science

How the Federal Government is thinking about Artificial Intelligence

Curated on September 1, 2016August 3, 2018 by Stefaan Verhulst

Mohana Ravindranath at NextGov: “Since May, the White House has been exploring the use of artificial intelligence and machine learning for the public: that is, how the federal government should be investing in the technology to improve its own operations. The technologies, often modeled after the way humans take in, store and use new information, could help researchers find patterns in genetic data or help judges decide sentences for criminals based on their likelihood to end up there again, among other applications. …

Here’s a look at how some federal groups are thinking about the technology:

Police data: At a recent White House workshop, Office of Science and Technology Policy Senior Adviser Lynn Overmann said artificial intelligence could help police departments comb through hundreds of thousands of hours of body-worn camera footage, potentially identifying the police officers who are good at de-escalating situations. It also could help cities determine which individuals are likely to end up in jail or prison and officials could rethink programs. For example, if there’s a large overlap between substance abuse and jail time, public health organizations might decide to focus their efforts on helping people reduce their substance abuse to keep them out of jail.
Explainable artificial intelligence: The Pentagon’s research and development agency is looking for technology that can explain to analysts how it makes decisions. If people can’t understand how a system works, they’re not likely to use it, according to a broad agency announcement from the Defense Advanced Research Projects Agency. Intelligence analysts who might rely on a computer for recommendations on investigative leads must “understand why the algorithm has recommended certain activity,” as do employees overseeing autonomous drone missions.
Weather detection: The Coast Guard recently posted its intent to sole-source a contract for technology that could autonomously gather information about traffic, crosswind, and aircraft emergencies. That technology contains built-in artificial intelligence technology so it can “provide only operational relevant information.”
Cybersecurity: The Air Force wants to make cyber defense operations as autonomous as possible, and is looking at artificial intelligence that could potentially identify or block attempts to compromise a system, among others.

While there are endless applications in government, computers won’t completely replace federal employees anytime soon….(More)”

How Tech Giants Are Devising Real Ethics for Artificial Intelligence

Curated on September 1, 2016August 3, 2018 by Stefaan Verhulst

John Markoff in the New York Times: “For years, science-fiction moviemakers have been making us fear the bad things that artificially intelligent machines might do to their human creators. But for the next decade or two, our biggest concern is more likely to be that robots will take away our jobs or bump into us on the highway.

Now five of the world’s largest tech companies are trying to create a standard of ethics around the creation of artificial intelligence. While science fiction has focused on the existential threat of A.I. to humans,researchers at Google’s parent company, Alphabet, and those from Amazon,Facebook, IBM and Microsoft have been meeting to discuss more tangible issues, such as the impact of A.I. on jobs, transportation and even warfare.

Tech companies have long overpromised what artificially intelligent machines can do. In recent years, however, the A.I. field has made rapid advances in a range of areas, from self-driving cars and machines that understand speech, like Amazon’s Echo device, to a new generation of weapons systems that threaten to automate combat.

The specifics of what the industry group will do or say — even its name —have yet to be hashed out. But the basic intention is clear: to ensure thatA.I. research is focused on benefiting people, not hurting them, according to four people involved in the creation of the industry partnership who are not authorized to speak about it publicly.

The importance of the industry effort is underscored in a report issued onThursday by a Stanford University group funded by Eric Horvitz, a Microsoft researcher who is one of the executives in the industry discussions. The Stanford project, called the One Hundred Year Study onArtificial Intelligence, lays out a plan to produce a detailed report on the impact of A.I. on society every five years for the next century….The Stanford report attempts to define the issues that citizens of a typicalNorth American city will face in computers and robotic systems that mimic human capabilities. The authors explore eight aspects of modern life,including health care, education, entertainment and employment, but specifically do not look at the issue of warfare..(More)”

Data and Democracy

Curated on August 30, 2016August 3, 2018 by Stefaan Verhulst

(Free) book by Andrew Therriault: “The 2016 US elections will be remembered for many things, but for those who work in politics, 2016 may be best remembered as the year that the use of data in politics reached its maturity. Through a collection of essays from leading experts in the field, this report explores how political data science helps to drive everything from overall strategy and messaging to individual voter contacts and advertising.

