Data Science newsletter – February 19, 2018

GROUP CURATION: N/A

… Let me explain what I think the problems are, and then I’ll suggest some solutions (or maybe just workarounds).

The fundamental problem is that the barrier between using a computer and programming a computer is getting higher.

When I got a Commodore 64 (in 1982, I think) this barrier was non-existent. When you turned on the computer, it loaded and ran a software development environment (SDE). In order to do anything, you had to type at least one line of code, even if all it did was another program (like Archon).

Since then, three changes have made it incrementally harder for users to become programmers

1) Computer retailers stopped installing development environments by default.

A collaboration between two professors – one of music and one of engineering – at Virginia Tech resulted in the creation of a new platform for data analysis that makes it possible to understand data better by turning it into sound.

This is a pioneering approach to studying spatially distributed data which instead of placing information into a visual context to show patterns or correlations – meaning, data visualization – uses an aural environment to leverage the natural affordances of the space and the user’s location within the sound field.

The technology used by Facebook, Google and Amazon to turn spoken language into text, recognize faces and target advertising could help doctors combat one of the deadliest killers in American hospitals.

Clostridium difficile, a deadly bacterium spread by physical contact with objects or infected people, thrives in hospitals, causing 453,000 cases a year and 29,000 deaths in the United States, according to a 2015 study in the New England Journal of Medicine. Traditional methods such as monitoring hygiene and warning signs often fail to stop the disease.

But what if it were possible to systematically target those most vulnerable to C-diff? Erica Shenoy, an infectious-disease specialist at Massachusetts General Hospital, and Jenna Wiens, a computer scientist and assistant professor of engineering at the University of Michigan, did just that when they created an algorithm to predict a patient’s risk of developing a C-diff infection, or CDI. Using patients’ vital signs and other health records, this method — still in an experimental phase — is something both researchers want to see integrated into hospital routines.

In a lab at the Georgia Institute of Technology, physicists run experiments with robots that look as though they came from the dollar store. The robots can’t move through space. They can’t communicate. Mostly they flap their little arms, like beetles stuck on their backs.

But put a lot of these objects together and you get something from nothing: They hit each other, nudge each other and tangle with each other. And eventually, they start to work as a unit.

Researchers are learning how to control these systems so that they function in a manner similar to swarms of bees or colonies of ants: Each individual operates in response to the same basic set of instructions. But when the swarm comes together, its members can carry out complex behaviors without any centralized direction.

“Our whole perspective is: What’s the simplest computational model that will achieve these complicated tasks?” said Dana Randall, a computer scientist at Georgia Tech and one of the lead researchers on the project. “We’re looking for elegance and simplicity.”

When Dustin Gordon’s high school invited juniors and seniors to meet with recruiters from colleges and universities, a handful of students showed up.

A few were serious about the prospect of continuing their educations, he said, “But I think some of them went just to get out of class.”

In his sparsely settled community in the agricultural countryside of southern Iowa, “there’s just no motivation for people to go” to college, says Gordon, who’s now a senior at the University of Iowa.

Can publicly available data from large-scale social media networks be used to help predict catastrophic events within the country’s infrastructure, such as threats to national security, the energy system or even the economy?

Conrad Tucker, associate professor of engineering design and industrial engineering at Penn State, has received funding from the U.S. Air Force to investigate whether crowd-sourced data from social media can be used to not only detect threats, but also prevent catastrophic events from happening in the future.

In terms of taming the chaos of science, I view advances in our software’s capabilities, and the development and evolution of our perspectives on data analysis, as a kind of ratchet that inexorably advances our science.

Papers, unless they accomplish the very difficult task of nailing down a concept and explaining it well, do very little to advance our lab’s science. They are merely artifacts that we produce because they meet metrics, with the side effect of being one relatively ineffective way to communicate methods and results.

