A new paper by several economists, including one directly involved in the trial, provides stark evidence that Harvard does give preferential treatment to affluent white applicants through legacy preferences and sports recruitment.
The researchers found that between 2009 and 2014, more than 40 percent of accepted white students were ALDC—athletes, legacies, “dean’s list” (meaning related to donors), or the children of faculty. Without such preferences, they said, three-quarters of those white students would have been rejected.
The study’s lead author, the Duke economist Peter Arcidiacono, was an expert witness against Harvard in a lawsuit accusing the university of discriminating against Asian applicants. The paper is based on data obtained during the trial.
Colleges are not producing large numbers of CS majors, and many of those who graduate with a CS degree are opting to go into industry rather than academia, which can pay twice as much as what professors earn. This is causing a perfect storm: a shortage of computer science teachers is making it harder for many students majoring in the discipline to get into the classes they need to graduate.
Finding enough qualified computer science teachers is also an issue in secondary education. Only 36 teachers graduated from universities with computer science degrees in 2017, compared with 11,157 math teachers and 11,905 science teachers, according to the nonprofit Code.org. In 2016, 75 teachers graduated from universities equipped to teach the subject, the organization reports.
“I can say at the K-12 level there’s a dramatic shortage” of computer science teachers, says Jake Baskin, executive director of the Computer Science Teachers Association (CSTA).
“Investors across the country have been asking state officials to expand the areas designated for the 2017 tax law’s opportunity zone tax breaks—and changes related to the 2020 Census may give those investors a new chance to get what they ask for,” writes Lydia O’Neal.
The Census Bureau will be revising census tract boundaries for the census next year based on population changes, including the expansion of low-income tracts eligible for opportunity zone designations.
It’s a common internet experience: throw a foreign phrase into Google Translate or any other online translation tool and out comes a farcical approximation of the real thing.
That’s why many experts — even Google itself — caution against relying on the popular Google Translate for complex tasks. Google advises users that its machine translation service is not “intended to replace human translators.”
Yet the U.S. government has decided that Google Translate and other machine translation tools are appropriate for one task: helping to decide whether refugees should be allowed into the United States.
Computer scientists at New York University Tandon School of Engineering today launched a new website that analyzes all Canadian Facebook political advertising in the leadup to the October 21 elections. The interactive, user-friendly website lets users easily explore the data and break down the advertising spend based on party, target demographic, region, and other factors. The full data is available at: onlinepoliticaltransparencyproject.org.
Tesla has acquired the machine learning startup DeepScale, CNBC, Techcrunch, and other news outlets have reported. The company’s CEO, Forrest Iandola, announced Monday that he had joined Tesla’s Autopilot team. … DeepScale focuses on improving the speed and efficiency of convolutional neural networks, drawing on Iandola’s past work as a computer science graduate student. The company’s techniques will be particularly helpful to Tesla. Tesla is relying heavily on machine learning techniques to achieve full self-driving capabilities without the lidar sensors or high-definition maps being used by most of Tesla’s competitors.
Augmenting human intelligence is the fastest way to get value from AI. The problem? Human-centered design (HCD) is missing from most attempts to augment human intelligence.
Sure, the AI teams doing this work sincerely care about humans — but that’s not the same thing as knowing and applying proven HCD methods like iterative prototyping paired with a disciplined observation of users. Think about it this way: What if designers believed they could produce rock-solid code because they care about quality, not recognizing that software quality requires proven software engineering methods?
The National Institute on Aging, part of the National Institutes of Health, has awarded a $37 million grant over five years to establish the Open-AD Drug Discovery Center. Sage Bionetworks will take a leading role in this Center, joining investigators at Emory University, the Structural Genomics Consortium (SGC), Stanford University, Oxford University, and University of North Carolina. The grant, part of a larger $73 million effort to launch two Alzheimer Centers for the Discovery of New Medicines, funds the development and open distribution of chemical tools that support experimental evaluation of biological mechanisms thought to be involved in Alzheimer’s disease (AD).
