Politics professor Todd Sechser and media studies professor Siva Vaidhyanathan will direct the two newest labs for interdisciplinary research sponsored by the University of Virginia’s Democracy Initiative. … Secher’s “Democratic Statecraft” lab and Vaidhyanathan’s “Deliberative Media Initiative” lab join the “Religion, Race and Democracy” and “Corruption Laboratory for Ethics, Accountability and the Rule of Law” projects the Democracy Initiative launched last year. Each of the labs aims to manage a series of interconnected research, teaching and public affairs projects related to their specific themes.
Yahoo News, Christian Science Monitor, Ann Scott Tyson
from
As the U.S. and China forge ahead as world powerhouses in the development and application of AI, the cautionary voices of researchers – and their choices about collaboration – could hold the key to promoting beneficial cooperation while preventing malicious or dangerous uses of the revolutionary and often unwieldy new technology, AI experts say.
In turn, their ability to discern between constructive and harmful AI sharing could help prevent a widening technological schism between the U.S. and China that, if allowed to grow, could spread globally as nations are forced to decide whether to align with the world’s leading democracy or its most populous authoritarian state.
Starting fall 2019, New Jersey Institute of Technology’s (NJIT) Ying Wu College of Computing (YWCC) will offer part-time and full-time graduate-level programs in data science at a location just steps from the Exchange Place PATH station in the waterfront district of Jersey City.
Hurricane Maria sent Puerto Ricans fleeing from the island to the U.S. mainland, but population surveys to assess the size of that migration would have taken at least a year to complete. A new study suggests, however, that a Facebook tool for advertisers could provide crude, real-time estimates for how many people are moving because of a natural disaster. That could help governments design policies to assist those displaced people.
The Facebook data revealed that, from October 2017 to January 2018, the Puerto Rican population on the mainland increased by some 17 percent, or about 185,200 residents. That would imply a 5.6 percent decrease in the population living the U.S. Caribbean territory.
Almost a third of those migrants, or about 65,400 people, went to Florida, the data suggest.
Google’s newest AI lab — located across the street from Princeton University’s Nassau Hall — officially launched on Thursday, May 2, with speeches and research presentations by state and local officials, Google executives, and University executives and students.
The lab, at 1 Palmer Square, is already playing a vital role in expanding New Jersey’s innovation ecosystem, as evidenced by the projects shared by Princeton University undergraduate and graduate students who have been working in the research lab and by the commitment to further expansion voiced by state governmental leaders.
ZDNet caught up with Wharton professor Kartik Hosanagar to talk about his book, “A Human’s Guide to Machine Intelligence,” and the growing pains of AI.
It’s already painfully clear that Facebook wants to do far more than just sell ads against News Feed attention to make money. That got them where they are, but it has its limits, and of late, it’s also attracted a volcano of furious attention, and a fake-news firestorm. So don’t look where their puck is; look where it’s going. Look at Facebook Marketplace; look at Facebook’s cryptocurrency plans; look at their purchase of WhatsApp and how Facebook Messenger was broken out into its own app.
It seems clear that what Facebook really wants next is for Messenger to become WeChat for the rest of the world.
Unfortunately, there’s no single solution capable of fixing the world’s traffic problems. Every city has a vast and unique set of problems that combine to create congestion and gridlock. Fixing them with AI requires targeted solutions.
Other solutions focus on a smaller problem, but still have the potential for immediate impact. Take, for example, Miami’s unpredictable drawbridges. According creative agency The Community, commuters spend between 10 and 20 minutes stopped if they have the misfortune of getting stuck waiting for one to open and reset. So, with some help from a local Mercedes-Benz dealership, it came up with a machine learning-powered method for determining ahead of time when the bridges would rise.
The solution put forward by The Community involves computer vision-equipped cameras and predictive AI. The company currently monitors three of the cities busiest drawbridges, using the data gleaned to power predictive analysis algorithms. It’s available now at BridgeForecast.com, where it’s already helping commuters spend less time waiting on ships to pass.
This is an edited transcript of a recent talk I gave at a blockchain conference, where I said I’d talk about “What will the world be like when computational intelligence and computational contracts are ubiquitous?”
“Every once in awhile, a new format comes along that changes everything,” said EmbodyMe founder and CEO Issay Yoshida from the stage. “And today, I am going to talk about a new technology that is going to change how visual content is created.”
The words seem straightforward, but the presentation wasn’t. That’s because Yoshida’s words were coming from the mouth of former Secretary of State and 2016 presidential candidate Hillary Clinton — or at least an image of her that has been combined with Yoshida’s voice (altered to sound very roughly like Clinton’s) and facial expressions to create a bizarre hybrid. As the assembled crowd giggles in disbelief, the presentation was quickly taken over by an interpreter for the Japanese inventor, who cheerily explains both the technology that powers EmbodyMe and the digital watermark system that keeps bad actors from abusing it.
