crowdsourcing

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Bold: How to Go Big, Create Wealth and Impact the World by Peter H. Diamandis, Steven Kotler

3D printing, additive manufacturing, adjacent possible, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, Boston Dynamics, Charles Lindbergh, cloud computing, company town, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deal flow, deep learning, dematerialisation, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Exxon Valdez, fail fast, Fairchild Semiconductor, fear of failure, Firefox, Galaxy Zoo, Geoffrey Hinton, Google Glasses, Google Hangouts, gravity well, hype cycle, ImageNet competition, industrial robot, information security, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, low earth orbit, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mars Rover, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, OpenAI, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, Scaled Composites, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, SpaceShipOne, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, Stuart Kauffman, superconnector, Susan Wojcicki, synthetic biology, technoutopianism, TED Talk, telepresence, telepresence robot, Turing test, urban renewal, Virgin Galactic, Wayback Machine, web application, X Prize, Y Combinator, zero-sum game

AbundanceHub content is driven by the Abundance360 community, my mastermind group of entrepreneurs whom I’ve committed to coach over a twenty-five-year period. Crowdsourcing.org: The industry-leading resource for everything crowdsourcing. Known as one of the most influential and credible authorities in the crowdsourcing space, they are recognized for their in-depth industry analyses, definitive crowdsourcing platform directory, and unbiased thought leadership. Their mission is to serve as a complete resource of information for analysts, researchers, journalists, investors, business owners, crowdsourcing experts, and participants in crowdsourcing platforms. Crowdsortium: Using the crowd to dissect, organize, and collectively move the young and evolving crowdsourcing industry forward, Crowdsortium helps organizations find, evaluate, and execute new ideas by working with online crowds and providing events, meet-ups, resources, and guides.

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And as of today, the site has about 300,000 users. So, while the point of the Freelancer and the Tongal case studies were to explore two different crowdsourcing platforms that offer today’s entrepreneur astounding leverage, the point of reCAPTCHA and Duolingo is the inverse—an example of the kind of crowdsourcing platform a bold entrepreneur might be interested in creating, the kind that both makes money and betters the world at the same time. How to Crowdsource As you can see from our case studies, crowdsourcing is a diverse and growing field, with more novel applications being dreamed up every day. So, before we dive into lessons learned, to give you a better sense of what’s going on, I’ve broken this section into four of the most common uses for crowdsourcing and provided a short explanation for each. 1.

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PREPARE YOUR DATA SET For non-design-specific tasks, there is usually a data set that you must submit for analysis, categorization, and so on. If you don’t have a perfectly formatted and ready-to-go .csv file, you can turn to the crowd for help. Most of the time crowdsourcing workers have actually crowdsourced projects themselves, so they know all the best ways to prepare your data for this process. vi. QUALIFY YOUR WORKERS Unfortunately, crowdsourcing does have the potential to create undesired results. The quality of the results can sometimes be inadequate, and crowdsourcing is not sheltered from the scammers and bots lurking on the Internet. Luckily, qualifying your workers and curating a trustworthy work force can help you avoid these issues.

Data Action: Using Data for Public Good by Sarah Williams

affirmative action, Amazon Mechanical Turk, Andrei Shleifer, augmented reality, autonomous vehicles, Brexit referendum, Cambridge Analytica, Charles Babbage, City Beautiful movement, commoditize, coronavirus, COVID-19, crowdsourcing, data acquisition, data is the new oil, data philanthropy, data science, digital divide, digital twin, Donald Trump, driverless car, Edward Glaeser, fake news, four colour theorem, global village, Google Earth, informal economy, Internet of things, Jane Jacobs, John Snow's cholera map, Kibera, Lewis Mumford, Marshall McLuhan, mass immigration, mass incarceration, megacity, military-industrial complex, Minecraft, neoliberal agenda, New Urbanism, Norbert Wiener, nowcasting, oil shale / tar sands, openstreetmap, place-making, precautionary principle, RAND corporation, ride hailing / ride sharing, selection bias, self-driving car, sentiment analysis, Sidewalk Labs, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Steven Levy, the built environment, The Chicago School, The Death and Life of Great American Cities, transatlantic slave trade, Uber for X, upwardly mobile, urban planning, urban renewal, W. E. B. Du Bois, Works Progress Administration

Jeff Howe codified the term's more contemporary meaning in a 2006 article in Wired titled “The Rise of Crowdsourcing,” using the term to describe the strategic appropriation of specialized labor markets to “create content, solve problems, even do corporate R&D.” 45 Howe tied crowdsourcing to Amazon's Mechanical Turk program, a “Web-based marketplace that helps companies find people to perform tasks computers are generally lousy at,” such as identifying images, sifting through interpretive information in documents, creating annotations, and more.46 This is where historical and contemporary treatments of the term “crowdsourcing” diverge: modern crowdsourcing privileges human intervention over computational algorithms, and relies on the dialectic relationship between humans and technology. Although the term “crowdsourcing” was coined for application to corporations, many public participation science research projects have employed elements of crowdsourcing methodologies in order to annotate or collect large amounts of data that could not otherwise be annotated with a smaller number of researchers addressing the problem. Crowdsourcing is controversial. One of the criticisms levelled at the crowdsourcing method of data collection involves the same criticism of participatory methods: data inaccuracy.

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Ultimately, the team developed a more streamlined sensor, which could be bought as a DIY kit from the KitHub website.44 Rather than going to test the sites themselves, the Safecast team decided to employ crowdsourcing techniques to collect data from all over Japan. Crowdsourcing is a term that describes a spectrum of initiatives involving (sometimes) massive numbers of people participating in task-based problem solving. Jeff Howe codified the term's more contemporary meaning in a 2006 article in Wired titled “The Rise of Crowdsourcing,” using the term to describe the strategic appropriation of specialized labor markets to “create content, solve problems, even do corporate R&D.” 45 Howe tied crowdsourcing to Amazon's Mechanical Turk program, a “Web-based marketplace that helps companies find people to perform tasks computers are generally lousy at,” such as identifying images, sifting through interpretive information in documents, creating annotations, and more.46 This is where historical and contemporary treatments of the term “crowdsourcing” diverge: modern crowdsourcing privileges human intervention over computational algorithms, and relies on the dialectic relationship between humans and technology.

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One of the criticisms levelled at the crowdsourcing method of data collection involves the same criticism of participatory methods: data inaccuracy. Some methods of crowdsourcing are extremely exploitative: at sites such as Amazon's Mechanical Turk, CrowdFlower, Clickworker, or Toluna, for example, workers are often paid well below minimum wage—exploiting labor pools at home and abroad.47 Data Action does not support using exploitative crowdsourcing. In many ways the popularity of crowdsourcing was driven by technology that allowed easy access to vast groups of people. The increased availability of smartphones beginning in 2007 with the release of the first iPhone and Android HTC device catalyzed widespread personal, mobile access to the internet.

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

Alan Greenspan, Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apollo 11, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, data science, driverless car, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, information security, job satisfaction, Johann Wolfgang von Goethe, lifelogging, machine readable, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, SpaceShipOne, speech recognition, statistical model, Steven Levy, supply chain finance, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

Although built from real data, the example decision trees in this chapter are not from the deployed Chase mortgage project. 8 Ian Ayres provides an informative overview of the fundamental intuition versus data debate in the Chapter “Experts versus Equations” of Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart (Bantam, 2007). 9 This non-existence of a universal solution to machine learning is known as the “no free lunch” theorem. 10 To view a non-buggy, proficient Tetris player we evolved, see www.predictionimpact.com/tetris. 11 CART® is a registered trademark licensed exclusively to Salford Systems. 12 The form of micro-risk relevant to the economic crisis is that of delinquent debtors, rather than the micro-risk predicted in this chapter’s case study, mortgage prepayments. But predicting delinquent accounts with PA is also subject to these same limitations. Chapter 5 The Ensemble Effect Netflix, Crowdsourcing, and Supercharging Prediction To crowdsource predictive analytics—outsource it to the public at large—a company launches its strategy, data, and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds?

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PA competitions do for data science what the X Prize did for rocket science. Mindsourced: Wealth in Diversity [Crowdsourcing is] a perfect meritocracy, where age, gender, race, education, and job history no longer matter; the quality of the work is all that counts. —Jeff Howe, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business When pursuing a grand challenge, from where will key discoveries appear? If we assume for the moment that one cannot know, there’s only one place to look: everywhere. Contests tap the greatest resource, the general public. A common way to enact crowdsourcing, an open competition brings together scientists from far and wide to compete for the win and cooperate for the joy.

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A common way to enact crowdsourcing, an open competition brings together scientists from far and wide to compete for the win and cooperate for the joy. With crowdsourcing, a company outsources to the world. The $1 million Netflix Prize attracted a white-hot spotlight and built a new appreciation for the influence crowdsourcing has to rally an international wealth of bright minds. In total, 5,169 teams formed to compete in this contest, submitting 44,014 entries by the end of the event. PA crowdsourcing reaps the rewards brought by a diverse brainshare. Chris Volinsky, a member of a leading Netflix Prize team named BellKor from AT&T Research, put it to me this way: “From the beginning, I thought it was awesome how many people in the top of the leaderboard were what could be called ‘amateurs.’

The Deepest Map by Laura Trethewey

9 dash line, airport security, Anthropocene, Apollo 11, circular economy, clean tech, COVID-19, crowdsourcing, digital map, Donald Trump, Elon Musk, en.wikipedia.org, Exxon Valdez, gentrification, global pandemic, high net worth, hive mind, Jeff Bezos, job automation, low earth orbit, Marc Benioff, microplastics / micro fibres, Neil Armstrong, Salesforce, Scramble for Africa, Silicon Valley, South China Sea, space junk, sparse data, TED Talk, UNCLOS, UNCLOS

That’s a bit of a myth,” said Brian Calder, the associate director of CCOM. Calder has written extensively about the uncertainties contained in professionally made maps, and he’s experimenting with crowdsourcing himself. Yes, trained hydrographers make good maps, he said, but so can a “superobserver,” his term for an untrained crowdsourcer committed to the cause. Both Calder and Desrochers dislike the crowdsourcing label. It naturally draws comparisons with the world’s best-known and most successful crowdsourcing effort: Wikipedia. The online encyclopedia’s legion of unpaid writers and editors validate one another’s work through sheer critical mass, eventually arriving at an approximate version of the truth.

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As seas rise and coastlines change, the small island states of the Pacific, such as the Republic of Palau, the Republic of Kiribati, and the Republic of the Marshall Islands, are all grappling with a dire need for new and improved charts. Crowdsourcing offers a way into the mapping world without the bigger budget of a developed country. A smaller local survey boat can reach shallow, hard-to-reach coastlines regularly where a larger survey vessel might get snagged or return only occasionally. Jennifer Jencks felt excited by the potential to crowdsource the seafloor. Jencks, who oversees the NOAA data center in Boulder, Colorado, where all of Seabed 2030’s maps are held, also chairs the Crowdsourced Bathymetry Working Group at the International Hydrographic Organization (IHO).

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But it was clear that their numbers weren’t growing, and neither was the map. To Jencks, those problems seemed interconnected. “Crowdsourced bathymetry came about a few years ago, when IHO was saying, ‘At this rate we’re never going to map the whole darn ocean. We really need to start looking outside the box,’” she remembered. As her working group started to spearhead crowdsourcing projects, she began to notice new faces at ocean-mapping events. When we spoke, she had just wrapped up a Caribbean mapping workshop during which Costa Rica had agreed to allow crowdsourcing in its waters under national jurisdiction. “The folks who are coming to the table is so much broader now,” she said.

The Data Journalism Handbook by Jonathan Gray, Lucy Chambers, Liliana Bounegru

Amazon Web Services, barriers to entry, bioinformatics, business intelligence, carbon footprint, citizen journalism, correlation does not imply causation, crowdsourcing, data science, David Heinemeier Hansson, eurozone crisis, fail fast, Firefox, Florence Nightingale: pie chart, game design, Google Earth, Hans Rosling, high-speed rail, information asymmetry, Internet Archive, John Snow's cholera map, Julian Assange, linked data, machine readable, moral hazard, MVC pattern, New Journalism, openstreetmap, Ronald Reagan, Ruby on Rails, Silicon Valley, social graph, Solyndra, SPARQL, text mining, Wayback Machine, web application, WikiLeaks

The only disappointment is the lack of absolute values—you only get relative percentages, which can be hard to interpret. Figure 4-11. Google Insights (Google) — Pete Warden, independent data analyst and developer Crowdsourcing Data at the Guardian Datablog Crowdsourcing, according to Wikipedia, is “a distributed problem-solving and production process that involves outsourcing tasks to a network of people, also known as the crowd.” The following is from an interview with Simon Rogers on how the Datablog used crowdsourcing to cover the MPs’ expenses scandal, drug use, and the Sarah Palin papers: Sometimes you will get a ton of files, statistics, or reports which it is impossible for one person to go through.

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What both of these projects have in common is that they are about issues that people really care about, so they are willing to spend time on them. A lot of the crowdsourcing we have done relies on help from obsessives. With the MPs’ expenses, we had a massive amount of traffic at the beginning and it really died down. But we still have people that are obsessively going through every page looking for anomalies and stories. One person has done 30,000 pages. They know a lot of stuff. We also used crowdsourcing with the Sarah Palin papers. Again this was a great help in scouring the raw information for stories. In terms of generating stories crowdsourcing has worked really well for us. People really liked it and it made the Guardian look good.

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My colleagues continued to use and build on the techniques learned on that day to create more ambitious and technical projects, such as a database of the administrative costs of housing. With this data, I made an interactive map in Fusion Tables. We asked our readers to play around with the data and crowdsourced results at http://bit.ly/scratchbook-crowdsourcing, for example. After a lot of questions on how we made a map in Fusion Tables, I also recorded a video tutorial. What did we learn? We learned a lot, but we also came along a lot of obstacles. We recognized these four: Where to begin: question or data? Almost all projects stalled when searching for information.

Reality Is Broken: Why Games Make Us Better and How They Can Change the World by Jane McGonigal

Abraham Maslow, airport security, Albert Einstein, Amazon Mechanical Turk, Anthropocene, citizen journalism, clean water, collaborative economy, crowdsourcing, delayed gratification, en.wikipedia.org, fear of failure, G4S, game design, hedonic treadmill, hobby farmer, Ian Bogost, jimmy wales, mass immigration, Merlin Mann, Network effects, new economy, oil shock, peak oil, planetary scale, Ralph Waldo Emerson, Richard Stallman, science of happiness, Search for Extraterrestrial Intelligence, SETI@home, Silicon Valley, SimCity, smart meter, Stewart Brand, The Wisdom of Crowds, Tony Hsieh, Tragedy of the Commons, urban planning, We are as Gods, web application, Whole Earth Catalog

So they decided to enlist the public’s direct help in uncovering whatever it was the authorities didn’t want uncovered. In other words, they “crowdsourced” the investigation. The term crowdsourcing, coined by technology journalist Jeff Howe in 2006, is shorthand for outsourcing a job to the crowd.3 It means inviting a large group of people, usually on the Internet, to cooperatively tackle a big project. Wikipedia, the collaboratively authored online encyclopedia created by a crowd of more than 10 million unpaid (and often anonymous) writers and editors, is a prime example. Crowdsourcing is a way to do collectively, faster, better, and more cheaply what might otherwise be impossible for a single organization to do alone.

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“MPs’ Expenses: Critics Attack Censorship as Redactions Black Out Documents.” Guardian, June 19, 2009. http://www.guardian.co.uk/politics/2009/jun/18/mps-expenses-censorship-black-out. 3 Jeff Howe. Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business (New York: Crown Business, 2008), 4-17. 4 Andersen, Michael. “Four Crowdsourcing Lessons from the Guardian’s (Spectacular) Expenses-Scandal Experiment.” Nieman Journalism Lab, June 23, 2009. http://www.niemanlab.org/2009/06/four-crowdsourcing-lessons-from-the-guardians-spectacular-expenses-scandal-experiment/. 5 “Participation on Web 2.0 Websites Remains Weak.” Reuters, April 18, 2007. http://www.reuters.com/article/technologyNews/idUSN1743638820070418. 6 Wright, Steven, and Jason Groves.

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Or investigate by political party. All the MPs’ records are on there now—so let us know what you find. Just three days into the game, it was clear that the crowdsourcing effort was an unprecedented success. More than 20,000 players had already analyzed more than 170,000 electronic documents. Michael Andersen, a member of the Nieman Journalism Lab at Harvard University and an expert on Internet journalism, reported at the time: “Journalism has been crowdsourced before, but it’s the scale of the Guardian’s project—170,000 documents reviewed in the first 80 hours, thanks to a visitor participation rate of 56 percent—that’s breathtaking.”4 A visitor participation rate measures the percentage of visitors who sign up and make a contribution to a network.

Doing Data Science: Straight Talk From the Frontline by Cathy O'Neil, Rachel Schutt

Amazon Mechanical Turk, augmented reality, Augustin-Louis Cauchy, barriers to entry, Bayesian statistics, bike sharing, bioinformatics, computer vision, confounding variable, correlation does not imply causation, crowdsourcing, data science, distributed generation, Dunning–Kruger effect, Edward Snowden, Emanuel Derman, fault tolerance, Filter Bubble, finite state, Firefox, game design, Google Glasses, index card, information retrieval, iterative process, John Harrison: Longitude, Khan Academy, Kickstarter, machine translation, Mars Rover, Nate Silver, natural language processing, Netflix Prize, p-value, pattern recognition, performance metric, personalized medicine, pull request, recommendation engine, rent-seeking, selection bias, Silicon Valley, speech recognition, statistical model, stochastic process, tacit knowledge, text mining, the scientific method, The Wisdom of Crowds, Watson beat the top human players on Jeopardy!, X Prize

connected graphs, Centrality Measures constraints, on algorithms at runtime, Runtime interpretability of, Interpretability scalability, Scalability thought experiments for, Logistic Regression–Media 6 Degrees Exercise understanding, You constructing features, How to Be a Good Modeler continuous density function, Probability distributions continuous variables in decision trees, Handling Continuous Variables in Decision Trees converged algorithms, Alternating Least Squares Conway, Drew, The Current Landscape (with a Little History), The Current Landscape (with a Little History) correlated variables, Feature Selection correlation vs. causality, Correlation Doesn’t Imply Causation–Confounders: A Dating Example cosine similarity, Similarity or distance metrics, Nearest Neighbor Algorithm Review Cosma Shalizi, The Current Landscape (with a Little History) CountSketch, Enter MapReduce Crawford, Kate, Populations and Samples of Big Data Crawshaw, David, About David Crawshaw creating competitions, Background: Data Science Competitions credit score example, Example with credit scores–Example with credit scores Cronkite Plaza (Thorp/Rubin/Hansen), Cronkite Plaza cross-validation, Adding in modeling assumptions about the errors, Last Thoughts on These Algorithms crowdsourcing DARPA and, Background: Crowdsourcing distributive, Background: Crowdsourcing InnoCentive and, Background: Crowdsourcing issues with, Background: Crowdsourcing Kaggle and, Background: Crowdsourcing Mechanical Turks vs., Background: Crowdsourcing organization, Background: Crowdsourcing Wikipedia and, Background: Crowdsourcing Cukier, Kenneth Neil, Datafication Cukierski, William, William Cukierski curves, goodness of, Research Experiment (Observational Medical Outcomes Partnership) D Dalessandro, Brian, Logistic Regression DARPA, Background: Crowdsourcing data abundance vs. scarcity, Data Abundance Versus Data Scarcity clustering, k-means extracting meaning from, Extracting Meaning from Data–Thought Experiment: What Is the Best Way to Decrease Concern and Increase Understanding and Control?

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using, Data Engineering: MapReduce, Pregel, and Hadoop, Enter MapReduce word frequency problems, Enter MapReduce mathematical models, Modeling matrix decomposition, The Dimensionality Problem maximize information gain, The Decision Tree Algorithm maximum likelihood estimation, Fitting a model, Estimating α and β Mayer-Schoenberger, Viktor, Datafication McKelvey, Jim, About Square MCMC methods, Inference for ERGMs mean absolute error, Evaluation mean squared error, Adding in modeling assumptions about the errors, Adding in modeling assumptions about the errors, Evaluation, How to Be a Good Modeler meaning of features, Causality measurement errors, Some Problems with Nearest Neighbors Mechanical Turks, Background: Crowdsourcing Amazon, Background: Crowdsourcing crowdsourcing vs., Background: Crowdsourcing Mechanize, Scraping the Web: APIs and Other Tools Media 6 Degrees (M6D), Your Mileage May Vary Media 6 Degrees (M6D) case study, M6D Logistic Regression Case Study–Evaluation click models for, Click Models Media 6 Degrees (M6D) exercise, Media 6 Degrees Exercise medical data thought experiment, Thought Experiment, Closing Thought Experiment meta-definition thought experiment, Thought Experiment: Meta-Definition Metamarket, The Current Landscape (with a Little History) methods, Three Basic Algorithms feature selection, Example: User Retention MCMC, Inference for ERGMs metrics, Nearest Neighbor Algorithm Review Microsoft Research, Populations and Samples of Big Data misclassification rate, Pick an evaluation metric mixed-method approaches, Moving from Descriptive to Predictive modeling, Linear Regression EDA and, But how do you build a model?

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—as well as what happens once you have your model, including visualization and communication. The team of Kaggle data scientists actually spends a lot of time creating the dataset and evaluation metrics, and figuring out what questions to ask, so the question is: while they’re doing data science, are the contestants? Background: Crowdsourcing There are two kinds of crowdsourcing models. First, we have the distributive crowdsourcing model, like Wikipedia, which is for relatively simplistic but large-scale contributions. On Wikipedia, the online encyclopedia, anyone in the world can contribute to the content, and there is a system of regulation and quality control set up by volunteers.

The Facebook era: tapping online social networks to build better products, reach new audiences, and sell more stuff by Clara Shih

Benchmark Capital, business process, call centre, Clayton Christensen, cloud computing, commoditize, conceptual framework, corporate governance, crowdsourcing, glass ceiling, jimmy wales, Marc Benioff, Mark Zuckerberg, Metcalfe’s law, Network effects, pets.com, pre–internet, rolodex, Salesforce, Savings and loan crisis, semantic web, sentiment analysis, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, social graph, social web, software as a service, tacit knowledge, Tony Hsieh, web application

See also friends blocking, 49 de-friending, 49 content aggregation, 27 continual iteration in innovation, 120 changing customers into partners, 121 crowdsourcing feedback, 120 with polls, 120 conversations in unsanctioned communities, listening to, 150-153 corporate alumni networks, 139-140 corporate governance, 195-196 cross-boundary collaboration, 196-197 industry standards and portability, 197-198 input from legal, IT, and PR departments, 200-202 From the Library of Kerri Ross 226 co r p o ra te g ove r n a n ce open networks versus closed networks, 196 risk management, 198-200 brand misrepresentation, 200 identity, privacy, security, 198-199 intellectual property, confidentiality, 199-200 user adoption levels of social networking, 197 corporate IT, social future of, 206-207 corporate participation in unsanctioned communities, 153-155 corporate presence establishing on social networking sites, 155-157, 160-161 selecting social networking sites for, 156 in unofficial communities, 161 cost-effectiveness of hypertargeting, 86-87 CPC ad pricing model, 171 CPM ad pricing model, 171 credibility, establishing, 64-65 CRM (customer relationship management), 61 social networking sites versus, 80 cross-boundary collaboration, 196-197 crowdsourcing, 77-78 crowdsourcing feedback, 120 crowdsourcing ideation, 111-112 customer engagement, 145146. See also marketing establishing social networking presence, 155-157, 160-161 finding unsanctioned communities, 148-155 strategic planning for, 146-148, 166 customer feedback crowdsourcing, 120 with polls, 120 on prototyping, 116 customer organizations, navigating relationships in, 69-71 customer rapport, building/sustaining, 75-76 customer references, 74-75 customer relationship management (CRM), 61 social networking sites versus, 80 customer support, maintaining, 77-78 customers changing into partners, 121 prospecting for, 65-67 Cyworld, 221 D Dawkins, Richard, 109 de-friending contacts, 49 deactivating accounts, 49 decision-making in technology revolutions, 22-23 Del Monte, 20 Dell Computer, 112 diffusion of innovation theory, 118-119 The Diffusion of Innovations (Rogers), 118 digital media, evolution of, 25 creation and storage, 26 distribution, 26-29 Facebook, 34-36 impact of technology revolutions on, 31 online social graph, 42 social filtering, 29-32, 34 discovery sites, 18-19 distribution of digital media, 26-29 behavioral targeting, 28-29 content aggregation, 27 impact of technology revolutions on, 31 search engine marketing, 27-28 Web site communities, 27 social distribution, 96-97 passive word of mouth, 97-98 reaching new audiences, 101 social ads, 98-99 social shopping and recommendations, 101-103 viral marketing, 99-101 Doostang recruiting via, 125 statistics, 221 Dow Chemical Company case study (corporate alumni networks), 140 Dunn, Andy, 88 From the Library of Kerri Ross Fa ce b o o k Pa g e s E early adopters (in social epidemics), 100 Eastman Kodak, 204 education level, as hypertargeting dimension, 165 employee recruiting.

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Because their ideas are heard, customers feel more accountable for providing input and more grateful when that input is incorporated in the design of new products. It is a win-win for companies and their customers. From the Library of Kerri Ross 108 Pa r t I I Tra n s fo r m i n g t h e Way We D o B u s i n e s s Concept Generation Social Processes: Meme Feeds, Crowdsourcing Ideas, Finding Expertise Continual Iteration Prototyping Social Processes: Crowdsourcing Feedback, Targeted Polling, Testing Ideas Social Processes: Crowdsourcing Feedback, Collaboration Commercial Implementation Social Processes: Winning Internal Buy-In, Persuading Customers to Adopt an Unproven Innovation Figure 6.1 The cycle of innovation typically follows four stages, each of which contains multiple social processes: concept generation, prototyping, commercial implementation, and continual iteration.

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Figure 6.3 Yammer is a status broadcasting tool that allows employees of the same company to answer and view colleagues’ answers to a simple question: “What are you working on?” This is salesforce.com’s Yammer page. From the Library of Kerri Ross C h a p te r 6 S o c i a l I n n ovat i o n 111 Crowdsourcing Ideation In addition to providing us with inspiration, other people are a wonderful source of good ideas. We introduced the notion of crowdsourcing in Chapter 4,“Social Sales,” in the context of customer support. Product managers can also use crowdsourcing techniques to generate ideas from the community for their products. Depending on the product and competitive situation, it might make sense to engage with internal communities, external communities, or both.

The Slow Fix: Solve Problems, Work Smarter, and Live Better in a World Addicted to Speed by Carl Honore

Albert Einstein, An Inconvenient Truth, Apollo 13, Atul Gawande, Broken windows theory, call centre, carbon credits, Checklist Manifesto, clean water, clockwatching, cloud computing, crowdsourcing, Dava Sobel, delayed gratification, drone strike, Enrique Peñalosa, Erik Brynjolfsson, Ernest Rutherford, Exxon Valdez, fail fast, fundamental attribution error, game design, Great Leap Forward, income inequality, index card, invention of the printing press, invisible hand, Isaac Newton, Jeff Bezos, John Harrison: Longitude, lateral thinking, lone genius, medical malpractice, microcredit, Netflix Prize, no-fly zone, planetary scale, Ralph Waldo Emerson, RAND corporation, reality distortion field, retail therapy, shareholder value, Silicon Valley, Skype, stem cell, Steve Jobs, Steve Wozniak, TED Talk, the scientific method, The Wisdom of Crowds, ultimatum game, urban renewal, War on Poverty

In 2010 the Icelandic Parliament set one up to gather input for the new national constitution. It also canvassed opinions on Twitter, YouTube and Facebook. To see how crowdsourcing politics works in the wild, I join an Assembly held in a school gymnasium on the outskirts of Reykjavik. Its mandate is to identify the “core competencies” upon which Iceland should be building its future. Some 150 people turn up on a grey, wet Saturday morning. True to the spirit of crowdsourcing, they represent a cross-section of Icelandic society plus a smattering of parliamentarians, a former mayor and the city’s chief of police. Most people are dressed casually, with many men sporting moustaches as part of an anti-cancer campaign.

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So has the Assembly done its job? Has Iceland’s experiment in crowdsourcing unleashed a torrent of creative solutions to the nation’s problems? Sitting at Table K, brainstorming with the locals, I did not feel like I was witnessing problem-solving on a Promethean scale. Sure, we came up with an intriguing list of suggestions, but so too could a few people sitting together in a bar. But that might be the wrong way to the think about it. Maybe I am trying to assess a pointilliste painting from too close up. When you step back and look at the big picture, Iceland’s experiment with crowdsourcing does seem to be churning up useful ideas that might one day make their way into government policy.

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And computer troubleshooting is just the tip of the iceberg: the Web is crammed with forums where crowds offer advice on everything from relationships to health to home repairs. Now is the time to sound a note of caution: collaboration and crowdsourcing both have their limits. Working together is not the only answer to every problem. Even teams with a fine track record can, over time, grow stale and narrow-minded. The crowd can make mistakes or be sabotaged by rogue members. With around 100,000 contributors constantly editing its content, Wikipedia is an example of the perils of crowdsourcing. Though a goldmine of information, the online encyclopedia is prone to inaccuracies and biases. The bubbles and crashes of recent years are a reminder that stock markets, another much-touted example of collective intelligence, are not always so clever.

