Southwest Research Institute (SwRI) and the Lyle School of Engineering at Southern Methodist University (SMU) announced the Seed Projects Aligning Research, Knowledge, and Skills (SPARKS) joint program, which aims to strengthen and cultivate long-term research collaboration between the organizations.
Research topics will vary for the annual funding cycles. The inaugural program selections will apply machine learning — a subset of artificial intelligence (AI) — to solve industry problems. A peer review panel selected two proposals for the 2020 cycle, with each receiving $125,000 in funding for a one-year term.
What if smartphones could alert you or your physician to a medical problem? Or even monitor your progress after you had suffered a concussion?
A University of Virginia research team, in collaboration with Lockheed Martin Corp., is developing smartphone applications that can identify symptoms of infectious disease and traumatic brain injury by monitoring a user’s daily activities.
“After you experience a concussion, can we see patterns of behavior changes as you go about your daily life that would indicate you are recovering or getting worse?” said Laura Barnes, a UVA associate professor in the Department of Engineering Systems and Environment, where a dozen faculty members focus on health care systems research. “Can we use everyday devices people carry, like their smartphones and smartwatches, to identify these changes? We are ultimately looking for objective, quantifiable, digital biomarkers of disease. Sensors already embedded in smartphones – including light, microphone, pedometers, GPS, to name a few – have the potential to provide these markers.
Fifty-two floors below the top of Salesforce Tower, I meet Paula Goldman in a glass-paneled conference room where the words EQUALITY OFFICE are spelled out on a patchwork bunting banner, the kind of decoration you might buy for a child’s birthday party.
Goldman has a master’s degree from Princeton and a Ph.D. from Harvard, where she studied how controversial ideas become mainstream. She arrived at Salesforce just over a year ago to become its first-ever Chief Ethical and Humane Use Officer, taking on an unprecedented and decidedly ambiguous title that was created specifically for her unprecedented, ambiguous, yet highly specific job: see to it that Salesforce makes the world better, not worse.
“I think we’re at a moment in the industry where we’re at this inflection point,” Goldman tells me. “I think the tech industry was here before, with security in the ’80s. All of a sudden there were viruses and worms, and there needed to be a whole new way of thinking about it and dealing with it. And you saw a security industry grow up after that. And now it’s just standard protocol. You wouldn’t ship a major product without red-teaming it or making sure the right security safeguards are in it.”
But sometimes people have accurate models of a phenomenon without any intuitive explanation or causation that provides an accurate picture of the situation. In many cases of physical phenomena, “explanations” contain causal loops where A causes B and B causes A.
There’s good reason to search for more powerful indicators of flood impacts: Sunny-day floods disrupt traffic, threaten infrastructure, and drain local economies. A study in Science last year found that high-tide flooding cost businesses in downtown Annapolis, MD, more than $100,000 in lost revenue in 2017.
Currently, the main sources of data on sunny-day floods are tide gauges, which are often few and far between. They also don’t paint a very detailed picture of how water levels will actually affect a community, said Katherine Mach, an environmental scientist at the University of Miami who led the Annapolis study.
“Most of what we know about coastal flooding is how it affects people through major disasters. We know less about nuisance floods, recurring, short-duration floods,” she said. So, “understanding how high-tide floods directly impact people is a really challenging issue that has been intractable so far.”
Powered by natural language processing and machine learning, the company’s new chatbot can communicate with students via text message in more than 100 languages and identifies trends in data to uncover hard-to-measure insights, such as a student’s sense of belonging or financial hardship. That data that can then be used by advisers to ensure students are directed to on-campus resources for support.
“A lot of times the issues that impact a student’s success are highly personal,” Carolina Recchi, co-CEO of EdSights, told EdScoop.
EdSights’ new chatbots — which are currently accessible by 50,000 students at universities including Bethel University in Minnesota, Baker University in Kansas and Missouri Western State University — can give students a way to openly communicate with their institutions and ensure they receive timely assistance with any challenges they may be facing.
Open source app helps predict brain tumour malignancy and patient survival
The power of artificial intelligence (AI) in medicine lies in its ability to find important statistical patterns in large datasets. A study published today is an important proof of concept for how AI can help doctors and brain tumour patients make better treatment decisions.
Meningiomas – tumours that arise from the membranes that surround the brain and spinal cord – are the most common primary central nervous system tumour, with an incidence of 8.14 per 100,000 population. While they generally have better outcomes than other brain tumours, there is a great deal of variability in aggressiveness. Being able to predict malignancy and accurately estimate survival is therefore incredibly important in deciding whether surgery is the best option for the patient.
Sometimes, you read news that’s equal parts exciting and potentially terrifying. Case in point — the University of Minnesota College of Science and Engineering announced it will be offering a new master’s degree in robotics.
Folks, I’ve seen “Terminator.” You’ve seen “Terminator.” We all know where this is headed.