“Current technologies can only track up to 70 two-dimensional points on the face,” the interpreter explained. “On the other hand, EmbodyMe’s 3D-dense face tracking technology can track up to 50,000 three-dimensional points. This allows for the highest accuracy and flexibility to match your facial expressions to any image or video.”
Here’s a groaner for you: The greyhound stopped to get a hare cut.
Don’t blame dad for this one. Blame the machines.
A pun generator might not sound like serious work for an artificial intelligence researcher—more the sort of thing knocked out over the weekend to delight the labmates come Monday. But for He He, who designed just that during her postdoc at Stanford, it’s an entry point to a devilish problem in machine learning.
It’s easy enough for students to sign up for online university courses, but getting them to finish is much harder. Dropout rates for online courses can be as high as 80 percent. Researchers have tried to help by developing early warning systems that predict which students are more likely to drop out. Administrators could then use these predictions to target at-risk students with extra retention efforts. And as these early warning systems become more sophisticated, they also reveal which variables are most closely correlated with dropout risk.
In a paper published 16 April in IEEE Transactions on Learning Technologies, a team of researchers in Spain describe an early warning system that uses machine learning to provide tailored predictions for both new and recurrent students. The system, called SPA (a Spanish acronym for Dropout Prevention System), was developed using data from more than 11,000 students who were enrolled in online programs at Madrid Open University (UDIMA) over the course of five years.
Facebook is working with online learning site Udacity to try to enable AI research that doesn’t hurt privacy.
At the social network’s F8 conference on Wednesday, Facebook announced that it’s offering scholarships to 5,000 people to take a new Udacity course called Secure and Private AI. This is the second phase of a Facebook program to help 300 people earn a Udacity “nanodegree.” The idea is to learn to apply techniques that AI powers are using like differential privacy at Apple and federated learning at Google.
New York, NY May 22-June 2. The program lineup “presents many of the world’s leading scientists, pioneers, artists, and influential thinkers – bringing science to life in imaginative ways throughout the five boroughs of New York City.” [tickets onsale]
“The ASA Biometrics Section invites applications for funding to support projects developing innovative outreach projects focused on enhancing awareness of biostatistics among quantitatively talented US students.” … “We anticipate funding one project this year, with total funding of up to $3,000.”
IDE Content Manager Paula Klein caught up with Tom for a preview of his discussions and to talk about some of the most vexing aspects of corporate AI implementation. Highlights of the conversation are featured here.
“In their recent publication, Brian McFee, NYU CDS Assistant Professor of Music Technology and Data Science, Jong Wook Kim, PhD student in Music Informatics at NYU, Mark Cartwright, Research Assistant Professor at NYU’s Music and Audio Research Laboratory, Justin Salamon, Adobe Research, Rachel Bittner, Research Scientist at Spotify, and Juan Pablo Bello, CDS-affiliated Professor of Music Technology, Computer Science, and Engineering, and Director of the Music and Audio Research Lab<, shared best practices for Open-Source Software (OSS) in MIR research. Drawing from expertise obtained over years of publishing their own open source software and open data within music research, the authors compiled a collection of tips spanning the development process. Notably, their suggestions are also relevant in other fields that employ complex systems."
For the last nine months we’ve experimented with paying our employees for any open source contributions to any project they do on their free time … We pay our employees $20/hr for contributions to OSS and tech communities, whether it’s a third-party library we use in our work like React, Next.js, or styled-components; one of our own OSS projects like Spectacle or Urql; or even a personal hack project purely for fun and learning, as long as it’s released under an OSI license. Contributions can be code, documentation, design work, pull request reviews, issue triage, community management, or really anything that the individual thinks counts as a contribution.
The movement towards open science is a consequence of seemingly pervasive failures to replicate previous research. This transition comes with great benefits but also significant challenges that are likely to affect those who carry out the research, usually early career researchers (ECRs). Here, we describe key benefits, including reputational gains, increased chances of publication, and a broader increase in the reliability of research. The increased chances of publication are supported by exploratory analyses indicating null findings are substantially more likely to be published via open registered reports in comparison to more conventional methods. These benefits are balanced by challenges that we have encountered and that involve increased costs in terms of flexibility, time, and issues with the current incentive structure, all of which seem to affect ECRs acutely. Although there are major obstacles to the early adoption of open science, overall open science practices should benefit both the ECR and improve the quality of research. We review 3 benefits and 3 challenges and provide suggestions from the perspective of ECRs for moving towards open science practices, which we believe scientists and institutions at all levels would do well to consider. [full text, uncorrected proof]