When Mike Franklin was being recruited to leave the University of California at Berkeley to head up the computer science department at the University of Chicago, it wasn’t exactly love at first sight. As he got a tour of the department’s proposed new home—the John Crerar Library, a onetime repository for scientific journals—Franklin considered the squat, dark, industrial-grade box and said, ” ‘There’s not enough light.’ They said, ‘We can change that,’ ” he recalls. “I said, ‘Hmm, maybe this could work.'”
And so workers cut big holes in the concrete walls for new windows, and today the space looks and feels more like a tech startup than a university. Opened in September, it’s the most visible part of U of C’s makeover of computer science that brought Franklin to Hyde Park two years ago. Windows may not seem like a big deal. But the renovation was a clear indication of just how serious U of C was about upgrading the program, and why Franklin was willing to leave Berkeley.
“It was a huge coup for (U of C President) Bob Zimmer to recruit him,” says Tom Siebel, founder of legendary Silicon Valley database-software maker Siebel Systems, who endowed a professorship at Berkeley held by Franklin, as well as similar positions at the University of Illinois (Siebel’s alma mater), Stanford and Princeton. “He’s a giant who’s universally respected, one of the innovators in Big Data. His academic credentials are second to none.”
One of the first sessions of the American Meteorological Society’s annual conference in Phoenix this weekend seemed like just the sort to attract plenty of government scientists: “Building Resilience to Extreme Political Weather: Advice for Unpredictable Times.”
But the conference, where more than 700 federal employees had been expected, will have few federal scientists in attendance. Many are barred from participating during the partial government shutdown, just one of the numerous consequences for the science community during the capital’s latest spending standoff.
“It’s a huge opportunity lost,” said Daniel A. Sobien, the president of the National Weather Service Employees Organization and a forecaster in the agency’s office near Tampa, Fla.
The Rockefeller Foundation and Chan Zuckerberg Initiative today announced that Benefits Data Trust (BDT) will receive a $1 million grant in the Communities Thrive Challenge— a $10-million effort to expand economic opportunity for low-income and financially insecure people and communities across the country. The Philadelphia-based organization is one of 10 grantees — from nine states and Puerto Rico — selected because of their demonstrated success and potential for future impact.
Millions of people are in need of food, healthcare, and housing but are not enrolled in programs that could help meet these needs. Each year, Benefits Data Trust helps tens of thousands of people receive critical supports using data, targeted outreach, policy change, and new technologies.
We are living in the world of Humans and Machines. These machines have to be programmed before they start following your instructions. But what if the machine started learning on their own from their experience, work like us, feel like us, do things more accurately than us? Well here’s where a Machine Learning Engineer Comes to Picture to make sure everything is working according to the procedures and Guidelines. In this article, we’ll discuss the Global Machine Learning Engineer Salary and see why every industry needs an ML Engineer. On an Average, an ML Engineer can expect a salary of ₹719,646 (IND) or $111,490 (US).
Our winner-take-all economic system (i.e. capitalism) is designed to grow at all cost, without concern for social, emotional or environmental implications. Our political system does not provide enough checks on this growth, and is in fact captured by the same interests who stand to benefit from its continuation. Both of these take advantage of superstition and bias to divide, alienate and conquer. Why should AI be any different, when the social and economic forces that create it are one and the same?
Others have questioned whether large-scale AI systems should be built at all, given their ambiguous moral, social, political and economic implications. While that is certainly a worthy question, in my mind there is one that is even more fundamental: wouldn’t we all be better off doing something else with our time and energy, instead of building these systems, or hand-wringing about their consequences?
In a landmark move, a group of MPs recently published a working definition of the term Islamophobia. They defined it as “rooted in racism”, and as “a type of racism that targets expressions of Muslimness or perceived Muslimness”.
In our latest working paper, we wanted to better understand the prevalence and severity of such Islamophobic hate speech on social media. Such speech harms targeted victims, creates a sense of fear among Muslim communities, and contravenes fundamental principles of fairness. But we faced a key challenge: while extremely harmful, Islamophobic hate speech is actually quite rare.
As scientific funding becomes increasingly scarce, professors in STEM fields spend more time in their offices writing grant applications: by one estimate, as much as one-fifth of their research time. That takes time and energy away from teaching students, training young researchers and making discoveries that boost our collective knowledge and well-being.
Two scientists believe that, with professors vying for such a small pool of funds, the grant-application process has become a competition not over who has the best ideas, but who is the best at writing grant applications. In a paper published Jan. 2 in the journal PLOS Biology, co-authors Carl Bergstrom, a professor of biology at the University of Washington, and Kevin Gross, a professor of statistics at North Carolina State University, use the economic theory of contests to illustrate how this competitive system has made the pursuit of research funding inefficient and unsustainable. They show that alternative methods, such as a partial lottery to award grants, could help get professors back in the lab where they belong.