Curated by Andrew Therriault, former Director of Data Science for the Democratic National Committee, this illuminating report includes first-hand accounts from Democrats, Republicans, and members of the media. Tech-savvy readers will get a comprehensive account of how data analysis has prevailed over political instinct and experience and examples of the challenges these practitioners face.

Essays include:

The Role of Data in Campaigns—Andrew Therriault, former Director of Data Science for the Democratic National Committee
Essentials of Modeling and Microtargeting—Dan Castleman, cofounder and Director of Analytics at Clarity Campaign Labs, a leading modeler in Democratic politics
Data Management for Political Campaigns—Audra Grassia, Deputy Political Director for the Democratic Governors Association in 2014
How Technology Is Changing the Polling Industry—Patrick Ruffini, cofounder of Echelon Insights and Founder/Chairman of Engage, was a digital strategist for President Bush in 2004 and for the Republican National Committee in 2006
Data-Driven Media Optimization—Alex Lundry, cofounder and Chief Data Scientist at Deep Root Analytics, a leading expert on media and voter analytics, electoral targeting, and political data mining
How (and Why) to Follow the Money in Politics—Derek Willis, ProPublica’s news applications developer, formerly with The New York Times
Digital Advertising in the Post-Obama Era—Daniel Scarvalone, Associate Director of Research and Data at Bully Pulpit Interactive (BPI), a digital marketer for the Democratic party
Election Forecasting in the Media—Natalie Jackson, Senior Polling Editor atThe Huffington Post…(More)”

Make Data Sharing Routine to Prepare for Public Health Emergencies

Curated on August 30, 2016August 3, 2018 by Stefaan Verhulst

Jean-Paul Chretien, Caitlin M. Rivers, and Michael A. Johansson in PLOS Medicine: “In February 2016, Wellcome Trust organized a pledge among leading scientific organizations and health agencies encouraging researchers to release data relevant to the Zika outbreak as rapidly and widely as possible [1]. This initiative echoed a September 2015 World Health Organization (WHO) consultation that assessed data sharing during the recent West Africa Ebola outbreak and called on researchers to make data publicly available during public health emergencies [2]. These statements were necessary because the traditional way of communicating research results—publication in peer-reviewed journals, often months or years after data collection—is too slow during an emergency.

The acute health threat of outbreaks provides a strong argument for more complete, quick, and broad sharing of research data during emergencies. But the Ebola and Zika outbreaks suggest that data sharing cannot be limited to emergencies without compromising emergency preparedness. To prepare for future outbreaks, the scientific community should expand data sharing for all health research….

Open data deserves recognition and support as a key component of emergency preparedness. Initiatives to facilitate discovery of datasets and track their use [40–42]; provide measures of academic contribution, including data sharing that enables secondary analysis [43]; establish common platforms for sharing and integrating research data [44]; and improve data-sharing capacity in resource-limited areas [45] are critical to improving preparedness and response.

Research sponsors, scholarly journals, and collaborative research networks can leverage these new opportunities with enhanced data-sharing requirements for both nonemergency and emergency settings. A proposal to amend the International Health Regulations with clear codes of practice for data sharing warrants serious consideration [46]. Any new requirements should allow scientists to conduct and communicate the results of secondary analyses, broadening the scope of inquiry and catalyzing discovery. Publication embargo periods, such as one under consideration for genetic sequences of pandemic-potential influenza viruses [47], may lower barriers to data sharing but may also slow the timely use of data for public health.

Integrating open science approaches into routine research should make data sharing more effective during emergencies, but this evolution is more than just practice for emergencies. The cause and context of the next outbreak are unknowable; research that seems routine now may be critical tomorrow. Establishing openness as the standard will help build the scientific foundation needed to contain the next outbreak.