But until an intrepid Gopher engineer pulls back a curtain and proudly reveals a T-800, the addition of the degree represents a step forward from the university in answering the need for robotics engineers and scientists worldwide. Medicine, manufacturing, transportation, agriculture — what aren’t robots involved with these days (he said in his most convincing Seinfeld impression)?
Educational Researcher journal, Elaine M. Allensworth and Kallie Clark
High school GPAs (HSGPAs) are often perceived to represent inconsistent levels of readiness for college across high schools, whereas test scores (e.g., ACT scores) are seen as comparable. This study tests those assumptions, examining variation across high schools of both HSGPAs and ACT scores as measures of academic readiness for college. We found students with the same HSGPA or the same ACT score graduate at very different rates based on which high school they attended. Yet, the relationship of HSGPAs with college graduation is strong and consistent and larger than school effects. In contrast, the relationship of ACT scores with college graduation is weak and smaller than high school effects, and the slope of the relationship varies by high school.
This Nature commentary by [Joshua Loftus] (at NYU) and Matt Kusner from UCL is about fundamental challenges in making algorithms fair and how we might begin to address some of them with causal modeling approaches. One of our takeaways: not all problems can be solved with machine learning, so sometimes the right decision is to not build such a system at all.
NBER Working Papers; Janet Currie, Henrik Kleven, Esmée Zwiers
The last 40 years have seen huge innovations in computing technology and data availability. Data derived from millions of administrative records or by using (as we do) new methods of data generation such as text mining are now common. New data often requires new methods, which in turn can inspire new data collection. If history is any guide, some methods will stick and others will prove to be a flash in the pan. However, the larger trends towards demanding greater credibility and transparency from researchers in applied economics and a “collage” approach to assembling evidence will likely continue.
Sitting between microwaves and infrared in the frequency spectrum, THz waves – or T-rays, have so far not been exploited by science due to their low energy. This led to a problem known in scientific circles as the terahertz gap. The new device enables the detection and amplification of the elusive waves and could potentially open up brand new technologies in medicine, cosmology, telecoms and satellites.
In this piece, we highlight 10 key takeaways from the Selected Readings on RD4C. Our review of the literature focused on policies, technical guidance, and other relevant documentation driving activity in the space. The review was ecosystem-wide, considering not only global policies uniquely focused on children’s data, but also documentation with any relevant guidance or lessons learned. For example, the review looked at documentation on a specific topical domain (e.g. guidance on handling data about refugee children) or policies guiding more general development or humanitarian action that featured some reflection on data handling.
Seattle, WA February 14, starting at 12 noon, Motif Seattle Hotel (1415 5th Avenue). “We will look at what quality standards could and should be in data science, and how to enable communities and users to press for them.” [free, registration required]
Healthcare Information and Management Systems Society
Orlando, FL March 9-13. “The can’t-miss health information and technology event of the year, where professionals throughout the global health ecosystem connect for the education, innovation and collaboration they need to reimagine health and wellness for everyone, everywhere. In a time of unprecedented healthcare disruption.” [$$$$]
Berlin, Germany August 10-13. “Just over a year ago, Project Jupyter announced it was reevaluating its annual community conference. An advisory committee of volunteers recommended a JupyterCon 2020 emphasizing a focus on access and leadership. We are now thrilled to announce a global Jupyter conference.” [save the date]
Brown University, Institute for Computational and Experimental Research in Mathematics
Providence, RI March 7 at Brown University (Kassar-Foxboro Auditorium). “Talks from professors of philosophy, neuroscience, computer science, and of course, mathematics. The idea is to investigate different conceptions of thinking systems across disciplines. In philosophy, we discuss what it means to be a thinking system. In neuroscience, we study how the most successful thinking system ever is built.” [free, registration required]
“As announced in 2019, each of the four hubs will receive $4 million over four years in our second phase, for a total investment of $16 million. This is double the budget for the first round of Big Data Hubs awards made in 2015. Be sure to catch up on the latest with these summary articles and links.” Deadline to participate is March
“We running the second in a series of data science competition where multiple groups attempt to use the same remote sensing data from low flying airplanes to infer the locations, sizes and species identities of millions of trees. This kind of collaborative data analysis challenge has proven highly effective in other fields for quickly improving methods for converting image data to useful information. This round of the competition focuses on exploring how methods generalize beyond a single forest.” Deadline for submissions is April 1.
“We just released V0.13 of Cortex, which includes support for all major ML frameworks, improved auto scaling, improved spot instance support, batched requests, and a variety of bug fixes/UX improvements.”
Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing (2015). In my most recent response, I was a little more detailed than usual, so I figured it’d make sense as a blog post too.
If my response comes across as negative, that’s certainly not the intention — being straight-forward about my experience is.
I learned a lot, it just wasn’t for me. Working by yourself on short(ish)-term things can get old.