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It by Marc Goodman

23andMe, 3D printing, active measures, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, Alvin Toffler, Apollo 11, Apollo 13, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Boston Dynamics, Brian Krebs, business process, butterfly effect, call centre, Charles Lindbergh, Chelsea Manning, Citizen Lab, cloud computing, Cody Wilson, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, data science, Dean Kamen, deep learning, DeepMind, digital rights, disinformation, disintermediation, Dogecoin, don't be evil, double helix, Downton Abbey, driverless car, drone strike, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, Firefox, Flash crash, Free Software Foundation, future of work, game design, gamification, global pandemic, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, Hacker News, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, information security, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, John Markoff, Joi Ito, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, knowledge worker, Kuwabatake Sanjuro: assassination market, Large Hadron Collider, Larry Ellison, Laura Poitras, Law of Accelerating Returns, Lean Startup, license plate recognition, lifelogging, litecoin, low earth orbit, M-Pesa, machine translation, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, mobile money, more computing power than Apollo, move fast and break things, Nate Silver, national security letter, natural language processing, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, offshore financial centre, operational security, optical character recognition, Parag Khanna, pattern recognition, peer-to-peer, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, printed gun, RAND corporation, ransomware, Ray Kurzweil, Recombinant DNA, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Ross Ulbricht, Russell Brand, Salesforce, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, SimCity, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, SoftBank, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, subscription business, supply-chain management, synthetic biology, tech worker, technological singularity, TED Talk, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, the long tail, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Virgin Galactic, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, Westphalian system, WikiLeaks, Y Combinator, you are the product, zero day

The boss’s gamification strategy paid off and received widespread attention among his workers, with the Ferrari reserved for the chosen “employee of the month.” From Crowdsourcing to Crime Sourcing Of all the business innovation techniques utilized by Crime, Inc., perhaps none has been as widely adopted as crowdsourcing. Crowdsourcing began as a legitimate tool to leverage the wisdom of crowds to solve complex business and scientific challenges. The concept of crowdsourcing first gained widespread attention in an article written in 2006 by Jeff Howe for Wired. Howe defined crowdsourcing as the act of “outsourcing a task to a large, undefined group of people through an open call.” While hundreds of examples of crowdsourcing have been documented with great results, these very same techniques can be harnessed for criminal purposes as well.

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Rather than having a small elite force of highly trained agents on hand to protect all of us, we are far better off enabling ordinary citizens to combat the problem as a group through crowdsourcing. To beat Crime, Inc. at its game, we must get good to scale, but bigger and better. The idea of crowdsourcing law enforcement is hardly new. In 1865, when John Wilkes Booth assassinated President Lincoln, he became the first fleeing felon to have his photograph appear on a wanted poster. Today, 150 years after the murder of our nation’s sixteenth president, government’s implementation of crowdsourced law enforcement has changed nary a bit. Cops distribute photographs to local news channels, and broadcasters warn that the wanted party is “armed and dangerous, please contact local law enforcement should you see him.”

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If any of the participants involved are arrested, they are unlikely to be able to “rat” on their co-conspirators, whom they met for the first time at the scene of the crime. Similar incidents have taken place in Chicago, Philadelphia, and Los Angeles. Some crowdsourcing techniques are meant to give potential lawbreakers a leg up on the police. In the United States, mobile apps such as DUI Dodger, Buzzed, and Checkpoint Wingman allow those who have had too much to drink to crowdsource the location of DUI checkpoints, view them on an interactive map on the iPhone or Android device, and receive alerts when checkpoints are moved or newly established. When the 2011 London riots against government spending cuts turned violent, protesters created an app called Sukey, which allowed them to photograph police and upload their geo-tagged images to a crowdsourced interactive map.

Too Big to Know: Rethinking Knowledge Now That the Facts Aren't the Facts, Experts Are Everywhere, and the Smartest Person in the Room Is the Room by David Weinberger

airport security, Alfred Russel Wallace, Alvin Toffler, Amazon Mechanical Turk, An Inconvenient Truth, Berlin Wall, Black Swan, book scanning, Cass Sunstein, commoditize, Computer Lib, corporate social responsibility, crowdsourcing, Danny Hillis, David Brooks, Debian, double entry bookkeeping, double helix, Dr. Strangelove, en.wikipedia.org, Exxon Valdez, Fall of the Berlin Wall, future of journalism, Future Shock, Galaxy Zoo, Gregor Mendel, Hacker Ethic, Haight Ashbury, Herman Kahn, hive mind, Howard Rheingold, invention of the telegraph, Jeff Hawkins, jimmy wales, Johannes Kepler, John Harrison: Longitude, Kevin Kelly, Large Hadron Collider, linked data, Neil Armstrong, Netflix Prize, New Journalism, Nicholas Carr, Norbert Wiener, off-the-grid, openstreetmap, P = NP, P vs NP, PalmPilot, Pluto: dwarf planet, profit motive, Ralph Waldo Emerson, RAND corporation, Ray Kurzweil, Republic of Letters, RFID, Richard Feynman, Ronald Reagan, scientific management, semantic web, slashdot, social graph, Steven Pinker, Stewart Brand, systems thinking, technological singularity, Ted Nelson, the Cathedral and the Bazaar, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Whole Earth Catalog, X Prize

Our traditional knowledge-based institutions are taking their first hesitant steps on land, and knowledge is beginning to show its new shape: Wide. When British media needed to pore through tens of thousands of pages of Parliamentarians’ expense reports, they “crowd-sourced” it, engaging thousands of people rather than relying on a handful of experts. It turns out that, with a big enough population engaged, sufficient width can be its own type of depth. (Note that this was not particularly good news for the Parliamentarians.) Boundary-free. Evaluating patent applications can’t be crowd-sourced because it would require expertise that crowds don’t have. So, when the US Patent Office was frustrated by how long its beleaguered staff was taking to research patent applications, it started a pilot project that enlists “citizen-experts” to find prior instances of the claimed inventions, across disciplinary and professional lines.

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Its massiveness alone gives rise to new possibilities for expertise—that is, for groups of unrelated people to collectively figure something out, or to be a knowledge resource about a topic far too big for any individual expert. The simplest forms are what Jeff Howe called “crowdsourcing” in a 2006 article in Wired.13 He intended it as a play on “outsourcing,” and his examples mainly were of “plugged-in enthusiasts” who will work for much less than traditional employees. But the term was so good that it quickly escaped its creator’s tether and now applies to just about any instance in which the mass of the Net lets us do things for little or no cost that otherwise would have been prohibitively expensive. The examples of crowdsourcing are familiar at this point. When Members of Parliament were found to be routinely taking frivolous deductions, the British newspaper The Guardian set up a site where 20,000 people pored through 700,000 expense claims.

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An excerpt of the chapter titled “The New York City Draft Riots of 1863” can be found at http://www.press.uchicago.edu/Misc/Chicago/317749.html. 11 Howard Rheingold, Smart Mobs: The Next Social Revolution (Basic Books, 2003). 12 James Surowiecki, The Wisdom of Crowds (Random House, 2004). 13 Jeff Howe, “The Rise of Crowdsourcing,” Wired 14, no. 6 (June 2006), http://www.wired.com/wired/archive/14.06/crowds.html. See also Howe’s Crowdsourcing (Crown Business, 2008). 14 “Darpa Network Challenge: We Have a Winner,” https://network-challenge.darpa.mil/Default.aspx. 15 “How It Works” (MIT), 2009, http://balloon.mit.edu/mit/payoff/. 16 Darren Murph, “MIT-Based Team Wins DARPA’s Red Balloon Challenge, Demonstrates Power of Social Networks (and Cold Hard Cash),” December 6, 2009, http://www.engadget.com/2009/12/06/mit-based-team-wins-darpas-red-balloon-challenge-demonstrates/. 17 See Jonathan Zittrain’s “Minds for Sale” video at http://www.youtube.com/watch?

The Knowledge Illusion by Steven Sloman

Affordable Care Act / Obamacare, Air France Flight 447, attribution theory, bitcoin, Black Swan, Cass Sunstein, combinatorial explosion, computer age, Computing Machinery and Intelligence, CRISPR, crowdsourcing, Dmitri Mendeleev, driverless car, Dunning–Kruger effect, Elon Musk, Ethereum, Flynn Effect, Great Leap Forward, Gregor Mendel, Hernando de Soto, Higgs boson, hindsight bias, hive mind, indoor plumbing, Isaac Newton, John von Neumann, libertarian paternalism, Mahatma Gandhi, Mark Zuckerberg, meta-analysis, Nick Bostrom, obamacare, Peoples Temple, prediction markets, randomized controlled trial, Ray Kurzweil, Richard Feynman, Richard Thaler, Rodney Brooks, Rosa Parks, seminal paper, single-payer health, speech recognition, stem cell, Stephen Hawking, Steve Jobs, technological singularity, The Coming Technological Singularity, The Wisdom of Crowds, Vernor Vinge, web application, Whole Earth Review, Y Combinator

The web has provided smart new tools for us to use, like GPS devices and operating systems that talk to us. But one of the most useful forms of web applications turns people themselves into tools. Crowdsourcing applications have created broader and more dynamic communities of knowledge than ever before by aggregating the knowledge and skills of large numbers of people. Crowdsourcing is the critical provider of information to sites and apps that integrate knowledge from different experiences, locations, and knowledge bases. Yelp and Amazon crowdsource reviews for services and products. Waze crowdsources maps of traffic conditions from the input of individual drivers on the road. Then there are sites like Reddit that allow users to ask questions and encourage anyone to provide an answer.

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Each of us has our little window on the world, a little bit of knowledge that we have access to. Crowdsourcing is a way of looking through tens, hundreds, and sometimes thousands of windows simultaneously. But crowdsourcing works only when it provides access to expertise. Without expertise, it can be useless and even detrimental. Pallokerho-35 (PK-35) is a Finnish soccer club. A few years ago, the team invited fans to participate in decisions regarding recruiting, training, and even game tactics. They voted using cell phones. The outcome was disastrous. The team did poorly, the coach was fired, and the experiment came to an abrupt end. For a crowdsourcing scheme to work, it’s not enough just to have a big community; the community needs to have the necessary expertise.

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Then there are sites like Reddit that allow users to ask questions and encourage anyone to provide an answer. When done right, crowdsourcing is the best way available to take advantage of expertise in the community. It gets as many people as possible involved in pursuing a goal. This means experts are more likely to offer their input. In the end, that’s the best way to achieve an objective: to make use of expertise. Yelp integrates knowledge from restaurant goers who purport to know how good a restaurant is, and Reddit tries to identify who has the most expertise to answer someone’s question. So crowdsourcing works best when those with the most expertise have sufficient incentive to participate in the community.

Whiplash: How to Survive Our Faster Future by Joi Ito, Jeff Howe

3D printing, air gap, Albert Michelson, AlphaGo, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Black Swan, Bletchley Park, blockchain, Burning Man, business logic, buy low sell high, Claude Shannon: information theory, cloud computing, commons-based peer production, Computer Numeric Control, conceptual framework, CRISPR, crowdsourcing, cryptocurrency, data acquisition, deep learning, DeepMind, Demis Hassabis, digital rights, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, Ford Model T, frictionless, game design, Gerolamo Cardano, informal economy, information security, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, move 37, Nate Silver, Network effects, neurotypical, Oculus Rift, off-the-grid, One Laptop per Child (OLPC), PalmPilot, pattern recognition, peer-to-peer, pirate software, power law, pre–internet, prisoner's dilemma, Productivity paradox, quantum cryptography, race to the bottom, RAND corporation, random walk, Ray Kurzweil, Ronald Coase, Ross Ulbricht, Satoshi Nakamoto, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, Simon Singh, Singularitarianism, Skype, slashdot, smart contracts, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, supply-chain management, synthetic biology, technological singularity, technoutopianism, TED Talk, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, Two Sigma, universal basic income, unpaid internship, uranium enrichment, urban planning, warehouse automation, warehouse robotics, Wayback Machine, WikiLeaks, Yochai Benkler

page=me_tab. 5 An earlier version of this section, including the quotes from Zoran Popović and Adrien Treuille, appeared in Slate. Jeff Howe, “The Crowdsourcing of Talent,” Slate, February 27, 2012, http://www.slate.com/articles/technology/future_tense/2012/02/foldit_crowdsourcing_and_labor_.html. 6 Jeff Howe, “The Rise of Crowdsourcing,” WIRED, June 1, 2006, http://www.wired.com/2006/06/crowds/. 7 Todd Wasserman, “Oxford English Dictionary Adds ‘Crowdsourcing,’ ‘Big Data,’” Mashable, June 13, 2013, http://mashable.com/2013/06/13/dictionary-new-words-2013/. 8 “Longitude Found: John Harrison,” Royal Museums Greenwich, October 7, 2015, http://www.rmg.co.uk/discover/explore/longitude-found-john-harrison. 9 Michael Franklin, “A Globalised Solver Network to Meet the Challenges of the 21st Century,” InnoCentive Blog, April 15, 2016, http://blog.innocentive.com/2016/04/15/globalised-solver-network-meet-challenges-21st-century/. 10 Karim R.

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“The labor isn’t always free, but it costs a lot less than paying traditional employees. It’s not outsourcing; it’s crowdsourcing.” The term, originally coined in a joking conversation between Jeff and his Wired editor Mark Robinson, was quickly adopted, initially by people in vocations like advertising and journalism in which crowdsourcing had taken root, and then by the public at large. (The word first appeared in the Oxford English Dictionary in 2013.)7 As a business practice crowdsourcing has become standard operating procedure in fields ranging from technology and media to urban planning, academia, and beyond.

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It’s the kind of result we’re beginning to see more and more of. A bigger circle benefits us all. PS: The Difference Difference Makes I spent much of 2007 and 2008 writing a book about crowdsourcing. I had no trouble identifying fascinating case studies; a burst of ambitious, if often ill-conceived, start-ups had sprung up in the years since Wired published my original article on the subject. But there were very few serious researchers studying the kinds of group behaviors that either made crowdsourcing click, or insured that it didn’t. Discovering Scott E. Page’s work on the mechanics of diversity represented a turning point—diversity was more than a policy plank or an indifferent bullet point on some HR presentation.

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, anti-fragile, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, behavioural economics, Ben Horowitz, bike sharing, bioinformatics, bitcoin, Black Swan, blockchain, Blue Ocean Strategy, book value, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, circular economy, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, data science, Dean Kamen, deep learning, DeepMind, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fail fast, game design, gamification, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, holacracy, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Joi Ito, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, low earth orbit, Lyft, Marc Andreessen, Mark Zuckerberg, market design, Max Levchin, means of production, Michael Milken, minimum viable product, natural language processing, Netflix Prize, NetJets, Network effects, new economy, Oculus Rift, offshore financial centre, PageRank, pattern recognition, Paul Graham, paypal mafia, peer-to-peer, peer-to-peer model, Peter H. Diamandis: Planetary Resources, Peter Thiel, Planet Labs, prediction markets, profit motive, publish or perish, radical decentralization, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Rutger Bregman, Salesforce, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, SpaceShipOne, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Jurvetson, subscription business, supply-chain management, synthetic biology, TaskRabbit, TED Talk, telepresence, telepresence robot, the long tail, Tony Hsieh, transaction costs, Travis Kalanick, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, urban planning, Virgin Galactic, WikiLeaks, winner-take-all economy, X Prize, Y Combinator, zero-sum game

This strategy failed, largely due to legal issues about ownership and licensing costs, although another problem was that the students lacked a sense of purpose and commitment insofar as the company was concerned, which resulted in almost no contribution to the platform [Experimentation]. Undaunted, Jones and Rogers took another shot at attracting community, this time via crowdsourcing. They were successful this time around, and in March 2008, Local Motors debuted as the first community to completely crowdsource a car. (The company currently has eighty-three employees and three micro-factories for manufacturing.) The Local Motors staff then turned its attention to evangelizing, sharing its passion for the product on numerous designer sites, which acted as magnets for a like-minded community [Community & Crowd].

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Here are some of the paper’s initiatives: In 2007, the Guardian offered a free blogging platform for thought leaders and created online forums and discussion groups [Community and Crowd]. Developers offered an open API to the paper’s website so they could leverage content on the site [Algorithms]. Investigative reporting for the millions of WikiLeaks cables fully crowdsourced [Community & Crowd]. The Guardian has institutionalized the crowdsourcing of investigative reporting and has successfully used that approach on several occasions, including after obtaining public documents from Sarah Palin’s tenure as governor of Alaska. Similarly, in 2009, when the UK government bowed to public pressure and released two million pages of parliamentary expense reports, the Guardian asked its readership to find any newsworthy needles in that vast haystack of words.

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CRM is initiated by companies, VRM by customers. Differential real-time pricing models Real-time monitoring will allow for the institution of real-time pricing to maximize pricing based on real-time demand (e.g., airline tickets). AIs will prove extremely valuable in this transition. Crowdsourced online marketplaces for marketing materials Using online marketplaces to crowdsource TV commercials (Tongal), logos and banners (99 designs), or any marketing expertise (Freelancer). PR & marketing will have to aim a lot further out to place business memes Due to accelerated pace of change it is mandatory to look further into the future to launch marketing and PR campaigns by identifying when a meme is booming (predictive planning) or, even better, when it first emerges.

Natural Language Annotation for Machine Learning by James Pustejovsky, Amber Stubbs

Amazon Mechanical Turk, bioinformatics, cloud computing, computer vision, crowdsourcing, easy for humans, difficult for computers, finite state, Free Software Foundation, game design, information retrieval, iterative process, language acquisition, machine readable, machine translation, natural language processing, pattern recognition, performance metric, power law, sentiment analysis, social web, sparse data, speech recognition, statistical model, text mining

In Chapter 4 we discussed interoperability and reproducibility as it applies to data formats, but the concept applies to the annotation task as well. Crowdsourcing Crowdsourcing is another approach that is being used more frequently in the annotation community. Essentially, instead of asking a small number of annotators to tag a large number of extents, the task is broken down into a large number of smaller tasks, and a large number of annotators are asked to tag only a few examples each. One popular crowdsourcing platform is Amazon’s Mechanical Turk (MTurk), a resource where people who have tasks that require human intelligence to perform can place requests that are then fulfilled by people across the country and around the world.

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“Annotating expressions of opinions and emotions in language.” Language Resources and Evaluation 39(2–3):165–210. References for Using Amazon’s Mechanical Turk/Crowdsourcing Aker, Ahmet, Mahmoud El-Haj, M-Dyaa Albakour, and Udo Kruschwitz. 2012. “Assessing Crowdsourcing Quality through Objective Tasks.” In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey. Filatova, Elena. 2012. “Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing.” In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey. Fort, Karën, Gilles Adda, and K.

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gold standard data set, Creating the Gold Standard (Adjudication) guidelines, writing, Writing the Annotation Guidelines–Example 4: Link Tags—Semantic Roles infrastructure of project for, The Infrastructure of an Annotation Project light annotation task, Semantic Roles machine learning algorithms, matching to, Matching Annotation to Algorithms MAMA cycle, Be Prepared to Revise metadata, Metadata Annotation: Document Classification–Multiple Labels: Film Genres multimodel, Semantic Roles POS tagsets and, Kinds of Annotation–Kinds of Annotation process of, Kinds of Annotation reporting on, About Your Annotation Task and Annotators semantic value, Kinds of Annotation specification vs. guidelines, Specification Versus Guidelines splitting files for, Splitting Up the Files for Annotation standards, Different Kinds of Standards–Other Standards Affecting Annotation, Applying and Adopting Annotation Standards–ISO Standards and You (see also annotation standards) syntactic bracketing, Kinds of Annotation text extent, Text Extent Annotation: Named Entities–Stand-off Annotation by Character Location TimeBank, creation of, Annotation: The Creation of TimeBank–Annotation: The Creation of TimeBank annotation-dependent feature, Decision Tree Learning annotators, The Infrastructure of an Annotation Project, Metadata, Preprocessed Data, Writing the Annotation Guidelines–Example 4: Link Tags—Semantic Roles, Example 2: Multiple Labels—Film Genres, Annotators–Annotators, Annotators, Annotators, Annotators, Annotators, About Your Annotation Task and Annotators, SputLink, Crowdsourcing Annotation–User-Generated Content bias with, potential for and avoiding, Metadata chosing, Annotators crowdsourcing as source of, The Infrastructure of an Annotation Project, Crowdsourcing Annotation–User-Generated Content finding, Annotators guidelines, writing for, Writing the Annotation Guidelines–Example 4: Link Tags—Semantic Roles and outside information, Example 2: Multiple Labels—Film Genres practical considerations for, Annotators preprocessed data and, Preprocessed Data reporting on, About Your Annotation Task and Annotators revising, Annotators SputLink vs., SputLink AP (Associated Press), Building the Corpus ARDA workshop, and the TimeML, Annotation: TimeML arity, Some Example Models and Specs Artificial Intelligence (AI), Language Data and Machine Learning Association for Computational Linguistics (ACL), Organizations and Conferences ATIS, as source for Penn TreeBank, What Is a Corpus?

Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia by Anthony M. Townsend

1960s counterculture, 4chan, A Pattern Language, Adam Curtis, air gap, Airbnb, Amazon Web Services, anti-communist, Apple II, Bay Area Rapid Transit, Big Tech, bike sharing, Boeing 747, Burning Man, business process, call centre, carbon footprint, charter city, chief data officer, clean tech, clean water, cloud computing, company town, computer age, congestion charging, congestion pricing, connected car, crack epidemic, crowdsourcing, DARPA: Urban Challenge, data acquisition, Deng Xiaoping, digital divide, digital map, Donald Davies, East Village, Edward Glaeser, Evgeny Morozov, food desert, game design, garden city movement, General Motors Futurama, gentrification, Geoffrey West, Santa Fe Institute, George Gilder, ghettoisation, global supply chain, Grace Hopper, Haight Ashbury, Hedy Lamarr / George Antheil, Herman Kahn, hive mind, Howard Rheingold, interchangeable parts, Internet Archive, Internet of things, Jacquard loom, Jane Jacobs, Jevons paradox, jitney, John Snow's cholera map, Joi Ito, Khan Academy, Kibera, Kickstarter, knowledge worker, Lewis Mumford, load shedding, lolcat, M-Pesa, machine readable, Mark Zuckerberg, megacity, megaproject, messenger bag, mobile money, mutually assured destruction, new economy, New Urbanism, Norbert Wiener, Occupy movement, off grid, One Laptop per Child (OLPC), openstreetmap, packet switching, PalmPilot, Panopticon Jeremy Bentham, Parag Khanna, patent troll, Pearl River Delta, place-making, planetary scale, popular electronics, power law, RFC: Request For Comment, RFID, ride hailing / ride sharing, Robert Gordon, scientific management, self-driving car, sharing economy, Shenzhen special economic zone , Silicon Valley, SimCity, Skype, smart cities, smart grid, smart meter, social graph, social software, social web, SpaceShipOne, special economic zone, Steve Jobs, Steve Wozniak, Stuxnet, supply-chain management, technoutopianism, Ted Kaczynski, telepresence, The Death and Life of Great American Cities, too big to fail, trade route, Twitter Arab Spring, Tyler Cowen, Tyler Cowen: Great Stagnation, undersea cable, Upton Sinclair, uranium enrichment, urban decay, urban planning, urban renewal, Vannevar Bush, working poor, working-age population, X Prize, Y2K, zero day, Zipcar

Wherever at the head of some new undertaking you see the government in France, or a man of rank in England, in the United States you will be sure to find an association.”48 Social technologies are but the latest upgrade to this urge that’s embedded in the DNA of American democracy. Crowdsourcing is a way of tapping and directing the inherent sociability of cities. But as powerful as this approach can be, we need to be cautious. While seemingly progressive, crowdsourcing can also open the door for those who would cut the legs out from under government. Where crowdsourced efforts fill gaps left behind by shrinking budgets, the appearance of an inefficient and ineffective public sector will be difficult to avoid. In cities in the developing world, where crowdsourcing offers services governments have never adequately provided, they may allow for a permanent offloading of obligations.

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Poor communities may not have the luxury of this level of engagement—the day-to-day realities of survival often leave few resources for volunteerism. Taken to its extreme, crowdsourcing is tantamount to the privatization of public services—the rich will provide for themselves and deny services to those outside their enclaves. Unless we are ready to embrace anarchy and institutionalize unequal access to public services, there will be limits to what crowdsourcing can accomplish. Crowdsourcing with care means limiting its use to areas where government needs to mobilize citizens around efforts where it lacks capacity, and there is broad consensus over desired outcomes.

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And there will always be an urge to “do something,” if only for self-preservation. As Heeks argues, “In a globalized world, the problems of the poor today can, tomorrow—through migration, terrorism, and disease epidemics—become the problems of those at the pyramid’s top.”50 This brings us to the final dilemma: crowdsourcing and the future role of government in delivering basic services. In smart cities, there will be many new crowdsourcing tools that, like OpenStreetMap, create opportunities for people to pool efforts and resources outside of government. Will governments respond by casting off their responsibilities? In rich countries, governments facing tough spending choices may simply withdraw services as citizen-driven alternatives expand, creating huge gaps in support for the poor.

Bulletproof Problem Solving by Charles Conn, Robert McLean

active transport: walking or cycling, Airbnb, Amazon Mechanical Turk, asset allocation, availability heuristic, Bayesian statistics, behavioural economics, Big Tech, Black Swan, blockchain, book value, business logic, business process, call centre, carbon footprint, cloud computing, correlation does not imply causation, Credit Default Swap, crowdsourcing, David Brooks, deep learning, Donald Trump, driverless car, drop ship, Elon Musk, endowment effect, fail fast, fake news, future of work, Garrett Hardin, Hyperloop, Innovator's Dilemma, inventory management, iterative process, loss aversion, megaproject, meta-analysis, Nate Silver, nudge unit, Occam's razor, pattern recognition, pets.com, prediction markets, principal–agent problem, RAND corporation, randomized controlled trial, risk tolerance, Silicon Valley, SimCity, smart contracts, stem cell, sunk-cost fallacy, the rule of 72, the scientific method, The Signal and the Noise by Nate Silver, time value of money, Tragedy of the Commons, transfer pricing, Vilfredo Pareto, walkable city, WikiLeaks

See Situation‐observation‐resolution Component disaggregation, 180 Component tree, 53–58 Compound growth, 115–116 Compounding, 212 Computers predictions, 160–161 usage, 161 Conclusions, linkage (failure), xxiii–xiv Conditional probabilities, estimation, 202 Conduct analyses, 111 Confirmation bias, 101, 218 avoidance, 180 Constructed experiments, 140 case study, 141, 149–151 Constructive challenging, usage, 83 Constructive confrontation, 104–105 Contested characters, 69e, 70e Cook, Chuck, 245–246 Co‐Opetition (Brandenberger/Nalebuff), 200 Correlation, impact, 145 Correlation tools, usage, 138, 140 Council for Aid to Education (CAE), xviii Courtney, Hugh, 197, 198, 201 Cripps, Sally, 148, 202 Critical analyses conducting, 6 findings, synthesis, 6 Critical path analyses, 185 finding, 88 Cross‐docking, 62 Crowd‐sourced algorithms, case study, 141 Crowd‐sourced problem solving, 140 Crowd‐sourced solutions, 136 Crowdsourcing, 156 algorithms, 164–176 Crowd, wisdom (principle), 165 Csapo, Beno, xvii CSIRO defense, 118, 168–171 patent infringement, 170e Cultivation, change stage, 228 D Dalio, Ray, 181 DALY. See Disability‐affected life years Data analytics, 225 bias, 164 data‐fishing expeditions, 137 overfitting, 146 synthesis/analysis, 180 visualization, case studies, 141 Data sets approach, 256 errors, 164 Data sources, broadening, 106 Decision‐making engine, usage, 161 Decision‐making processes, 160–161, 163 Decision tree, 68–71, 165–166, 166e argument structure, 192e example, 70e final storyline structure, 190–191 format, representation, 123–124 game theory problem usage, 174 Deductive arguments, 188e Deductive conclusion, 58 Deductive logic trees, 58–59, 67 Deductive reasoning, 188 Deductive statement, structuring, 58 Deep‐domain expertise, usage, 255 Deep‐learning algorithms, 162 Deep memory system, 100 Define (step), 44 Delp, Scott L., 143 Design thinking flow, 45e steps, 43–46 Development options inclusion, 214–216 value, 215 Dialectic standard, 104 Di, Q., 143 Direction, understanding, 112 Disability‐affected life years (DALY), 238 Disaggregation, 53, 98.

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Could you imagine delegating your decision to a machine about which city to move to, an algorithm that made the decision based on carefully weighing the preferences of thousands of other individuals and considering your situation? Clearly not—though you might be interested in seeing the results. Crowdsourcing Algorithms Besides doing sophisticated analysis within your own team, we now have the ability to go outside the organization to harness more analytic power. Enter crowdsourcing as a way to pair enterprises searching for solutions with teams of individuals that have ideas for implementation. Participants often compete for a prize to solve challenging problems, which previously required the contracting of consultants and experts.

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If you are solving your problem solo, find a way to brainstorm your ideas with a diverse range of others, insiders and outsiders, at each stage of the process. Phillip Tetlock's work on forecasting shows that teams always outperform individuals in this form of problem solving, even very high‐performing solo practitioners. And the best superforecasting teams even beat crowd‐sourcing and prediction markets.5 Always try multiple trees/cleaves: Even when a problem seems perfectly designed for your favorite framework, try multiple cleaving frames to see what different questions and insights emerge. When we decided to look at obesity as a wicked problem, discussed in Chapter 9, we had a team meeting where we tried out various alternative‐cleaving frames.

