Nick Bilton in NYT: “It looks as if the theory of the “wisdom of crowds” doesn’t apply to terrorist manhunts. Last week after the Boston Marathon bombings, the Internet quickly offered to help find the people responsible. In a scene metaphorically reminiscent of a movie in which vigilantes swarm the streets with pitchforks and lanterns, people took to Reddit, the popular community and social news Web site, and started scouring images posted online from the bombings.
One Reddit forum told users to search for ”people carrying black bags,” and noted that “if they look suspicious, then post them. Then people will try and follow their movements using all the images.” In the process, each time a scrap of information was discovered — the color of a hat, the type of straps on a backpack, the weighted droop of a bag — it was passed out on Twitter like “Wanted” posters tacked to lampposts. It didn’t matter whether it was right, wrong or even completely made up (some images posted to forums had been manipulated) — off it went, fiction and fact indistinguishable. Some misinformation online landed on the front page of The New York Post, incorrectly identifying an innocent high school student as a suspect. Later in the week, the Web wrongly identified one of the suspects as a student from Brown University who went missing earlier this month…
Perhaps the scariest aspect of these crowd-like investigations is that when information is incorrect, no one is held responsible.
As my colleague David Carr noted in his column this week, “even good reporters with good sources can end up with stories that go bad.” But the difference between CNN, The Associated Press or The New York Post getting it wrong, is that those names are held accountable when they publish incorrect news. No one is going to remember, or punish, the users on Reddit or Twitter who incorrectly identify random high school runners and missing college students as terrorists.”
Reinvent Roundtable that will take place on April 23, 2013 11:00 am PT : “Tim O’Reilly has some big ideas about how to dramatically modernize the entire notion of government regulation, particularly “algorithmic regulation” that harnesses computer power, much like top tech companies in Silicon Valley, to oversee the financial industry, which is using those same tools. This roundtable features some top talent from the Valley to apply their brains to figuring out how we could reinvent much more iterative regulation that constantly gets refined through analyzing data and processing feedback loops – much like Google refines its search techniques. In fact, we’ll have a top person from Google Search as well as someone from the US Treasury Department to work on these ideas. Watch Now →”
New Scientist: “Diagnosing rare illnesses could get easier, thanks to new web-based tools that pool information from a wide variety of sources…CrowdMed, launched on 16 April at the TedMed conference in Washington DC, uses crowds to solve tough medical cases.
Anyone can join CrowdMed and analyse cases, regardless of their background or training. Participants are given points that they can then use to bet on the correct diagnosis from lists of suggestions. This creates a prediction market, with diagnoses falling and rising in value based on their popularity, like stocks in a stock market. Algorithms then calculate the probability that each diagnosis will be correct. In 20 initial test cases, around 700 participants identified each of the mystery diseases as one of their top three suggestions….
Frustrated patients and doctors can also turn to FindZebra, a recently launched search engine for rare diseases. It lets users search an index of rare disease databases looked after by a team of researchers. In initial trials, FindZebra returned more helpful results than Google on searches within this same dataset.”
Eric Schmidt, Google’s executive chairman and former CEO. and Jared Cohen, director of Google Ideas in the WSJ: “…While technology has great potential to bring about change, there is a dark side to the digital revolution that is too often ignored. There is a turbulent transition ahead for autocratic regimes as more of their citizens come online, but technology doesn’t just help the good guys pushing for democratic reform—it can also provide powerful new tools for dictators to suppress dissent.
Fifty-seven percent of the world’s population still lives under some sort of autocratic regime. In the span of a decade, the world’s autocracies will go from having a minority of their citizens online to a majority. From Tehran to Beijing, autocrats are building the technology and training the personnel to suppress democratic dissent, often with the help of Western companies….
Dictators and autocrats in the years to come will attempt to build all-encompassing surveillance states, and they will have unprecedented technologies with which to do so. But they can never succeed completely. Dissidents will build tunnels out and bridges across. Citizens will have more ways to fight back than ever before—some of them anonymous, some courageously public.
The digital revolution will continue. For all the complications this revolution brings, no country is worse off because of the Internet. And with five billion people set to join us online in the coming decades—perhaps someday even the Pyongyang traffic police and the students in the Potemkin computer lab we visited in North Korea among them—the digital future can be bright indeed, despite its dark side.”
See also: “The New Digital Age: Reshaping the Future of People, Nations and Business,
Dave Girouard, co-founder and CEO of Upstart in Wired: “A total $2.7 billion was pledged by individual donors through crowdfunding last year, according to reports by research firm Massolution — up 81% from the year before. This space is only going to heat up further when SEC rules for the JOBS Act are released this year, paving the way for equity crowdfunding….
Crowdfunders and angel investors, while not purely philanthropic, share the common desire to participate and be involved in the creation of something new.Put another way, it’s more about cause than cash (a phrase I picked up from Kiva co-founder Jessica Jackley). And that desire is the disruptive “feature” of crowdfunding….
Predicting success for a newbie startup is notoriously difficult. But investing in people is one of the only ways to get a risk/return/volatility investment profile that actually works. It’s a model that could also appeal to quant (not just cause) investors as well…companies like Upstart (which I founded) and Pave make it easy for people to invest in other people.
