Data Science newsletter – January 5, 2019

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“The tech companies are not flinching,” said Bob Staedler, a Silicon Valley consultant. “Nothing has hit them on the nose hard enough to tell them to cut back. Instead, they are expanding. They’re going around the country acquiring the best human capital so they can create the next whiz-bang thing.”

There is so much of life that remains undisrupted. The companies are competing to own the cloud — to become, in essence, the internet’s landlord. They have designs on cities: Google made a deal in 2017 to reimagine a chunk of waterfront Toronto from the ground up. Amazon is reworking the definition of community from the inside, as warehouses in rural areas provide the urban middle class with everything they want to stay home all weekend.

Below find insights from Google Brain cofounder Andrew Ng, Cloudera general manager of ML and Fast Forward Labs founder Hilary Mason, Facebook AI Research founder Yann LeCun, and Accenture’s responsible AI global lead Dr. Rumman Chowdhury. We wanted to get a sense of what they saw as the key milestones of 2018 and hear what they think is in store for 2019.

Amid a recap of the year and predictions for the future, some said they were encouraged to be hearing fewer Terminator AI apocalypse scenarios, as more people understand what AI can and cannot do. But these experts also stressed a continued need for computer and data scientists in the field to adopt responsible ethics as they advance artificial intelligence.

In his research on college students’ productivity, Cornell Tech graduate student Fabian Okeke heard many accounts of time lost to social media, beginning with a click over to Facebook or YouTube for a quick distraction.

But the distraction was not always so quick.

“You have cases where a few minutes becomes an hour, because these programs have been designed to keep pulling people back in,” said Okeke, a doctoral student in the field of information science at Cornell Tech. “So we started thinking about ways we could design different kinds of interventions that could help people focus back on work.”

Okeke and his colleagues didn’t want to block Facebook or other potentially addictive apps – blocking tools already exist, these sites can be helpful or entertaining, and people might avoid signing up for a tool that bars them from certain apps completely. Instead, they looked to the theories of behavioral economics and psychology and developed an app that uses negative reinforcement, in the form of persistent smartphone vibrations, to remind users they’d exceeded their predetermined time limit.

Few people get a bigger whiff of the entrepreneurial spirit taking hold among biosciences faculty members than Thelma Tennant. “I walk down the halls of the lab buildings regularly,” Tennant said, “and I don’t think I’ve made it to a meeting without having someone come out a door and say, ‘Hey, I’ve been meaning to call you.’”

Tennant, whose doctorate is in cancer biology, is the oncology lead for technology commercialization in the University of Chicago’s Polsky Center for Entrepreneurship and Innovation. For the past year, she’s also been one of several Polsky staffers embedded in the Duchossois Family Institute: Harnessing the Microbiome and Immunity for Human Health.

The Duchossois Family Institute was founded with a $100 million gift from The Duchossois Group Inc. Chairman and CEO Craig Duchossois; his wife, Janet Duchossois; and The Duchossois Family Foundation to accelerate research and interventions based on how the immune system, microbiome and genetics interact to maintain health. The entrepreneurial infrastructure includes commercialization specialists with a deep understanding of science and the path to market. Embedded within the DFI, they work closely with faculty and students to build strong patent applications and the relationships with investors and industry needed to successfully develop and license these technologies.

Improving the reproducibility of neuroscience research is of great concern, especially to early-career researchers (ECRs). Here I outline the potential costs for ECRs in adopting practices to improve reproducibility. I highlight the ways in which ECRs can achieve their career goals while doing better science and the need for established researchers to support them in these efforts.

Anthropologists often call attention to the problems posed by social inequality, but academic anthropology also reproduces many of the very inequalities that its practitioners work to critique. Past research on US academic hiring networks has shown evidence of systematic inequality and hierarchy, attributed in significant part to the influence of academic prestige, which is not necessarily a reflection of merit or academic productivity. Using anthropology departments’ websites, we gathered information on all tenured and tenure‐track faculty in PhD‐granting anthropology programs in the United States, totaling 1,918 individuals in all. For each faculty member, we noted their current institution and PhD‐granting institution, which we treated as a “tie” between those academic programs. With those data, we applied both statistical and social network analysis (SNA) methods to explain variation in faculty placement as well as the network’s overall structure. In this article, we report on our findings and discuss how they can be used to help rethink academic reproduction in American anthropology. [free access]

Truly revolutionary political transformations are naturally of great interest to historians, and the French Revolution at the end of the 18th century is widely regarded as one of the most influential, serving as a model for building other European democracies. A paper published last summer in the Proceedings of the National Academy of Sciences offers new insight into how the members of the first National Constituent Assembly hammered out the details of this new type of governance.

Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature.

We asked young scientists these questions: Have you ever encountered a particularly stark difference between an old and new position in your education or career? What was the difference between the positions, and what advice would you give to someone making a similar transition? Here, respondents share the challenges they faced when they took on new responsibilities and roles, changed fields, or moved to new places. To others in similar situations, they advise: Be confident, prepared, and patient; communicate; and always ask for help when needed.

Something strange happened over the course of 2018: As more and more people had their pipes evaluated in 2018, fewer and fewer inspections were finding lead pipes. In November 2017, according to meeting notes obtained by local news outlet MLive’s Zahra Ahmad, the city’s head of public works estimated that about 10,000 of Flint’s homes still had lead pipes, roughly in line with the number other experts have floated. The new contractor hasn’t been efficiently locating those pipes: As of mid-December 2018, 10,531 properties had been explored and only 1,567 of those digs found lead pipes to replace. That’s a lead-pipe hit rate of just 15 percent, far below the 2017 mark.

There are reasons for the slowdown. AECOM discarded the machine-learning model’s predictions, which had guided excavations. And facing political pressure from some residents, Weaver demanded that the firm dig across the city’s wards and in every house on selected blocks, rather than picking out the homes likely to have lead because of age, property type, or other characteristics that could be correlated with the pipes.

[Luis] von Ahn set out with his developers to make the app as addictive as Candy Crush and other popular games—in a good way. The addiction Duolingo cultivates, he says, isn’t harmful in the way the World Health Organization says compulsive video-game playing is; the organization classifies excessive video-gaming alongside opioid or amphetamine abuse. Duolingo really is about self-improvement, von Ahn says—time otherwise spent playing games, on social media, or doing nothing, is applied to developing a skill. What’s so bad about that?

Good or bad, Duolingo’s addiction rate is way up. Next-day retention is 55 percent, up from 13 percent in 2012. “That’s about as good as a middle-of-the-road game,” von Ahn says. And with about 300 million users, Duolingo is the largest language-teaching company in the world, by user base.

Senate lawmakers late Wednesday confirmed Kelvin Droegemeier, an extreme-weather expert, as the White House’s top science and tech adviser, filling a critical administration role that had been vacant for nearly two years under President Trump.

Droegemeier, who had served as a top meteorologist at the University of Oklahoma, is set to become leader of the Office of Science and Technology Policy, an arm of the White House that helps guide federal research spending and informs the government’s policies in areas such as artificial intelligence, climate change, precision medicine and online privacy.

Linguists estimate at least half of the world’s estimated 7,000 spoken languages will become extinct by the century’s end due to forces ranging from globalization to cultural assimilation.

Part of the challenge of documenting and revitalizing endangered languages is a lack of texts and speech recordings to work with. Seneca, a language of one of the six Iroquois Nations in North America, has only about 100 first-language speakers and several hundred more second-language learners.

Automatic speech recognition (ASR) technology is widely used to transcribe languages with millions or billions of speakers, like English and Mandarin. But it has only scratched the surface with languages like Seneca, which have vastly fewer speakers and significantly less data to work with.

Now a team of researchers at the Rochester Institute of Technology in New York, along with colleagues from the University at Buffalo, is tapping deep learning to bolster the ability of ASR. And while its focus is on Seneca, the project’s vision encompasses the preservation of languages globally as well as an important part of our shared cultural history.

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams’ challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners’ needs.

La Jolla, CA March 14-15. “Individualized medicine takes into account a person’s genes—and genomics will remain a core topic for exploration and discussion—but it also considers the full spectrum of a person’s uniqueness from their biologic, physiologic, anatomic, lifestyle and environmental information. The Future of Individualized Medicine conference will thus incorporate perspectives from the emerging fields of digital medicine, artificial intelligence and machine learning, behavioral science and others.” [$$$]

Lying at the heart of modern data science and analysis, Jupyter Notebooks are an incredibly powerful tool at both ends of the project lifecycle. Whether you’re rapidly prototyping ideas, demonstrating your work, or producing fully fledged reports, notebooks can provide an efficient edge over IDEs or traditional desktop applications.

Following on from “Jupyter Notebook for Beginners: A Tutorial”, this guide will take you on a journey from the truly vanilla to the downright dangerous. That’s right! Jupyter’s wacky world of out-of-order execution has the power to faze, and when it comes to running notebooks inside notebooks, things can get complicated fast.