The pitch was simple: Forget accredited graduate schools and think big at Singularity University. Google co-founder Larry Page and futurist Ray Kurzweil could be among your lecturers in the Graduate Studies Program at Singularity, named for the notion that humans will someday merge with machines. You’d work in a kind of combination think tank and startup incubator, trying to address challenges as grand as renewable energy and space travel. Kurzweil announced the program during a TED Talk in 2009, adding that the Singularity team had leased its campus from NASA, just east of the agency’s historic Hangar One in Mountain View, Calif. The team received 1,200 applications for its first class of 40 later that year.

Reality hasn’t matched the hype. Previously unreported police files, other documents, and interviews with current and former students and staff paint the picture that almost from the beginning, some Singularity staffers weren’t able to curb their worst impulses.

The University of Waterloo will be a key partner with leading Canadian companies and sectors chosen to help grow our country’s global competitiveness through significant investments in the areas of artificial intelligence (AI) and advanced manufacturing.

As part of the Government of Canada’s $950 million Innovation Supercluster Initiative, Waterloo will take a leading research role in two of the five winning bids announced today. The effort will see researchers and innovators from Waterloo become key contributors in industry-led consortia.

“The University of Waterloo is very proud to be participating in two of the five initiatives selected by government to help advance our nation’s economy,” said Feridun Hamdullahpur, president and vice-chancellor at Waterloo. “With the discoveries already made to date in Waterloo, we can only imagine what can be accomplished when our knowledge and expertise in the fields of artificial intelligence and advanced manufacturing join with the power of some of Canada’s most important industrial players.”

The biggest threat and benefit to U.S. consumers in the next 10 years will be artificial intelligence. U.S. search professionals will not only need to learn the nuances of platforms from Amazon, Bing, Google Oath, and others, but they will need to relearn how to rewrite and re-strategize campaigns to achieve a change in mindset among consumers who also are trying to readjust.

Earlier this week, IAB CEO Randell Rothenberg called for advertising industry professionals to step up and support the “direct brand economy” because he says “a two-way relationship is more valuable than a one-way impression.” That goes for search too, as consumers wake up to a world where machines interact more prominently with humans.

Northeastern University and Gallup have released findings from a survey that gauges public perceptions about artificial intelligence (AI). The study — Optimism and Anxiety: Views on the Impacts of Artificial Intelligence and Higher Education’s Response — analyzes responses from 3,297 U.S. adults between September 15 and October 10, 2017, to explore the impact AI will have on people’s lives and work; their educational choices; and the potential interventions from higher education, government and private industry.

You have a report deadline in 20 minutes and your software keeps incorrectly reformatting your document. Or you’re driving along when another car cuts you off at the intersection. Or you’re upset at your boss and decide to finally tell him how you really feel about him in an email.

Wouldn’t it be great if technology could detect your feelings and step in to fix the problem, prevent you from doing something dangerous, or pointed out the benefits of holding onto your job?

Welcome to the world of affective computing, otherwise known as artificial emotional intelligence or Emotion AI. Rapidly being incorporated into everything from market research testing to automotive interfaces to chatbots and social robotics, this is a branch of AI that will continue to rapidly grow over the next few decades. According to research group Markets and Markets, they expect the global affective computing market to grow from $12.20 Billion in 2016 to $53.98 Billion by 2021, at a compound annual growth rate (CAGR) of 34.7%.

Landscape ecologist Sharon Collinge says she’s happiest in the field. But next week she’ll move indoors to lead the National Ecological Observatory Network (NEON) as it evolves from a beleaguered construction project into a unique user facility.

Collinge says she couldn’t be more excited by the prospect of “working in a leadership position with an organization that can have an impact on society.” That could be a tall order, however, given the troubled history of the $469 million project, which endured nearly a decade of false starts before construction began in 2012.

The longtime professor at the University of Colorado (CU) in Boulder will set up shop at NEON’s headquarters only a few blocks away from the CU campus. Although dozens of scientists work there, the real action will take place at 81 sites in 20 ecological domains scattered across the United States.

Their current system has been ramping up for years. Given the space and resources to think the problem through, develop a set of complementary approaches, and assess their performance in an ongoing way, administrators and teachers have been able to not just think outside the box but bring what they learned back inside it. The breakthroughs haven’t come at the cost of the structure — they’ve strengthened it.

