The Brown Institute for Media Innovation, founded in 2012, is located at both Columbia’s Graduate School of Journalism and Stanford’s Graduate School of Engineering. Each year it awards upwards of $1 million in grants and fellowships, called Magic Grants, to fund projects and prototypes that devise new tools and new ways to tell stories.
The “magic” is inspired by the language of the institute’s founding donor, legendary Cosmopolitan magazine editor Helen Gurley Brown, who wanted to provide students and others in the early stages of a project with the resources to follow their passions. As the Brown Institute prepares to select the next set of Magic Grant recipients, its director, Mark Hansen, discussed the breadth of the institute’s work.
Query understanding and relevance are key aspects of search, but they don’t tell the whole story. A holistic framework for search calls for a broader perspective. Search is about computers helping humans help themselves, a framework also known as human-computer information retrieval (HCIR).
In order to understand search as HCIR, we need to consider it from both the human and computational perspectives.
The big difference between the United States and the rest of the advanced economies around the world is that the U.S. is the only country that believes having no plan will solve this issue. We are the only country to completely abandon federal oversight of an uncompetitive, highly concentrated market that sells critical services to all people, yet we expect widely available, affordable, ultra-fast services. But if you live in a low-income neighborhood or in a rural market today, you know very well this is not working and the status quo is going to cement in your local broadband options to either one choice or no choice.
The consequences of these data discrepancies have already proven very real. Doctors have known for decades that people of African, Puerto Rican, and Mexican descent suffer unusually high rates of asthma-related deaths, but only recently learned why this might be: These groups commonly carry genetic variants that could be making them less sensitive to albuterol, a drug used in inhalers. The connection might have been obvious when looking at a pool of diverse genomic data—but over 90 percent of lung research has been done on populations of European descent.
The Seattle neighborhood populated by thousands of Amazonians everyday will soon see a major influx of Googlers.
Starting in the mid-to-late summer, Google will begin moving into its massive campus in the South Lake Union neighborhood that has been under construction for nearly two years, a company spokeswoman told GeekWire.
There’s a “factory of the future” being built in Shanghai, with $150 million in investment from Swiss-Swedish automation giant ABB. Slated for completion in 2020, the factory is a place where “robots will make robots,” according to ABB. But the cutting-edge robotics technology the facility hopes to showcase won’t cater only to heavy industrial needs. It will also largely feature “collaborative automation solutions” — known as cobots — that work with humans instead of replacing them. The facility is evidence of an emerging Chinese automation strategy that’s beginning to reshape the world’s approach to robotics.
The archetypal image of a futuristic factory involves droves of fully automated robots working nonstop — not a human worker in sight. In countries such as Indonesia, Vietnam and the Philippines, the United Nations’ International Labor Organization predicts automated machines will replace some 137 million workers over the next 20 years. But a scenario like that could prove catastrophic for China, the world’s most populous nation. That’s why China — already at the forefront of the global robot revolution — is laying the foundations for an alternative model: an industrial landscape where cobots will work alongside human workers to create a semiautonomous production process drawing on the strengths of both.
My two interests serendipitously came together in 2017. I had been retained as a technical expert to assist two colleagues with the patent portfolio of a great startup team that is designing and commercializing an impressive and innovative accelerator for machine learning. In essence, I was offered an opportunity to become an expert in hardware architecture for machine learning and it was an easy choice to dive in.
MLPerf started as a collaboration between companies such as Baidu and Google and academic researchers at Berkeley, Harvard and Stanford to measure performance for machine learning systems. The effort was inspired in many ways by the success of the SPEC benchmarks for CPUs, and adapted for the 21st century to be incredibly open and embrace the guiding principles of rapid development and iteration.
The dissemination of fake news received widespread attention from journalists, scholars, and the public after the 2016 election, with some people even ascribing the election outcome to the spread of false information on social media. But a new study from CDS affiliate faculty members Jonathan Nagler and Joshua Tucker, Professors of Politics and co-Directors of the NYU Social Media and Political Participation (SMaPP) lab, and Andrew Guess of Princeton University and former Moore Sloan Data Science Environment postdoc, reveals that the incidence of fake news sharing on Facebook was actually quite rare — over 90% of people in the researchers’ sample shared no links to fake news websites stories during the sample period.