Pax Technica: How the Internet of Things May Set Us Free or Lock Us Up by Philip N. Howard

Aaron Swartz, Affordable Care Act / Obamacare, Berlin Wall, bitcoin, blood diamond, Bretton Woods, Brian Krebs, British Empire, butter production in bangladesh, call centre, Chelsea Manning, citizen journalism, Citizen Lab, clean water, cloud computing, corporate social responsibility, creative destruction, crowdsourcing, digital map, Edward Snowden, en.wikipedia.org, Evgeny Morozov, failed state, Fall of the Berlin Wall, feminist movement, Filter Bubble, Firefox, Francis Fukuyama: the end of history, Google Earth, Hacker News, Howard Rheingold, income inequality, informal economy, information security, Internet of things, John Perry Barlow, Julian Assange, Kibera, Kickstarter, land reform, M-Pesa, Marshall McLuhan, megacity, Mikhail Gorbachev, mobile money, Mohammed Bouazizi, national security letter, Nelson Mandela, Network effects, obamacare, Occupy movement, off-the-grid, packet switching, pension reform, prediction markets, sentiment analysis, Silicon Valley, Skype, spectrum auction, statistical model, Stuxnet, Tactical Technology Collective, technological determinism, trade route, Twitter Arab Spring, undersea cable, uranium enrichment, WikiLeaks, zero day

Such initiatives are rarely able to cover an entire country in a systematic way, and they often need the backing of funding and skills from neutral outsiders like the National Democratic Institute.32 But even the most humble projects to map voting irregularities, film the voting process, or crowd-source polling results help expose and document electoral fraud. Such projects allow citizens to surveil government behavior at a local level, though democracy at the national level isn’t necessarily an outcome. Still, the highlight reels of voter fraud can end up online, wearing down bad government.33 Ushahidi is a user-friendly, open-source platform for mapping and crowd-sourcing information. These days, there are well over thirty-five thousand Ushahidi maps in thirty languages.34 In complex humanitarian disasters, most governments and United Nations agencies now know they need to take public crisis mapping seriously.

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Research on China’s feeble attempts at open government demonstrates that crowd sourcing doesn’t work well, and in China’s context is better thought of as “cadre sourcing.”18 This is because the kinds of information sought by the government have already been distorted by the government, enthusiastic cadre participants are more likely to report favorable information than accurate information, and news about independent crowd-sourcing initiatives don’t circulate far. Only during complex humanitarian disasters do people decide to take on the risks of contributing quality information. As device networks spread, civic initiatives will always have more positive impacts in open societies. Authoritarian societies are structurally prevented from making use of people’s goodwill and altruism. In the bounded device networks of an authoritarian regime, crowd-sourcing initiatives are likely to create negative feedback loops and big data efforts are likely to generate misinformation about the actual conditions of public life.

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For example, the latest smartphones, watches, and wearable technologies reveal how immersive and pervasive the internet of things will be. Cell phones have the ability to take one location point per second, but don’t do so because their power supply is limited. If you give an application on your phone permission to use location information, it will send information to a server at the rate the developer chooses. If you use a crowd-sourcing application for traffic data, your phone is sending data about your commute. If you use an application to keep track of your jogging, your phone is generating geotagged data about your movements relative to other people. Every time you take a picture, check in with your favorite social networking application, or track your health, data is sent from your phone to a cell phone tower or router and over a vast network of digital switches.

The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind

23andMe, 3D printing, Abraham Maslow, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, Blue Ocean Strategy, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, commoditize, computer age, Computer Numeric Control, computer vision, Computing Machinery and Intelligence, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, death of newspapers, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, full employment, future of work, Garrett Hardin, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, Large Hadron Collider, lifelogging, lump of labour, machine translation, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, Nick Bostrom, optical character recognition, Paul Samuelson, personalized medicine, planned obsolescence, pre–internet, Ray Kurzweil, Richard Feynman, Second Machine Age, self-driving car, semantic web, Shoshana Zuboff, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, Susan Wojcicki, tacit knowledge, TED Talk, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, transaction costs, Turing test, Two Sigma, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, world market for maybe five computers, Yochai Benkler, young professional

At PatientsLikeMe, 300,000 people connect with other people who share their conditions (at the moment, around 2,300 conditions), and swap experiences and treatments.37 It is reported that Facebook is planning to develop online ‘support communities’ in this spirit.38 One-third of doctors in the United States use the network known as Sermo to distribute research, post clinical cases, and talk amongst each other.39 The same proportion use a network known as QuantiaMD, with similar functionality.40 More than half of the doctors in the United States are members of Doximity, another doctor-specific networking tool.41 Medicine is also making use of crowdsourcing, where large numbers of individuals are drawn upon for their collective ideas and support. At CrowdMed, people post their symptoms and crowdsource diagnoses from an online community of 2,000 doctors—so-called ‘Medical Detectives’.42 At InnoCentive, medical institutions crowdsource ideas by offering large online rewards for those who solve their medical ‘challenges’.43 At Watsi, people in need of medical care, but unable to afford it themselves, can use their online crowdfunding platform to raise finance from donors.44 3-D printing techniques enable many medical objects, from casts to prosthetics to dentist’s caps and crowns, to be personalized and then printed on demand.

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In Chapter 2 we observed cases where experts and lay people collaborate with each other, with the former, for example, reviewing, editing, or supplementing the user-generated content. A third type of online collaboration is crowdsourcing. Here large numbers of people—experts or non-specialists—are invited to contribute to a well-defined project or problem that is so large that it requires many hands, or is so difficult that it might benefit from the ‘wisdom of crowds’,24 or so obscure that an answer is most likely to be found if the net of inquiry is spread widely enough. WikiHouse and Arcbazar in architecture, and Open Ideo and WikiStrat in consulting, have run crowdsourcing projects in this manner. Realization of latent demand One of the attractions of the new approaches to professional work and, in particular, of various online services, is that practical expertise is steadily becoming more affordable and accessible.

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In this way, for example, it is easy jointly to co-author complex documents, even if the contributors are in different countries. A related form of production occurs when online humans crowdsource (as is discussed briefly in section 3.7). Ordinarily, this involves large numbers of people being called upon to co-operate on discrete projects whose completion would be beyond the scope of individuals or conventional organizations. A common approach adopted here is to break some large task down into a manageable number of sub-tasks and invite a community of users each to undertake some of them. Crowdsourcing draws on networks of human beings to solve particular problems, to carry out pieces of work, or even to raise finance for given initiatives.

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences by Rob Kitchin

Bayesian statistics, business intelligence, business process, cellular automata, Celtic Tiger, cloud computing, collateralized debt obligation, conceptual framework, congestion charging, corporate governance, correlation does not imply causation, crowdsourcing, data science, discrete time, disruptive innovation, George Gilder, Google Earth, hype cycle, Infrastructure as a Service, Internet Archive, Internet of things, invisible hand, knowledge economy, Large Hadron Collider, late capitalism, lifelogging, linked data, longitudinal study, machine readable, Masdar, means of production, Nate Silver, natural language processing, openstreetmap, pattern recognition, platform as a service, recommendation engine, RFID, semantic web, sentiment analysis, SimCity, slashdot, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart grid, smart meter, software as a service, statistical model, supply-chain management, technological solutionism, the scientific method, The Signal and the Noise by Nate Silver, transaction costs

Clearly the production of such life-logs raise a number of questions concerning privacy, the ownership of the data produced, and how such data are used (Dodge and Kitchin 2007b). Crowdsourcing Crowdsourcing is the collective generation of media, ideas and data undertaken voluntarily by many people to solve a particular task. While social media content can be said to be crowdsourced in the sense that it is sourced from a large number of people, its purpose is diffuse and lacking in focus. Instead, crowdsourcing focuses on the collective production of information and on creating solutions to particular issues by drawing on the energy, knowledge, skills and consensual and collective action of a crowd of people (Howe 2008).

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Instead, crowdsourcing focuses on the collective production of information and on creating solutions to particular issues by drawing on the energy, knowledge, skills and consensual and collective action of a crowd of people (Howe 2008). Howe (2008) argues that there are four developments that support the growth of crowdsourcing: a renaissance of amateurism (often at professional standards), the emergence of the open source software movement, the increasing availability of the tools of production outside of firms, and the rise of vibrant online communities organised according to people’s interests. Together these enable distributed and democratised pooling of ideas, tools, and materials. There are three main types of crowdsourcing operating at present: those that collectively produce a solution (e.g., OpenStreetMap, Wikipedia); those that use crowd to evaluate multiple solutions/products (e.g., Amazon, Tripadvisor reviews); and those that seek single or multiple solutions from a crowd (e.g., InnoCentive, hackathons).

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These translate approximately into scientific and financial gains, the cumulative effect of benefits, and who gains from such infrastructures. The scientific arguments for the storing, sharing and scaling of data within data infrastructures centre on the promises of new discoveries and innovations through the combination of datasets and the crowdsourcing of minds. Individual datasets are valuable in their own right, but when combined with other datasets or examined in new ways fresh insights can be potentially discerned and new questions answered (Borgman 2007). By combining datasets, it is contended that the cumulative nature and pace of knowledge building is accelerated (Lauriault et al. 2007).

The Open Organization: Igniting Passion and Performance by Jim Whitehurst

Airbnb, behavioural economics, cloud computing, content marketing, crowdsourcing, digital capitalism, en.wikipedia.org, fail fast, Google Hangouts, Infrastructure as a Service, job satisfaction, Kaizen: continuous improvement, market design, meritocracy, Network effects, new economy, place-making, platform as a service, post-materialism, profit motive, risk tolerance, Salesforce, shareholder value, side project, Silicon Valley, Skype, Snapchat, Steve Jobs, subscription business, TED Talk, The Wisdom of Crowds, Tony Hsieh

This book will show leaders and aspiring leaders—in companies large and small, and in established companies as well as start-ups struggling to grow—how to develop a new, open organizational model that uniquely matches the speed and complexity that businesses must master today. From Crowdsourcing to Open Sourcing Much has been written recently about a new way of working called “crowdsourcing,” which is the power of mass participation to generate phenomenal ideas, solve complex problems, and organize broad movements. We’ve seen examples, such as Wikipedia or the Linux operating system (which played a key role in Red Hat’s start), where communities of people spontaneously self-organize around a problem or activity.

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There are also competitions like the Ansari XPRIZE, which awarded $10 million to the first nongovernment organization to launch a reusable manned spacecraft into space twice within two weeks, or Kaggle, which crowdsources solutions to big data–type analytical challenges, that get a multitude of participants to deliver a bunch of ideas or solutions, where the single best of the bunch wins and receives an award. The Limitations of Crowdsourcing As effective as these “tapping the wisdom of the crowd” approaches are at providing companies with new ideas and solutions to problems, they are often limited in that they are either timebound, say, for the duration of a competition, or are narrowly focused on a single, specific goal, such as generating an idea for a new product.

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So, while many companies have tapped the power of participation in targeted ways, few have leveraged its power more broadly within their own organizations. What if you could make this kind of engagement standard, not just one-and-done, for how work gets done in your organization, so that you’re engaging at this level every single day? Another problem with crowdsourcing is that it’s a one-way transaction. Crowdsourcing approaches typically depend on the contributions of volunteers—people who contribute to the product primarily for the reputational advantage, not necessarily for a monetary one. And, too often, it seems that companies approach these volunteers with the goal of extracting value, with what has been called a “Tom Sawyer” model of collaboration.1 As you might recall from your childhood reading, Tom was a bit of a manipulator, someone who was always trying to get out of doing chores.

The End of Big: How the Internet Makes David the New Goliath by Nicco Mele

4chan, A Declaration of the Independence of Cyberspace, Airbnb, Amazon Web Services, Andy Carvin, Any sufficiently advanced technology is indistinguishable from magic, Apple's 1984 Super Bowl advert, barriers to entry, Berlin Wall, big-box store, bitcoin, bread and circuses, business climate, call centre, Cass Sunstein, centralized clearinghouse, Chelsea Manning, citizen journalism, cloud computing, collaborative consumption, collaborative editing, commoditize, Computer Lib, creative destruction, crony capitalism, cross-subsidies, crowdsourcing, David Brooks, death of newspapers, disruptive innovation, Donald Trump, Douglas Engelbart, Douglas Engelbart, en.wikipedia.org, Evgeny Morozov, Exxon Valdez, Fall of the Berlin Wall, Filter Bubble, Firefox, global supply chain, Google Chrome, Gordon Gekko, Hacker Ethic, Ian Bogost, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John Perry Barlow, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, Lean Startup, lolcat, machine readable, Mark Zuckerberg, military-industrial complex, minimum viable product, Mitch Kapor, Mohammed Bouazizi, Mother of all demos, Narrative Science, new economy, Occupy movement, off-the-grid, old-boy network, One Laptop per Child (OLPC), peer-to-peer, period drama, Peter Thiel, pirate software, public intellectual, publication bias, Robert Metcalfe, Ronald Reagan, Ronald Reagan: Tear down this wall, satellite internet, Seymour Hersh, sharing economy, Silicon Valley, Skype, social web, Steve Jobs, Steve Wozniak, Stewart Brand, Stuxnet, Ted Nelson, Ted Sorensen, Telecommunications Act of 1996, telemarketer, the Cathedral and the Bazaar, the long tail, The Wisdom of Crowds, transaction costs, uranium enrichment, Whole Earth Catalog, WikiLeaks, Zipcar

It makes me feel better; I know I’m safe because of an interconnecting web of institutions that operate to encourage the best and make sure my interests are protected. In a crowd-sourced, maker world, we’re missing some of the institutions that have helped to provide accountability and reliability. Just as the end of Big News has meant a demonstrable loss of accountability journalism, we might worry that the end of Big Companies could mean a demonstrable loss of accountability, especially in industries where technical expertise or competency is required. While I tend to sympathize with the views of Ben Kaufman of Quirky and others who believe that anything—literally anything—can be crowd-sourced, I’m not sure everything should be crowd-sourced. Would you want to fly on a small airline that uses a craft- or maker-produced airplane whose engines come from a craft- or maker-produced engine company?

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In another study, “just 4 percent of consumers would be willing to stick with a brand if its competitors offered better value for the same price.24 It turns out that the more technology you use, the more likely you are to have a lot less brand loyalty.25 From Groupon to iPad shopping apps to Amazon, consumers respond overwhelmingly to price and to the recommendations of trusted peers in their social networks—not to brands. Crowdsourcing works well for designing large, complex, expensive products purchased by organizations, not just small consumer items. The U.S. military has begun designing its vehicles using crowdsourcing—with some initial promise. In four months, a team designed, built, and deployed an “experimental crowd-derived combat support vehicle” or XC2V for short. The site used to design it is called LocalMotors, which “harnesses the creativity of the world’s underemployed car designers.”26 Thousands of people on the site participate in the design of vehicles, some of which actually find their way into production.

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Smith explained that the site focused on assembling strong content and waited for people to discover it on their own; with social media and the dynamics of viral sharing holding sway, “push is a much more effective way of reaching people than pull.”29 Continuing to build on its success, the Tribune shows that a new organization can carry the torch of accountability and journalistic integrity formerly carried by “big” institutions at their best. CSI (Crowd-Sourced Investigations) The Texas Tribune is an example of a new organization providing accountability journalism, but Britain’s venerable Guardian newspaper (founded in 1821) offers a compelling glimpse of an old big institution experimenting and trying to find its way. In recent years, the Guardian has offered a free blogging platform to online opinion leaders, hosted chatrooms and discussion boards, and emerged as an early convert to podcasting.

How to Stand Up to a Dictator by Maria Ressa

2021 United States Capitol attack, activist lawyer, affirmative action, Affordable Care Act / Obamacare, airport security, anti-communist, Asian financial crisis, Big Tech, Brexit referendum, business process, business process outsourcing, call centre, Cambridge Analytica, citizen journalism, cognitive bias, colonial rule, commoditize, contact tracing, coronavirus, COVID-19, crowdsourcing, delayed gratification, disinformation, Donald Trump, fake news, future of journalism, iterative process, James Bridle, Kevin Roose, lockdown, lone genius, Mahatma Gandhi, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Milgram experiment, move fast and break things, natural language processing, Nelson Mandela, Network effects, obamacare, performance metric, QAnon, recommendation engine, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Steven Levy, surveillance capitalism, the medium is the message, The Wisdom of Crowds, TikTok, Twitter Arab Spring, work culture

One area in which we wanted to have real impact was the Philippines’ record on elections and encourage more of our citizens to vote. We wisely aimed all of our news network initiatives toward the May 2010 presidential elections, for which I co-opted two ideas popularized by American writers: the tipping point and crowdsourcing.8 The idea of the tipping point has its roots in epidemiology: when a virus multiplies below the radar screen and then hits the point when it changes the entire system. Crowdsourcing suggests that if a group’s members have diversity of ideas, independence of one another, a decentralized structure, and a mechanism for turning judgments into a collective decision, they can make smarter decisions than any lone genius can.

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Nieto predicted that Rappler would close down in 2018. In April, I flew to San Jose, California, for the F8, Facebook’s annual developer conference. Facebook had asked me to attend, meet with its officers and partners, and talk about our work at Rappler. A year earlier, Facebook had featured Rappler and our crowdsourcing efforts during disasters37 at F8. #ProjectAgos, the crowdsourcing platform we had built for typhoons and disasters, had been included in the rollout of Facebook’s Free Basics, the low-bandwidth app available on mobile phones for free. But my feelings about the company had changed in the intervening year. My warnings to our Facebook partners in Singapore the previous August had been entirely ignored.

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My ambition was to create a new standard of investigative journalism in my country, one that would harness the social media platforms to build communities of action for better governance and stronger democracies. At the time, I was the truest of true believers in the power of social media to do good in the world. Using Facebook and other platforms, we were able to crowdsource breaking news, find pivotal sources and tips, harness collective action for climate change and good governance, and help increase voter knowledge and participation in our elections. We were a fast success, but by Rappler’s fifth year of existence, we had gone from being lauded for our ideas to being targeted by our government—all because we continued to do our jobs as journalists: to tell the truth and hold power to account.

Humans as a Service: The Promise and Perils of Work in the Gig Economy by Jeremias Prassl

3D printing, Affordable Care Act / Obamacare, Airbnb, algorithmic management, Amazon Mechanical Turk, Andrei Shleifer, asset light, autonomous vehicles, barriers to entry, call centre, cashless society, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, death from overwork, Didi Chuxing, disruptive innovation, Donald Trump, driverless car, Erik Brynjolfsson, full employment, future of work, George Akerlof, gig economy, global supply chain, Greyball, hiring and firing, income inequality, independent contractor, information asymmetry, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, low skilled workers, Lyft, machine readable, Mahatma Gandhi, Mark Zuckerberg, market friction, means of production, moral hazard, Network effects, new economy, obamacare, pattern recognition, platform as a service, Productivity paradox, race to the bottom, regulatory arbitrage, remote working, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Rosa Parks, scientific management, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Simon Singh, software as a service, Steve Jobs, TaskRabbit, TechCrunch disrupt, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, transaction costs, transportation-network company, Travis Kalanick, two tier labour market, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, warehouse automation, work culture , working-age population

Lilly Irani, ‘The cultural work of microwork’ (2013) 17(5) New Media & Society 720, 729. 13. Ibid., 732. 14. Ibid., 738. 15. Jeff Howe, Crowdsourcing: How the Power of the Crowd Is Driving the Future of Business (Random House 2009), 15. 16. Frank Pasquale, ‘Two narratives of platform capitalism’ (2016) 35(1) Yale Law & Policy Review 309, 312. chapter 1 1. Jeff Howe, ‘The rise of crowdsourcing’, Wired (1 June 2006), http://www. wired.com/2006/06/crowds/, archived at https://perma.cc/44XE-MLDR 2. Ibid. See also Jeff Howe, Crowdsourcing: How the Power of the Crowd Is Driving the Future of Business (Random House 2009). Crowdsourcing is by no means limited to labour markets: consumers, governments, and businesses have turned to the Internet in a wide range of areas—from the US National Aeronautics and Space Administration (NASA) asking citizens for help in its quest to identify exoplanets (http://www.zooniverse.org/projects/marckuchner/backyard-worlds-planet-9, archived at https://perma.cc/LR8S-7QUF), to start-ups raising capital for new business ideas through platforms such as Kickstarter (http://www.kickstarter.

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The long-standing distinction between employees working for large companies and amateurs engaging in their craft as a hobby had become increasingly blurred: Hobbyists, part-timers, and dabblers suddenly have a market for their efforts, as smart companies in industries as disparate as pharmaceuticals and television discover ways to tap the latent talent of the crowd. The labor isn’t always free, but it costs a lot less than paying traditional employees. It’s not outsourcing; it’s crowdsourcing.1 Crowdsourcing, he subsequently explained, is the ‘act of taking a job trad- itionally performed by employees and outsourcing it to an undefined, gen- erally large group of people in the form of an open call’.2 Its rise from nowhere has been little short of meteoric: a mere decade later, recourse to the crowd has begun to permeate our daily lives.

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* * * Understanding the Gig Economy 13 To grapple with the constant evolution of the on-demand economy, sev- eral academics have attempted to come up with a taxonomy of gig-economy work. One of the leading authors in this field is Jan Marco Leimeister of Kassel University in Germany. Together with his team, he has developed an extensive classificatory scheme of crowdsourcing and crowdwork. This distinguishes, for example, between ‘internal’ and ‘external’ crowdwork, depending on whether on-demand workers are employed by the platform operator or not. The latter category is then subdivided into a series of ‘arche- types’, including categories such as ‘Microtask’, where ‘tasks are predomin- antly simple and repetitive’, or ‘Marketplace platforms’, through which ‘more long-term and complex jobs are given into crowd’.5 This understanding of crowdwork as a purely digital form of on-demand labour, whereby tasks can be completed behind a computer anywhere, is usually contrasted with gigwork, whereby tasks mediated through a platform have to be completed offline—think food delivery or cleaning.6 The axes along which the field could be organized are nearly unlimited.

The Internet of Us: Knowing More and Understanding Less in the Age of Big Data by Michael P. Lynch

Affordable Care Act / Obamacare, Amazon Mechanical Turk, big data - Walmart - Pop Tarts, bitcoin, Cass Sunstein, Claude Shannon: information theory, cognitive load, crowdsourcing, data science, Edward Snowden, Firefox, Google Glasses, hive mind, income inequality, Internet of things, John von Neumann, meta-analysis, Nate Silver, new economy, Nick Bostrom, Panopticon Jeremy Bentham, patient HM, prediction markets, RFID, sharing economy, Steve Jobs, Steven Levy, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Twitter Arab Spring, WikiLeaks

It allows researchers to pass their work directly to those who might be interested in it, or benefit from it. Inclusivity of a different sort can come about by what Wired’s Jeff Howe dubbed “crowdsourcing” in 2006. Crowdsourcing is not simply any activity that uses the World Wide Web as a platform for people to network about problems—like Intrade or rankings on Amazon. As computer scientist Daren Brabham defines it, crowdsourcing is an online problem-solving and production model that “leverages the collective intelligence of online communities to serve specific organizational goals.”2 In other words, it is the top-down organized use of the Internet hive-mind.

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The popular incentive-based innovation platform InnoCentive is often cited as an example of the inclusivity of crowdsourcing. (InnoCentive is ancient in Web 2.0 terms: it was founded in 2002.) Here’s how it works: nonprofits and businesses post prize competitions for solutions to challenges. These can run the gamut—from retail product positioning to early detection mechanisms for inflammatory bowel disease. The prizes themselves vary in size, with some topping nearly a million dollars but many being significantly less. InnoCentive is only one example of how crowdsourcing can work, of course. Other famous examples include Amazon’s Mechanical Turk, which allows companies (and scientific researchers) to outsource specific tasks to a huge network of “Turkers” to perform tasks that humans are still better at than computers, such as image identification and translation.

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“The traditional dream of rags to riches is being supplanted by a new dream of sustainable quality of life”—a life where we can spend more time engaged in pursuits that interest us, such as making music, cooking better food and thinking about philosophy.6 Rifkin thinks the same revolution is happening at the level of knowledge—perhaps especially there, since the wide availability of knowledge is the fuel powering the rest of our economy. But while Rifkin’s collaborative vision might be appealing, the death of capitalism—and exploitative versions of it—is hardly near. Take crowdsourcing as an example. Instead of an economy of skilled laborers that require resources to train, equip and compensate, crowdsourcing makes it possible for companies to distribute and generate knowledge without the expense of hiring those experts. This isn’t necessarily more “democratic.” But it is more capitalistic. Even some of the most active workers for Amazon’s Mechanical Turk can make very little, two to five dollars an hour.

Frugal Innovation: How to Do Better With Less by Jaideep Prabhu Navi Radjou

3D printing, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, barriers to entry, Baxter: Rethink Robotics, behavioural economics, benefit corporation, Bretton Woods, business climate, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, circular economy, cloud computing, collaborative consumption, collaborative economy, Computer Numeric Control, connected car, corporate social responsibility, creative destruction, crowdsourcing, disruptive innovation, driverless car, Elon Musk, fail fast, financial exclusion, financial innovation, gamification, global supply chain, IKEA effect, income inequality, industrial robot, intangible asset, Internet of things, job satisfaction, Khan Academy, Kickstarter, late fees, Lean Startup, low cost airline, M-Pesa, Mahatma Gandhi, Marc Benioff, megacity, minimum viable product, more computing power than Apollo, new economy, payday loans, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, planned obsolescence, precision agriculture, race to the bottom, reshoring, risk tolerance, Ronald Coase, Salesforce, scientific management, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, six sigma, smart grid, smart meter, software as a service, standardized shipping container, Steve Jobs, supply-chain management, tacit knowledge, TaskRabbit, TED Talk, The Fortune at the Bottom of the Pyramid, the long tail, The Nature of the Firm, Tony Fadell, transaction costs, Travis Kalanick, unbanked and underbanked, underbanked, value engineering, vertical integration, women in the workforce, work culture , X Prize, yield management, Zipcar

Yet the Booz study estimates that only 8% of large R&D budgets are spent on digital tools that, among other things, track changing customer needs and help firms work with customers to create solutions.8 It is an obvious way to improve front-end innovation performance and, thanks to plummeting technology costs, affordable digital tools and techniques now exist to improve the depth and breadth of customer engagement. Deploy crowdsourcing and social media Crowdsourcing is a cost-effective technique for collecting customers’ ideas and ascertaining specific, explicit needs. For instance, SoapBox, a Toronto-based online crowdsourcing site, provides companies with a platform for individuals to talk about their ideas and gauge initial public reactions through its thumbs-up or thumbs-down voting system. Ideas that garner sufficient support are packaged with supporting data and sent to the relevant managers, who in turn signal their view of its potential to executives responsible for taking such ideas forward.

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Chapter 6 looks at ways consumers – especially the tech-savvy millennial generation (those born between 1982 and 2004) – are evolving from passive individual users into communities of empowered “prosumers”, who collectively design, create and share the products and services they want. As a result, R&D and marketing leaders at firms like Auchan are working with do-it-yourself (DIY) and crowdsourcing pioneers, such as TechShop and Quirky, to bolster and harness the collective ingenuity and skills of consumer communities. Additionally, big brands such as IKEA are linking up with start-ups such as Airbnb to develop a “sharing economy” in which consumers share goods and services. The chapter also outlines how sales and marketing managers can build greater brand affinity and deepen their engagement with customers by co-creating greater value for all.

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Quirky claims that 10% of its direct revenues are shared with its online community; in 2013, inventors and online influencers shared a pot of $3.8 million. According to Ben Kaufman, the company’s CEO, two consumer products are developed every week. Quirky has launched several products based on crowdsourced ideas, including a flexible power strip, an egg separator and a smartphone-controlled air conditioner. Kaufman uses this as evidence to explain to corporate leaders he meets that the most innovative ideas do not necessarily come from the boardroom or from within the company, but from consumers and the general public.

Bank 3.0: Why Banking Is No Longer Somewhere You Go but Something You Do by Brett King

3D printing, Abraham Maslow, additive manufacturing, Airbus A320, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apollo 11, Apollo 13, Apollo Guidance Computer, asset-backed security, augmented reality, barriers to entry, behavioural economics, bitcoin, bounce rate, business intelligence, business process, business process outsourcing, call centre, capital controls, citizen journalism, Clayton Christensen, cloud computing, credit crunch, crowdsourcing, disintermediation, en.wikipedia.org, fixed income, George Gilder, Google Glasses, high net worth, I think there is a world market for maybe five computers, Infrastructure as a Service, invention of the printing press, Jeff Bezos, jimmy wales, Kickstarter, London Interbank Offered Rate, low interest rates, M-Pesa, Mark Zuckerberg, mass affluent, Metcalfe’s law, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, operational security, optical character recognition, peer-to-peer, performance metric, Pingit, platform as a service, QR code, QWERTY keyboard, Ray Kurzweil, recommendation engine, RFID, risk tolerance, Robert Metcalfe, self-driving car, Skype, speech recognition, stem cell, telepresence, the long tail, Tim Cook: Apple, transaction costs, underbanked, US Airways Flight 1549, web application, world market for maybe five computers

If you aren’t in the game already, it’s getting harder and harder to get a proper seat at the table. There’s no technological fix to being able to tell whether or not people like you. There’s only the ability to change the way you talk to your audience. Crowdsourcing—use the power of the crowd The “occupy” movement we talked about earlier is an example of how communities work in the social, hyperconnected landscape of today. However, there is a mechanism for using crowdsourcing as a mechanism for designing new products and services that are immediately advocated by customers because they were designed by the crowd, for the crowd. Commonwealth Bank in Australia has invited the crowd to submit, discuss and vote on ideas that improve the Australian banking experience.

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Commonwealth is hoping to drum up more ideas along the lines of Kaching, a mobile app that lets people make P2P payments via email, Facebook and wireless phone numbers. Figure 8.6: Use the power of the crowd for good, not evil (Credit: Washington Post) The concept of an idea lab or crowdsource engagement platform is hardly new. In fact, CommBank’s IdeaBank appears to be a close facsimile of First Direct’s “lab” launched five months earlier. First Direct was the UK’s first financial provider to exploit the power of crowdsourcing. First Direct Lab is, according to the official press release, a social platform “that will be populated with content every month such as product designs, service innovations and website concepts.