“Through these centers, NIH will expand the use of open-science and open-source principles to de-risk novel drug targets with the goal of facilitating the development of new treatments for Alzheimer’s,” said NIH Director Francis S. Collins, MD, PhD.
What are the genes that influence survival and reproduction? What happens to the genetics of a population when it becomes threatened by extinction? Why do some individuals fare better than others? How do natural populations evolve over short time-scales?
These are questions at the crux of research conducted by evolutionary biologist Nancy Chen, an assistant professor of biology at the University of Rochester.
Chen annually travels to Venus, Florida, to study Florida Scrub-Jays, an endangered population of wild birds that have been individually marked and monitored since 1969. Using 50 years of collected data, Chen creates family trees and develops pedigree simulations to analyze the ways the birds’ genetic material is changing over time due to habitat loss. The tools she develops based on the field data allow her to investigate how different evolutionary forces shape genetic variation in a population and affect the probability of extinction.
I want a smart home that just does the right thing. I want to walk into a room and see the lights turn on. I want the TV to put on my favorite show. I want the temperature not to be too hot or too cold. And I don’t want to have to flip a switch, or touch a remote, or touch a thermostat for it to happen. Most of all, I don’t want to be handcuffed to my phone. That’s what we’ve been promised about the smart home. Yet here all of us are today, fumbling with our smart phones, and calling to our smart speakers (assuming one is even in the room), experiencing a home that isn’t intuitive or easy, let alone “smart”.
So is all lost? Should we simply give up on the idea of the smart home? Of course not! This is a challenge, and an exciting one at that. It’s also a challenge I think we can meet… but with robots, not smarter phones.
On Sept. 30, metaresearch got another boost when an international coalition of policymakers, funders, universities, publishers, and researchers launched the Research on Research Institute (RoRI), which will be dedicated to tackling metascience questions on a mass scale. James Wilsdon, a research policy scholar at the University of Sheffield and the institute’s founding director, says that the scientific community is “woefully underinvesting” in metascience given the benefits it could have for the “efficiency, dynamism and sustainability” of the research enterprise.
Through a National Science Foundation grant, a team of researchers at the University of Illinois at Urbana-Champaign’s Grainger College of Engineering and the Georgia Institute of Technology are working on an advanced autopilot, which will autonomously evaluate unforeseen circumstances, take the best course of action, and land the plane safely.
Lui Sha, the Donald B. Gillies Chair in Computer Science, is the principal investigator on the project.
“We cannot be all Captain Sully,” said Lui Sha, the Donald B. Gillies Chair in Computer Science at Illinois, an expert in safety critical real-time systems, and the principal investigator on this project. “He is a genius. He is to pilots what Michelangelo is to painters. However, not all pilots have his expertise.”
London, England November 11, starting at 6 p.m., Arts One Lecture Theatre (Mile End Road). “An event that will discuss how the exponential accumulation of data from everyday online and offline activities raises tensions about who has the rights to produce and own such data.” Engin Isin will chair the panel with Elspeth Guild, Jennifer Gabrys and Didier Bigo. [free, registration required]
The maps people use on many of our apps to shop, find jobs, support causes, and more are all powered by OpenStreetMap (OSM). OSM is a community-driven project built by mappers all over the world who contribute and maintain data about roads and locations. The OSM global copy receives up to 5 million changes every day, which means our local copy would quickly become outdated if we didn’t regularly update it. To reduce the risk of bad edits, whether intentional (vandalism) or unintentional, we don’t update our local copy directly. Instead, changes between the two versions are reviewed and accepted into the local copy. This all needs to be done on a regular cadence, or the growing difference between the global and local versions will require significant time and effort to catch up. We developed two new tools to help us keep pace: Logical changesets (LoChas) and our new machine-augmented automatic review system (MaRS).
LoChas break OSM changesets into individual CRUD operations and then cluster them for more efficient human review. MaRS uses a blend of heuristics and machine learning (ML) techniques to automate evaluation of LoChas that don’t require a further nuanced review. The ultimate goal of these tools is to create a funnel where machine-augmented techniques reduce the workload that requires human intervention.