A promising field of research on social behaviour struggled after investigators couldn’t repeat key findings. Now researchers are trying to establish what’s worth saving.
As we entered this decade, a small cohort of companies — 23andMe, its Silicon Valley neighbor Navigenics, and Icelandic competitor deCODE Genetics — were selling a future of personalized medicine: Patients would hold the keys to longer and healthier lives by understanding the risks written into their DNA and working with their doctors to reduce them. … But in reality, the 2010s would be when genetics got social. As the decade comes to a close, few of us have discussed our genes with our doctors, but millions of us have uploaded our DNA profiles to online databases to fill in the details of our family trees, explore our ethnic roots, and find people who share overlapping sequences of DNA.
Women are harder to study than men, and because science is extremely competitive and demands a rapid pace of publication, when there aren’t checks and balances in the system to ensure that women get studied, few people will study them. Because females’ hormones change cyclically, biomedical research using females as participants has to account for cycle phase. Although this might not sound like that big of a deal to have to do, the logistics of exercising this kind of control when collecting data on a large number of participants is nightmarishly tricky and can easily triple the amount of time and money it takes to answer a research question.
Just to give you a sense for what this sort of thing looks like in terms of day-to-day lab operations, I’ll tell you about a study that we did recently looking at the relationship between immune function and decision-making in women and men.
Qualtrics today unveiled a series of new features to beef up its “experience management” platform through the infusion of artificial intelligence, automation and machine learning. The new techs comes at a time of rapid growth for the company, and it just announced an expansion in Dallas.
The new features are able to analyze and detect issues with products and services through a number of feedback channels, including social media, product reviews and more. When an “experience gap” is detected, the technology pushes personalized mobile alerts to the right teams to rectify the issue.
Case Western Reserve University has launched a new Computer & Data Sciences Department in the Case School of Engineering and announced the Kevin J. Kranzusch Professorship, which will be held by the future chair of the new department.
The new department was made possible primarily with a $5 million gift from Kranzusch, a Case School of Engineering alumnus, who said a spike in computer sciences enrollment, coupled with the advent of Artificial Intelligence (AI), inspired him to make the commitment.
The University of Waterloo has launched a new program that brings academia and industry together to collaborate on developing new quantum technologies.
The Quantum Alliance program is led by Transformative Quantum Technologies, an
existing research initiative led by the University of Waterloo. The university calls the program a “first-of-its-kind collaboration” between quantum academia and industry, where its partners would have access to, and be able to test,the research group’s Quantum Innovation Cycle
About eight years ago, leaders of the University of Washington’s computer science department decided to zero in on artificial intelligence. The goal was to recruit machine learning and AI “superstars” to lead the department into this new frontier of technology, according to UW Computer Science Chair Hank Levy.
The challenge? Competing for experts with bigger names in academia, like Stanford and MIT.
But the University of Washington had an asset those institutions didn’t: Jeff Bezos in its backyard.
“So we thought, what the hell? We’ll send Jeff an email,” Levy said Thursday, speaking at the Technlogy Alliance’s AI Policy Matters Summit in Seattle. The event brought together industry experts, scholars, and elected officials to discuss the state of AI and machine learning and recommend policies to govern the new technology.
While AI is delivering unprecedented progress and convenience, the increasing implementation of AI technologies has also triggered public fears regarding autonomous vehicle safety, data misuse and job losses. One of the latest concerns to capture mainstream media headlines is the danger of human social biases leaking into AI models.
Stanford Computer Science PhD student Reid Pryzant is well aware of the public skepticism. “Bias is one of these trust issues we need to solve in order to actually get the technology out there and into our society in a safe way,” Pryzant told Synced in an email.
What if AI could also be used to fight bias?
Pryzant and other Stanford researchers partnered with researchers from Kyoto University and Georgia Institute of Technology to develop a novel natural language model that can identify and neutralize biased framings, presuppositions, attitudes, etc. in text.
As I returned home from this year’s Women in Statistics and Data Science Conference, one word rang loudly in my ears: patriarchy.
In a presentation on the impact of gender on women’s careers, Carnegie Mellon statistician Dalene Stangl boldly claimed that although the term may be out of favor, patriarchy is “alive and well” and that “it happened to me.”
At first, there was little I recognized in Stangl’s story. The term “patriarchy” is loaded with the history of gender oppression that, thanks to the determination of women like Stangl who came before me, seemed worlds apart from my own experience. I’ve had many male supporters and have never felt discriminated against, even as a female student in a male-dominated field. And yet, as Stangl spoke, I slowly realized that despite my initial resistance to the word itself, I had indeed encountered patriarchy — in the data from my own research.
As the largest federal civilian agency in the U.S., the Department of Health and Human Services is rich with data. However, it has lacked ways to scale and use the data to its full potential, in part due to considerable fears people have about sharing research data with others and with the involvement of artificial intelligence.