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Users will have the chance to critique the content through a comment facility and forum, with the feedback collated and inputted into the product or service teams before release”. Figure 8.7: First Direct was the first to launch a crowdsourcing platform for regular interaction with the crowd (Credit: First Direct UK) Finding out how customers interact with financial services can be tricky and expensive. Crowdsourcing appears to be an effective tool that vastly simplifies that, but only if you’re an engaged brand with strong social media values. On the one hand, it makes customers feel heard and valued; on the other, it can also drive innovation very cheaply.

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

"World Economic Forum" Davos, active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, AOL-Time Warner, Bear Stearns, behavioural economics, Black Swan, business cycle, butterfly effect, carbon credits, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, coherent worldview, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Future Shock, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, Herman Kahn, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Laplace demon, Long Term Capital Management, loss aversion, medical malpractice, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, social contagion, social intelligence, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, tacit knowledge, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, Tragedy of the Commons, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

The solution is the Mullet Strategy: In the back pages where few people will see any particular story, let a thousand flowers bloom (or a million of them); then selectively promote material from the back to the front page, with all its premium advertising space, and keep that under strict editorial control.6 The Mullet Strategy is also an example of “crowdsourcing,” a term coined in a 2006 Wired article by Jeff Howe to describe the outsourcing of small jobs to potentially very large numbers of individual workers. Online journalism, in fact, is increasingly moving toward a crowdsourced model—not just for generating community activity around a news story but also for creating the stories themselves, or even deciding what topics to cover in the first place. The Huffington Post, for example, relies on thousands of unpaid bloggers who contribute content either out of passion for the topic they write about or else to benefit from the visibility they receive from being published on a widely read news site.

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And BuzzFeed—a platform for launching “contagious media”—keeps track of hundreds of potential hits and only promotes those that are already generating enthusiastic responses from users.8 As creative as they are, these examples of crowdsourcing work best for media sites that already attract millions of visitors, and so automatically generate real-time information about what people like or don’t like. So if you’re not Bravo or Cheezburger or BuzzFeed—if you’re just some boring company that makes widgets or greeting cards or whatnot—how can you tap into the power of the crowd? Fortunately, crowdsourcing services like Amazon’s Mechanical Turk (which Winter Mason and I used to run our experiments on pay and performance that I discussed in Chapter 2) can also be used to perform fast and inexpensive market research.

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Journal of Law, Economics & Organization 7:24–52. Hoorens, Vera. 1993. “Self-Enhancement and Superiority Biases in Social Comparison.” European Review of Social Psychology 4 (1):113–39. Howard, Philip K. 1997. The Death of Common Sense. New York: Warner Books. Howe, Jeff. 2006. “The Rise of Crowdsourcing.” Wired Magazine 14 (6):1–4. ———. 2008. Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business. New York: Crown Business. Hu, Ye, Leonard M. Lodish, and Abba M. Krieger. 2007. “An Analysis of Real World TV Advertising Tests: A 15-year Update.” Journal of Advertising Research 47 (3):341. Huckfeldt, Robert, Paul E.

Likewar: The Weaponization of Social Media by Peter Warren Singer, Emerson T. Brooking

4chan, active measures, Airbnb, augmented reality, barriers to entry, battle of ideas, Bellingcat, Bernie Sanders, Black Lives Matter, British Empire, Cambridge Analytica, Cass Sunstein, citizen journalism, Citizen Lab, Comet Ping Pong, content marketing, crony capitalism, crowdsourcing, data science, deep learning, digital rights, disinformation, disintermediation, Donald Trump, drone strike, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fake news, false flag, Filter Bubble, global reserve currency, Google Glasses, Hacker Conference 1984, Hacker News, illegal immigration, information security, Internet Archive, Internet of things, invention of movable type, it is difficult to get a man to understand something, when his salary depends on his not understanding it, Jacob Silverman, John Gilmore, John Markoff, Kevin Roose, Kickstarter, lateral thinking, lolcat, Mark Zuckerberg, megacity, Menlo Park, meta-analysis, MITM: man-in-the-middle, Mohammed Bouazizi, Moneyball by Michael Lewis explains big data, moral panic, new economy, offshore financial centre, packet switching, Panopticon Jeremy Bentham, Parag Khanna, pattern recognition, Plato's cave, post-materialism, Potemkin village, power law, pre–internet, profit motive, RAND corporation, reserve currency, sentiment analysis, side project, Silicon Valley, Silicon Valley startup, Snapchat, social web, South China Sea, Steve Bannon, Steve Jobs, Steven Levy, Stewart Brand, systems thinking, too big to fail, trade route, Twitter Arab Spring, UNCLOS, UNCLOS, Upton Sinclair, Valery Gerasimov, We are Anonymous. We are Legion, We are as Gods, Whole Earth Catalog, WikiLeaks, Y Combinator, yellow journalism, Yochai Benkler

There is a word for this: “crowdsourcing.” An idea that had danced excitedly on the lips of Silicon Valley evangelists for years, crowdsourcing had been originally conceived as a new way to outsource software programming jobs, the internet bringing people together to work collectively, more quickly and cheaply than ever before. As social media use had skyrocketed, the promise of crowdsourcing had extended into a space beyond business. Mumbai proved an early, powerful demonstration of the concept in action. The incidents would swiftly multiply from there. At its core, crowdsourcing is about redistributing power—vesting the many with a degree of influence once reserved for the few.

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At its core, crowdsourcing is about redistributing power—vesting the many with a degree of influence once reserved for the few. Sometimes, crowdsourcing might be about raising awareness, other times about money (also known as “crowdfunding”). It can kick-start new businesses or throw support to people who might once have languished in the shadows. It was through crowdsourcing, for instance, that septuagenarian socialist Bernie Sanders became a fundraising juggernaut in the 2016 U.S. presidential election, raking in $218 million online. Of course, like any useful tool, crowdsourcing has also been bent to the demands of war. A generation ago, Al Qaeda was started by the son of a Saudi billionaire.

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Hezbollah, the Iranian-sponsored terror group that allied with the Syrian regime, ran an “equip a mujahid” campaign on Facebook and Twitter. It similarly allowed online supporters to fulfill their religious obligations by buying weapons and ammunition for the war. As radical transparency merges with crowdsourcing, the result can wander into the grotesque. In 2016, a hard-line Iraqi militia took to Instagram to brag about capturing a suspected ISIS fighter. The militia then invited its 75,000 online fans to vote on whether to kill or release him. Eager, violent comments rolled in from around the world, including many from the United States.

Networks of Outrage and Hope: Social Movements in the Internet Age by Manuel Castells

"World Economic Forum" Davos, access to a mobile phone, banking crisis, call centre, centre right, citizen journalism, cognitive dissonance, collective bargaining, conceptual framework, crowdsourcing, currency manipulation / currency intervention, disintermediation, en.wikipedia.org, Glass-Steagall Act, housing crisis, income inequality, microcredit, military-industrial complex, Mohammed Bouazizi, Occupy movement, offshore financial centre, Port of Oakland, social software, statistical model, Twitter Arab Spring, We are the 99%, web application, WikiLeaks, World Values Survey, young professional, zero-sum game

Landamore, H. (2014) We, All the People. Five lessons from Iceland’s failed experiment in creating a crowdsourced constitution. Future Tense, [Online] July 31. Available at: <http://www.slate.com/articles/technology/future_tense/2014/07/five_lessons_from_iceland_s_failed_crowdsourced_constitution_experiment.html> [Accessed on November 17, 2014]. Siddique, H. (2011) Mob rule: Iceland crowdsources its next constitution. The Guardian, [Online] June 9. Available at: <http://www.guardian.co.uk/world/2011/jun/09/iceland-crowdsourcing-constitution-facebook/print> [Accessed on January 9, 2012]. On Iceland’s financial crisis Journal articles Wade, R. and Sigurgeirsdottir, S. (2010) Lessons from Iceland.

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On the Icelandic revolution Web resources Bennett, N. (2011) Iceland’s crowdsourced constitution – a lesson in open source marketing. [Online] Available at: <http://socialmediatoday.com/nick-bennett/305690/icelands-crowdsourced-constitution-lesson-opensource-marketing> [Accessed on January 9, 2012]. Boyes, R. (2009) Age of Testosterone comes to end in Iceland. The Times, [Online] February 7. Available at: <http://www.timesonline.co.uk/tol/news/world/europe/article5679378.ece> [Accessed on January 9, 2012]. Brown, M. (2011) Icelanders turn in first draft of crowdsourced constitution. Wired News, [Online] August 1. Available at: <http://www.wired.co.uk/news/archive/2011-08/01/iceland-constitution> [Accessed on January 9, 2012].

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That the Constitution of a country could explicitly reflect principles that, in the context of global capitalism, are revolutionary shows the direct link between a process of genuinely popular crowdsourcing and the content resulting from such a participatory process. It should be remembered that the consultation and elaboration took place in four months as requested by the parliament, belying the notion of the ineffectiveness of participatory democracy. Granted, Iceland has only 320,000 citizens. But the defenders of the experience argue that with the Internet and with full Internet literacy and unrestricted access, this model of political participation and crowdsourcing of the legislative process is scalable. The reference that the Icelandic revolution came to be for European social movements battling the consequences of a devastating financial crisis is explained by its direct connection to the main issues that induced the protests.

New Power: How Power Works in Our Hyperconnected World--And How to Make It Work for You by Jeremy Heimans, Henry Timms

"Susan Fowler" uber, "World Economic Forum" Davos, 3D printing, 4chan, Affordable Care Act / Obamacare, Airbnb, algorithmic management, augmented reality, autonomous vehicles, battle of ideas, benefit corporation, Benjamin Mako Hill, Big Tech, bitcoin, Black Lives Matter, blockchain, British Empire, Chris Wanstrath, Columbine, Corn Laws, crowdsourcing, data science, David Attenborough, death from overwork, Donald Trump, driverless car, Elon Musk, fake news, Ferguson, Missouri, future of work, game design, gig economy, hiring and firing, holacracy, hustle culture, IKEA effect, impact investing, income inequality, informal economy, job satisfaction, John Zimmer (Lyft cofounder), Jony Ive, Kevin Roose, Kibera, Kickstarter, Lean Startup, Lyft, Mark Zuckerberg, Minecraft, Network effects, new economy, Nicholas Carr, obamacare, Occupy movement, post-truth, profit motive, race to the bottom, radical decentralization, ride hailing / ride sharing, rolling blackouts, rolodex, Salesforce, Saturday Night Live, sharing economy, side hustle, Silicon Valley, six sigma, Snapchat, social web, subscription business, TaskRabbit, tech billionaire, TED Talk, the scientific method, transaction costs, Travis Kalanick, Uber and Lyft, uber lyft, upwardly mobile, web application, WikiLeaks, Yochai Benkler

Participatory budgeting is an idea that started decades ago in the Brazilian city of Porto Alegre but is now spreading throughout the world and merging with internet crowdsourcing culture. Paris’s ambitious participatory budgeting program, led by Mayor Anne Hidalgo, has led to hundreds of thousands of Parisians passionately debating and voting on projects close to their daily lives, everything from greening public space to aiding the homeless. This mirrors the success of the citizen engagement and budgeting program in Reykjavik, which has attracted nearly 60 percent of that city’s people. Plans like this must account for the bias inherent in any crowdsourcing exercise—especially the challenge of distinguishing between what is vital and what has viral appeal, and ensuring that they don’t mostly benefit the already privileged.

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This patient had learned about her condition through PatientsLikeMe, an online community of over 500,000 people living with more than 2,700 diseases, each of whom shares their personal medical data and experiences with others on the platform, creating tens of millions of data points. Think of it as a massive support group, learning community, and data set, all rolled into one. Patients on the platform have even worked together to crowd-source their own drug trials, such as when a group of ALS patients conducted a test of lithium as a treatment in a fraction of the time it would have taken the health authorities. Letitia Browne-James, another member of the community, stumbled upon PatientsLikeMe “out of desperation.” She had suffered from epilepsy her whole life, enduring frequent and debilitating seizures that were just getting worse.

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Thanks to today’s ubiquitous connectivity, we can come together and organize ourselves in ways that are geographically boundless and highly distributed and with unprecedented velocity and reach. This hyperconnectedness has given birth to new models and mindsets that are shaping our age, as we’ll see in the pages ahead. That’s the “new” in new power. A popular thread on Reddit, the link-sharing platform, crowd-sourced memories of growing up in the 1990s, when life felt very different. For those who were there, the posts offered warm nostalgia. For those who weren’t yet born, it told stories of an alien world: The anxiety of waiting for your yearbook photo to arrive, which was “the only time you saw a picture of you and your friends at school.”

Brick by Brick: How LEGO Rewrote the Rules of Innovation and Conquered the Global Toy Industry by David Robertson, Bill Breen

barriers to entry, Blue Ocean Strategy, business logic, business process, Clayton Christensen, creative destruction, crowdsourcing, Day of the Dead, Dean Kamen, digital divide, disruptive innovation, financial independence, game design, global supply chain, Jeff Bezos, Kickstarter, Mark Zuckerberg, Minecraft, Rubik’s Cube, Silicon Valley, Steve Jobs, subscription business, systems thinking, The Wisdom of Crowds, Wall-E, work culture

Keep in mind that LEGO launched its experiment with crowdsourcing in 2004, a full year before James Surowiecki came out with his groundbreaking book The Wisdom of Crowds, in which he posited that because groups of people are “often smarter than the smartest people in them,” a crowd’s “collective intelligence” will produce better outcomes than a small group of experts. Since the publication of that and other books on customer cocreation, initiatives ranging from LINUX to Wikipedia to more than 240,000 open-source software development projects (according to SourceForge.net) have amply demonstrated that crowdsourcing opens an organization up to a broad swath of insights and ideas that it could never muster by itself.

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During the first years of the past decade, the heaving growth of massive online communities inspired books such as Open Innovation, Wikinomics, and The Wisdom of Crowds, which showed how creative companies were harnessing the collective genius of virtual communities to spur innovation and growth. LEGO, a conservative company whose numerous battles over patent infringements had made it hyperprotective of its intellectual property, certainly did not rush into the crowdsourcing craze. But LEGO did take some tentative first steps. For much of its history, LEGO was a monolith—a massively intractable organization that viewed its fans solely as consumers, never as cocreators. The company’s mostly Danish designers believed that when it came to conjuring the next cycle of brick-based construction toys, they were the smartest guys in the room.

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Most tellingly, such sites let fans share online photos and videos of their strikingly clever “MOCs,” otherwise known as “My Own Creations,” which offered tangible evidence that not even the LEGO Group’s most talented designers could consistently outinnovate the millions of LEGO fanatics from across the globe. The creative output of its sprawling, online community of independent brick masters convinced LEGO to give crowdsourcing a modest test. In mid-2000, LEGO commissioned a software development outfit to begin work on the LEGO Digital Designer, a computer-aided design program that let enthusiasts build their own dream models using virtual 3-D bricks (see insert photo 6). The company’s strategy was to let fans use the Designer software (which was based on the Darwin project’s technology) to create virtual models and upload them to a LEGO website, which would eventually come to be called LEGO Factory.

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, carbon tax, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, congestion pricing, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, driverless car, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, G4S, game design, general purpose technology, global village, GPS: selective availability, Hans Moravec, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, Jevons paradox, jimmy wales, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kiva Systems, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, One Laptop per Child (OLPC), pattern recognition, Paul Samuelson, payday loans, post-work, power law, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Robert Solow, Rodney Brooks, Ronald Reagan, search costs, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, the Cathedral and the Bazaar, the long tail, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

It was an example of what Amazon CEO Jeff Bezos called “artificial artificial intelligence,” and another way for people to race with machines, although not one with particularly high wages.26 Mechanical Turk, which quickly became popular, was an early instance of what came to be called crowdsourcing, defined by communications scholar Daren Brabham as “an online, distributed problem-solving and production model.”27 This model is interesting because instead of using technology to automate a process, crowdsourcing makes it deliberately labor intensive. The labor is provided not by a preidentified group of employees, as is the case with most industrial processes, but instead by one or more people (often many more), not identified in advance, who choose to participate. In less than a decade, crowdsourced production has become an important phenomenon.

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His recombination of theory and data earned him a thirty-thousand-dollar reward from the space agency. In recent years, many organizations have adopted NASA’s strategy of using technology to open up their innovation challenges and opportunities to more eyeballs. This phenomenon goes by several names, including ‘open innovation’ and ‘crowdsourcing,’ and it can be remarkably effective. The innovation scholars Lars Bo Jeppesen and Karim Lakhani studied 166 scientific problems posted to Innocentive, all of which had stumped their home organizations. They found that the crowd assembled around Innocentive was able to solve forty-nine of them, for a success rate of nearly 30 percent.

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One of its most successful products, a flexible electrical power strip called Pivot Power, sold more than 373 thousand units in less than two years and earned the crowd responsible for its development over $400,000. Affinnova, yet another young company supporting recombinant innovation, helps its customers with the second of Weitzman’s two phases: sorting through the possible combinations of building blocks to find the most valuable ones. It does this by combining crowdsourcing with Nobel Prize–worthy algorithms. When Carlsberg breweries wanted to update the bottle and label for Belgium’s Grimbergen, the world’s oldest continually produced abbey beer, it knew it had to proceed carefully. The company wanted to update the brand without sacrificing its strong reputation or downplaying its nine hundred years of history.

How to Predict the Unpredictable by William Poundstone

accounting loophole / creative accounting, Albert Einstein, Bernie Madoff, Brownian motion, business cycle, butter production in bangladesh, buy and hold, buy low sell high, call centre, centre right, Claude Shannon: information theory, computer age, crowdsourcing, Daniel Kahneman / Amos Tversky, Edward Thorp, Firefox, fixed income, forensic accounting, high net worth, index card, index fund, Jim Simons, John von Neumann, market bubble, money market fund, pattern recognition, Paul Samuelson, Ponzi scheme, power law, prediction markets, proprietary trading, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Rubik’s Cube, statistical model, Steven Pinker, subprime mortgage crisis, transaction costs

Use the phrase to remember the password. See notes on this chapter Nine How to Outguess Crowd-Sourced Ratings We live in the golden age of crowd sourcing. Anyone with a smartphone can rate restaurants, books, movies, and songs on the go (1 to 5 stars). Focus groups rate cars, carbonara sauces, and candidates (on a scale of 1 to 10). What, if anything, do we learn from that? It might seem that ratings ought to peak in the exact middle of the scale. They usually don’t. They’re more like report card grades where C is effectively below average. Crowd-sourced ratings have a tendency to peak at about 7 out of 10 (or around 70 percent of the maximum rating, whatever it is).

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The film isn’t intended to appeal to everyone, and there are always some online reviewers who select the “wrong” movie to see. When a business has a broader audience (such as a blockbuster movie or a family restaurant), the 0-star reviews are more informative. They help gauge the likelihood of a bad experience. Recap: How to Outguess Crowd-Sourced Ratings • People asked to think of a number between 1 and 10 most often pick 7. This can distort ratings of focus groups and crowd-sourced Web reviews. • The percentage of raters giving a product a perfect 10 (or 5 out of 5 stars) may be a better measure of the product’s sales potential than its average score. See notes on this chapter Ten How to Outguess Fake Numbers Mark Nigrini grew up in Cape Town, South Africa, charmed by the magic of numbers.

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— Dan Guterman See notes on this chapter Contents Prologue: The Outguessing Machine Part One: The Randomness Experiment 1 The Zenith Broadcast 2 How to Outguess Rock, Paper, Scissors 3 How to Outguess Multiple-Choice Tests 4 How to Outguess the Lottery 5 How to Outguess Tennis Serves 6 How to Outguess Football Penalty Kicks 7 How to Outguess Card Games 8 How to Outguess Passwords 9 How to Outguess Crowd-Sourced Ratings 10 How to Outguess Fake Numbers 11 How to Outguess Manipulated Numbers 12 How to Outguess Ponzi Schemes Part Two: The Hot Hand Theory 13 In the Zone 14 How to Outguess Football Bets 15 How to Outguess Oscar Pools 16 How to Outguess Big Data 17 How to Outguess Retail Prices 18 How to Outguess Property Prices 19 How to Outguess the Unpredictable 20 How to Outguess the Stock Market Epilogue: Fortune’s Wheel Acknowledgments Notes Bibliography Index Prologue The Outguessing Machine The outguessing machine began with a messy laboratory accident.

Ghost Work: How to Stop Silicon Valley From Building a New Global Underclass by Mary L. Gray, Siddharth Suri

"World Economic Forum" Davos, Affordable Care Act / Obamacare, AlphaGo, Amazon Mechanical Turk, Apollo 13, augmented reality, autonomous vehicles, barriers to entry, basic income, benefit corporation, Big Tech, big-box store, bitcoin, blue-collar work, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, cognitive load, collaborative consumption, collective bargaining, computer vision, corporate social responsibility, cotton gin, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, deindustrialization, deskilling, digital divide, do well by doing good, do what you love, don't be evil, Donald Trump, Elon Musk, employer provided health coverage, en.wikipedia.org, equal pay for equal work, Erik Brynjolfsson, fake news, financial independence, Frank Levy and Richard Murnane: The New Division of Labor, fulfillment center, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, independent contractor, industrial robot, informal economy, information asymmetry, Jeff Bezos, job automation, knowledge economy, low skilled workers, low-wage service sector, machine translation, market friction, Mars Rover, natural language processing, new economy, operational security, passive income, pattern recognition, post-materialism, post-work, power law, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, scientific management, search costs, Second Machine Age, sentiment analysis, sharing economy, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, Skype, software as a service, speech recognition, spinning jenny, Stephen Hawking, TED Talk, The Future of Employment, The Nature of the Firm, Tragedy of the Commons, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, Wayback Machine, women in the workforce, work culture , Works Progress Administration, Y Combinator, Yochai Benkler

The MTurk API, and those that followed, allowed programmers to use humans to do tasks that are beyond a computer’s capacity, like accurately making a quick judgment call, as Kala and Joan do when they determine what is and isn’t adult content. In fact, anyone sitting in front of a web browser could now answer an automated request for help. Businesses call this mix of APIs, rote computation, and human ingenuity “crowdsourcing,” “microwork,” or “crowdwork.” Computer scientists call it “human computation.” Any project that can be broken down into a series of discrete tasks can be solved using human computation. Software can use these APIs to manage the workflow and process the output of computers and individuals and even pay people for their contributions once they have completed the task.

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She just accepted a job routed from Uber to CrowdFlower’s software, and now she is an invisible yet integral part of the ride. CrowdFlower and its competitors with similarly hip-techy names, like CloudFactory, Playment, and Clickworker, offer their platform’s software as a service to anyone who needs quick access to a ready crowd of workers. Tens of thousands of people like Ayesha log on to crowdsourcing platforms like CrowdFlower every day, looking for task-based work. Now Ayesha—and any other invisible workers who happen to have responded to CrowdFlower’s request—will determine whether Sam picks up Emily. Uber and CrowdFlower are two links in a growing supply chain of services that use APIs and human computation to put people to work.

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Along with a call for transparency, Ghost Work holds lessons for tech entrepreneurs who want a productive workforce, engineers who are building the labor platforms of the future, and policy makers charged with shaping this new commercial landscape. But the still untold story of the invisible workers who power the apps on our phones and the websites we look at should interest a wide range of general readers who’ve seen some coverage of “gigging it” or “Turk work,” not to mention “crowdsourcing” and “microwork,” and heard a lot about the rise of robots but want a deeper look at how, exactly, AI reshapes the working world and what, precisely, people do in the shadow of it. We offer a textured, nuanced, and ultimately hopeful account. Among other things, we show how moving beyond the full-time-freelance divide alone could go a long way toward sharing the wealth generated by the internet with those tasked to grapple with the paradox of automation’s last mile.

Masters of Scale: Surprising Truths From the World's Most Successful Entrepreneurs by Reid Hoffman, June Cohen, Deron Triff

"Susan Fowler" uber, 23andMe, 3D printing, Airbnb, Anne Wojcicki, Ben Horowitz, bitcoin, Blitzscaling, Broken windows theory, Burning Man, call centre, chief data officer, clean water, collaborative consumption, COVID-19, crowdsourcing, data science, desegregation, do well by doing good, Elon Musk, financial independence, fulfillment center, gender pay gap, global macro, growth hacking, hockey-stick growth, Internet of things, knowledge economy, late fees, Lean Startup, lone genius, Marc Benioff, Mark Zuckerberg, minimum viable product, move fast and break things, Network effects, Paul Graham, Peter Thiel, polynesian navigation, race to the bottom, remote working, RFID, Ronald Reagan, Rubik’s Cube, Ruby on Rails, Salesforce, Sam Altman, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, social distancing, Steve Jobs, Susan Wojcicki, TaskRabbit, TechCrunch disrupt, TED Talk, the long tail, the scientific method, Tim Cook: Apple, Travis Kalanick, two and twenty, work culture , Y Combinator, zero day, Zipcar

Looking forward, she says, “I think that’s going to happen both inside Bumble and outside of Bumble in terms of our physical presence in the world.” I believe crowdsourcing can scale your business in unexpected ways—as long as you align your mission with your crowd’s motivation. Luis von Ahn uncovered a hugely valuable resource in crowdsourcing. In fact, it was the resource that would shoot Duolingo to massive scale. And he found it by reaching out to his passionate crowd of users who loved the company’s mission of offering a free way to learn language enough to help him fulfill it. A similar effect happened with Charles Best when he created the first crowdsourcing platform, DonorsChoose.org, back in 2001.

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Charles’s particular do-good mission aligned perfectly with his crowd’s motivation: so much so, in fact, that he also was able to recruit a volunteer crowd of experienced DonorsChoose teachers to authenticate all the other teachers’ project requests, saving time and money. And this is the kind of alignment you need to get crowdsourcing right. Crowdsourcing is a way to tap skills—and scale—that you don’t have in-house. When it works, it opens extraordinary opportunities. It lets you scale faster and further than you ever could on your own. But if you get the alignment wrong, you can end up with anything from a weak fizzle to a fiery disaster. Rallying people who share your mission to gather beneath your banner is the Holy Grail—or maybe the Rosetta Stone in Luis’s case—of crowdsourcing. To keep them there, you have to make sure you’re directing their efforts toward something that engages them and feels like a worthwhile use of their passion.

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Mariam had stumbled onto the power of crowdsourcing—the idea that ordinary people, coming together in large numbers, can do work once reserved for experts. The implications were clear: Because of new opportunities created by technology, almost anyone could become a stationery designer. And if their designs were good enough, they could rise up—and gain exposure and a following. “It was a true meritocracy that you could actually build and unleash,” Mariam says. There was just one problem: Out of the $2.5 million originally raised, Mariam had only about $100,000 left to promote these crowdsourced designs. Mariam’s plan to start a lifestyle business hadn’t panned out, and now she didn’t have enough funding left to adequately support her Plan B.

The Patient Will See You Now: The Future of Medicine Is in Your Hands by Eric Topol

23andMe, 3D printing, Affordable Care Act / Obamacare, Anne Wojcicki, Atul Gawande, augmented reality, Big Tech, bioinformatics, call centre, Clayton Christensen, clean water, cloud computing, commoditize, computer vision, conceptual framework, connected car, correlation does not imply causation, creative destruction, crowdsourcing, dark matter, data acquisition, data science, deep learning, digital divide, disintermediation, disruptive innovation, don't be evil, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, gamification, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, information asymmetry, interchangeable parts, Internet of things, Isaac Newton, it's over 9,000, job automation, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, Lyft, Mark Zuckerberg, Marshall McLuhan, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, quantum cryptography, RAND corporation, randomized controlled trial, Salesforce, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Snapchat, social graph, speech recognition, stealth mode startup, Steve Jobs, synthetic biology, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, traumatic brain injury, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize

Murphy, “Crowdfunding Tips for Turning Inspiration into Reality,” New York Times, January 23, 2014, http://www.nytimes.com/2014/01/23/technology/personaltech/crowdfunding-tips-for-turning-inspiration-into-reality.html. 125. B. L. Ranard et al., “Crowdsourcing—Harnessing the Masses to Advance Health and Medicine, a Systematic Review,” Journal of General Internal Medicine 29, no. 1 (2013): 187–203. 126. S. Novella, “CureCrowd—Crowdsourcing Science,” Science Based Medicine, May 7, 2014, http://www.sciencebasedmedicine.org/curecrowd-crowdsourcing-science/. 127. “Open-Source Medical Devices: When Code Can Kill or Cure,” The Economist, May 31, 2012, http://www.economist.com/node/21556098. Chapter 12 1.

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., “Detection of a Recurrent DNAJB1-PRKACA Chimeric Transcript in Fibrolamellar Hepatocellular Carcinoma,” Science 343, no. 6174 (2014): 1010–1014. 102. A. D. Marcus, “Frustrated ALS Patients Concoct Their Own Drug,” Wall Street Journal, April 15, 2012, http://online.wsj.com/news/articles/SB10001424052702304818404577345953943484054?mg=reno64-wsj. 103. D. L. Scher, “Crowdsourced Clinical Studies: A New Paradigm in Health Care?,” Digital Health Corner, March 30, 2012, http://davidleescher.com/2012/03/30/crowdsourced-clinical-studies-a-new-paradigm-in-health-care/. 104. A. Hamilton, “Could ePatient Networks Become the Superdoctors of the Future?,” Fast Coexist, September 28, 2012, http://www.fastcoexist.com/1680617/could-epatient-networks-become-the-superdoctors-of-the-future. 105.