Since 2016 the growth in iPhone sales has stalled. But Apple’s focus is on services: its upcoming Netflix competitor, Apple Music, and the App Store leave it in a commanding position
When young visitors from around the world headed to Australia’s Groovin the Moo music festival in Canberra last summer, public health policy researchers were watching carefully. In a rare decision, local officials there had allowed the installation of a pill-testing facility, allowing festivalgoers to have illegal substances examined for unusual and potentially even more dangerous additives, without having to fear arrest.
For a long time, authorities at festivals in Australia and elsewhere almost entirely focused on preventing people from taking drugs in the first place. That approach has done little to drive down the number of drug-related deaths, however, and a mounting body of research suggests that pill-testing facilities might be a more promising strategy.
Originally started as a free-access university course, Finland’s “1 percent” AI scheme is now being rolled out nationally with the support of private companies and the government.
For Helsinki, there is also a clear economic incentive to training large numbers of Finns in the basics of AI: Doing so may allow Finland to stay competitive amid ever growing competition between China and the United States, and in the aftermath of the rapid decline of Nokia, the national mobile champion that has fallen on hard times.
The narrative in 2019 is one of dozens of companies—particularly Amazon.com (AMZN) and Google—driving the growth of AI-powered devices in homes, Dan O’Connell, chief strategy officer and head of AI at Dialpad, a Silicon Valley startup behind UberConference and other business phone systems, told Barron’s in a phone interview.
“There will be tons of voice assistance, especially for cars and home appliances,” said John Foster, CEO of Aiqudo. The startup, which recently announced a voice-assistant agreement with Motorola, is attending CES.
The economic implications of AI are broad and sweeping: By 2030, up to a third of the American workforce—16 million to 54 million—will need to switch to new occupations, depending on adoption of AI, according to a report published by McKinsey Global Institute in late 2017.
Los Angeles has unveiled the US’s first publicly available earthquake early-warning app, a development seismologists hope will move cities across the west coast to invest in wide-scale alert technology.
ShakeAlertLA uses mobile app technology to warn Los Angeles county residents when the US Geological Survey’s early warning sensor network indicates an earthquake of 5.0 magnitude or larger is about to shake their location.
Cambridge, MA September 28, “a meeting of statisticians and quantitative analysts connected with sports teams, sports media, and universities to discuss common problems of interest in statistical modeling and analysis of sports data.” Deadline for submissions is June 15.
Munich, Germany June 11-14. “Dataset paper submissions must comprise two parts: a dataset or group of datasets, and metadata describing the content, quality, structure, potential uses of the dataset(s), and methods employed for data collection. Descriptive statistics may be included in the metadata (more sophisticated analyses should be part of a regular paper submission).” Deadline for submissions is January 15.
“Betaworks Camp is a highly selective eleven-week program for early stage startups hosted out of the Betaworks offices in New York City. Each Camp has a specific theme related to our wider investment thesis around emerging technologies and interfaces.” Deadline to apply is January 15.
New Haven, CT June 3-14 at Yale University. “Animal Movement Analysis summer school is offered as a two-week professional training course, that targets students, researchers and conservation practitioners that have collected animal relocation data and want to learn how to analyze these data.” Deadline to apply is February 1.
I was recently asked by a startup I’m consulting (BigPanda) to give my opinion about the structure and flow of data science projects, which made me think about what makes them unique. Both managers and the different teams in a startup might find the differences between a data science project and a software development one unintuitive and confusing. If not stated and accounted for explicitly, these fundamental differences might cause misunderstanding and clashes between the data scientist and his peers.
Respectively, researchers coming from academia (or highly research-oriented industry research groups) might have their own challenges when arriving at a startup or a smaller company. They might find it challenging to incorporate new types of inputs, such as product and business needs, tighter infrastructure and compute constraints and costumer feedback, into their research and development process.
The aim of this post, then, is to present the characteristic project flow that I have identified in the working process of both my colleagues and myself in recent years. Hopefully, this can help both data scientists and the people working with them to structure data science projects in a way that reflects their uniqueness.
We present a language for generic computation using Factor Graphs, a computationally convenient data structure abstraction that has been popularly utilized for efficient inference in the framework of probabilistic graphical models cf. [22, 15, 30]. We show that message passing over Factor Graphs is Turing-complete. As an important contribution of this work, we show that a Factor Graph can be realized using any Publisher-Subscriber (PubSub) infrastructure.