On Monday, LeCun appeared at Harvard to deliver the first of his Morris Loeb Lectures, sponsored by Harvard’s physics department. Most Loeb lecturers are important physicists (last semester, Nobel laureate Donna Strickland came to speak). LeCun is not, but no one seemed to care: this year the series had to be moved into the Science Center because of overwhelming interest—reflecting the breadth of faculty and student engagement in computer science and its increasing applications across a dizzying array of fields of inquiry. At LeCun’s first talk, every seat was filled and crowds packed into the back of the lecture hall.
LeCun moved quickly between sophisticated mathematics and gentle humor, usually at the expense of physicists (all physics is, he said, is fiddling with fundamental constants until the universe pops out). He outlined a few breakthroughs in physics that have been made possible by his work, pointing to a few discoveries made at Harvard. For example, professor of physics Matthew Schwartz has used neural networks to identify subatomic particles in supercolliders from the fragments they leave behind; the computer learns high-energy physics on its own in a matter of days, leaving in its dust the graduate students who have suffered through Schwartz’s legendary quantum field theory class.
Modern scientists operate under massive pressure to publish consistently groundbreaking work in prestigious scientific journals. Like me, most of them also do their work with very few people looking over their shoulder, especially during data collection. This dynamic creates a situation where, for many scientists, being dishonest by falsifying data is both potentially lucrative for one’s career and surprisingly easy to do. And while most scientists are honest, data falsification does happen.
A new approach to research, however, called “citizen science” is changing this dynamic by allowing members of the general public to collect and analyze scientific data. By spreading responsibility for data collection across dozens, hundreds or thousands of different people, and by making this data accessible throughout the research process, citizen science has the potential to stamp out many aspects of data dishonesty in fields that use this kind of data.
WHY WOULD A COMPANY LIKE FACEBOOK Inc., which earns almost all of its revenue from advertising, choose to adopt an apparently anti-advertising technology like end-to-end encryption?
Of the big tech companies, Facebook Inc. seems to have the worst brand perception among consumers. In a survey conducted by Fortune and Harris Poll, only 22% of the respondents said they would trust Facebook Inc. with their personal data. The company has been part of widely publicized scandals such as the Cambridge Analytica debacle and the Russian disinformation campaign during the 2016 elections, and many more. It has been accused of endangering democracies and enabling horrific crimes. It has repeatedly apologized and promised to change its ways, but its privacy scandals and abuses of its power have repeated just as frequently.
Humana’s new digital health hub in Boston has the look and feel of a tech start-up, and that is very much the point. The Medicare giant’s CEO has told his managers digital engagement has to be at the heart of everything they do.
“We’re trying to bring different disciplines together to wrap around the whole idea of agile development, and the ability to have consumer design individuals, analytics capability, data scientists,” explained CEO Bruce Broussard at the company’s new Studio H facilities.
Several equipment makers are developing or ramping up a new class of wafer inspection systems that address the challenges in finding defects in advanced chips.
At each node, the feature sizes of the chips are becoming smaller, while the defects are harder to find. Defects are unwanted deviations in chips, which impact yield and performance. The new inspection systems promise to address the challenges, but they are also more expensive than the previous tools, and chipmakers may need to buy a mix of them.
Used to find defects for both logic and memory chips in the fab, wafer inspection is split into two categories—optical systems and e-beam. Both types are often seen as complimentary, but there are some tradeoffs. Optical inspection tools are fast, but they have some resolution limits. Single beam, e-beam inspection systems have better resolution, but they are slower.
In March 2018 the artist Nan Goldin, who struggled with a three-year-long addiction to the painkiller OxyContin, staged a protest at the Metropolitan Museum of Art in New York. Her group, Prescription Addiction Intervention Now, or PAIN, dumped medicine bottles with labels reading “Prescribed to you by the Sackler family” into the reflecting pool at Temple of Dendur in the museum’s Sackler Wing.
The Sacklers, who have been donors to cultural institutions around the world, own Purdue Pharma, which developed and sold OxyContin. The company has been sued by thousands of cities and counties as well as most states over the crisis of opioid addiction and overdose deaths. Members of the Sackler family are also named in the lawsuits.
Goldin’s group has kept up the pressure for more than a year at the Met and at other museums.
The company transforms construction equipment – including excavators, bulldozers and skid steers – into fully autonomous robots. The company’s automated guidance systems can be installed on existing equipment from any manufacturer, while maintaining complete manual operation capabilities, Built Robotics said. The upgraded equipment can then perform common tasks autonomously, such as digging trenches, excavating foundations, and grading building pads. The fleet can be managed via a web-based platform, which lets remote equipment operators to supervise the robots.
The Whitehead Institute board of directors today announced the selection of Ruth Lehmann, a world-renowned developmental and cell biology researcher, as the institute’s fifth director. Lehmann will succeed current Director David Page on July 1, 2020.