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The information itself is quite useful to researchers as well. The company’s scientists have published seventeen publications in peer-reviewed journals, having discovered novel gene variants on conditions that span from Parkinson’s disease to photic sneeze.82 They have received large peer-reviewed grants from the National Institutes of Health for crowdsourcing data from individuals with such conditions as allergies or asthma. Much of this work has been done with a database of only a few hundred thousand individuals. If the 23andMe objective of twenty-five million customers were ever reached, even with the rather limited part of the genome that 23andMe assays, the opportunity to make discoveries on the root cause of diseases, how our DNA interacts with medications, and enriched understanding of genetic circuitry—how a variant in one gene cancels out the effect of another, for instance—would all be markedly improved.

Just Keep Buying: Proven Ways to Save Money and Build Your Wealth by Nick Maggiulli

Airbnb, asset allocation, Big Tech, bitcoin, buy and hold, COVID-19, crowdsourcing, cryptocurrency, data science, diversification, diversified portfolio, financial independence, Hans Rosling, index fund, it's over 9,000, Jeff Bezos, Jeff Seder, lifestyle creep, mass affluent, mortgage debt, oil shock, payday loans, phenotype, price anchoring, risk-adjusted returns, Robert Shiller, Sam Altman, side hustle, side project, stocks for the long run, The 4% rule, time value of money, transaction costs, very high income, William Bengen, yield curve

While buying individual farmland is no small undertaking, the most common way for investors to own farmland is through a publicly traded REIT or a crowdsourced solution. The crowdsourced solution can be nice because you have more control over which farmland properties you specifically invest in. The downside of crowdsourced solutions is that they are only typically available to accredited investors (people with a net worth >$1 million or annual income >$200,000 for the last three years). In addition, the fees for these crowdsourced platforms can be higher than with other public investments. I don’t think these fees are predatory given the amount of work that goes into structuring these deals, but if you hate the idea of fees, this is something to keep in mind.

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May generate higher returns than public market offerings. Publicly non-traded REITs: Not traded on a stock exchange, but available to all public investors through crowdsourcing. More regulatory oversight than private REITs. Minimum investment requirements. Less liquid due to longer required holding period. May generate higher returns than public market offerings. Though I have only ever invested in publicly traded REIT ETFs, real estate crowdsourcing firms are a non-traded alternative that could offer higher long-term returns. Why You Should/Shouldn’t Invest in REITs No matter how you decide to invest in REITs, they generally have stock-like returns (or better) with a somewhat low correlation (0.5–0.7) to stocks during good times.

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However, like most other risky assets, publicly traded REITs tend to sell off during stock market crashes. Therefore, don’t expect diversification benefits from REITs on the downside. How Do You Invest in REITs? As mentioned above, you can either invest in publicly traded REITs available through any brokerage platform, or go to a crowdsourced site to buy publicly non-traded or private REITs. I personally lean towards publicly traded REITs simply because they are more liquid (i.e., easier to buy/sell), but there can be benefits to looking at publicly non-traded or private options where you get to pick which specific properties you invest in.

Superminds: The Surprising Power of People and Computers Thinking Together by Thomas W. Malone

Abraham Maslow, agricultural Revolution, Airbnb, Albert Einstein, Alvin Toffler, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, Asperger Syndrome, Baxter: Rethink Robotics, bitcoin, blockchain, Boeing 747, business process, call centre, carbon tax, clean water, Computing Machinery and Intelligence, creative destruction, crowdsourcing, data science, deep learning, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental economics, Exxon Valdez, Ford Model T, future of work, Future Shock, Galaxy Zoo, Garrett Hardin, gig economy, happiness index / gross national happiness, independent contractor, industrial robot, Internet of things, invention of the telegraph, inventory management, invisible hand, Jeff Rulifson, jimmy wales, job automation, John Markoff, Joi Ito, Joseph Schumpeter, Kenneth Arrow, knowledge worker, longitudinal study, Lyft, machine translation, Marshall McLuhan, Nick Bostrom, Occupy movement, Pareto efficiency, pattern recognition, prediction markets, price mechanism, radical decentralization, Ray Kurzweil, Rodney Brooks, Ronald Coase, search costs, Second Machine Age, self-driving car, Silicon Valley, slashdot, social intelligence, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, technological singularity, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Tim Cook: Apple, Tragedy of the Commons, transaction costs, Travis Kalanick, Uber for X, uber lyft, Vernor Vinge, Vilfredo Pareto, Watson beat the top human players on Jeopardy!

Of course, Climate CoLab wasn’t solely responsible for the development of all these ideas. Some of them were based on work their developers had been doing for years. But Climate CoLab’s crowdsourcing approach provides a way of framing important problems, finding people who have good ideas about these problems wherever they are, encouraging them to develop the ideas into a form that can be shared, systematically comparing these ideas, and then helping to bring attention and other resources to the most promising ones. This crowdsourcing approach to problem solving is very different from the conventional problem-solving approach where you hire the best people you can find, pay them to work on your problem, and then hope they are successful.

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This crowdsourcing approach to problem solving is very different from the conventional problem-solving approach where you hire the best people you can find, pay them to work on your problem, and then hope they are successful. Neither is guaranteed to work, but crowdsourcing can often solve problems the conventional approach can’t. And crowdsourcing is only one of a number of possibilities we’ll see for how new technologies can make groups smarter by involving more individuals. THE BRUTE-FORCE EFFECT As we’ve already seen, there are many tasks in the physical world for which larger groups (like armies or companies) are better than smaller ones. We can call this the brute-force effect. The same principle holds true for many tasks in the information world, too.

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“Wikipedia:Bots,” accessed August 18, 2016, https://en.wikipedia.org/wiki/Wikipedia:Bots. 7. Aniket Kittur, Boris Smus, Susheel Khamkar, and Robert E. Kraut, “CrowdForge: Crowdsourcing Complex Work,” in Proceedings of the ACM Symposium on User Interface Software and Technology (New York: ACM Press, 2011), http://smus.com/crowdforge/crowdforge-uist-11.pdf. 8. Figure from Kittur A., Smus, B., Khamkar, S., Kraut, R.E., “CrowdForge: Crowdsourcing Complex Work.” UIST 2011: Proceedings of the ACM Symposium on User Interface Software and Technology. New York: ACM Press, http:doi.acm.org/10.1145/2047196.2047202. © 2011 Association for Computing Machinery, Inc.

The Organized Mind: Thinking Straight in the Age of Information Overload by Daniel J. Levitin

Abraham Maslow, airport security, Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, autism spectrum disorder, Bayesian statistics, behavioural economics, big-box store, business process, call centre, Claude Shannon: information theory, cloud computing, cognitive bias, cognitive load, complexity theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, deep learning, delayed gratification, Donald Trump, en.wikipedia.org, epigenetics, Eratosthenes, Exxon Valdez, framing effect, friendly fire, fundamental attribution error, Golden Gate Park, Google Glasses, GPS: selective availability, haute cuisine, How many piano tuners are there in Chicago?, human-factors engineering, if you see hoof prints, think horses—not zebras, impulse control, index card, indoor plumbing, information retrieval, information security, invention of writing, iterative process, jimmy wales, job satisfaction, Kickstarter, language acquisition, Lewis Mumford, life extension, longitudinal study, meta-analysis, more computing power than Apollo, Network effects, new economy, Nicholas Carr, optical character recognition, Pareto efficiency, pattern recognition, phenotype, placebo effect, pre–internet, profit motive, randomized controlled trial, Rubik’s Cube, Salesforce, shared worldview, Sheryl Sandberg, Skype, Snapchat, social intelligence, statistical model, Steve Jobs, supply-chain management, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, traumatic brain injury, Turing test, Twitter Arab Spring, ultimatum game, Wayback Machine, zero-sum game

Kiva applied the concept to banking, using crowdsourcing to kick-start economic independence by sponsoring microloans that help start small businesses in developing countries. In its first nine years, Kiva has given out loans totaling $500 million to one million people in seventy different countries, with crowdsourced contributions from nearly one million lenders. The people who make up the crowd in crowdsourcing are typically amateurs and enthusiastic hobbyists, although this doesn’t necessarily have to be the case. Crowdsourcing is perhaps most visible as a form of consumer ratings via Yelp, Zagat, and product ratings on sites such as Amazon.com. In the old, pre-Internet days, a class of workers existed who were expert reviewers and they would share their impressions of products and services in newspaper articles or magazines such as Consumer Reports.

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Whenever we read of terrorist attacks or war atrocities, the most wrenching and visceral reactions are to descriptions of children being harmed. This feeling appears to be culturally universal and innate. The Amber Alert is an example of crowdsourcing—outsourcing to a crowd—the technique by which thousands or even millions of people help to solve problems that would be difficult or impossible to solve any other way. Crowdsourcing has been used for all kinds of things, including wildlife and bird counts, providing usage examples and quotes to the editors of the Oxford English Dictionary, and helping to decipher ambiguous text. The U.S. military and law enforcement have taken an interest in it because it potentially increases the amount of data they get by turning a large number of civilians into team members in information gathering.

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A large number of people—the public—can often help to solve big problems outside of traditional institutions such as public agencies. Wikipedia is an example of crowdsourcing: Anyone with information is encouraged to contribute, and through this, it has become the largest reference work in the world. What Wikipedia did for encyclopedias, Kickstarter did for venture capital: More than 4.5 million people have contributed over $750 million to fund roughly 50,000 creative projects by filmmakers, musicians, painters, designers, and other artists. Kiva applied the concept to banking, using crowdsourcing to kick-start economic independence by sponsoring microloans that help start small businesses in developing countries.

The End of Absence: Reclaiming What We've Lost in a World of Constant Connection by Michael Harris

4chan, Albert Einstein, algorithmic management, AltaVista, Andrew Keen, augmented reality, Burning Man, Carrington event, cognitive dissonance, crowdsourcing, dematerialisation, disinformation, en.wikipedia.org, Evgeny Morozov, Filter Bubble, Firefox, Google Glasses, informal economy, information retrieval, invention of movable type, invention of the printing press, invisible hand, James Watt: steam engine, Jaron Lanier, jimmy wales, Kevin Kelly, Lewis Mumford, lifelogging, Loebner Prize, low earth orbit, Marshall McLuhan, McMansion, moral panic, Nicholas Carr, off-the-grid, pattern recognition, Plato's cave, pre–internet, Republic of Letters, Silicon Valley, Skype, Snapchat, social web, Steve Jobs, technological solutionism, TED Talk, the medium is the message, The Wisdom of Crowds, traumatic brain injury, Turing test

The process of intellectual exploration, once highly idiosyncratic, becomes an opportunity to promote whatever material has the highest view count. “Until now,” Ng told me, “education has been a largely anecdotal science, and we can now make it data-driven.” This reminded me, of course, of Karthik Dinakar, eager to “harden” the soft sciences of psychology and psychiatry with reams of crowdsourced data. The crowdsourcing of education is further highlighted by Ng’s interest in Wiki lecture notes. “At Stanford,” he explained to me, “I taught a class for a decade, and writing the lecture notes would take forever. And then, every year, students would find more bugs, more errors, in my notes. But for online classes, I put up a Wiki and invite students to write their own lecture notes; students watch my lectures and create the notes themselves.

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Dinakar watched the program, figuring there must be a way to stem such cruelty, to monitor and manage unacceptable online behavior. Most social Web sites leave it to the public. Facebook, Twitter, and the like incorporate a button that allows users to “flag this as inappropriate” when they see something they disapprove of. In the age of crowdsourced knowledge like Wikipedia’s, such user-driven moderation sounds like common sense, and perhaps it is.8 “But what happens,” Dinakar explains, “is that all flagging goes into a stream where a moderation team has to look at it. Nobody gets banned automatically, so the problem becomes how do you deal with eight hundred million users throwing up content and flagging each other?”

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Unfortunately, Feldman (though she may well be a real person) did not invent the hair iron, and on September 15, 2009, Wikipedia was forced to consign the Feldman affair to their growing list of hoaxes. Not a particularly scandalous or even interesting hoax, but such is the banality of error. Four years later, I asked Wiki.Answers.com (the largest Q&A site online) who Erica Feldman is and was redirected to a set of crowdsourced “Relevant Answers” that claimed she is both alive and that she invented the hair straightener in 1872 (making her more than 140 years old). I was also informed of the Feldman hairdo, which involves “slicked-back long hair with one strand hanging over the forehead.” These results were displayed alongside a number of hair-focused advertisements.

We Are Bellingcat: Global Crime, Online Sleuths, and the Bold Future of News by Eliot Higgins

4chan, active measures, Andy Carvin, anti-communist, anti-globalists, barriers to entry, belling the cat, Bellingcat, bitcoin, blockchain, citizen journalism, Columbine, coronavirus, COVID-19, crowdsourcing, cryptocurrency, data science, deepfake, disinformation, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, failed state, fake news, false flag, gamification, George Floyd, Google Earth, hive mind, Julian Assange, Kickstarter, lateral thinking, off-the-grid, OpenAI, pattern recognition, post-truth, rolodex, Seymour Hersh, Silicon Valley, Skype, Tactical Technology Collective, the scientific method, WikiLeaks

doi=10.1.1.552.239&rep=rep1&type=pdf 65 www.aljazeera.com/news/2019/03/brother-muslim-worshipper-words-gunman-190315152715528.html 66 www.washingtonpost.com/world/2019/03/15/with-strobe-lights-guns-bearing-neo-nazi-slogans-new-zealand-gunman-plotted-massacre/ 67 www.bellingcat.com/news/rest-of-world/2019/03/15/shitposting-inspirational-terrorism-and-the-christchurch-mosque-massacre/ 68 www.cjr.org/analysis/christchurch-shooting-media-coverage.php 69 www.buzzfeed.com/markdistefano/the-daily-mail-let-readers-download-the-new-zealand-mosque 70 www.bellingcat.com/news/americas/2019/04/28/ignore-the-poway-synagogue-shooters-manifesto-pay-attention-to-8chans-pol-board/ 71 www.bellingcat.com/news/americas/2019/08/04/the-el-paso-shooting-and-the-gamification-of-terror/ 72 www.bellingcat.com/news/americas/2019/04/28/ignore-the-poway-synagogue-shooters-manifesto-pay-attention-to-8chans-pol-board/ 73 encyclopediadramatica.rs/High_Score 74 www.nytimes.com/2019/08/04/technology/8chan-shooting-manifesto.html 75 www.nytimes.com/2019/08/04/technology/8chan-shooting-manifesto.html 76 www.theverge.com/2019/11/4/20947429/8chan-8kun-online-image-board-shooter-gunmen-manifesto 77 twitter.com/rcallimachi/status/734053117109624836 78 twitter.com/rcallimachi/status/734053117109624836 79 twitter.com/jenanmoussa/status/734054748031160320 80 twitter.com/jenanmoussa/status/734105719612407808 81 www.bellingcat.com/news/uk-and-europe/2017/06/01/crowdsourcing-europols-stop-child-abuse-trace-object-campaign/ www.bellingcat.com/news/uk-and-europe/2017/06/15/update-crowdsourcing-europols-stop-child-abuse-trace-object-campaign/www.youtube.com/watch?v=XrOAy6PfYlYwww.europol.europa.eu/stopchildabuse 82 www.europol.europa.eu/newsroom/news/you-have-identified-70-objects-taken-child-sexual-abuse-imageswww.europol.europa.eu/newsroom/news/your-help-we-are-21-000-steps-closer-to-saving-child-sexual-abusewww.bellingcat.com/news/uk-and-europe/2017/08/28/stop-child-abuse-2nd-update/ 83 www.bellingcat.com/news/uk-and-europe/2020/04/22/creating-impact-a-year-on-stop-child-abuse-trace-an-object/ 84 www.europol.europa.eu/newsroom/news/241-victims-of-child-sexual-abuse-safeguarded-thanks-to-global-law-enforcement-efforts 85 en.wikipedia.org/wiki/Russians_in_the_Baltic_states 86 www.thedailybeast.com/the-baltic-elves-taking-on-pro-russian-trolls 87 www.bbc.co.uk/news/av/world-europe-48401922/how-finland-fights-the-fake-news-trolls 88 disinfoportal.org/governments-countering-disinformation-the-case-of-sweden/ 89 stacks.stanford.edu/file/druid:fv751yt5934/SHEG%20Evaluating%20Information%20Online.pdfamp.usatoday.com/amp/2769781002?

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First came a gush of identical posts on Russian social media, in tweets and on Facebook, all insisting that the video of the Buk with a missile gone had been filmed in a Ukrainian-controlled town. Next, identical fake posts turned up on Western social media, then Russian state TV and separatist websites. Last stop in the disinformation chain was the Russian Defence Ministry briefing of 21 July 2014. ‘This was a deliberate attempt to simulate “crowd-sourcing” of geolocation efforts,’26 she said. Unfortunately for the Kremlin, our efforts were not over. A fresh clip appeared on YouTube, seemingly filmed from within another building, this time overlooking a communal patch of dry grass and a roadway. A flatbed truck passed, transporting the Buk. The person who had posted this seemed aware of the need to verify online claims, so included geographical coordinates – 48°01’01.1″N, 38°18’06.6″E – and the time of day of the footage.

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SLEUTHING AROUND THE GLOBE The MH17 case helped make Bellingcat, but our new form of investigation could be applied to almost any topic. Every new inquiry – as I had found when moving from the Libya conflict to phone-hacking to the Syrian civil war – connected to a fresh node of obsessives online, expanding the network of crowd-sourced expertise, while spreading word of what digital sleuthing could achieve. During our first couple of years, Bellingcat conducted cases from Latin America and the United States, to the Arabian Peninsula and Turkey. A French freelancer, Aliaume Leroy, geolocated Mexican cartel hitmen via YouTube videos and produced a study of drug lord bling on social media.44 We published on the illegal activities of a Detroit street gang, as tracked down via their habit of scrawling graffiti with a hashtag.45 We delved into the civil war in Yemen,46 and gave an award-winning glimpse into a Turkish military coup via the plotters’ leaked WhatsApp feed.

The Net Delusion: The Dark Side of Internet Freedom by Evgeny Morozov

"World Economic Forum" Davos, A Declaration of the Independence of Cyberspace, Alvin Toffler, Ayatollah Khomeini, Berlin Wall, borderless world, Buckminster Fuller, Californian Ideology, Cass Sunstein, citizen journalism, cloud computing, cognitive dissonance, Columbine, computer age, conceptual framework, crowdsourcing, digital divide, disinformation, Dissolution of the Soviet Union, don't be evil, Evgeny Morozov, failed state, Fall of the Berlin Wall, Francis Fukuyama: the end of history, global village, Google Earth, Herbert Marcuse, illegal immigration, invention of radio, invention of the printing press, invisible hand, John Markoff, John Perry Barlow, John von Neumann, lolcat, Marshall McLuhan, Mitch Kapor, Naomi Klein, Network effects, new economy, New Urbanism, off-the-grid, Panopticon Jeremy Bentham, peer-to-peer, pirate software, pre–internet, Productivity paradox, public intellectual, RAND corporation, Robert Solow, Ronald Reagan, Ronald Reagan: Tear down this wall, Silicon Valley, Silicon Valley startup, Sinatra Doctrine, Skype, Slavoj Žižek, social graph, Steve Jobs, Streisand effect, technological determinism, technoutopianism, TED Talk, The Wisdom of Crowds, urban planning, Washington Consensus, WikiLeaks, women in the workforce

(At least for most nonlawyers, those are probably far less satisfying than the porn pages they were seeking to visit.) The practice of “crowdsourcing” censorship is becoming popular in democracies as well. Both the British and the French authorities have similar schemes for their citizens to report child pornography and several other kinds of illegal content. As there are more and more websites and blogs to check for illegal material, it’s quite likely that such crowdsourcing schemes will become more common. The Thai, Saudi, and British authorities rely on citizens’ goodwill, but a new scheme in China actually offers monetary awards to anyone submitting links to online pornography.

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One such tool, Ushahidi, was first designed to report on violence during the postelection crisis in Kenya and since then has been successfully deployed all over the world, including in the devastating earthquakes in Haiti and Chile in early 2010. But the reason why many projects that rely on crowdsourcing produce trustworthy data in natural disasters is because those are usually apolitical events. There are no warring sides, and those who report data do not have any incentives to manipulate it. The problem with using such crowdsourced tools for other purposes—for example, documenting human rights abuses or monitoring elections, some of the other uses to which Ushahidi has been put—is that the accuracy of such reports is impossible to verify and easy to manipulate.

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World Politics: A Quarterly Journal of International Relations 4, no. 2 (1952): 145-158. Currion, Paul. “Better the Devil We Know: Obstacles and Opportunities in Humanitarian GIS.” Humanitarian.info, January 25, 2006. www.humanitarian.info/humanitarian-gis/. ———. “Correcting Crowdsourcing in a Crisis.” Humanitarian.info, March 30, 2009. www.humanitarian.info/2009/03/30/correcting-crowdsourcing-in-a-crisis/. “Cyber-Nationalism: The Brave New World of E-hatred.” Economist, July 24, 2008. Dahlberg, L. “Rethinking the Fragmentation of the Cyberpublic: From Consensus to Contestation.” New Media & Society 9, no. 5 (2007): 827. Dewan, Shaila.

Data and the City by Rob Kitchin,Tracey P. Lauriault,Gavin McArdle

A Declaration of the Independence of Cyberspace, algorithmic management, bike sharing, bitcoin, blockchain, Bretton Woods, Chelsea Manning, citizen journalism, Claude Shannon: information theory, clean water, cloud computing, complexity theory, conceptual framework, corporate governance, correlation does not imply causation, create, read, update, delete, crowdsourcing, cryptocurrency, data science, dematerialisation, digital divide, digital map, digital rights, distributed ledger, Evgeny Morozov, fault tolerance, fiat currency, Filter Bubble, floating exchange rates, folksonomy, functional programming, global value chain, Google Earth, Hacker News, hive mind, information security, Internet of things, Kickstarter, knowledge economy, Lewis Mumford, lifelogging, linked data, loose coupling, machine readable, new economy, New Urbanism, Nicholas Carr, nowcasting, open economy, openstreetmap, OSI model, packet switching, pattern recognition, performance metric, place-making, power law, quantum entanglement, RAND corporation, RFID, Richard Florida, ride hailing / ride sharing, semantic web, sentiment analysis, sharing economy, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, smart grid, smart meter, social graph, software studies, statistical model, tacit knowledge, TaskRabbit, technological determinism, technological solutionism, text mining, The Chicago School, The Death and Life of Great American Cities, the long tail, the market place, the medium is the message, the scientific method, Toyota Production System, urban planning, urban sprawl, web application

As public and private sectors become further intertwined in delivering ‘smart cities’ to the urban public, the clash between the need for data transparency and claims to confidential business information will need to be addressed. In some cases, the crowd-sourcing of unofficial crime maps has been used as a means of providing a counter-narrative to official visualizations. For example, HarassMap3 invites women to map and describe incidents of sexual harassment in Egypt, within a cultural context in which such harassment typically remains invisible and unreportable. While not a crime map per se (as it compiles data on harassment that ranges from criminal to non-criminal conduct), this is an example of how the crowd-sourcing of data can draw attention to a problem, and can be used in powerful visualization tools to create an alternative representation of the gendered experience of urban space.

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While not a crime map per se (as it compiles data on harassment that ranges from criminal to non-criminal conduct), this is an example of how the crowd-sourcing of data can draw attention to a problem, and can be used in powerful visualization tools to create an alternative representation of the gendered experience of urban space. While crowd-sourced maps are interesting, they present many challenges. Stability and continuity are one challenge – both in terms of management of the project as well as in terms of public participation. In the case of crimes like sexual assault there are also significant privacy issues, risks of further victimization, other liability issues, as well as risks that first-person reports could be used by defence lawyers in criminal prosecutions to discredit victim witnesses. Nevertheless, crowd-sourcing remains an interesting tool particularly in contexts where certain crimes are not dealt with effectively by public authorities (Friedman 2014).

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These are being complemented with big data generated by: (a) commercial companies such as mobile phone operators (location/movement, app use, activity), travel and accommodation sites (reviews, location/movement, consumption), social media sites (opinions, photos, personal info, location/movement), transport providers (routes, traffic flow), website owners (clickstreams), financial institutions and retail chains (consumption, in-store movement, location), and private surveillance and security firms (location, behaviour) that are increasingly selling and leasing their data through data brokers, or making their data available through APIs (e.g. Twitter and Foursquare); (b) crowdsourcing (e.g. OpenStreetMap) and citizen science (e.g. personal weather stations) initiatives, wherein people collaborate on producing a shared data resource or volunteer data. Other kinds of more irregular urban big data include digital aerial photography via planes or drones, or spatial video, LiDAR (light detection and ranging), thermal or other kinds of electromagnetic scans of environments that enable the mobile and realtime 2D and 3D mapping of landscapes.

Collaborative Futures by Mike Linksvayer, Michael Mandiberg, Mushon Zer-Aviv

4chan, AGPL, Benjamin Mako Hill, British Empire, citizen journalism, cloud computing, collaborative economy, corporate governance, crowdsourcing, Debian, Eben Moglen, en.wikipedia.org, fake news, Firefox, informal economy, jimmy wales, Kickstarter, late capitalism, lolcat, loose coupling, Marshall McLuhan, means of production, Naomi Klein, Network effects, optical character recognition, packet switching, planned obsolescence, postnationalism / post nation state, prediction markets, Richard Stallman, semantic web, Silicon Valley, slashdot, Slavoj Žižek, stealth mode startup, technoutopianism, The future is already here, the medium is the message, The Wisdom of Crowds, web application, WikiLeaks, Yochai Benkler

One of them is the Twi er Vote Report used in the 2008 US presidential elections <twi ervotereport.com> and its later incarnation as Swi River, a tool for crowdsourcing situational awareness: “Swi hopes to expand [Twi er Vote Report’s] approach into a general purpose toolkit for crowdsourcing the semantic structuring of data so that it can be reused in other applications and visualizations. The developers of Swi are particularly interested in crisis reporting (Ushahidi) and international media criticism (Meedan), but by providing a general purpose crowdsourcing tool we hope to create a tool reusable in many contexts. Swi engages self-interested teams of “citizen editors” who curate publicly available information about a crisis or any event or region as it happens” —Swi Project, 2010 <h p://github.com/unthinkingly/swi river_rails> These activist hacker initiatives are realizing the potential of loosely coordinated distributed action.

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Process Fetishism There's a risk of making a fetish of process over product, of the act of collaboration over the artifact that results from it. How important is it that a product was produced through an open, distributed network if, in the end, it serves the interests of the status quo? If it's just another widget, another distraction, an added value that some giant conglomerate can take advantage of, as in some cases of crowdsourcing? Does open collaboration serve a purpose or is it more like a drum circle, way more fun and interesting for the participants than for those who are forced to listen to it? Collaboration is fundamental to human experience. It should be no surprise that collaboration also occurs online. The important question is what goals these new opportunities for cooperation and creation across space and time are put in service of.

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Open appears to be an assertion of —though it is more accurately an aspiration towards—egalitarianism, inclusion, non-coerciveness, freedom. But what kind of freedom? Free as in unscripted and improvisatory, free as in freely chosen, free as in unpaid, or free as in it won't tie you down? As books like Jeff Howe’s Crowdsourcing: Why The Power of the Crowd is Driving the Future of Business show, corporate America is ready to collaborate. They want to have an open relationship with their workforce, because who can beat free? And by turning their consumers into collaborators, the bond between the company and their customers is made even stronger.

Gigged: The End of the Job and the Future of Work by Sarah Kessler

"Susan Fowler" uber, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, basic income, bitcoin, blockchain, business cycle, call centre, cognitive dissonance, collective bargaining, crowdsourcing, data science, David Attenborough, do what you love, Donald Trump, East Village, Elon Musk, financial independence, future of work, game design, gig economy, Hacker News, income inequality, independent contractor, information asymmetry, Jeff Bezos, job automation, law of one price, Lyft, Mark Zuckerberg, market clearing, minimum wage unemployment, new economy, opioid epidemic / opioid crisis, payday loans, post-work, profit maximization, QR code, race to the bottom, ride hailing / ride sharing, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, TaskRabbit, TechCrunch disrupt, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, working-age population, Works Progress Administration, Y Combinator

And as a daycare provider, when she’d been stiffed by most of her clients the week before Christmas (because they were also struggling to make ends meet), she had marched to each of their homes with a letter explaining that she could not afford her own Christmas dinner and demanding payment. If Kristy couldn’t figure out how to make a living, it wouldn’t be because she hadn’t tried, and it wouldn’t be because she wasn’t a fighter. Her first idea was to work more hours on Mechanical Turk. Founded in 2005, Mechanical Turk is an online “crowdsourcing” marketplace run by Amazon. Its clients post work tasks on a dashboard that a “crowd” of workers can choose to complete. The process doesn’t work that much differently than Gigster’s process. But the tasks on Mechanical Turk are often simple and pay just cents each. They’re jobs like adding tags to images, filling out spreadsheets with contact information, or writing product descriptions for websites.

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Another layer bigger, and you’d see Arise, the big customer service company that had made a contract with the IBO. Only after another layer would you find Sears, the company that the customer thought he was dealing with all along. Arise, no surprise, presented this setup as innovation: Under the heading “Leveraging the power of crowdsourcing,” the company’s “about” page at the time explained to potential customers that Arise takes advantage of “innovative breakthroughs in technology and our own award winning, proprietary and patented technologies” and “provide[s] entrepreneurial opportunities to many underserved populations, where small business owners have the ability to create flexible schedules based on their lifestyle needs.”

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A local chapter of the Teamsters in Seattle lobbied for a law that would allow Uber drivers to form a union. It passed. (Shortly later, the US Chamber of Commerce sued the city, saying the law conflicted with anti-trust law.) In Germany, a group of workers’ organizations created a list of best practices that, as of 2017, eight crowdsourcing companies had pledged to follow. Together they established an office where workers could report violations of this code.16 Regardless of the strategy, long-established unions faced the same problem as novice organizers like Abe, which was that they didn’t know who worked for gig economy companies.

Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism by Robin Chase

Airbnb, Amazon Web Services, Andy Kessler, Anthropocene, Apollo 13, banking crisis, barriers to entry, basic income, Benevolent Dictator For Life (BDFL), bike sharing, bitcoin, blockchain, Burning Man, business climate, call centre, car-free, carbon tax, circular economy, cloud computing, collaborative consumption, collaborative economy, collective bargaining, commoditize, congestion charging, creative destruction, crowdsourcing, cryptocurrency, data science, deal flow, decarbonisation, different worldview, do-ocracy, don't be evil, Donald Shoup, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, Eyjafjallajökull, Ferguson, Missouri, Firefox, Free Software Foundation, frictionless, Gini coefficient, GPS: selective availability, high-speed rail, hive mind, income inequality, independent contractor, index fund, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jane Jacobs, Jeff Bezos, jimmy wales, job satisfaction, Kickstarter, Kinder Surprise, language acquisition, Larry Ellison, Lean Startup, low interest rates, Lyft, machine readable, means of production, megacity, Minecraft, minimum viable product, Network effects, new economy, Oculus Rift, off-the-grid, openstreetmap, optical character recognition, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, Post-Keynesian economics, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Salesforce, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, smart cities, smart grid, Snapchat, sovereign wealth fund, Steve Crocker, Steve Jobs, Steven Levy, TaskRabbit, The Death and Life of Great American Cities, The Future of Employment, the long tail, The Nature of the Firm, Tragedy of the Commons, transaction costs, Turing test, turn-by-turn navigation, Uber and Lyft, uber lyft, vertical integration, Zipcar

What I failed to appreciate back then was the much larger movement made possible by the Internet. Zipcar was a trailblazer. When you can connect and share assets, people, and ideas, everything changes, not just how you rent a car. Google, eBay, Facebook, OKCupid, YouTube, Waze, Airbnb, WhatsApp, Duolingo—all are part of this transformation of capitalism. Web 2.0, the sharing economy, crowdsourcing, collaborative production, collaborative consumption, and network effects are simply terms we’ve created along the way in an effort to capture what is going on. Attributing all this to “the Internet” misses the building blocks and therefore the ability to replicate this type of activity in a more controlled way.

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A $69 million financing in November 2013, including $30 million from GE, allowed Quirky to spin off Wink, a wholly owned subsidiary.27 Wink provides a technology ecosystem (a platform) that makes it simple to bring together connected-home devices with smartphones, giving GE a way to participate in both the Internet of Things and crowd-sourced innovation. (Chapter 8 will delve into the ways in which large mainstream companies are adapting to the new organizational paradigm.) The first GE + Quirky–branded product was the Aros air conditioner, which lets you change the room temperature setting from a distance when you are away, and which automatically instructs your Aros to begin cooling the room to a predetermined temperature when your smartphone is within a certain proximity of home.

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Agency for International Development’s Ebola Grand Challenge is the late-breaking but now deep-within-the-crisis Peers Inc approach, using the OpenIdeo platform to collect insight into the situation on the ground and integrate learning from hospitals and universities.13 Today, we connected peers have access to crowdsourced best practices plus supercomputing power plus the insight of people who are right there on the ground and just a step ahead of us. Institutions tasked with responsibilities as enormous as fighting diseases such as Ebola are catching up as fast as they can. Even the U.S. Department of Defense has a disaster relief expert coordination team, STAR-TIDES, striving to provide the deft touch, the fluid access to expertise required to assist teams in the field with a connection to the right people at the right time.

Curation Nation by Rosenbaum, Steven

Amazon Mechanical Turk, Andrew Keen, AOL-Time Warner, barriers to entry, citizen journalism, cognitive dissonance, commoditize, creative destruction, crowdsourcing, disintermediation, en.wikipedia.org, Ford Model T, future of journalism, independent contractor, Jason Scott: textfiles.com, Mary Meeker, means of production, off-the-grid, PageRank, pattern recognition, post-work, postindustrial economy, pre–internet, Sand Hill Road, Silicon Valley, Skype, social graph, social web, Steve Jobs, Tony Hsieh, Yogi Berra

CONTENT STRATEGISTS While the emerging curation ecosystem may leave the highbrow and pedigreed museum curation crowd with a furrowed brow, there’s another group who are equally troubled by the rise of human-powered finding and filtering—and that’s the code-centric solutions crowd that has been searching for the holy grail of machine-powered (or crowd-sourced) finding and filtering. This is the aggregation camp. And they too are anxious to see the emerging but noisy curation community replaced by elegant code. Blogger Clinton Forry has the most cogent distinction I’ve read so far: Aggregation is automated Aggregation collects content based on criteria in the form of metadata or keywords Criteria can be adjusted, but remain static otherwise Follows a preset frequency of publishing [as available, weekly, etc.]

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Bonin Bough, the director of digital and social media at PepsiCo explains, “If you listen to what people have to say and give voice to their perspectives, you can inspire people and empower their ideas.” This may not seem like the words of a soda and snack food company, but Pepsi is putting its brand and its money where its mouth is by pledging more than 20 million dollars to a crowd-sourced grant program, with public voting determining who gets the grants. Each month, Pepsi will award up to $1.3 million to the winning ideas across six categories: Health, Arts and Culture, Food and Shelter, The Planet, Neighborhoods, and Education. It’s a listening campaign that is meant to send a message to a new generation of connected consumers.

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And it appears that some of the companies that first were exposed to the power of consumer voice online and didn’t pay attention are drinking the Kool-Aid. Jarvis says Dell got the message, and years later Dell CEO Michael Dell told Jarvis, “No company can exist anymore on the idea that it’s just three people.” The era of the all-powerful CEO, CMO, and COO has been replaced by a crowd-sourced aggregation of suggestions, feedback, and complaints. TAKING CONTROL OF THE BRAND So what are the action items that this change from mass media to consumer-controlled conversations can offer? Well, there is a shift in how the buyers engage companies: no longer do they need to accept the “take it or leave it” attitude of many companies they do business with.

The Digital Party: Political Organisation and Online Democracy by Paolo Gerbaudo

Airbnb, barriers to entry, basic income, Bernie Sanders, bitcoin, Californian Ideology, call centre, Cambridge Analytica, centre right, creative destruction, crowdsourcing, data science, digital capitalism, digital divide, digital rights, disintermediation, disruptive innovation, Donald Trump, Dunbar number, Edward Snowden, end-to-end encryption, Evgeny Morozov, feminist movement, gig economy, industrial robot, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, jimmy wales, Joseph Schumpeter, Mark Zuckerberg, Network effects, Occupy movement, offshore financial centre, oil shock, post-industrial society, precariat, Ralph Waldo Emerson, Richard Florida, Richard Stallman, Ruby on Rails, self-driving car, Silicon Valley, Skype, Slavoj Žižek, smart cities, Snapchat, social web, software studies, Stewart Brand, technological solutionism, technoutopianism, the long tail, Thomas L Friedman, universal basic income, vertical integration, Vilfredo Pareto, WikiLeaks

The Five Star Movement, in less than a decade from its birth, has managed to become the largest party in Italy, and is currently heading the national government, while many other formations have had a similarly explosive growth trajectory. Like social networks, it is a party that feeds on the ‘engagement’ which its supporters and sympathisers provide. It is constantly busy eliciting feedback from its member/user base, crowdsourcing ideas from it, balloting on issues, measuring the response of the public, and modifying its strategy and messaging accordingly. It is a party that adopts the free sign-up process of social media and apps, to lower as much as possible the barrier to entry and its definition of membership, and exploit the close-to-zero marginal costs of communicating online with an ever-expanding base of members.

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The organisational template introduced by the digital party has the merit of updating the party form to the technological and social conditions of our era. The digital party has demonstrated the ability to operate efficiently despite extremely limited economic resources, and introduced new forms of membership involvement, as seen in processes of participatory legislation, where ideas for new parliamentary initiatives are crowdsourced from members. However, such organisational restructuring does not result, as some platform party advocates would like us to believe, in a radical diffusion of power in the organisation, nor does it lead to a situation in which ‘everyone is of equal worth’, as suggested by the Five Star Movement slogan (ognuno vale uno).

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It examines how different decision-making software integrate different visions of democracy, with some leaning more on deliberative functions and others more on representative and plebiscitary balloting. Chapter 7 discusses the process of online decision-making and how it is managed by the party staff. All digital parties involve some deliberative functions, crowdsourcing policy ideas from the party membership. However, preponderance goes to more top-down functions, in particular to referenda through which the leadership seeks to constantly renew their mandate, with most ballots returning the expected results. Chapter 8 explores the figure of the hyperleader. The hyperleader is a plebiscitary-charismatic figure tasked with representing the party in the media and internet spectacle, by attending TV talk shows and intervening obsessively on social media.

After the Gig: How the Sharing Economy Got Hijacked and How to Win It Back by Juliet Schor, William Attwood-Charles, Mehmet Cansoy

1960s counterculture, Airbnb, algorithmic management, Amazon Mechanical Turk, American Legislative Exchange Council, back-to-the-land, barriers to entry, bike sharing, Californian Ideology, carbon footprint, clean tech, collaborative consumption, collaborative economy, Community Supported Agriculture, COVID-19, creative destruction, crowdsourcing, deskilling, driverless car, en.wikipedia.org, financial independence, future of work, gentrification, George Gilder, gig economy, global supply chain, global village, haute cuisine, income inequality, independent contractor, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Jean Tirole, Jeff Bezos, jitney, job satisfaction, John Perry Barlow, John Zimmer (Lyft cofounder), Kevin Kelly, Lyft, Marshall McLuhan, Mason jar, mass incarceration, Mitch Kapor, Network effects, new economy, New Urbanism, Occupy movement, peer-to-peer rental, Post-Keynesian economics, precariat, profit maximization, profit motive, race to the bottom, regulatory arbitrage, rent gap, rent-seeking, ride hailing / ride sharing, Ruby on Rails, selection bias, sharing economy, Silicon Valley, Silicon Valley ideology, Skype, smart cities, social distancing, Stewart Brand, TaskRabbit, technological determinism, technoutopianism, Telecommunications Act of 1996, The Nature of the Firm, the payments system, Tragedy of the Commons, transaction costs, transportation-network company, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, urban planning, wage slave, walking around money, Whole Earth Catalog, women in the workforce, working poor, Yochai Benkler, Zipcar

The boss is a jerk. I’ve spent much of my professional life studying these issues. When the financial system crashed in 2008, a powerful idea emerged from the rubble: digital technology could solve the problem of work. This is not because machines will replace people but because algorithms and crowdsourced information can make bosses redundant. Software can reorganize economic activity into a person-to-person structure. This empowers individuals to take control of their lives. Vast swathes of the economy, especially in services, are ripe for this transformation. This vision came to be called “the sharing economy.”

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The vision was of a high-tech Jeffersonian world10 populated by small-scale asset-holders who self-govern in “harmonious community.”11 Many of these ideas would carry over into the original discourse of the sharing economy—personal empowerment through decentralized task-based work, egalitarianism, and community. An additional dimension of New Communalist thinking—cybernetics, the study of machine-based control—is also present in the design of sharing platforms. It focused on information, feedback loops, and self-correcting mechanisms, which appeared as dynamic algorithms, surge pricing, and crowd-sourced data.12 As it happened, the gelling of the Californian Ideology coincided with the neoliberal policy turn of the 1980s. Cyberutopians jumped on the “free market” bandwagon,* combining “the free-wheeling spirit” of hippies with the “entrepreneurial zeal” of yuppies.13 In the process they came to believe in what Thomas Frank calls “market populism,” the view that “markets enjoyed some mystic, organic connection to the people while governments were fundamentally illegitimate.”14 Technology corporations were heralded as agents of revolution,15 and the task of those in the digital vanguard was to free the companies from the pesky regulations of government bureaucrats.16 The crucial intellectual move here was the conflation of individual liberty with freedom for corporations, an equivalence that made sense to cyberutopians who had been heavily engaged with Silicon Valley companies for years.17 The 1996 Telecommunications Act deregulated the sector, and platforms such as Google, Facebook, and Amazon morphed into monopolies with virtually no opposition.

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This was less true of platform employees, who were more aware of problems, and researchers, who from the beginning debated the pros and cons of sharing platforms, especially their economics.41 The Economics of the Platforms The conversation about what is propelling the growth of the platform sector has broken down along familiar lines. One group—mainstream economists, management scholars, and the companies—points to digital innovation and market structure, in particular algorithms, crowdsourced information, and network effects.42 Sociologists, legal scholars, and political economists point to platforms’ ability to exploit the labor and capital of sellers and to evade regulation. Both sides have a point. Sharing platforms are “two-sided markets,” whose function is to coordinate individual buyers and sellers.43 Traditionally, anonymous person-to-person markets have been stymied by the time involved in searching for the right match (think yard sales) and the risk of transacting with an unknown individual.

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott

"World Economic Forum" Davos, Airbnb, altcoin, Alvin Toffler, asset-backed security, autonomous vehicles, barriers to entry, behavioural economics, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, Bretton Woods, business logic, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, carbon credits, carbon footprint, clean water, cloud computing, cognitive dissonance, commoditize, commons-based peer production, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, crowdsourcing, cryptocurrency, currency risk, decentralized internet, digital capitalism, disintermediation, disruptive innovation, distributed ledger, do well by doing good, Donald Trump, double entry bookkeeping, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, failed state, fiat currency, financial innovation, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, future of work, Future Shock, Galaxy Zoo, general purpose technology, George Gilder, glass ceiling, Google bus, GPS: selective availability, Hacker News, Hernando de Soto, Higgs boson, holacracy, income inequality, independent contractor, informal economy, information asymmetry, information security, intangible asset, interest rate swap, Internet of things, Jeff Bezos, jimmy wales, Kickstarter, knowledge worker, Kodak vs Instagram, Lean Startup, litecoin, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, microcredit, mobile money, money market fund, Neal Stephenson, Network effects, new economy, Oculus Rift, off grid, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, performance metric, Peter Thiel, planetary scale, Ponzi scheme, prediction markets, price mechanism, Productivity paradox, QR code, quantitative easing, radical decentralization, ransomware, Ray Kurzweil, renewable energy credits, rent-seeking, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Salesforce, Satoshi Nakamoto, search costs, Second Machine Age, seigniorage, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, smart grid, Snow Crash, social graph, social intelligence, social software, standardized shipping container, Stephen Hawking, Steve Jobs, Steve Wozniak, Stewart Brand, supply-chain management, systems thinking, TaskRabbit, TED Talk, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, The Soul of a New Machine, The Wisdom of Crowds, transaction costs, Turing complete, Turing test, Tyler Cowen, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, unorthodox policies, vertical integration, Vitalik Buterin, wealth creators, X Prize, Y2K, Yochai Benkler, Zipcar

Combining the concepts of transparency, crowdsourcing, and online citizen participation—over a blockchain—we can envision reintroducing concepts of ancient Athenian democracy into the twenty-first century.55 CrowdJury56 looks to transform the justice system by putting several judicial processes online, using both crowdsourcing and the blockchain, including filing a charge or complaint, gathering and vetting of evidence, engaging citizens in open trials online and as online jurors, and issuing a verdict. Think transparent processes with crowdsourced discovery, crowdsourced analysis, and crowdsourced decision making and presto—you get an accurate outcome in a much shorter time frame and at vastly reduced cost.

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“A wallet could be controlled by a piece of software that has no ownership and so you have the possibility of completely autonomous software agents that control their own money.”15 An autonomous agent could pay for its own Web hosting and use evolutionary algorithms to spread copies of itself by making small changes and then allowing those copies to survive. Each copy could contain new content that it discovers or even crowdsources somewhere on the Internet. As some of these copies become very successful, the agent could sell ads back to users, ad revenue could go into a bank account or posted on a secure place on the blockchain, and the agent could use this growing revenue to crowdsource more ad content and proliferate itself. The agent would repeat the cycle so that appealing content propagates and hosts itself successfully, and unsuccessful content basically dies because it runs out of money to host itself.

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Patent Application of David Chaum, “Random Sample Elections,” June 19, 2014; http://patents.justia.com/patent/20140172517. 54. https://blog.ethereum.org/2014/08/21/introduction-futarchy/. 55. Federico Ast (@federicoast) and Alejandro Sewrjugin (@asewrjugin), “The CrowdJury, a Crowdsourced Justice System for the Collaboration Era,” https://medium.com/@federicoast/the-crowdjury-a-crowdsourced-court-system-for-the-collaboration-era-66da002750d8#.e8yynqipo. 56. http://crowdjury.org/en/. 57. The entire process is described in Ast and Sewrjugin, “The CrowdJury.” 58. A brief description of the jury selection process in early Athens is described at www.agathe.gr/democracy/the_jury.html. 59.

The New Digital Age: Transforming Nations, Businesses, and Our Lives by Eric Schmidt, Jared Cohen

access to a mobile phone, additive manufacturing, airport security, Amazon Mechanical Turk, Amazon Web Services, Andy Carvin, Andy Rubin, anti-communist, augmented reality, Ayatollah Khomeini, barriers to entry, bitcoin, borderless world, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, crowdsourcing, data acquisition, Dean Kamen, disinformation, driverless car, drone strike, Elon Musk, Evgeny Morozov, failed state, false flag, fear of failure, Filter Bubble, Google Earth, Google Glasses, Hacker Conference 1984, hive mind, income inequality, information security, information trail, invention of the printing press, job automation, John Markoff, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, Mary Meeker, means of production, military-industrial complex, MITM: man-in-the-middle, mobile money, mutually assured destruction, Naomi Klein, Nelson Mandela, no-fly zone, off-the-grid, offshore financial centre, Parag Khanna, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Singer: altruism, power law, Ray Kurzweil, RFID, Robert Bork, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, social graph, speech recognition, Steve Jobs, Steven Pinker, Stewart Brand, Stuxnet, Susan Wojcicki, The Wisdom of Crowds, upwardly mobile, Whole Earth Catalog, WikiLeaks, young professional, zero day

Imagine if the end goal of the Chinese users was not to harass the kitten-stomper but to bring her to justice through official channels. In a conflict scenario, where institutions have broken down or are not trusted by the population, crowd-sourced energy will help to produce more comprehensive and accurate information, help track down wanted criminals and create demand for accountability even in the most difficult circumstances. But the importance and utility of crowd-sourced justice pales in comparison to the other modern development: data permanence. The exposure of atrocities in real time and in front of a global audience is vital, as is permanently storing it and making it searchable for everyone who wants to refer to it (for prosecutions, legislation or later study).

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As people come online, they will quite suddenly have access to almost all the world’s information in one place in their own language. This will even be true for an illiterate Maasai cattle herder in the Serengeti, whose native tongue, Maa, is not written—he’ll be able to verbally inquire about the day’s market prices and crowd-source the whereabouts of any nearby predators, receiving a spoken answer from his device in reply. Mobile phones will allow formerly isolated people to connect with others very far away and very different from themselves. On the economic front, they’ll find ways to use the new tools at their disposal to enlarge their businesses, make them more efficient and maximize their profits, as the fisherwomen did much more locally with their basic phones.

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Disillusioned with Russia’s liberal opposition parties, Navalny, a real-estate lawyer, started his own blog dedicated to exposing corruption in major Russian companies, initially supplying the disclosures himself by taking small stakes in the businesses and invoking shareholder rights to force them to share information. He later crowd-sourced his approach, instructing supporters to try to do the same, with some success. Eventually, his blog grew into a full-blown secret-spilling platform, where visitors were encouraged to donate toward its operating costs via PayPal. Navalny’s profile grew as his collection of scoops swelled, most notably with a set of leaked documents that revealed the misuse of $4 billion at the state-owned oil pipeline company Transneft in 2010.

Chasing My Cure: A Doctor's Race to Turn Hope Into Action; A Memoir by David Fajgenbaum

Atul Gawande, Barry Marshall: ulcers, crowdsourcing, data science, Easter island, friendly fire, medical residency, personalized medicine, phenotype, placebo effect, randomized controlled trial, Saturday Night Live, Silicon Valley, the scientific method

When thinking about how the CDCN would accelerate research into how Castleman disease works and how to best diagnose and treat it, I came up with an ambitious but quite simple plan (I thought!): Instead of waiting for the stars to align, we would align the stars. First, we’d need to identify, connect, and build a global community to identify and prioritize high-impact research projects. Toward this end, we imagined an online crowdsourcing process that allowed any patient, physician, or researcher to suggest lines of inquiry or studies that seemed most important, regardless of that person’s ability to perform the study. This was a novel approach: The norm in medical research dictates that research ideas are pursued if they’re conceived by a researcher who can also garner the necessary funding and do the work.

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This was a novel approach: The norm in medical research dictates that research ideas are pursued if they’re conceived by a researcher who can also garner the necessary funding and do the work. Promising ideas, if conceived by someone who can’t obtain the funding or perform the work, go unharnessed. We couldn’t afford to let that happen. We also proposed the creation of a scientific advisory board to prioritize the crowdsourced ideas based on their likely impact, feasibility, and rational order within an overarching framework (e.g., you shouldn’t perform in-depth investigations of the inner workings of a particular cell type until after you have identified that the particular cell type is in fact important in Castleman disease).

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And when medications were repurposed for use in iMCD, we’d systematically track their effectiveness to guide future use and identify promising candidates for clinical trials. Finally, we understood that feeding information back to the community would be the critical component to keep all of this going—sharing findings would promote continued rounds of crowdsourcing, prioritization, expert recruitment, and execution. What we were proposing would maximize efficiency and give the best hope for breakthroughs in the shortest amount of time. Put simply, we’d bring all the key stakeholders together and then apply hyperfocus to research. Instead of soliciting grant applications and hoping that the right researcher would apply to conduct the right research, we’d ensure that the right research is done by the right researcher right now.

To Save Everything, Click Here: The Folly of Technological Solutionism by Evgeny Morozov

"World Economic Forum" Davos, 3D printing, algorithmic bias, algorithmic trading, Amazon Mechanical Turk, An Inconvenient Truth, Andrew Keen, augmented reality, Automated Insights, behavioural economics, Berlin Wall, big data - Walmart - Pop Tarts, Buckminster Fuller, call centre, carbon footprint, Cass Sunstein, choice architecture, citizen journalism, classic study, cloud computing, cognitive bias, creative destruction, crowdsourcing, data acquisition, Dava Sobel, digital divide, disintermediation, Donald Shoup, driverless car, East Village, en.wikipedia.org, Evgeny Morozov, Fall of the Berlin Wall, Filter Bubble, Firefox, Francis Fukuyama: the end of history, frictionless, future of journalism, game design, gamification, Gary Taubes, Google Glasses, Ian Bogost, illegal immigration, income inequality, invention of the printing press, Jane Jacobs, Jean Tirole, Jeff Bezos, jimmy wales, Julian Assange, Kevin Kelly, Kickstarter, license plate recognition, lifelogging, lolcat, lone genius, Louis Pasteur, machine readable, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, moral panic, Narrative Science, Nelson Mandela, Nicholas Carr, packet switching, PageRank, Parag Khanna, Paul Graham, peer-to-peer, Peter Singer: altruism, Peter Thiel, pets.com, placebo effect, pre–internet, public intellectual, Ray Kurzweil, recommendation engine, Richard Thaler, Ronald Coase, Rosa Parks, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, Slavoj Žižek, smart meter, social graph, social web, stakhanovite, Steve Jobs, Steven Levy, Stuxnet, surveillance capitalism, systems thinking, technoutopianism, TED Talk, the built environment, The Chicago School, The Death and Life of Great American Cities, the medium is the message, The Nature of the Firm, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, transaction costs, Twitter Arab Spring, urban decay, urban planning, urban sprawl, Vannevar Bush, warehouse robotics, WikiLeaks, work culture , Yochai Benkler

Sol Schwimmer is suing me”: Woody Allen, The Complete Prose of Woody Allen (New York: Wings Books, 1991), 105. 35 “when we think of information technology”: David Edgerton, Shock of the Old: Technology and Global History Since 1900 (London: Profile Books, 2011), xvi. 36 “the most wrenching cultural transformation since the Industrial Revolution”: “‘Antichrist of Silicon Valley,’ Andrew Keen Wary of Online Content Sharing,” Economic Times, May 29, 2012. 37 they don’t always capture the historical complexity: on the longitude problem, see Dava Sobel’s accessible history Longitude: The True Story of a Lone Genius Who Solved the Greatest Scientific Problem of His Time, reprint ed. (New York: Walker & Company, 2007). On early crowdsourcing efforts by the Smithsonian, see “Smithsonian Crowd-sourcing since 1849!,” Smithsonian Institution Archives, April 14, 2011, http://siarchives.si.edu/blog/smithsonian-crowdsourcing-1849. I learned of Toyota’s efforts via this blog post on pre-Internet crowd-sourcing efforts: “Crowdsourcing Is Not New—the History of Crowdsourcing (1714 to 2010),” DesignCrowd, October 28, 2010, http://blog.designcrowd.com/article/202/crowdsourcing. 37 “Knowledge is taking on the shape of the Net”: David Weinberger, Too Big to Know: Rethinking Knowledge Now that the Facts Aren’t the Facts, Experts Are Everywhere, and the Smartest Person in the Room Is the Room (New York: Basic Books, 2012), 17.

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Such circularity—whereby “the Internet” is seen as revolutionary because of Factor X, but Factor X is seen as revolutionary because of “the Internet”—is silly, but in an era of profound and revolutionary change, this passes for deep insight. Take the fake novelty of a term like “crowdsourcing”—supposedly, one of the chief attributes of the Internet era, an idea that gave us that great source of didactic knowledge, Wikipedia. “Crowdsourcing” is certainly a very effective term; calling some of the practices it enables as “digitally distributed sweatshop labor”—for this seems like a much better description of what’s happening on crowdsource-for-money platforms like Amazon’s Mechanical Turk—wouldn’t accomplish half as much. But effective euphemisms come with trade-offs; they don’t always capture the historical complexity of the processes they purport to describe.

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Didn’t the British government turn to “crowdsourcing”—in 1714!—to solve the “longitude problem” and solicit proposals for how to better navigate at sea? Didn’t the Smithsonian Institution—in 1849!—turn to a network of over six hundred volunteer observers (in Canada, Mexico, Latin America, and the Caribbean) to submit monthly weather reports (published in 1861 as the first of a two-volume compilation of climactic data)? Didn’t Toyota hold a contest—in 1936!—to redesign its logo, only to receive 27,000 entries in return? Didn’t Zagat turn to a form of “crowdsourcing” to generate its restaurant reviews long before Yelp made online reviews fashionable?

Designing Social Interfaces by Christian Crumlish, Erin Malone

A Pattern Language, Amazon Mechanical Turk, anti-pattern, barriers to entry, c2.com, carbon footprint, cloud computing, collaborative editing, commons-based peer production, creative destruction, crowdsourcing, en.wikipedia.org, Firefox, folksonomy, Free Software Foundation, game design, ghettoisation, Howard Rheingold, hypertext link, if you build it, they will come, information security, lolcat, Merlin Mann, Nate Silver, Network effects, Potemkin village, power law, recommendation engine, RFC: Request For Comment, semantic web, SETI@home, Skype, slashdot, social bookmarking, social graph, social software, social web, source of truth, stealth mode startup, Stewart Brand, systems thinking, tacit knowledge, telepresence, the long tail, the strength of weak ties, The Wisdom of Crowds, web application, Yochai Benkler

As seen on Amazon Mechanical Turk (http://www.mturk.com/mturk/welcome) Assignment Zero (http://zero.newassignment.net/) The ESP Game (http://www.cs.cmu.edu/~biglou/ESP.pdf) iStockphoto (http://istockphoto.com) ReCAPTCHA (http://recaptcha.net/) SETI@home (http://setiathome.ssl.berkeley.edu/) Threadless (http://threadless.com) Download at WoweBook.Com 330 Chapter 12: Barnraising Further Reading “Berners-Lee on the read/write web,” BBC News, August 9, 2005, http://news.bbc.co.uk/2/ hi/technology/4132752.stm Cross Cultural Collaboration, http://crossculturalcollaboration.pbwiki.com/ “Deriving Process-driven Collaborative Editing Pattern from Collaborative Learning Flow Patterns,” by Olivera Marjanovic, Hala Skaf-Molli, Pascal Molli, and Claude Godart, http://www.ifets.info/journals/10_1/12.pdf “Edit This Page,” by Dave Winer, http://www.scripting.com/davenet/1999/05/24/editThisPage.html Edit This Page PHP, http://sourceforge.net/projects/editthispagephp/ Paylancers blog, http://paylancers.blogspot.com/ The Power of Many, http://thepowerofmany.com Regulating Prominence: A Design Pattern for Co-Located Collaboration (http://www.ida.liu.se/~matar/coop04arvola-web.pdf) “The Rise of Crowdsourcing,” by Jeff Howe, Wired 14.06, http://www.wired.com/wired/archive/14.06/crowds.html “The Simplest Thing That Could Possibly Work,” by Bill Venners, http://www.artima.com/intv/simplest.html Universal Edit Button, http://universaleditbutton.org/Universal_Edit_Button Wiki Design Principles, http://c2.com/cgi/wiki?WikiDesignPrinciples “The Wiki Way,” by Jon Udell, http://weblog.infoworld.com/udell/2004/10/19.html Wired Crowdsourcing blog, http://crowdsourcing.typepad.com/ Download at WoweBook.Com Chapter 13 Social Media Junkies Unite!