Recent epidemics were surprises—Zika and chikungunya sweeping through the Americas; an Ebola pandemic with more than 10,000 deaths; the emergence of severe acute respiratory syndrome and Middle East respiratory syndrome, and an influenza pandemic (influenza A[H1N1]pdm09) originating in Mexico—and we can be sure there are more surprises to come. Opening all research provides the best chance to accelerate discovery and development that will help during the next surprise….(More)”

Effect of Government Data Openness on a Knowledge-based Economy

Curated on August 9, 2016August 3, 2018 by Stefaan Verhulst

Jae-Nam Lee, Juyeon Ham and Byounggu Choi at Procedia Computer Science: “Many governments have recently begun to adopt the concept of open innovation. However, studies on the openness of government data and its effect on the global competitiveness have not received much attention. Therefore, this study aims to investigate the effects of government data openness on a knowledge-based economy at the government level. The proposed model was analyzed using secondary data collected from three different reports. The findings indicate that government data openness positively affects the formation of knowledge bases in a country and that the level of knowledge base of a country positively affects the global competitiveness of a country….(More)”

Taking a More Sophisticated Look at Human Beings

Curated on August 9, 2016October 10, 2018 by Stefaan Verhulst

Nathan Collins at Pacific Standard: “Are people fundamentally selfish, or are they cooperators? Actually, it’s kind of an odd question—after all, why are those the only options? The answer is that those options are derived in large part from philosophy and classical economic theory, rather than data. In a new paper, researchers have flipped the script, using observations of simple social situations to show that optimism, pessimism, envy, and trust, rather than selfishness and sacrifice, are the basic ingredients of our behavior.

That conclusion advances wider “efforts toward the identification of basic behavioral phenotypes,” or categories of behavior, and the results could be usefully applied in social science, policy, and business, Julia Poncela-Casasnovas and her colleagues write in Science Advances.

Classical economic theory has something of a bad reputation these days, and not without reason. For one thing, most economic theory assumes people are rational, in the sense that they are strategic and maximize their payoffs in all that they do. The list of objections to that approach is long and well-documented, but there’s a counter objection—amid a slew of objections and anecdotes, there’s little in the way of a cohesive alternative theory.

Optimism, pessimism, envy, and trust are the basic ingredients of our behavior.

Poncela-Casasnovas and her colleagues’ experiments are, they hope, a step toward such a theory. Their idea was to put ordinary people in simple social situations with economic tradeoffs, observe how those people act, and then construct a data-driven classification of their behavior…. Using standard statistical methods, the researchers identified four such player types: optimists (20 percent), who always go for the highest payoff, hoping the other player will coordinate to achieve that goal; pessimists (30 percent), who act according to the opposite assumption; the envious (21 percent), who try to score more points than their partners; and the trustful (17 percent), who always cooperate. The remaining 12 percent appeared to make their choices completely at random.

Those results don’t yet add up to anything like a theory of human behavior, but they do “open the door to making relevant advances in a number of directions,” the authors write. In particular, the researchers hope their results will help explain behavior in other simple games, and aid those hoping to understand how people may respond to new policy initiatives….(More)”

Legal confusion threatens to slow data science

Curated on August 5, 2016August 3, 2018 by Stefaan Verhulst

Simon Oxenham in Nature: “Knowledge from millions of biological studies encoded into one network — that is Daniel Himmelstein’s alluring description of Hetionet, a free online resource that melds data from 28 public sources on links between drugs, genes and diseases. But for a product built on public information, obtaining legal permissions has been surprisingly tough.

Menche rapidly gave consent — but not everyone was so helpful. One research group never replied to Himmelstein, and three replied without clearing up the legal confusion. Ultimately, Himmelstein published the final version of Hetionet in July — minus one data set whose licence forbids redistribution, but including the three that he still lacks clear permission to republish. The tangle shows that many researchers don’t understand that simply posting a data set publicly doesn’t mean others can legally republish it, says Himmelstein.