Focusing on the need to advance health science as well as a human-centered design practice to alleviate data-sharing concerns, the HHS Office of the CTO launched a data science platform in support of its Data Insights Initiative that aims to address these issues, its chief Ed Simcox told attendees of the GovernmentCIO Media & Research AI and RPA CXO Tech Forum last week.
University of California-San Francisco, News & Media
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What if instead of needing a different blood draw for each suspected health issue, doctors could order up a single blood test that would combine a hundred or more different measures to reveal a holistic portrait of your overall health – as well as your risk for a whole host of diseases? Such a test could be administered widely to inform early individualized interventions designed to keep everyone healthy longer.
This is the dream of UC San Francisco physician Peter Ganz, MD, and colleagues at Colorado-based SomaLogic Inc., who are developing what they call “liquid health check” technology – a single blood test capable of painting a detailed portrait of a person’s current health and future disease risks. The technology uses tiny fragments of DNA and RNA called aptamers that are able to identify and measure thousands of different circulating proteins from a small blood sample to reveal information about the health of organs across the body.
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Even before the election officially began, Boris Johnson’s refusal to publish an independent report on Russian influence in the 2016 EU referendum prompted criticisms that the UK prime minister was suppressing evidence of his party’s links with Russian donors.
The UK Cabinet Office has only recently set up an “election cell” to safeguard polls against a spectrum of threats, from low-level hackers to hostile states, but the controversy over the leaked trade dossier has exposed its weaknesses.
Reddit and a social media analysis group called Graphika have both published evidence linking the leak of the document to accounts that had previously been involved in a Russian disinformation campaign, known as Secondary Infektion.
The Antarctic Circumpolar Current flows in a loop around Antarctica, connecting the Atlantic, Pacific and Indian oceans. It is one of the most significant ocean currents in our climate system because it facilitates the exchange of heat and other properties among the oceans it links.
But how the current transfers heat, particularly vertically from the top layer of the ocean to the bottom layers and vice versa, is still not fully understood. This current is very turbulent, producing eddies – swirling vortices of water similar to storms in the atmosphere – between 30 to 125 miles (50 to 200 kilometers) in diameter. It also spans some 13,000 miles (21,000 kilometers) through an especially remote and inhospitable part of the world, making it one of the most difficult currents for scientists – as least those of the human variety – to observe and measure.
Luckily for Lia Siegelman, a visiting scientist at NASA’s Jet Propulsion Laboratory in Pasadena, California, the rough seas posed no challenge for her scientific sidekick: a tagged southern elephant seal.
The most interesting and telling reason for why TVs are now so cheap is because TV manufacturers have found a new revenue stream: advertising. If you buy a new TV today, you’re most likely buying a “smart” TV with software from either the manufacturer itself or a third-party company like Roku. The cut of the advertising revenue from those pre-installed video channels is big business for actual TV makers, as is the business of selling user viewing data and other information to marketers.
Because only 37 percent of U.S. homes currently have a smart TV, according to the market researchers at Forrester, that means there’s a lot of revenue these companies have yet to squeeze. TV makers, understandably, are doubling down: This week Vizio announced that it was launching its own in-house advertising unit, looking to capture a bigger slice of the estimated $4.4 billion video-streaming ad market in 2020.
The story of the cheap HD television in the last decade is, in many ways, the story of the American economy writ large. Prices may be low, but so are most people’s wages. And although customers are getting a glut of new free content from TV products, that’s because customers are now themselves the products that TCL can sell to outside marketing companies.
“Researchers are an important part of Kaggle’s community and we want to expand our support for them through the first ever Kaggle Open Data Research Grant. Graduate students, PhD candidates, research scientists, post-doctoral fellows, and faculty at accredited universities are all invited to apply.” Deadline for applications is January 9, 2020.
“The Caselaw Access Project (CAP) expands public access to U.S. law. Our goal is to make all published U.S. court decisions freely available to the public online, in a consistent format, digitized from the collection of the Harvard Law Library.”
“Text analysis is taught in most major universities; many have entire courses dedicated to the topic. This article offers a systematic review of 45 syllabi of text-analysis courses around the world. From these syllabi, we extracted data that allowed us to rank canonical sources and discuss the variety of software used in teaching. Furthermore, we argue that our empirical method for building a text-analysis syllabus could easily be extended to syllabi for other courses. For instance, scholars can use our technique to introduce their graduate students to the field of systematic reviews while improving the quality of their syllabi.”
“We recently released a beta version of Fairness Indicators, a suite of tools that enable regular computation and visualization of fairness metrics for binary and multi-class classification, helping teams take a first step towards identifying unjust impacts. Fairness Indicators can be used to generate metrics for transparency reporting, such as those used for model cards, to help developers make better decisions about how to deploy models responsibly.”
