On Tuesday, the Charles Koch Foundation announced that it would be making a significant change: The philanthropic behemoth would begin publishing details about the multiyear contracts that it makes with universities. The contracts, known as “grant agreements,” lay out the “term, scope, and purpose” of the funds the foundation gives to organizations. The effort at transparency was big news, not least because it came on the heels of a controversy over what exactly was in the libertarian organization’s agreement with George Mason University.
“There has been a lot of mischaracterization of our grants in the past,” Brian Hooks, the foundation’s president, told The Wall Street Journal. “The opportunity to be crystal-clear about how our foundation interacts with universities is a good opportunity.” The foundation awarded more than $49 million to more than 250 colleges in 2016, according to the Associated Press. And a new grant agreement that Koch shared with The Atlantic—the first since the announcement of the foundation’s transparency push— shows exactly what goes into those contracts.
The new grant is with Arizona State University, and is being given to the Academy for Justice, a coalition of criminal-justice scholars housed at the Sandra Day O’Connor College of Law; it is a five-year grant for $6.5 million.
At the first-ever National Cybersecurity Summit in New York City on July 31, Vice President Mike Pence gave an in-depth speech about what the Trump administration is doing, and what it says past administrations didn’t do, to address cybersecurity. The text below is from the official speech posted on the White House website.
PLOS One; Tom E. Hardwicke and John P. A. Ioannidis
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The vast majority of scientific articles published to-date have not been accompanied by concomitant publication of the underlying research data upon which they are based. This state of affairs precludes the routine re-use and re-analysis of research data, undermining the efficiency of the scientific enterprise, and compromising the credibility of claims that cannot be independently verified. It may be especially important to make data available for the most influential studies that have provided a foundation for subsequent research and theory development. Therefore, we launched an initiative—the Data Ark—to examine whether we could retrospectively enhance the preservation and accessibility of important scientific data. Here we report the outcome of our efforts to retrieve, preserve, and liberate data from 111 of the most highly-cited articles published in psychology and psychiatry between 2006–2011 (n = 48) and 2014–2016 (n = 63). Most data sets were not made available (76/111, 68%, 95% CI [60, 77]), some were only made available with restrictions (20/111, 18%, 95% CI [10, 27]), and few were made available in a completely unrestricted form (15/111, 14%, 95% CI [5, 22]). Where extant data sharing systems were in place, they usually (17/22, 77%, 95% CI [54, 91]) did not allow unrestricted access. Authors reported several barriers to data sharing, including issues related to data ownership and ethical concerns. The Data Ark initiative could help preserve and liberate important scientific data, surface barriers to data sharing, and advance community discussions on data stewardship. [full text]
A new study using machine learning has identified brain-based dimensions of mental health disorders, an advance towards much-needed biomarkers to more accurately diagnose and treat patients. A team at Penn Medicine led by Theodore D. Satterthwaite, MD, an assistant professor in the department of Psychiatry, mapped abnormalities in brain networks to four dimensions of psychopathology: mood, psychosis, fear, and disruptive externalizing behavior. The research is published in Nature Communications this week.
Currently, psychiatry relies on patient reporting and physician observations alone for clinical decision making, while other branches of medicine have incorporated biomarkers to aid in diagnosis, determination of prognosis, and selection of treatment for patients. While previous studies using standard clinical diagnostic categories have found evidence for brain abnormalities, the high level of diversity within disorders and comorbidity between disorders has limited how this kind of research may lead to improvements in clinical care.
“Psychiatry is behind the rest of medicine when it comes to diagnosing illness,” said Satterthwaite. “For example, when a patient comes in to see a doctor with most problems, in addition to talking to the patient, the physician will recommend lab tests and imaging studies to help diagnose their condition. Right now, that is not how things work in psychiatry. In most cases, all psychiatric diagnoses rely on just talking to the patient. One of the reasons for this is that we don’t understand how abnormalities in the brain lead to psychiatric symptoms. This research effort aims to link mental health issues and their associated brain network abnormalities to psychiatric symptoms using a data-driven approach.”
Boeing, the world’s largest aerospace company, will soon become part of the MIT/Kendall Square innovation fabric. The company has agreed to lease approximately 100,000 square feet at MIT’s building to be developed at 314 Main St., in the heart of Kendall Square in Cambridge.
The agreement makes Boeing the first major tenant to commit to MIT’s Kendall Square Initiative, which includes six sites slated for housing, retail, research and development, office, academic, and open space uses. The building at 314 Main St. (“Site 5” on the map above) is located between the MBTA Red Line station and the Kendall Hotel. Boeing is expected to occupy its new space by the end of 2020.
PayPal, the MIT Media Lab, and the Humanity Centered Robotics Initiative at Brown University are among 18 organisations that have joined the Partnership on AI — a consortium set up to study and formulate best practices on artificial intelligence.
The Partnership on AI was established in September 2016 by Google, DeepMind (an AI lab acquired by Google), Facebook, Amazon, IBM, and Microsoft. It was set up to ensure AI is developed safely, ethically, and transparently.