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Synchronous Versus Asynchronous Communication Sign In to Participate Communicating Forums Public Conversation Private Conversation Group Conversation Arguments Flame Wars Vendettas Download at WoweBook.Com 291 292 292 292 296 298 302 304 304 305 xii Contents Sock Puppets Further Reading 305 305 12. Barnraising.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Collaboration Manage Project Voting Collaborative Editing Edit This Page The Wiki Way 307 309 312 315 319 323 325 330 Crowdsourcing Further Reading 13. Social Media Junkies Unite!.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Keeping Up Tuning In Following Filtering Recommendations Social Search Real-Time Search Conversational Search Pivoting Further Reading 331 334 335 335 340 342 343 346 349 350 Part IV.

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To Andrew Hinton, information philosopher, advanced practitioner, and community leader, for his illumination of the problems of context in these new environments. To Andrius Kuliskaukus and the Minciu Sodas collective, for their contributions to freedom and self-sufficiency around the world, and some crowdsourced thoughts on the value of the public domain as a preferred licensing option. To Derek Powazek, trailblazer of community-oriented design and communication, for his exploration of people as meaning-making machines. To Harjeet S. Gulati, who found our project through the wiki, added a wealth of definitions and other useful contributions there, and then consented to contribute his thoughts on knowledge management in the enterprise.

Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It by Azeem Azhar

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 23andMe, 3D printing, A Declaration of the Independence of Cyberspace, Ada Lovelace, additive manufacturing, air traffic controllers' union, Airbnb, algorithmic management, algorithmic trading, Amazon Mechanical Turk, autonomous vehicles, basic income, Berlin Wall, Bernie Sanders, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, book value, Boris Johnson, Bretton Woods, carbon footprint, Chris Urmson, Citizen Lab, Clayton Christensen, cloud computing, collective bargaining, computer age, computer vision, contact tracing, contact tracing app, coronavirus, COVID-19, creative destruction, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Graeber, David Ricardo: comparative advantage, decarbonisation, deep learning, deglobalization, deindustrialization, dematerialisation, Demis Hassabis, Diane Coyle, digital map, digital rights, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, Elon Musk, emotional labour, energy security, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Firefox, Frederick Winslow Taylor, fulfillment center, future of work, Garrett Hardin, gender pay gap, general purpose technology, Geoffrey Hinton, gig economy, global macro, global pandemic, global supply chain, global value chain, global village, GPT-3, Hans Moravec, happiness index / gross national happiness, hiring and firing, hockey-stick growth, ImageNet competition, income inequality, independent contractor, industrial robot, intangible asset, Jane Jacobs, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Just-in-time delivery, Kickstarter, Kiva Systems, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, lockdown, low skilled workers, lump of labour, Lyft, manufacturing employment, Marc Benioff, Mark Zuckerberg, megacity, Mitch Kapor, Mustafa Suleyman, Network effects, new economy, NSO Group, Ocado, offshore financial centre, OpenAI, PalmPilot, Panopticon Jeremy Bentham, Peter Thiel, Planet Labs, price anchoring, RAND corporation, ransomware, Ray Kurzweil, remote working, RFC: Request For Comment, Richard Florida, ride hailing / ride sharing, Robert Bork, Ronald Coase, Ronald Reagan, Salesforce, Sam Altman, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, software as a service, Steve Ballmer, Steve Jobs, Stuxnet, subscription business, synthetic biology, tacit knowledge, TaskRabbit, tech worker, The Death and Life of Great American Cities, The Future of Employment, The Nature of the Firm, Thomas Malthus, TikTok, Tragedy of the Commons, Turing machine, Uber and Lyft, Uber for X, uber lyft, universal basic income, uranium enrichment, vertical integration, warehouse automation, winner-take-all economy, workplace surveillance , Yom Kippur War

In time, this type of activity garnered a new name: ‘crowdsourcing’. The internet could connect people who needed something done with thousands, perhaps millions, of those with the time and the skill to do it. According to Jeff Howe, the professor of journalism who coined the term, crowdsourcing would unleash ‘the latent talent of the crowd’.47 In these early days, the notion of crowdsourcing had a utopian feel: millions of people working together, perhaps voluntarily, to build some incredible tool like Wikipedia. Within a few years, crowdsourcing platforms had multiplied. Services like Elance and oDesk sprung up for complicated tasks like programming or copywriting; Fiverr and PeoplePerHour were created for tasks that were less complex than programming but more complex than an Amazon HIT.

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In-built GPS meant that phones always knew where they were – and allowing crowdsourcing platforms to offer up local, highly convenient services. Soon we could order taxis, takeaway food and massages from the comfort of our couches. TaskRabbit, now owned by furniture giant Ikea, will today dispatch someone to help you assemble your new bookcase. Talkspace will help you find a therapist. Wag! will find a walker for your dog. In time, the term ‘crowdsourcing’ – which often referred to unpaid, non-commercial work – gave way to a new term: the ‘gig economy’. This whole new way of working was underpinned by the emerging exponential economy. Crowdsourcing depended on digital platforms.

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, Wall Street Journal, 1 April 2019 <https://www.wsj.com/articles/will-ai-destroy-more-jobs-than-it-creates-over-the-next-decade-11554156299> [accessed 11 January 2021]. 45 ‘Company Information’, Uber Newsroom Pakistan <https://www.uber.com/en-PK/newsroom/company-info/> [accessed 21 September 2020]. 46 ‘Mechanical Turk: Research in the Crowdsourcing Age’, Pew Research Center: Internet & Technology, 11 July 2016 <https://www.pewresearch.org/internet/2016/07/11/research-in-the-crowdsourcing-age-a-case-study/> [accessed 28 September 2020]. 47 Jeff Howe, ‘The Rise of Crowdsourcing’, Wired, 1 June 2006 <https://www.wired.com/2006/06/crowds/> [accessed 28 September 2020]. 48 Nicole Lyn Pesce, ‘This Chart Shows How Uber Rides Sped Past NYC Yellow Cabs in Just Six Years’, MarketWatch, 9 August 2019 <https://www.marketwatch.com/story/this-chart-shows-how-uber-rides-sped-past-nyc-yellow-cabs-in-just-six-years-2019-08-09> [accessed 7 January 2021]. 49 Kelle Howson et al., ‘Platform Workers, the Future of Work and Britain’s Election’, Media@LSE, 11 December 2019 <https://blogs.lse.ac.uk/medialse/2019/12/11/platform-workers-the-future-of-work-and-britains-election/> [accessed 7 January 2021]. 50 James Manyika et al., Connecting Talent with Opportunity in the Digital Age (McKinsey & Company, 1 June 2015) <https://www.mckinsey.com/featured-insights/employment-and-growth/connecting-talent-with-opportunity-in-the-digital-age> [accessed 6 October 2020]. 51 Neil Munshi, ‘Tech Start-Ups Drive Change for Nigerian Truckers’, Financial Times, 26 August 2019 <https://www.ft.com/content/c6a3d1f2-c27d-11e9-a8e9-296ca66511c9> [accessed 21 September 2020]. 52 ‘Upwork Reports Fourth Quarter and Full Year 2020 Financial Results’, Upwork Inc., 23 February 2021 <https://investors.upwork.com/news-releases/news-release-details/upwork-reports-fourth-quarter-and-full-year-2020-financial> [accessed 21 April 2021]. 53 Lijin Yeo, ‘The U.S.

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future by Orly Lobel

2021 United States Capitol attack, 23andMe, Ada Lovelace, affirmative action, Airbnb, airport security, Albert Einstein, algorithmic bias, Amazon Mechanical Turk, augmented reality, barriers to entry, basic income, Big Tech, bioinformatics, Black Lives Matter, Boston Dynamics, Charles Babbage, choice architecture, computer vision, Computing Machinery and Intelligence, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, crowdsourcing, data science, David Attenborough, David Heinemeier Hansson, deep learning, deepfake, digital divide, digital map, Elon Musk, emotional labour, equal pay for equal work, feminist movement, Filter Bubble, game design, gender pay gap, George Floyd, gig economy, glass ceiling, global pandemic, Google Chrome, Grace Hopper, income inequality, index fund, information asymmetry, Internet of things, invisible hand, it's over 9,000, iterative process, job automation, Lao Tzu, large language model, lockdown, machine readable, machine translation, Mark Zuckerberg, market bubble, microaggression, Moneyball by Michael Lewis explains big data, natural language processing, Netflix Prize, Network effects, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, occupational segregation, old-boy network, OpenAI, openstreetmap, paperclip maximiser, pattern recognition, performance metric, personalized medicine, price discrimination, publish or perish, QR code, randomized controlled trial, remote working, risk tolerance, robot derives from the Czech word robota Czech, meaning slave, Ronald Coase, Salesforce, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, social distancing, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, surveillance capitalism, tech worker, TechCrunch disrupt, The Future of Employment, TikTok, Turing test, universal basic income, Wall-E, warehouse automation, women in the workforce, work culture , you are the product

Rob Davies, “Everything to Play for as Employers Turn to Video Games in Recruitment Drive,” Guardian, Nov. 28, 2015, https://www.theguardian.com/money/2015/nov/28/psychometric-tests-games-recruitment-interview. 31. Meredith Somers, “This CEO Wields Transparency and Openness to Crowdsource Ideas,” Ideas Made to Matter (blog), MIT Sloan School of Management, September 9, 2020, https://mitsloan.mit.edu/ideas-made-to-matter/ceo-wields-transparency-and-openness-to-crowdsource-ideas. 32. Zoe Rohrich, “Why These Companies Are Rethinking the Use of AI in Hiring,” PBS NewsHour, November 26, 2019, https://www.pbs.org/newshour/world/agents-for-change/why-these-companies-are-rethinking-the-use-of-ai-in-hiring. 33.

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Just like information, knowledge, innovation, and our talent pools more generally, AI and data should be understood as a commons—a shared resource capable of addressing some of the world’s toughest problems: global health and pandemics, world hunger, environmental sustainability and climate change, and poverty and inequality. We should move toward more open-source big data as well as public initiatives to crowdsource data collection for public goals. And we need to incentivize more competition and less concentration in the development and use of new technologies. Competition lowers costs and fuels choice, customizable options, and user-driven preferences. We can create AI that challenges stereotypes. We need to build machines and design robots that subvert long-standing constructions of identity.

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Earlier studies on the software found that certain algorithms charged with flagging who is likely to reoffend are inherently flawed, labeling Black defendants as future criminals twice as often as white defendants and frequently mislabeling white defendants as “low risk.” One high-profile, much-cited study from 2018 found that COMPAS is no more accurate than predictions by people with no criminal justice expertise. The study looked at 400 online participants on Amazon’s Mechanical Turk who crowdsourced short descriptions of real defendants from a publicly available COMPAS data set. The participants were asked to predict whether the defendants were likely to commit a crime in the future. The study found that the overall accuracy of the lay humans was 62 percent, compared to the 65 percent accuracy of algorithmic COMPAS predictions.12 More recent studies do question these findings, however: one from 2020 argues that the first research wasn’t a good reflection of human judgment in the real world, and that the first study’s experimental setting focused participants’ attention on the most predictive factors and gave them feedback along the way, artificially boosting their results.13 In each of the fifty rounds, participants made a prediction, were informed whether the prediction was correct—that is, received immediate feedback—and then received the next scenario.

Groundswell: Winning in a World Transformed by Social Technologies by Charlene Li, Josh Bernoff

business process, call centre, centre right, citizen journalism, crowdsourcing, demand response, Donald Trump, estate planning, Firefox, folksonomy, John Markoff, Kickstarter, knowledge worker, off-the-grid, Parler "social media", Salesforce, Silicon Valley, skunkworks, social bookmarking, social intelligence, Streisand effect, the long tail, Tony Hsieh

All the brand equity the bank had built up would ebb away, and worse, it would cease to tap into its customers’ innovations and go back to business as usual. a few words about crowdsourcing The Crédit Mutuel story is an example of crowdsourcing—asking the groundswell to provide you with ideas. Crowdsourcing is all the rage right now. It’s especially popular with advertising agencies, which are increasingly asking people to create television ads as part of some sort of contest.8 Frito-Lay’s Doritos ad in the 2007 Super Bowl was crowdsourced.9 It was pretty good, too. Crowdsourcing by itself is not the same as embracing your customers. Crowdsource an ad campaign, and you might save a few bucks on ad production—but you won’t have to do the hard work of changing the way you interact with customers.

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And Crédit Mutuel, if it keeps going in the same direction, will be a very responsive bank in the future. On the other hand, Frito-Lay probably learned very little from crowdsourcing its Super Bowl Doritos commercial. It’s unlikely to turn over significant parts of its ad creation process to consumers on a regular basis. Its customers are not changing the company’s product development pipeline, supporting each other, or energizing each other in any sustainable way. Crowdsourced ads are a flash in the pan—they tap the groundswell for a moment, rather than move the company in a positive direction. CASE STUDY loblaw: reviews that drive continuous improvement Jim Osborne helped turn a grocery store and its store brand into a hotbed of innovation.

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It’s especially popular with advertising agencies, which are increasingly asking people to create television ads as part of some sort of contest: The New York Times examined this phenomenon and noted that not only is crowdsourcing commercials sometimes an expensive proposition, but it also leads to expressions of the brand that the company might find less than ideal. See “The High Price of Creating Free Ads” by Louise Story, New York Times, May 26, 2007, visible at http://forr.com/gsw9-8. 9. Frito-Lay’s Doritos ad in the 2007 Super Bowl was crowdsourced: The Frito-Lay’s 2007 Super Bowl ad site is no longer visible. chapter 10 1. Fadra blogs and tweets about her experiences, especially as a mom.

Super Thinking: The Big Book of Mental Models by Gabriel Weinberg, Lauren McCann

Abraham Maslow, Abraham Wald, affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, Apollo 13, Apple Newton, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, dark pattern, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Dunning–Kruger effect, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fake news, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Goodhart's law, Gödel, Escher, Bach, heat death of the universe, hindsight bias, housing crisis, if you see hoof prints, think horses—not zebras, Ignaz Semmelweis: hand washing, illegal immigration, imposter syndrome, incognito mode, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, karōshi / gwarosa / guolaosi, lateral thinking, loss aversion, Louis Pasteur, LuLaRoe, Lyft, mail merge, Mark Zuckerberg, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nocebo, nuclear winter, offshore financial centre, p-value, Paradox of Choice, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, power law, precautionary principle, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, Salesforce, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Streisand effect, sunk-cost fallacy, survivorship bias, systems thinking, The future is already here, The last Blockbuster video rental store is in Bend, Oregon, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Tragedy of the Commons, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, vertical integration, Vilfredo Pareto, warehouse robotics, WarGames: Global Thermonuclear War, When a measure becomes a target, wikimedia commons

One way to do so is actively to seek out people from different backgrounds to participate. Another way, easily enabled by the internet, is to crowdsource ideas, where you seek (source) ideas quite literally from anyone who would like to participate (the crowd). Crowdsourcing has been effective across a wide array of situations, from soliciting tips in journalism, to garnering contributions to Wikipedia, to solving the real-world problems of companies and governments. For example, Netflix held a contest in 2009 in which crowdsourced researchers beat Netflix’s own recommendation algorithms. Crowdsourcing can help you get a sense of what a wide array of people think about a topic, which can inform your future decision making, updating your prior beliefs (see Bayesian statistics in Chapter 5).

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While you cannot expect similar results in all situations, Surowiecki explains the key conditions in which you can expect good results from crowdsourcing: Diversity of opinion: Crowdsourcing works well when it draws on different people’s private information based on their individual knowledge and experiences. Independence: People need to be able to express their opinions without influence from others, avoiding groupthink. Aggregation: The entity doing the crowdsourcing needs to be able to combine the diverse opinions in such a way as to arrive at a collective decision. If you can design a system with these properties, then you can draw on the collective intelligence of the crowd.

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In general, drawing on collective intelligence makes sense when the group’s collective pool of knowledge is greater than what you could otherwise get access to; this helps you arrive at a more intelligent decision than you would arrive at on your own. “The crowd” can help systematically think through various scenarios, get new data and ideas, or simply help improve existing ideas. One direct application of crowdsourcing to scenario analysis is the use of a prediction market, which is like a stock market for predictions. In a simple formulation of this concept, the price of each stock can range between $0 and $1 and represents the market’s current probability of an event taking place, such as whether a certain candidate will be elected.

Beautiful Data: The Stories Behind Elegant Data Solutions by Toby Segaran, Jeff Hammerbacher

23andMe, airport security, Amazon Mechanical Turk, bioinformatics, Black Swan, business intelligence, card file, cloud computing, computer vision, correlation coefficient, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, data acquisition, data science, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Gregor Mendel, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, Large Hadron Collider, longitudinal study, machine readable, machine translation, Mars Rover, natural language processing, openstreetmap, Paradox of Choice, power law, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social bookmarking, social graph, SPARQL, sparse data, speech recognition, statistical model, supply-chain management, systematic bias, TED Talk, text mining, the long tail, Vernor Vinge, web application

He has a PhD in organic chemistry and has published articles and obtained patents in the areas of synthetic and mechanistic chemistry, gene therapy, nanotechnology, and scientific knowledge management. Lukas Biewald is founder and CEO of Dolores Labs, a company making crowdsourcing easy and reliable. Dolores Labs’ blog (http://blog.doloreslabs.com) is full of fun crowdsourcing and data visualization experiments. Prior to Dolores Labs, he worked as a senior scientist at Powerset, and before that he built Yahoo! Japan’s search engine ranking algorithm. He received a BS in math and an MS in computer science from Stanford University, where he 349 Download at Boykma.Com worked in the AI Lab and published two papers on machine learning applications.

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(author), 323–332 Kimball, Ralph (The Data Warehouse Toolkit), 76 Klump, Valdean (author), 149–165 Koblin, Aaron (author), 149–165 Krumme, Coco (author), 205–217 L Lake Wobegon effect, 217 Lang, Andrew (author), 259–277 language identification of corpus data, 239 “Learning Organization” concept, 78 Lewin, Kurt (“action research” concept), 78 libraries, as Information Platforms, 73 Lidar scanner (see Velodyne Lidar scanner) Lindenbaum, Pierre (author), 259–277 Lindsay, Jeff (Web Hooks concept), 127 Linguistic Data Consortium, 219 location information, representation of for Geograph archive, 95–98 for Oakland Crimespotting project, 174–181 for PEIR system, 8–11 for political data, 330 for sense.us website, 188–194 Luhn, Hans Peter (“A Business Intelligence System”), 75 luxury product, survey for (see customer survey project) M machine translation of corpus data, 240 Madhaven, Jayant (author), 133–147 maps (see location information, representation of) Mars Lander system (see Phoenix Mars Lander system) mastership of records, 60, 61 materialized views, 66 Matlab (data analysis package), 282 Matplotlib (data analysis package), 282 Matsumoto, Yukihiro (Ruby programming language), 89, 98 MECA Optical Microscope (OM) camera, 38 mediator accessing Deep Web using, 135 for social data (see Gnip) Medicare website, 337 message boards, public data available from, 337 Microsoft Azure SDS, 70 Microsoft’s data management stack, 82 Migurski, Michal (author), 167–182 MObStor system, 71 Modest Maps library, 8 Morville, Peter (“findability” concept) motivation considerations for data collection, 21, 30 music video based on data (see Radiohead’s “House of Cards” video) N narrative fallacy, 207 National Center for Biotechnology Information (NCBI) website, 336 natural language corpus data, 219, 240 author identification of, 239 DNA sequencing of, 240 document unshredding of, 240 language identification of, 239 machine translation of, 240 search strategies used for, 241 secret codes in, analysis of, 228–234 spam detection in, 239 spelling correction of, 234–239 word segmentation analysis of, 221–227 NCBI (National Center for Biotechnology Information) website, 336 INDEX Download at Boykma.Com 361 Neylon, Cameron (author), 259–277 normalization of social data, 128–131 Norvig, Peter (author), 219–242 Num Py (data analysis package), 282 O Oakland Crimespotting project, 167 data collection from CrimeWatch, 169–174 visualizing data online, 174–181 Oakland CrimeWatch application, 169–174 OAuth, 130 O’Connor, Brendan (author), 279–301 Open Notebook Science, 261 Optical Microscope (OM) camera, 38 P P2P protocol, 121 partitioning data, 56 patterns, people’s skill at recognizing, 206 PEIR (Personal Environmental Impact Report) system, 2 data collection for, 3, 4 data processing for, 6 data visualization for, 8–12 database design for, 5 participating in, 15 sharing data from, 12 perception considerations for data collection, 20–21 persistent context, 115 personal data collection of, 3–5 visualization of, 7–14 Personal Environmental Impact Report (see PEIR system) Phoenix Mars Lander system, 35–40 cameras (imagers) for, 38, 53 computer used for, 37 data collection for, 37 data packing for, 40 data processing for, 42, 46–51, 53 data storage for, 43–46 data transfer for, 37, 52 image compression for, 50 websites about, 54 photographs (see Geograph archive; Phoenix Mars Lander system) planning fallacy, 212 PNUTS system, 56 comparison with Azure SDS, 70 comparison with BigTable system, 68 comparison with Cassandra system, 70 comparison with Dynamo system, 69 geo-replication of data in, 56, 58 362 partitioning data for scale-out, 56, 62 querying data, 64–67 updating data, 57–64 political data, 323 age, effect on vote choice, 328 graphics used for, 323 mapping partisanship in Pennsylvania, 330 predicting vote choice, 326 redistricting, effect on partison bias, 324 supreme court nominees, senate voting patterns on, 328 polling, 123 Poole, David (author), 303–321 Popper, Karl (statement about falsifiability), 209, 213 predictions, difficulty in making from data, 213 privacy, with “data finds data” systems, 118 probabilistic model, 221 probability, 215, 220 public data, sources of, 336 Purves, Ross (research using Geograph), 92 PyHive framework, 81 Q Quants, as Data Scientists, 84 R R (data analysis package), 282, 300 RAC (Robotic Arm Camera), 38 Radiohead’s “House of Cards” video, 149 data capture equipment for, 150–154 data capturing process for, 155–159, 164 data processing for, 160 data sample for, 154 launching, 161–164 Ramakrishnan, Raghu (author), 55–71 range-partitioned data, 62 rate limiting, used with polling, 123 raw data, providing to users application for querying live data, 265 collecting crowdsourced data, 260 further experiments suggested by, 271–274 integrating data with other data resources, 266 problems created by, 275–277 reasons for, 259, 274, 276 representing data online, 263–271 self-describing data formats for, 269 unique identifiers required for, 263, 269 validating crowdsourced data, 262 Raynard, Robert (Secret Code Breaker), 233 RDF (Resource Description Framework), 269 INDEX Download at Boykma.Com real estate sales, analysis of (see housing market analysis) record-level mastership, 61 relational model for data, 76 replication of DNA, 247 geo-replication of data, 56, 58 reporting on data results (see data visualization) REpresentational State Transfer (REST), 122 Resource Description Framework (RDF), 269 resources (see books and publications; website resources) REST (REpresentational State Transfer), 122 Rice algorithm for compression, 51 Robotic Arm Camera (RAC), 38 roulette wheel example of “data finds data”, 107–111 S San Francisco housing market analysis (see housing market analysis) Sanger Institute’s sequencing platform for DNA data, 254–257 SAS (data analysis package), 282 scale-out feature for data storage, 56 Sci Py (data analysis package), 282 search engines, accessing Deep Web from (see surfacing) search strategies for corpus data, 241 Secret Code Breaker (Raynard), 233 secret codes in corpus data, analyzing, 228–234 Securities and Exchange Commission website, 336 Segaran, Toby (author), 335–348 semantically reconciled and relationshipaware directories, 114 semantically reconciled directories, 114 Senge, Peter (The Fifth Discipline), 78 sense.us website, 184, 186 Birthplace Voyager graph, 191 census data used for, 186–188 collaboration features of, 194–199, 201 doubly linked discussions, 195 field tests of, 199–203 Job Voyager graph, 191 pointing with graphical annotations, 196 population pyramid, 192 scatter plot display, 192 social navigation, 198 state map, 192 views, collecting and linking, 197 views, sharing, 194 visualization of data, 188–194 Sequencescape tools, 254 sequencing platform for DNA data, 254–257 shift ciphers, 228 Singh, Simon (The Code Book), 230 social data, 119 business value of, 129–131 formats for, current, 121 normalizing, 128–131 public versus private data, 130 sharing and collaborating on, 194–199, 201 transporting, APIs for, 122–128 transporting, current methods for, 120 visualization and analysis of, 184, 199 visualization of, 188–194 social networks, public data available on, 336 social stereotypes, researching, 279 clustering types of people, 295–300 data analysis, 282, 290–294 gendered words, determining, 294 preprocessing the data, 280 presentation of data results, 285–290 Sokol, Lisa (author), 105–118 space missions (see Phoenix Mars Lander system) spam detection in corpus data, 239 spelling correction of corpus data, 234–239 SPSS (data analysis package), 282 Srivastava, Utkarsh (author), 55–71 Stata (data analysis package), 282 Stereo Surface Imager (SSI), 38 stereotypes (see social stereotypes, researching) storage cloud, 56, 70 (see also PNUTS system) stories created from data, 208, 211 stylometry of corpus data, 239 substitution ciphers in corpus data, analyzing, 228–234 surfacing, 135, 136 challenges of, 136 informative test for, 136, 142 inputs for, selecting, 140, 144–146 queries for, selecting, 138–144 query templates for, 139, 141, 143 survey project (see customer survey project) Swayne, Deborah F.

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Conclusion 208 217 NATURAL LANGUAGE CORPUS DATA by Peter Norvig 219 Word Segmentation Secret Codes Spelling Correction Other Tasks Discussion and Conclusion 221 228 234 239 240 LIFE IN DATA: THE STORY OF DNA by Matt Wood and Ben Blackburne 243 DNA As a Data Store DNA As a Data Source Fighting the Data Deluge The Future of DNA 243 250 253 257 BEAUTIFYING DATA IN THE REAL WORLD by Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen 259 The Problem with Real Data Providing the Raw Data Back to the Notebook Validating Crowdsourced Data Representing the Data Online Closing the Loop: Visualizations to Suggest New Experiments Building a Data Web from Open Data and Free Services 259 260 262 263 271 274 SUPERFICIAL DATA ANALYSIS: EXPLORING MILLIONS OF SOCIAL STEREOTYPES by Brendan O’Connor and Lukas Biewald 279 Introduction Preprocessing the Data Exploring the Data Age, Attractiveness, and Gender Looking at Tags Which Words Are Gendered?

Roads and Bridges by Nadia Eghbal

AGPL, Airbnb, Amazon Web Services, barriers to entry, Benevolent Dictator For Life (BDFL), corporate social responsibility, crowdsourcing, cryptocurrency, data science, David Heinemeier Hansson, Debian, DevOps, en.wikipedia.org, Firefox, Free Software Foundation, GnuPG, Guido van Rossum, Ken Thompson, Khan Academy, Kickstarter, leftpad, Marc Andreessen, market design, Network effects, platform as a service, pull request, Richard Stallman, Ruby on Rails, Salesforce, side project, Silicon Valley, Skype, software is eating the world, the Cathedral and the Bazaar, Tragedy of the Commons, Y Combinator

Before open source software existed, technology firms treated software like any other paid product: a team of employees built new software internally, then sold it to the public. While this meant software had a clear business model, it also came with increased development costs. Proprietary software requires a full-time paid team to support its development, including developers, designers, marketers, and lawyers. It’s far cheaper to simply crowdsource software, built and maintained by a community of volunteer developers. Open source is easier to distribute. People are more likely to adopt software that is free to use and modify than software that cost s hundreds of dollars to license and was developed in a black box. Not only will developers want to use it for free, but they might be inclined to tell their friends to use it as well, amplifying the effects of its distribution.

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Developers could use Swift to write software for Apple devices, but not contribute to the language’s core development. In 2015, Swift was open sourced under the Apache License 2.0.[49] The incentives for a company to maintain an open source project are numerous. Opening up a project to the public can mean less work for the company, which is essentially crowdsourcing improvements. It builds goodwill and awareness among developers, who might then be incentivized to use other company resources to build things. Having an active community of developers creates a recruiting pipeline for talent. And sometimes, open sourcing a project helps a company strengthen their user base and brand, or even drown out competition.

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Several months later, Atwood followed up with Solara to ask how he decided to spend the donation. Solara replied that the grant money was still untouched. It’s not easy to use it….What would you suggest? Atwood wrote that he was crushingly disappointed by the response. [73] The decentralized nature of open source has made it what it is: crowdsourced software that anyone can build, share and contribute to. But when it comes to discussing organizational needs or sustainability, it can be difficult to make authoritative decisions. These transitions to long-term sustainability can be drawn out and painful. One of the more prominent examples is the Linux kernel, an open source project used in many operating systems worldwide, including Android and Chrome OS.

Revolution in the Age of Social Media: The Egyptian Popular Insurrection and the Internet by Linda Herrera

citizen journalism, crowdsourcing, decentralized internet, Evgeny Morozov, fake news, Google Earth, informal economy, Julian Assange, knowledge economy, minimum wage unemployment, Mohammed Bouazizi, moral panic, Nelson Mandela, Occupy movement, RAND corporation, Rosa Parks, Silicon Valley, Skype, Slavoj Žižek, WikiLeaks

There were also attempts to penetrate the most popular online chat rooms of young Arabs, but this proved far more difficult due to the atomized and interactive nature of chat rooms. Chat rooms, like social media after it, do not lend themselves to packaged, top-down messaging in the style of traditional media, but rely on people-to-people crowdsourcing. Christopher Ross, Special Coordinator for Public Diplomacy at the State Department, describes the difficulty of working in chat rooms: There are many, many chat rooms in which the Middle East, and U.S. policy towards the Middle East, are openly discussed. Many of their participants are from the Middle East.

