Facebook says it wants Facebook Watch programming that’s unique to its platform — with big names and built-in distribution, if possible. That excludes the short-form, unscripted, lifestyle programming that’s ubiquitous on the web and that Facebook bought in droves when it first commissioned shows for Facebook Watch.
Facebook entertainment video executives, including Ricky Van Veen, the company’s head of global creative strategy, have been telling potential production partners that they’re interested in funding Watch shows that can get off to a “hot start” on Facebook. Sources described “hot start” shows as shows that star or are made by high-profile creators who already have a big Facebook following. They include “Red Table Talk,” an interview series hosted by Jada Pinkett Smith (who has more than 7.9 million Facebook fans) and “The Real World” reboot with Bunim-Murray Productions (which also makes “Ball in the Family” for Facebook Watch) and MTV (which has more than 46 million followers on Facebook).
“They’re not buying anything else — everything apart from news and sports is talent based,” said one publishing source that has produced multiple shows for Facebook Watch in the past year and recently pitched Facebook on a few projects.
The Harvard Crimson, Amy L. Jia and Sanjana L. Narayanan,
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About a quarter of today’s tenured Statistics faculty is female, a development that administrators, professors, and alumni see as a sea-change from the department of decades past.
Senior Vice Provost for Faculty Development and Diversity Judith D. Singer, who received her PhD in Statistics from Harvard in 1983, said the Statistics department was “very, very much gender-imbalanced for decades.”
“This was a department that always had female graduate students but never had female faculty, ever,” Singer said.
Singer attributed the imbalance to the small size of the department and a conspicuous lack of undergraduate concentrators in its early years.
As demand increases for computer science programs, the University will welcome four new professors to the department over the course of this academic year. But despite the department’s expansion, some challenges facing computer science at Yale persist, according to department chair Zhong Shao.
The new professors — Marynel Vazquez, Abhishek Bhattacharjee, Yang Cai and Nisheeth Vishnoi — bring expertise in robotics; economics and computation; and computer networking and theory. Vazquez joined the Yale faculty in July while Bhattacharjee, Cai and Vishnoi will join the department in January of 2019. The hiring of the new professors falls in line with the University’s commitment to expanding the size of the Department of Computer Science, outlined in a 2015 announcement.
But while Shao spoke highly of the new hires, he outlined a number of obstacles facing his department, such as personnel shortages and a lack of facilities relative to peer institutions. Although the number of students majoring in computer science at Yale has quadrupled over the past 10 years, for a large portion of that time, the number of faculty members has remained largely constant.
GraphQL, the Facebook -incubated data query language, is moving into its own open-source foundation. Like so many other similar open-source foundations, the aptly named GraphQL Foundation will be hosted by the Linux Foundation.
AI is changing the world, whether we’re ready for it or not. We are already seeing examples of AI algorithms being applied to reduce doctors’ workloads by intelligently triaging patients, connect journalists with global audiences because of accurate language translations, and reduce customer service wait time, according to Google. But even as we begin to benefit from AI, there is still an air of uncertainty and unease about the technology.
For example, Google recently backed out of a controversial military contract using AI after receiving public backlash.
Now, the company is taking the future of responsible AI more seriously. In June, Google laid out its AI principles, and this week it began opening up a discussion of the concerns that customers most frequently have about AI. The concerns are broken into four areas: unfair bias, interpretability, changing workforce, and doing good.
While we wait for the next AI-generated work to hit the block, there’s a lot more to learn. To find out about the interesting work being created with machine learning—and the complex boundaries it’s pushing—we’ve assembled a list of nine pioneering artists to watch.
Five new Artificial Intelligence centres are to be established in the UK to help hospitals make scans and biopsy images digital in a bid to cut down manual reporting.
Greg Clark Secretary of State for Business, Energy and Industrial Strategy announced five new Artificial Intelligence centres of excellence for digital pathology and imaging, including radiology, using artificial intelligence (AI) medical advances. Established to assist hospitals make scans and biopsy images digital, the centres have the aim to free up more staff time for direct patient care in the NHS and is part of a bid to find new ways to speed up diagnosis of diseases to improve to outcomes for patients.