Other new members include Deutsche Telekom, the Wikimedia Foundation, and the United Nations Development Programme.
Since GPS devices first appeared in cars in 1995, routing technology has been guiding us from Point A to Point B. In addition to its utility at the individual level, this now-ubiquitous technology can provide insights on wider human mobility when collected and aggregated. But how can we use these new derivative data streams in conjunction with spatial data science methodologies to help understand the health of our road networks, make them safer, and more efficient?
For traffic engineers and analysts working in a city like New York (with its over 6000 miles of road) looking to use these data streams to tackle big challenges, like reducing the number of car crashes, it is often helpful to start with some basic questions:
Microsoft is warning investors that artificial intelligence technology as well as any changes to trade policies around the world may present risks to its business.
In a regulatory filing Friday, the Redmond, Wash.-based tech powerhouse said that “AI algorithms may be flawed. Datasets may be insufficient or contain biased information. Inappropriate or controversial data practices by Microsoft or others could impair the acceptance of AI solutions.”
It’s the first time the company has specifically mentioned AI technology in such detail in its 10-K filing as part of its standard list of potential risk factors.
To make real progress in Artificial Intelligence we need the best, brightest and most diverse minds to exchange ideas and build on each other’s work. Research in isolation, or in secret, falls behind the leading edge.
According to Nature Index Science Inc. 2017, publications resulting from collaborations not just among academics, which comes most naturally, but between academia and industry more than doubled from 12,672 in 2012 to 25,962 in 2016. The burgeoning dual-affiliation model — where academics actually work inside industry for a time, while maintaining their academic position — makes possible not only technological advances like better speech recognition, image recognition, text understanding, and language translation systems, but also fundamental scientific advances in our understanding of intelligence.
Dual affiliation is a boon. It benefits not just the AI economy but individual academics — both researchers and students — as well as industry. We need to champion it.
The search for life, developing flagship telescopes, partnering with the private sector, and maintaining Earth science programs should be top priorities for the space agency, say witnesses.
In reality, Silicon Valley listens to two masters: financial incentives and regulation. Technology companies have largely ignored fake news because, until now, they didn’t have to pay attention to it. There were no consequences for policing their platforms. Sure, the media issued harangues, but their stocks were peaking. And Snap, which briefly tried to model itself as a safe haven, appeared to be cratering; many users just didn’t care. But then came the first murmurs of a double-barreled reckoning. Last week we saw tech stocks plummet (and continue to do so) after Facebook reported that users had started to abandon its services, and that ad dollars seemed likely to follow in the coming quarter. On Monday, Democratic Senator Mark Warner released a paper outlining a vision for legislating these big tech companies. It delineated 20 different options for addressing the industry’s unchecked power, from micro-level solutions to sweeping policy changes. It called for combating disinformation, protecting user privacy, promoting competition, and combatting fake news. “Due to Section 230 of the Communications Decent Act, Internet intermediates like social-media platforms are immunized from state torts and criminal liability,” the paper notes. “However, the rise of technology like DeepFakes . . . is poised to usher in an unprecedented wave of false and defamatory content.” While the paper acknowledges that changing Section 230 would meet with dissent from tech providers and digital-liberties organizations, it suggests that there is a problem here that needs to be fixed before things spiral even further out of control.
If Warner’s recommendations had come out weeks ago, I think they would have been glossed over by tech executives, possibly as a media-hungry gesture from a canny senator who may have larger political aspirations. But after the massive Facebook-led tech-stock implosion, which erased tens of billions in market capitalization for these giant companies, many observers now believe that Wall Street has a limit to what it’s willing to tolerate.
Atlanta, GA Monday, August 20. “The Institute for Robotics and Intelligent Machines and the Machine Learning Center present “Deep Learning to Learn” by Pieter Abbeel of Berkeley University. The event will be held in the auditorium of the Calloway GTMI Building from 12:15-1:15 p.m. and is open to the public.”
“We anticipate that the 2019 Summit will attract between 250-300 guests. Please keep this figure in mind as you answer the questions below.” Deadline to submit interest form is August 24.
At the 2018 Bio-IT World Conference & Expo, we dedicated the Wednesday morning plenary session to a panel discussion on data science. It was rich conversation. Panelists laid out their own definitions of “data science”, discussed how to build and structure a data science team, and debated what role expertise (data science or domain) should play in experimental design. As we talked, we collected questions from the audience—more than we were able to use. Instead of abandoning the community’s contributions, I’ve collected the top voted questions from the panel and offered them back to our community.
The Lever is Google Developers Launchpad’s new resource for sharing applied-Machine Learning (ML) content to help startups innovate and thrive. In partnership with experts and leaders across Google and Alphabet, The Lever is operated by Launchpad, Google’s global startup acceleration program. The Lever will publish the Launchpad community’s experiences of integrating ML into products, and will include case studies, insights from mentors, and best practices from both Google and global thought leaders.