project can be simply getting enough relevant data to train the algorithms. That’s where Superb AI, a member of the Y Combinator Winter 2019 class, can help. The startup helps companies create customized data sets to meet the requirements of any project, using AI to speed up the tagging process.
Hyun Kim, who is CEO and co-founder at the startup, says one of the big stumbling blocks for companies trying to incorporate AI and machine learning into their applications is coming up with a set of suitable data to train the models. “Superb AI uses AI to make customized AI training data for large tech companies. Clients work with us to develop machine learning-based features in their products multiple times faster than they could themselves,” Kim told TechCrunch.
A new report investigates how 13 jurisdictions, including Chicago and New Orleans, were feeding systems data sullied by “unconstitutional and racially biased stops, searches, and arrests.”
Students in the master’s program at the Data Science Institute (DSI) learn the most advanced data-science techniques, may it be machine learning, statistical inference and modeling, or deep learning. And now, thanks to a program called Campus Connections, many of them are using those data techniques to assist Columbia professors with their research.
Campus Connections unites DSI students with professors who need assistance on research projects, especially with data analysis.
Seattle, WA March 12, starting at 12:30 p.m., eScience Institute WRF Data Science Studio (3910 15th Ave NE). “Join us for a presentation and discussion with representatives from the UW Privacy Office and Microsoft Research about GDPR’s impact on research, regulatory developments related to data and privacy, and the University’s response to this shifting landscape.” [rsvp requested]
New York, NY March 15, starting at 9 a.m., Columbia University Davis Auditorium. “The goal of the Columbia DSI/TRIPODS Deep Learning Workshop is to showcase research in the foundations and applications of deep learning going on at Columbia University and beyond.” [registration required]
“If you’re a developer, security researcher, or otherwise interested in developing apps that use confidential computing, this is your chance to make an impact in this growing field. Google Cloud, in collaboration with Intel, is hosting the Confidential Computing Challenge to generate new ideas in the future of computing.” Deadline for submissions is April 1.
Today, we are proud to launch the 2019 Indaba Annual Awards programme. We again look to our continent to celebrate the African heroes who push forward the frontiers of research and its applications. Deadline for nominations is 12 April 2019.
“Kaggle, the data science crowdsourcing community, and Google subsidiary are sponsoring a competition to help the nonprofit CareerVillage.org use AI to efficiently match students to the perfect career advisors.” Deadline for submissions is April 23.
arXiv, Computer Science > Machine Learning; Jonathan Shen et al.
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Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, and experiment configurations are centralized and highly customizable. Distributed training and quantized inference are supported directly within the framework, and it contains existing implementations of a large number of utilities, helper functions, and the newest research ideas. Lingvo has been used in collaboration by dozens of researchers in more than 20 papers over the last two years. This document outlines the underlying design of Lingvo and serves as an introduction to the various pieces of the framework, while also offering examples of advanced features that showcase the capabilities of the framework.