Lehmann is now the Laura and Isaac Perlmutter Professor of Cell Biology and chair of the Department of Cell Biology at New York University (NYU), where she also directs the Skirball Institute of Biomolecular Medicine and The Helen L. and Martin S. Kimmel Center for Stem Cell Biology. She is currently an investigator of the Howard Hughes Medical Institute. The Whitehead Institute appointment represents a homecoming: Lehmann was a Whitehead Institute member and a faculty member of MIT from 1988 to 1996, before beginning a distinguished 23-year career at NYU.
The purpose of this blog post is to describe a) what we did, b) what we think worked well, and c) what we will do differently next time. We hope that this document will be useful to other people organizing similar Summer Institutes, as well as people who are organizing partner locations for the 2020 Summer Institutes in Computational Social Science. If you are interested in hosting a partner location of SICSS 2020 at your university, company, NGO, or governmental organization, please read our information for potential partner locations.
There are valid reasons why tech companies listen to your dialogue with their smart devices. These companies review a small sample — less than 1 percent for Google and Apple — of user conversations. They say they do this because the recordings help make their products better. The workers who listen to these recordings take note of common mistakes — say an Amazon Echo hearing the word “elections” as “Alexa” — with the intent of improving the software.
The samples tech companies review are anonymized, so they don’t include personally identifiable information. But that’s not entirely reassuring because if you really want to figure out who someone is, in some cases you can just listen to what they’re saying.
“Anyone who’s dealt with voice systems will have worked in call centers, and all of them use these practices: Humans looking at errors, assessing them, and feeding corrections back into the system for machine learning to improve performance,” Bret Kinsella, founder of Voicebot.ai, a publication that covers voice-activated technology like smart speakers, told Recode. “You can’t run the system without recording, and it’s difficult to improve the system without human reviewers annotating errors.”
As the ability to harness the power of artificial intelligence grows, so does the need to consider the difficult decisions and trade-offs humans make about privacy, bias, ethics, and safety.
Climate change is one of the most important social issues of our time. The climate science community faces the immediate, important task of informing difficult decisions that must be made regarding our economic, environmental, and public health systems. Confidence in the effectiveness of these decisions derives from confidence in the underlying climate science. Appropriate statistical analyses can increase such confidence.
Hence, for climate science research to be most successful, statisticians and climate scientists must better integrate their research teams. Indeed, we believe that climate research requires multidisciplinary teams that include what we call climostatisticians. Such integration of these two scientific communities would likely require collaboration between divisions of research funding agencies, and might require reshaping of current reward systems in academic departments and government labs.
The University of Michigan will continue its work to create a nationally integrated repository of criminal justice data that will be used to support research and evidence-based policy-making. Researchers are working to collect individual-level data across all parts of the criminal justice system and link it with social and economic data to examine research questions that were previously unanswerable.
Tax law and big data are growing in alignment. And the Graduate Tax Program at the University of California, Irvine School of Law is looking to push the envelope when it comes to the two forces. It has entered into a partnership with Alteryx, Inc. (NYSE: AYX), a computer software company based in Irvine, revolutionizing business through data science and analytics.
Data science is quickly becoming an integral part of legal tax practice. Data automation tools, big data analytics, machine learning, and other foundations of artificial intelligence-based technologies are already being used in tax compliance, tax planning and tax advisory.
Striving to prepare graduates for the tax practice of tomorrow, the program is launching a tax and data science practicum in cooperation with Alteryx. The UCI/Alteryx Tax Law and Data Analytics Practicum will debut in the spring semester 2020.
Manipulated images and videos that appear “perfectly real” will be accessible to everyday people in “half-a-year to a year,” deepfake pioneer Hao Li said on CNBC on Friday.
“It’s still very easy, you can tell from the naked eye most of the deepfakes,” Li, an associate professor of computer science at the University of Southern California, said on “Power Lunch.”
“But there also are examples that are really, really convincing,” Li said, adding those require “sufficient effort” to create.
Toronto, ON, Canada October 26, starting at 12 p.m. “Rohan Alexander and Prof Kelly Lyons run a multidisciplinary brown bag that focuses on data cleaning. The series is a chance to share what we do in these crucial, yet little discussed, stages.” [free]
“Applications are now open for the 2020 cohorts of MassChallenge FinTech and MassChallenge HealthTech. Apply at https://accelerate.masschallenge.org by October 8, 2019 at 12:00PM (noon) ET.”
In 2014, Statistics Netherlands (CBS) rolled out a portal to make its open data more publicly available. Since then, the use of open data has grown explosively. More and more users, ranging from public authorities to businesses and private citizens, are finding their way to the portal. In a bid to lend a further boost to the use of open data, CBS made available a number of user-friendly instruction guides on its website that enable users to get started quickly and easily with Python or R as programming languages.
“This reading list gives an overview of the ethical concerns specific to data analysis, data science, and artificial intelligence.” … “This list is intended to spark new ideas and prompt critical thinking about data system design and integration into business processes in an organization. This is not an endorsement of all viewpoints represented in the readings below – except to say that each of the readings raise questions, put forward ideas, and make critiques that are worthy of your deep consideration.”