Why is investing in people a safer bet? Because there are clear — and measurable — signals reflecting their accomplishments and hinting at their potential. It’s not unlike the logic used by big companies or universities faced with countless candidates, by recruiting firms and talent agents, and others. By using data and algorithms — in this case, a sophisticated regression model that considers variables like school, area of study, standardized test scores, internships, job offers — we can statistically predict a person’s future income.
Such a model allows a person to “borrow” from his or her future self.”
Tom Kalil, Deputy Director for Technology and Innovation at OSTP : “As we enter the second year of the Big Data Initiative, the Obama Administration is encouraging multiple stakeholders, including federal agencies, private industry, academia, state and local government, non-profits, and foundations to develop and participate in Big Data initiatives across the country. Of particular interest are partnerships designed to advance core Big Data technologies; harness the power of Big Data to advance national goals such as economic growth, education, health, and clean energy; use competitions and challenges; and foster regional innovation.
The National Science Foundation has issued a request for information encouraging stakeholders to identify Big Data projects they would be willing to support to achieve these goals. And, later this year, OSTP, NSF, and other partner agencies in the Networking and Information Technology R&D (NITRD) program plan to convene an event that highlights high-impact collaborations and identifies areas for expanded collaboration between the public and private sectors.”
Steve Lohr from the New York Times: “Work-force science, in short, is what happens when Big Data meets H.R….Today, every e-mail, instant message, phone call, line of written code and mouse-click leaves a digital signal. These patterns can now be inexpensively collected and mined for insights into how people work and communicate, potentially opening doors to more efficiency and innovation within companies.
Digital technology also makes it possible to conduct and aggregate personality-based assessments, often using online quizzes or games, in far greater detail and numbers than ever before. In the past, studies of worker behavior were typically based on observing a few hundred people at most. Today, studies can include thousands or hundreds of thousands of workers, an exponential leap ahead.
“The heart of science is measurement,” says Erik Brynjolfsson, director of the Center for Digital Business at the Sloan School of Management at M.I.T. “We’re seeing a revolution in measurement, and it will revolutionize organizational economics and personnel economics.”
The data-gathering technology, to be sure, raises questions about the limits of worker surveillance. “The larger problem here is that all these workplace metrics are being collected when you as a worker are essentially behind a one-way mirror,” says Marc Rotenberg, executive director of the Electronic Privacy Information Center, an advocacy group. “You don’t know what data is being collected and how it is used.”
Carl Fillichio who heads the Labor Department’s Office of Public Affairs at (Work in Progress): “Since we published a department-wide API two years ago, developers across the country have used it to create apps that educate users about workplace safety and health, employers’ compliance with wage and hour laws, and improving employment opportunities for disabled workers, just to name a few!
Releasing data through an API was a big step forward, but it was not exactly groundbreaking. However, since then, my team has been working hard to develop software development kits that are truly innovative because they make using our API even easier.
These kits (also known as SDKs) contain application code for six different platforms − iOS, Android, Blackberry, .Net, PHP and Ruby − that anyone creating a mobile or Web-based app using our data could incorporate. By using the kits, experienced developers will save time and novice developers will be able to work with DOL data in just a few minutes…. All of these kits can be downloaded from our developer site. Additionally, in keeping with the federal digital government strategy, each has been published as an open source project on github, a popular code-sharing site. For a list of federal APIs that are supported by our kits, check the github repository’s wiki page. This list will be updated as the kits are tested with additional federal APIs.”
New paper on “Bridging narrative scenario texts and formal policy modeling through conceptual policy modeling” in Artificial Intelligence and Law.
Abstract: “Engaging stakeholders in policy making and supporting policy development with advanced information and communication technologies including policy simulation is currently high on the agenda of research. In order to involve stakeholders in providing their input to policy modeling via online means, simple techniques need to be employed such as scenario technique. Scenarios enable stakeholders to express their views in narrative text. At the other end of policy development, a frequently used approach to policy modeling is agent-based simulation. So far, effective support to transform narrative text input to formal simulation statements is not widely available. In this paper, we present a novel approach to support the transformation of narrative texts via conceptual modeling into formal simulation models. The approach also stores provenance information which is conveyed via annotations of texts to the conceptual model and further on to the simulation model. This way, traceability of information is provided, which contributes to better understanding and transparency, and therewith enables stakeholders and policy modelers to return to the sources that informed the conceptual and simulation model.”
Press Release: “The Digital Public Library of America (DPLA) launched a beta of its discovery portal and open platform today. The portal delivers millions of materials found in American archives, libraries, museums, and cultural heritage institutions to students, teachers, scholars, and the public. Far more than a search engine, the portal provides innovative ways to search and scan through its united collection of distributed resources. Special features include a dynamic map, a timeline that allow users to visually browse by year or decade, and an app library that provides access to applications and tools created by external developers using DPLA’s open data…
With an application programming interface (API) and maximally open data, the DPLA can be used by software developers, researchers, and others to create novel environments for learning, tools for discovery, and engaging apps. The DPLA App Library features an initial slate of applications built on top of the platform; developers and hobbyists of all skill levels are freely able to make use of the data provided via the platform….
With its content partners, the DPLA has developed a number of diverse virtual exhibitions that tell the stories of people, places, and historical events both here in the US and abroad; all are available freely via the portal.”