Thanks to innovative data analysis, advisers, who are each individually responsible for some 300 students, don’t get overwhelmed. Nor do they wake up one morning to discover that students have fallen through the cracks.

Then there’s the adaptive learning software that, paired with student-led supplemental instruction, flows attention and resources to those who need it most, where they need it most.

In 2016 [Sophia] Lin, along with Andrew Kelley, co-founded Keel, a fintech startup that functions almost like a social network for investing, connecting rookie investors with more seasoned ones who can show them the ropes.

The company’s core technology is an algorithm that aggregates investment data from hundreds of brokers. This lets people closely follow their returns and also highlights when people have outperformed the market. Investors can then pay a fee to follow those who have performed well, helping them make smarter investment decisions.

Here is what Lin learned about starting and launching a fintech company:

Whose medical diagnosis would you trust more: an experienced doctor’s or an algorithm’s? It turns out that — at least when it comes to pneumonia — artificial intelligence can decode chest X-rays more accurately than radiologists can.

We can use the help. According to the CDC, pneumonia sends 1 million people to U.S. hospitals each year. The lung infection can be tough to spot, and roughly 50,000 people in the United States die from it annually.

The project to create a machine-learning diagnostic tool began with a large data set released by the National Institutes of Health of more than 100,000 frontal-view chest X-rays, labeled with 14 possible diagnoses. The NIH included some preliminary algorithms for deciphering the illnesses and asked for help advancing them.

A group of Stanford computer scientists teamed up with assistant professor of radiology Matthew Lungren to set about the task. They enlisted four Stanford radiologists to analyze 420 of the images for indications of pneumonia, which served as a baseline for diagnostic performance. The computer scientists, meanwhile, designed CheXNet, an algorithm that learned in about a week’s time to identify 10 of the 14 diagnoses in the original data set more accurately than previous algorithms had been able to. After a month, CheXNet was ahead in all 14 categories.

We are private industry leaders in AI research and development, and we want to discuss how AI has moved from the lab to the everyday world, whether the field has finally escaped its past boom and bust cycles, and what we can expect from AI in the coming years.

Ask us anything!

Brooklyn, NY Sunday, March 4, starting at 10 a.m., Genspace (140 32nd Street). “We will explore the rich visual culture of science and discuss why a well-crafted drawing may be more useful than a long explanation. If you have a scientific project or topic that you want to share with the world you’re encouraged to draw a comic about it.” [$$]

Grapevine, TX March 5-8. “At Gartner Data & Analytics Summit 2018, we’ll help you create the future — a future based on data you can trust, analytics you can rely on, and the insight needed to make game-changing business decisions.” [$$$$]

Stanford, CA March 1, starting at 6 p.m., Knight Management Center. “Join us for an evening of unique perspectives as we host panelists who helped build the original IBM Watson, who are working on robots for NASA’s mission to Mars, and who can decipher fact from fiction in our understanding of AI.” [$$]

… Amy Bruckman, a professor and associate chair in the School of Interactive Computing at Georgia Tech, has two sons, ages 12 and 14. As many parents do, Bruckman initially tried to keep track of her kids’ screen time, especially their video game use. Then she decided to switch up her focus.

Bruckman’s new plan let her sons choose their own amounts of screen time on the weekends as long as they also read a book for an hour every day, exercised and, ultimately, led more balanced lives. Her plan worked “well beyond” her expectations, she said. Why? They “embraced the values behind the new system” ― that is, the boys came to appreciate the importance of reading, exercise and simply other things beyond technology.

“I also tell them, ‘Look, if you were doing nothing but practicing your musical instrument with every second you had, I would say that was a problem, too,’” Bruckman said.

Symposium videos.

This blog post is mostly for myself but maybe it’s useful to others. It contains my current R markdown blog template. I initially posted this as a question at StackOverflow. Then I read how much a burden we put in Yihui Xie and decided that my current setup (copy-pasting) works just fine. In any case using blogdown with the RStudio IDE is much simpler than what I used to do in the past with jekyll or with even my prior setup with blogdown.