Alphabet is convening employees from across its various health units, including life-sciences R&D unit Verily and health-focused artificial intelligence project Google Brain, for an invitation-only two-day conference at the company’s Sunnyvale campus.
The event showcases Alphabet’s growing interest in the space. It’s also one of the first times that Alphabet has organized a big gathering for its health groups, which are spread out across the organization.
Some of these teams sit within Google, like the Google Fit wearables team; home automation group Nest, which CNBC has reported is interested in health-tech scenarios, such as helping seniors living independently for longer; and Google Brain. Others are independent companies within Google holding company Alphabet, including Calico, which is doing anti-aging research, and Verily.
Amazon’s critics were apoplectic at what they called a bait-and-switch.
“I was shocked,” said Robert B. Engel of the Free & Fair Markets Initiative, a nonprofit that is a determined foe of the retailer on all fronts. “They’ve duped more than the bidders. They’ve duped all of us. They can’t even live up to a promise that wasn’t fair to anyone but Amazon.”
From the company’s point of view, however, things seem to be working out rather nicely.
Research and scientific discovery are rooted in a rich, fluid ecosystem of shared information that includes data, publications, software, physical samples, and a myriad of other research products. A combination of technological advances and increasing pressures on global resources is prompting a major shift in how data and research products are shared and valued in the Earth, space, and environmental sciences (ESES). This shift is complicated by legacy systems of communication, incentives, and cultural norms. Open sharing [European Commission, 2016] of data and research products will mitigate many of these challenges and enable new frontiers of discovery. Toward this goal, scientific publishers, geoscience data repositories, funders, and other stakeholders recently met as part of the Enabling FAIR Data project, funded by the Laura and John Arnold Foundation through AGU. By leveraging the FAIR principles [Wilkinson et al., 2016]—findable, accessible, interoperable, and reusable—this emerging community is working together to ensure that data, physical samples, and software are treated as first-class research products to open new opportunities for ESES research.
As data science systems become more widespread, effectively governing and managing them has become a top priority for practitioners and researchers. While data science allows researchers to chart new frontiers, it requires varied forms of discretion and interpretation to ensure its credibility. Central to this is the notion of trust – how do we reliably know the trustworthiness of data, algorithms and models?
This is the basis of research from Samir Passi, doctoral student in information science, and Steven Jackson, associate professor of information science, whose paper “Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects,” received a Best Paper Award at the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), held Nov. 3-7. Passi and Jackson also received a Best Paper Award for “Data Vision: Learning to See Through Algorithmic Abstraction” at last year’s CSCW conference.
Ann Arbor, MI May 17-19, 2019. “CAED invites submissions of paper proposals for its 15th conference on research using enterprise microdata.” Deadline for submissions is January 19, 2019.
PDF files hold lots of information. Most of the information contained in a PDF file is used to render a document in a reproducible way across many different platforms, be it a PDF of a contract, an instruction manual, or your favorite cat meme.
But did you know there’s also an abundance of metadata in a PDF with information about dates and times of creation and editing, what application was used, the subject of the document, the title and author, and more? These are examples of the standard set of metadata properties, but there are also ways to insert custom metadata into a PDF and insert hidden comments in the middle of the format. This post will introduce some different forms of metadata and show where to look for them.
We built an assembly line for building, testing, and deploying data products, which we called the machine learning platform. With it, we could now deploy a model to production in minutes. We no longer had to wait as long to enjoy a return on our analytics investments.
What we learned along the way
Along the way we learned some important rules about how to build, test, and deploy machine learning models safely and quickly. These rules changed how we work, hopefully you’ll find them useful for you and your organization.
Zero-shot learning (ZSL) is a process by which a machine learns to recognize objects it has never seen before. Researchers at Facebook have developed a new, more accurate ZSL model that uses neural net architectures called generative adversarial networks (GANs) to read and analyze text articles, and then visually identify the objects they describe. This novel approach to ZSL allows machines to classify objects based on category, and then use that information to identify other similar objects, as opposed to learning each object individually, as other models do.