The confusion has the power to slow down science, he says, because researchers will be discouraged from combining data sets into more useful resources. It will also become increasingly problematic as scientists publish more information online. “Science is becoming more and more dependent on reusing data,” Himmelstein says….

Himmelstein is not convinced that he is legally in the clear — and feels that such uncertainty may deter other scientists from reproducing academic data. If a researcher launches a commercial product that is based on public data sets, he adds, the stakes of not having clear licensing are likely to rise. “I think these are largely untested waters, and most academics aren’t in the position to risk setting off a legal battle that will help clarify these issues,” he says….(More)”

Expanding citizen science models to enhance open innovation

Curated on August 4, 2016August 3, 2018 by Stefaan Verhulst

Kendra L. Smith in the Conversation: “Over the years, citizen scientists have provided vital data and contributed in invaluable ways to various scientific quests. But they’re typically relegated to helping traditional scientists complete tasks the pros don’t have the time or resources to deal with on their own. Citizens are asked to count wildlife, for instance, or classify photos that are of interest to the lead researchers.

This type of top-down engagement has consigned citizen science to the fringes, where it fills a manpower gap but not much more. As a result, its full value has not been realized. Marginalizing the citizen scientists and their potential contribution is a grave mistake – it limits how far we can go in science and the speed and scope of discovery.

Instead, by harnessing globalization’s increased interconnectivity, citizen science should become an integral part of open innovation. Science agendas can be set by citizens, data can be open, and open-source software and hardware can be shared to assist in the scientific process. And as the model proves itself, it can be expanded even further, into nonscience realms.

…

The time is right for citizen science to join forces with open innovation. This is a concept that describes partnering with other people and sharing ideas to come up with something new. The assumption is that more can be achieved when boundaries are lowered and resources – including ideas, data, designs and software and hardware – are opened and made freely available.

Open innovation is collaborative, distributed, cumulative and it develops over time. Citizen science can be a critical element here because its professional-amateurs can become another significant source of data, standards and best practices that could further the work of scientific and lay communities.

Globalization has spurred on this trend through the ubiquity of internet and wireless connections, affordable devices to collect data (such as cameras, smartphones, smart sensors, wearable technologies), and the ability to easily connect with others. Increased access to people, information and ideas points the way to unlock new synergies, new relationships and new forms of collaboration that transcend boundaries. And individuals can focus their attention and spend their time on anything they want.

We are seeing this emerge in what has been termed the “solution economy” – where citizens find fixes to challenges that are traditionally managed by government.

Consider the issue of accessibility. Passage of the 1990 Americans with Disabilities Act aimed to improve accessibility issues in the U.S. But more than two decades later, individuals with disabilities are still dealing with substantial mobility issues in public spaces – due to street conditions, cracked or nonexistent sidewalks, missing curb cuts, obstructions or only portions of a building being accessible. These all can create physical and emotional challenges for the disabled.

To help deal with this issue, several individual solution seekers have merged citizen science, open innovation and open sourcing to create mobile and web applications that provide information about navigating city streets. For instance, Jason DaSilva, a filmmaker with multiple sclerosis, developed AXS Map – a free online and mobile app powered by Google Places API. It crowdsources information from people across the country about wheelchair accessibility in cities nationwide….

Perhaps the most pressing limitation of scaling up the citizen science model is issues with reliability. While many of these projects have been proven reliable, others have fallen short.

For instance, crowdsourced damage assessments from satellite images following 2013’s Typhoon Haiyan in the Philippines faced challenges. But according to aid agencies, remote damage assessments by citizen scientists had a devastatingly low accuracy of 36 percent. They overrepresented “destroyed” structures by 134 percent….(More)”

Network Science

Curated on August 3, 2016August 3, 2018 by Stefaan Verhulst

Book by Albert-László Barabási: “Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do. Using numerous real-world examples, this innovatively designed text includes clear delineation between undergraduate and graduate level material. The mathematical formulas and derivations are included within Advanced Topics sections, enabling use at a range of levels. Extensive online resources, including films and software for network analysis, make this a multifaceted companion for anyone with an interest in network science….(More)”