1. One of the most celebrated successes of Deep Learning is image labeling, using CNNs, Convolutional Neural Networks, but the very essence of CNNs is that the front end of the network is designed by humans to manage translational invariance, the idea that objects can appear anywhere in the frame. To have a Deep Learning network also have to learn that seems pedantic to the extreme, and will drive up the computational costs of the learning by many orders of magnitude.
Researchers hope participants stay in the longitudinal study for a decade or more. “It’s easy and interesting for them to do,” Cotler said. “We hope they feel they are making a contribution to something bigger than themselves.”
Later this year, All of Us expects to launch Project Fitbit, a pilot effort to provide free activity trackers to 10,000 randomly selected participants who are already enrolled in the research project. This will provide information about how people use – or don’t use – digital health monitors, Cotler said.
The All of Us Research Program is a key element of the Precision Medicine Initiative, which launched in 2016 when $130 million was allocated to NIH to build a national, large-scale research participant group.
EPFL scientists have come up with a systematic method for studying and even predicting gene expression – without using cells. Using their innovative, quantitative approach, they measured important parameters governing gene regulation. This allowed them to design and construct a synthetic biological logic gate, which could one day be used to introduce new functions into cells. Their research has just been published in PNAS.
Proceedings of the National Academy of Sciences; Morgan R. Frank et al.
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Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.
Researchers, including my group at Harvard Medical School and at Brigham and Women’s Hospital, in Boston, have been developing chip-based systems with multiple organoids—systems with a miniature heart, a diminutive liver, even a basic brain. Many of these are 3D printed and all are connected by a circulatory system of microfluidic pumps and channels.
The University of Toronto has received its largest-ever donation, a $100-million gift to further the school’s research on artificial intelligence, biomedicine and how new technologies can disrupt and enrich lives.
The donation from the Gerald Schwartz and Heather Reisman Foundation will in part go to a new 750,000-square-foot complex to be built at the northeast corner of College St. and Queen’s Park starting this fall, school president Meric Gertler announced at a Monday news conference.
Detroit, MI April 9-11. “From IoT, Big Data and connectivity to automated and unmanned vehicles, and from safety, blockchain and powertrain to sustainability and cybersecurity, WCX covers every corner of the industry—right in the beating heart of The Motor City.” [$$$$]
Amsterdam, The Netherlands July 17, part of IC2S2. “IC2S2 brings together researchers in computational science, complexity, and social science, and provides a platform for new work in the field of computational social science.” [$$$]
“WiST supports university students eager to begin their careers in the sports tech industry. We define the sports tech industry broadly*, and encourage proposals that combine the themes of sports and technology creatively. Fellowships are an integral part of how we propel women into important roles and leadership positions in our industry.” Deadline for applications is March 31.
“Doctoral students enrolled in their fourth year or beyond of PhD studies and who are underrepresented in the field of computing may apply directly.” Deadline for applications is March 31.
To provide website visitors the ability to prevent their data from being used by Google Analytics, we have developed the Google Analytics opt-out browser add-on for the Google Analytics JavaScript (ga.js, analytics.js, dc.js).
Cluttered spaces can have negative effects on our stress and anxiety levels, as well as our ability to focus, our eating choices, and even our sleep. Much of the research (and much of the public enthusiasm) around tidiness and clutter is currently focused on the home, but with workplace stress costing American businesses up to $190 billion every year in health care costs alone, it’s time to recognize the role that clutter plays in our work lives — and to do something to clean up the mess.
AI2 celebrated 5 years this past January, and there is more going on in the institute than ever. We’re excited to start offering regular updates about our newest events, reports, and research initiatives.
Mozilla wants to make it easier to create, view, and replicate data visualizations on the web, and toward that end, it today unveiled Iodide, an “experimental tool” meant to help scientists and engineers write and share interactive documents using an iterative workflow. It’s currently in alpha, and available from GitHub in open source.