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Contact the media—print, radio, television, online—whenever possible to tell your story. If a sympathetic foreign dignitary or organization happens to visit, try to meet with them, or organize a protest to coincide with their arrival, which can get you some international press attention. Through crowdsourcing, the page started formulating its media plan as early as June 14, four days into the launch. The admin wrote: Hey everyone, tonight our plan is for all of us to call the talk shows. Please, can people send us the telephone numbers so we can use these in our [media] plan? We want you to find the numbers of all the talk shows in Egypt.

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The goal was to create an “internet government” by the people to monitor security forces and police at three main sites: checkpoints in the streets, police stations, and public universities. The plan was to create a Twitter hashtag that any citizen with an internet connection could use to tweet and report their encounter with the police, whether good or bad. The admins of the site would use a crowdsourcing mapping application to monitor where high numbers of violations were taking place. Police stations that respected people and the law would be rewarded with good ratings, whereas the abusers would be publicized and subject to citizen action. As plans were underway for Police Watch, the Khaled Said incident took place and diverted his energies.

Alternatives to Capitalism by Robin Hahnel, Erik Olin Wright

affirmative action, basic income, crowdsourcing, inventory management, iterative process, Kickstarter, loose coupling, means of production, Pareto efficiency, profit maximization, race to the bottom, tacit knowledge, transaction costs

But there is also a practical problem with Erik’s suggestions about risk and innovation when applied in the context of a participatory economy. Even if those who received start-up funds from crowd-sourcing agreed that the enterprise would be run according to participatory economic principles, and even if investors received no return on their investment, the new enterprise would have to obtain the inputs it needs to operate through the participatory planning procedure. And it can’t do this without being certified as “credible.” A worker council can’t buy its inputs with funds raised through crowd-sourcing in a participatory economy. I think, instead, what is needed are multiple ways for groups who want to start up new enterprises to demonstrate their credibility so they can participate in the planning procedure.

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Consider the following example: Suppose a group of people have an idea for some new product but they cannot convince the relevant council or federation to provide them the needed capital equipment and raw materials to produce it. There is just too much skepticism about the viability of the project. An alternative way of funding the project could be through a form of crowdsourcing finance along the lines of Kickstarter. The workers involved would post a description of the project online and explain their specific needs for material inputs. They appeal to people (in their role of consumers) to allocate part of their annual consumption allowances to the project. Consumers might decide, for example, to put in extra hours at work in order to acquire the extra funds needed for their contribution, or they might just decide to consume less of some discretionary part of their consumption bundle.

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But potential contributors to the project might also only be interested in contributing if they got a positive return on their “investment”. This would look much closer to market investment. The question, then, is should such practices be prohibited in a participatory economy? Especially if a positive return on crowd-sourced investments is allowed, these projects would constitute a kind of quasi-market niche in the participatory economy. Robin argues that new worker councils should be prohibited from raising capital outside of the planning process. Here is what he says about new startup worker councils: In a participatory economy new worker councils bid for the resources they need to get started in the participatory planning process.

The Secret War Between Downloading and Uploading: Tales of the Computer as Culture Machine by Peter Lunenfeld

Albert Einstein, Andrew Keen, anti-globalists, Apple II, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, Burning Man, business cycle, business logic, butterfly effect, Charles Babbage, computer age, Computing Machinery and Intelligence, creative destruction, crowdsourcing, cuban missile crisis, Dissolution of the Soviet Union, don't be evil, Douglas Engelbart, Douglas Engelbart, Dynabook, East Village, Edward Lorenz: Chaos theory, Fairchild Semiconductor, Fall of the Berlin Wall, folksonomy, Francis Fukuyama: the end of history, Frank Gehry, Free Software Foundation, Grace Hopper, gravity well, Guggenheim Bilbao, Herman Kahn, Honoré de Balzac, Howard Rheingold, Ian Bogost, invention of movable type, Isaac Newton, Ivan Sutherland, Jacquard loom, Jane Jacobs, Jeff Bezos, John Markoff, John von Neumann, Jon Ronson, Kickstarter, Mark Zuckerberg, Marshall McLuhan, Mercator projection, Metcalfe’s law, Mother of all demos, mutually assured destruction, Neal Stephenson, Nelson Mandela, Network effects, new economy, Norbert Wiener, PageRank, pattern recognition, peer-to-peer, planetary scale, plutocrats, post-materialism, Potemkin village, RFID, Richard Feynman, Richard Stallman, Robert Metcalfe, Robert X Cringely, Schrödinger's Cat, Search for Extraterrestrial Intelligence, seminal paper, SETI@home, Silicon Valley, Skype, social bookmarking, social software, spaced repetition, Steve Ballmer, Steve Jobs, Steve Wozniak, technological determinism, Ted Nelson, the built environment, the Cathedral and the Bazaar, The Death and Life of Great American Cities, the medium is the message, Thomas L Friedman, Turing machine, Turing test, urban planning, urban renewal, Vannevar Bush, walkable city, Watson beat the top human players on Jeopardy!, William Shockley: the traitorous eight

Formerly at <http://www.jennyeverywhere.com>; now available at <http://theshifterarchive.com>. 14 . This neologism is credited to journalist Jeff Howe in his article “The Rise of Crowdsourcing” Wired 14.06 (June 2006): 176–183. <http://www.wired.com/ wired/archive/14.06/crowds.html>. See his book, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business (New York: Crown Business, 2008) and blog, http://crowdsourcing.typepad.com/. His “white paper” version of the definition of crowdsourcing specifically foregrounds the economic relationships: “Crowdsourcing is the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call.” 15 .

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According to the Web site where she was born and lives her (many) lives: “The character of Jenny Everywhere is available for use by anyone, with only one condition. This paragraph must be included in any publication involving Jenny Everywhere, in order that others may use this property as they wish. All rights reversed.”13 The openendedness, the unfinish of Jenny Everywhere, distinguishes it from the similar neologism “crowdsourcing.” Crowdsourcing is also about the deployment of multiple, online “eyeballs,” but the concept’s link to the “outsourcing” of globalization ties it tightly to the economic realm.14 Imagination, on the other hand, encompasses but is not limited to those projects that can be monetized, so it is less “problem solving” than “situation enabling” that is needed.

Tomorrowland: Our Journey From Science Fiction to Science Fact by Steven Kotler

adjacent possible, Albert Einstein, Alexander Shulgin, autonomous vehicles, barriers to entry, Biosphere 2, Burning Man, carbon footprint, carbon tax, Colonization of Mars, crowdsourcing, Dean Kamen, Dennis Tito, epigenetics, gravity well, Great Leap Forward, haute couture, Helicobacter pylori, interchangeable parts, Kevin Kelly, life extension, Louis Pasteur, low earth orbit, North Sea oil, Oculus Rift, off-the-grid, oil shale / tar sands, peak oil, personalized medicine, Peter H. Diamandis: Planetary Resources, private spaceflight, RAND corporation, Ray Kurzweil, Richard Feynman, Ronald Reagan, self-driving car, SpaceShipOne, stem cell, Stephen Hawking, Stewart Brand, synthetic biology, theory of mind, Virgin Galactic, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, WikiLeaks

It’s about humans who have the audacity to join in nature’s creative process, in her quest for more than mere self-improvement, in her quest for self reinvention. Radical self-reinvention.” 1. This is how the future arrived. It began innocuously. In the early 2000s, businesses started to realize that highly skilled jobs formerly performed in-house, by a single employee, could more efficiently be crowdsourced to a larger group via the Internet. Initially, offerings were simple. We crowdsourced the design of T-shirts (Threadless.com) and the writing of encyclopedias (Wikipedia.com), but it didn’t take long for the trend to start making inroads into the harder sciences. Pretty soon, the hunt for extraterrestrial life, the development of self-driving cars, and the folding of enzymes into new and novel proteins were being done this way.

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Pretty soon, the hunt for extraterrestrial life, the development of self-driving cars, and the folding of enzymes into new and novel proteins were being done this way. With the fundamental tools of genetic manipulation — tools that cost millions of dollars not ten years ago — dropping precipitously in price, the crowdsourced design of biological agents was just the next logical step. In 2008, casual DNA design competitions with small prizes arose; then, in 2011, with the launch of GE’s $100 million cancer challenge, the field moved onto serious contests. By early 2015, as personalized gene therapies for end-stage cancer became medicine’s bleeding edge, viral design sites began to appear where people could upload information about their disease, and virologists could post designs for a customized cure.

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Iraqi insurgents subsequently repurposed Skygrabber — Russian software developed to steal satellite television and available for $29.95 — to intercept the video feeds of US predator drones, giving them the information needed to monitor and evade American military operations. Lately, organized crime has even taken up crowdsourcing, outsourcing areas of their illegal operations — printing up fake credit cards, money laundering, even murder — to those with greater expertise. With the anonymous nature of the online crowd, this development makes it all but impossible for law-enforcement to track these efforts. Added together, the historical data is clear: Whenever novel technologies enter the market, illegitimate uses quickly follow legitimate ones.

One Way Forward: The Outsider's Guide to Fixing the Republic by Lawrence Lessig

collapse of Lehman Brothers, crony capitalism, crowdsourcing, en.wikipedia.org, Filter Bubble, jimmy wales, Occupy movement, Ronald Reagan, Yochai Benkler

Both fueled irresponsible monetary and banking policy and, eventually, economic collapse. So the citizens of Iceland launched the most ambitious crowdsourced-sovereignty project in modern history. As a first step, a network of private grassroots organizations called the Anthill gathered a statistically significant portion of the nation to brainstorm a vision for the country. This “National Assembly” of more than fifteen hundred Icelandic citizens used open-source principles to “energize the wisdom of the population,” as it was promoted, and “to crowdsource a socio-economic political manifesto.”45 The idea, according to the assembly’s architect, was to “focus on the process.

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They had no idea what this was about. … The pros, when we made the mistake of consulting them, would warn very very strongly, “Do not just send volunteers out to do this work.” But, of course, volunteers became the lifeblood of this new genre of political movement. They constituted the energy in “crowdsourced” politics, and they defined its power. MoveOn’s wave has repeated itself again and again in the decade or so since. Not just on the tech-enabled Left but also on the traditional Left (Obama) and then on the Right (the Tea Party), then on the Gen X/millennial Left (Occupy Wall Street), and now in the unaligned Internet (the Wikipedia-driven anti-SOPA/PIPA campaign).

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The National Assembly set the stage for the next extraordinary step of popular sovereignty. In June 2010, the Icelandic parliament passed the Act on a Constitutional Assembly, delegating the “intensely legalistic task” of writing a constitution to a group of citizens acting in a constitutional council. That council then convened a National Forum in November 2010, which “crowdsourced the norms and values of the population of 21st century Iceland,” through a series of questions and interviews. Then, building on the results from that survey and the work of the 2009 assembly, the forum divided citizens into groups focused upon particular themes. At the same time, the council initiated elections to a twenty-five-seat drafting commission, which would have ultimate responsibility for drafting a constitution.

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost, Tom Fawcett

Albert Einstein, Amazon Mechanical Turk, Apollo 13, big data - Walmart - Pop Tarts, bioinformatics, business process, call centre, chief data officer, Claude Shannon: information theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, data acquisition, data science, David Brooks, en.wikipedia.org, Erik Brynjolfsson, Gini coefficient, Helicobacter pylori, independent contractor, information retrieval, intangible asset, iterative process, Johann Wolfgang von Goethe, Louis Pasteur, Menlo Park, Nate Silver, Netflix Prize, new economy, p-value, pattern recognition, placebo effect, price discrimination, recommendation engine, Ronald Coase, selection bias, Silicon Valley, Skype, SoftBank, speech recognition, Steve Jobs, supply-chain management, systems thinking, Teledyne, text mining, the long tail, The Signal and the Noise by Nate Silver, Thomas Bayes, transaction costs, WikiLeaks

If we apply our general framework of considering the costs and benefits in data science projects explicitly, it leads us to new thinking about investing in data. Final Example: From Crowd-Sourcing to Cloud-Sourcing The connectivity between businesses and “consumers” brought about by the Internet has changed the economics of labor. Web-based systems like Amazon’s Mechanical Turk and oDesk (among others) facilitate a type of crowd-sourcing that might be called “cloud labor”—harnessing via the Internet a vast pool of independent contractors. One sort of cloud labor that is particularly relevant to data science is “micro-outsourcing”: the outsourcing of large numbers of very small, well-defined tasks.

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as craft, Superior Data Scientists as strategic asset, Data and Data Science Capability as a Strategic Asset–Data and Data Science Capability as a Strategic Asset baseline methods of, Summary behavior predictions based on past actions, Example: Hurricane Frances Big Data and, Data Processing and “Big Data”–Data Processing and “Big Data” case studies, examining, Examine Data Science Case Studies classification modeling for issues in, Generalizing Beyond Classification cloud labor and, Final Example: From Crowd-Sourcing to Cloud-Sourcing–Final Example: From Crowd-Sourcing to Cloud-Sourcing customer churn, predicting, Example: Predicting Customer Churn data mining about individuals, Privacy, Ethics, and Mining Data About Individuals–Privacy, Ethics, and Mining Data About Individuals data mining and, The Ubiquity of Data Opportunities, Data Mining and Data Science, Revisited–Data Mining and Data Science, Revisited data processing vs., Data Processing and “Big Data”–Data Processing and “Big Data” data science engineers, Deployment data-analytic thinking in, Data-Analytic Thinking–Data-Analytic Thinking data-driven business vs., Data Processing and “Big Data” data-driven decision-making, Data Science, Engineering, and Data-Driven Decision Making–Data Science, Engineering, and Data-Driven Decision Making engineering, Data Science, Engineering, and Data-Driven Decision Making–Data Science, Engineering, and Data-Driven Decision Making engineering and, Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist evolving uses for, From Big Data 1.0 to Big Data 2.0–From Big Data 1.0 to Big Data 2.0 fitting problem to available data, Changing the Way We Think about Solutions to Business Problems–Changing the Way We Think about Solutions to Business Problems fundamental principles, The Ubiquity of Data Opportunities history, Machine Learning and Data Mining human interaction and, What Data Can’t Do: Humans in the Loop, Revisited–What Data Can’t Do: Humans in the Loop, Revisited human knowledge and, What Data Can’t Do: Humans in the Loop, Revisited–What Data Can’t Do: Humans in the Loop, Revisited Hurricane Frances example, Example: Hurricane Frances learning path for, Superior Data Scientists limits of, What Data Can’t Do: Humans in the Loop, Revisited–What Data Can’t Do: Humans in the Loop, Revisited mining mobile device data example, Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data–Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data opportunities for, The Ubiquity of Data Opportunities–The Ubiquity of Data Opportunities principles, Data Science, Engineering, and Data-Driven Decision Making, Business Problems and Data Science Solutions privacy and ethics of, Privacy, Ethics, and Mining Data About Individuals–Privacy, Ethics, and Mining Data About Individuals processes, Data Science, Engineering, and Data-Driven Decision Making software development vs., A Firm’s Data Science Maturity structure, Machine Learning and Data Mining techniques, Data Science, Engineering, and Data-Driven Decision Making technology vs. theory of, Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist–Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist understanding, The Ubiquity of Data Opportunities, Data Processing and “Big Data” data science maturity, of firms, A Firm’s Data Science Maturity–A Firm’s Data Science Maturity data scientists academic, Attracting and Nurturing Data Scientists and Their Teams as scientific advisors, Attracting and Nurturing Data Scientists and Their Teams attracting/nurturing, Attracting and Nurturing Data Scientists and Their Teams–Attracting and Nurturing Data Scientists and Their Teams evaluating, Superior Data Scientists–Superior Data Scientists managing, Superior Data Science Management–Superior Data Science Management Data Scientists, LLC, Attracting and Nurturing Data Scientists and Their Teams data sources, Evaluation, Baseline Performance, and Implications for Investments in Data data understanding, Data Understanding–Data Understanding expected value decomposition and, From an Expected Value Decomposition to a Data Science Solution–From an Expected Value Decomposition to a Data Science Solution expected value framework and, The Expected Value Framework: Structuring a More Complicated Business Problem–The Expected Value Framework: Structuring a More Complicated Business Problem data warehousing, Data Warehousing data-analytic thinking, Data-Analytic Thinking–Data-Analytic Thinking and unbalanced classes, Problems with Unbalanced Classes for business strategies, Thinking Data-Analytically, Redux–Thinking Data-Analytically, Redux data-driven business data science vs., Data Processing and “Big Data” understanding, Data Processing and “Big Data” data-driven causal explanations, Data-Driven Causal Explanation and a Viral Marketing Example–Data-Driven Causal Explanation and a Viral Marketing Example data-driven decision-making, Data Science, Engineering, and Data-Driven Decision Making–Data Science, Engineering, and Data-Driven Decision Making benefits, Data Science, Engineering, and Data-Driven Decision Making discoveries, Data Science, Engineering, and Data-Driven Decision Making repetition, Data Science, Engineering, and Data-Driven Decision Making database queries, as analytic technique, Database Querying–Database Querying database tables, Models, Induction, and Prediction dataset entropy, Example: Attribute Selection with Information Gain datasets, Models, Induction, and Prediction analyzing, Introduction to Predictive Modeling: From Correlation to Supervised Segmentation attributes of, Overfitting in Mathematical Functions cross-validation and, From Holdout Evaluation to Cross-Validation limited, From Holdout Evaluation to Cross-Validation Davis, Miles, Example: Jazz Musicians, Example: Jazz Musicians Deanston single malt scotch, Understanding the Results of Clustering decision boundaries, Visualizing Segmentations, Classification via Mathematical Functions decision lines, Visualizing Segmentations decision nodes, Supervised Segmentation with Tree-Structured Models decision stumps, Evaluation, Baseline Performance, and Implications for Investments in Data decision surfaces, Visualizing Segmentations decision trees, Supervised Segmentation with Tree-Structured Models decision-making, automatic, Data Science, Engineering, and Data-Driven Decision Making deduction, induction vs., Models, Induction, and Prediction Dell, Data preparation, Achieving Competitive Advantage with Data Science demand, local, Example: Hurricane Frances dendrograms, Hierarchical Clustering, Hierarchical Clustering dependent variables, Models, Induction, and Prediction descriptive attributes, Data Mining and Data Science, Revisited descriptive modeling, Models, Induction, and Prediction Dictionary of Distances (Deza & Deza), * Other Distance Functions differential descriptions, * Using Supervised Learning to Generate Cluster Descriptions Digital 100 companies, Data-Analytic Thinking Dillman, Linda, Data Science, Engineering, and Data-Driven Decision Making dimensionality, of nearest-neighbor reasoning, Dimensionality and domain knowledge–Dimensionality and domain knowledge directed marketing example, Targeting the Best Prospects for a Charity Mailing–A Brief Digression on Selection Bias discoveries, Data Science, Engineering, and Data-Driven Decision Making discrete (binary) classifiers, ROC Graphs and Curves discrete classifiers, ROC Graphs and Curves discretized numeric variables, Selecting Informative Attributes discriminants, linear, Linear Discriminant Functions discriminative modeling methods, generative vs., Summary disorder, measuring, Selecting Informative Attributes display advertising, Example: Targeting Online Consumers With Advertisements distance functions, for nearest-neighbor reasoning, * Other Distance Functions–* Other Distance Functions distance, measuring, Similarity and Distance distribution Gaussian, Regression via Mathematical Functions Normal, Regression via Mathematical Functions distribution of properties, Selecting Informative Attributes Doctor Who (television show), Example: Evidence Lifts from Facebook “Likes” document (term), Representation domain knowledge data mining processes and, Dimensionality and domain knowledge nearest-neighbor reasoning and, Dimensionality and domain knowledge–Dimensionality and domain knowledge domain knowledge validation, Associations Among Facebook Likes domains, in association discovery, Associations Among Facebook Likes Dotcom Boom, Results, Formidable Historical Advantage double counting, Costs and benefits draws, statistical, * Logistic Regression: Some Technical Details E edit distance, * Other Distance Functions, * Other Distance Functions Einstein, Albert, Conclusion Elder Research, Attracting and Nurturing Data Scientists and Their Teams Ellington, Duke, Example: Jazz Musicians, Example: Jazz Musicians email, Why Text Is Important engineering, Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist, Business Understanding engineering problems, business problems vs., Other Data Science Tasks and Techniques ensemble method, Bias, Variance, and Ensemble Methods–Bias, Variance, and Ensemble Methods entropy, Selecting Informative Attributes–Selecting Informative Attributes, Selecting Informative Attributes, Example: Attribute Selection with Information Gain, Summary and Inverse Document Frequency, * The Relationship of IDF to Entropy change in, Selecting Informative Attributes equation for, Selecting Informative Attributes graphs, Example: Attribute Selection with Information Gain equations cosine distance, * Other Distance Functions entropy, Selecting Informative Attributes Euclidean distance, Similarity and Distance general linear model, Linear Discriminant Functions information gain (IG), Selecting Informative Attributes Jaccard distance, * Other Distance Functions L2 norm, * Other Distance Functions log-odds linear function, * Logistic Regression: Some Technical Details logistic function, * Logistic Regression: Some Technical Details majority scoring function, * Combining Functions: Calculating Scores from Neighbors majority vote classification, * Combining Functions: Calculating Scores from Neighbors Manhattan distance, * Other Distance Functions similarity-moderated classification, * Combining Functions: Calculating Scores from Neighbors similarity-moderated regression, * Combining Functions: Calculating Scores from Neighbors similarity-moderated scoring, * Combining Functions: Calculating Scores from Neighbors error costs, ROC Graphs and Curves error rates, Plain Accuracy and Its Problems, Error rates errors absolute, Regression via Mathematical Functions computing, Regression via Mathematical Functions false negative vs. false positive, Evaluating Classifiers squared, Regression via Mathematical Functions estimating generalization performance, From Holdout Evaluation to Cross-Validation estimation, frequency based, Probability Estimation ethics of data mining, Privacy, Ethics, and Mining Data About Individuals–Privacy, Ethics, and Mining Data About Individuals Euclid, Similarity and Distance Euclidean distance, Similarity and Distance evaluating models, Decision Analytic Thinking I: What Is a Good Model?

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–Summary baseline performance and, Evaluation, Baseline Performance, and Implications for Investments in Data–Evaluation, Baseline Performance, and Implications for Investments in Data classification accuracy, Plain Accuracy and Its Problems–Generalizing Beyond Classification confusion matrix, The Confusion Matrix–The Confusion Matrix expected values, A Key Analytical Framework: Expected Value–Costs and benefits generalization methods for, Generalizing Beyond Classification–Generalizing Beyond Classification procedure, Flaws in the Big Red Proposal evaluating training data, Holdout Data and Fitting Graphs evaluation in vivo, Evaluation purpose, Evaluation evaluation framework, Evaluation events calculating probability of, Combining Evidence Probabilistically–Combining Evidence Probabilistically independent, Joint Probability and Independence–Joint Probability and Independence evidence computing probability from, Bayes’ Rule, Bayes’ Rule determining strength of, Example: Targeting Online Consumers With Advertisements likelihood of, Applying Bayes’ Rule to Data Science strongly dependent, Advantages and Disadvantages of Naive Bayes evidence lift Facebook “Likes” example, Example: Evidence Lifts from Facebook “Likes”–Example: Evidence Lifts from Facebook “Likes” modeling, with Naive Bayes, A Model of Evidence “Lift”–A Model of Evidence “Lift” eWatch/eBracelet example, Co-occurrences and Associations: Finding Items That Go Together–Co-occurrences and Associations: Finding Items That Go Together examining clusters, Understanding the Results of Clustering examples, Models, Induction, and Prediction analytic engineering, Targeting the Best Prospects for a Charity Mailing–From an Expected Value Decomposition to a Data Science Solution associations, Associations Among Facebook Likes–Associations Among Facebook Likes beer and lottery association, Example: Beer and Lottery Tickets–Example: Beer and Lottery Tickets biases in data, What Data Can’t Do: Humans in the Loop, Revisited Big Red proposal, Example Data Mining Proposal–Flaws in the Big Red Proposal breast cancer, Example: Logistic Regression versus Tree Induction–Example: Logistic Regression versus Tree Induction business news stories, Example: Clustering Business News Stories–The news story clusters call center metrics, Profiling: Finding Typical Behavior–Profiling: Finding Typical Behavior cellular churn, Problems with Unbalanced Classes, Problems with Unequal Costs and Benefits centroid-based clustering, Nearest Neighbors Revisited: Clustering Around Centroids–Nearest Neighbors Revisited: Clustering Around Centroids cloud labor, Final Example: From Crowd-Sourcing to Cloud-Sourcing–Final Example: From Crowd-Sourcing to Cloud-Sourcing clustering, Clustering–* Using Supervised Learning to Generate Cluster Descriptions consumer movie-viewing preferences, Data Reduction, Latent Information, and Movie Recommendation cooccurrence/association, Co-occurrences and Associations: Finding Items That Go Together–Co-occurrences and Associations: Finding Items That Go Together, Example: Beer and Lottery Tickets–Example: Beer and Lottery Tickets cross-validation, From Holdout Evaluation to Cross-Validation–From Holdout Evaluation to Cross-Validation customer churn, Example: Predicting Customer Churn, Example: Addressing the Churn Problem with Tree Induction–Example: Addressing the Churn Problem with Tree Induction, From Holdout Evaluation to Cross-Validation–From Holdout Evaluation to Cross-Validation, A Firm’s Data Science Maturity data mining proposal evaluation, Example Data Mining Proposal–Flaws in the Big Red Proposal data-driven causal explanations, Data-Driven Causal Explanation and a Viral Marketing Example–Data-Driven Causal Explanation and a Viral Marketing Example detecting credit-card fraud, Profiling: Finding Typical Behavior directed marketing, Targeting the Best Prospects for a Charity Mailing–A Brief Digression on Selection Bias evaluating proposals, Scenario and Proposal–Flaws in the GGC Proposal evidence lift, Example: Evidence Lifts from Facebook “Likes”–Example: Evidence Lifts from Facebook “Likes” eWatch/eBracelet, Co-occurrences and Associations: Finding Items That Go Together–Co-occurrences and Associations: Finding Items That Go Together Facebook “Likes”, Example: Evidence Lifts from Facebook “Likes”–Example: Evidence Lifts from Facebook “Likes”, Associations Among Facebook Likes–Associations Among Facebook Likes Green Giant Consulting, Scenario and Proposal–Flaws in the GGC Proposal Hurricane Frances, Example: Hurricane Frances information gain, attribute selection with, Example: Attribute Selection with Information Gain–Example: Attribute Selection with Information Gain iris overfitting, An Example of Mining a Linear Discriminant from Data, Example: Overfitting Linear Functions–Example: Overfitting Linear Functions Jazz musicians, Example: Jazz Musicians–Example: Jazz Musicians junk email classifier, Advantages and Disadvantages of Naive Bayes market basket analysis, Associations Among Facebook Likes–Associations Among Facebook Likes mining linear discriminants from data, An Example of Mining a Linear Discriminant from Data–Summary mining mobile device data, Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data–Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data mining news stories, Example: Mining News Stories to Predict Stock Price Movement–Results mushroom, Example: Attribute Selection with Information Gain–Example: Attribute Selection with Information Gain Naive Bayes, Evidence in Action: Targeting Consumers with Ads nearest-neighbor reasoning, Example: Whiskey Analytics–Example: Whiskey Analytics overfitting linear functions, Example: Overfitting Linear Functions–Example: Overfitting Linear Functions overfitting, performance degradation and, * Example: Why Is Overfitting Bad?

The Innovators: How a Group of Inventors, Hackers, Geniuses and Geeks Created the Digital Revolution by Walter Isaacson

1960s counterculture, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AltaVista, Alvin Toffler, Apollo Guidance Computer, Apple II, augmented reality, back-to-the-land, beat the dealer, Bill Atkinson, Bill Gates: Altair 8800, bitcoin, Bletchley Park, Bob Noyce, Buckminster Fuller, Byte Shop, c2.com, call centre, Charles Babbage, citizen journalism, Claude Shannon: information theory, Clayton Christensen, commoditize, commons-based peer production, computer age, Computing Machinery and Intelligence, content marketing, crowdsourcing, cryptocurrency, Debian, desegregation, Donald Davies, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, driverless car, Dynabook, El Camino Real, Electric Kool-Aid Acid Test, en.wikipedia.org, eternal september, Evgeny Morozov, Fairchild Semiconductor, financial engineering, Firefox, Free Software Foundation, Gary Kildall, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, Haight Ashbury, Hans Moravec, Howard Rheingold, Hush-A-Phone, HyperCard, hypertext link, index card, Internet Archive, Ivan Sutherland, Jacquard loom, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, Joseph-Marie Jacquard, Leonard Kleinrock, Lewis Mumford, linear model of innovation, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, Neil Armstrong, new economy, New Journalism, Norbert Wiener, Norman Macrae, packet switching, PageRank, Paul Terrell, pirate software, popular electronics, pre–internet, Project Xanadu, punch-card reader, RAND corporation, Ray Kurzweil, reality distortion field, RFC: Request For Comment, Richard Feynman, Richard Stallman, Robert Metcalfe, Rubik’s Cube, Sand Hill Road, Saturday Night Live, self-driving car, Silicon Valley, Silicon Valley startup, Skype, slashdot, speech recognition, Steve Ballmer, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, Stewart Brand, Susan Wojcicki, technological singularity, technoutopianism, Ted Nelson, Teledyne, the Cathedral and the Bazaar, The Coming Technological Singularity, The Nature of the Firm, The Wisdom of Crowds, Turing complete, Turing machine, Turing test, value engineering, Vannevar Bush, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Whole Earth Review, wikimedia commons, William Shockley: the traitorous eight, Yochai Benkler

This led to many changes and additions as well as an entirely new section (on Dan Bricklin and VisiCalc). I want to thank the hundreds of collaborators, some of whom I have now gotten to know, who helped me in this crowdsourcing process. (Speaking of which, I hope that someone will soon invent a cross between an enhanced eBook and a wiki so that new forms of multimedia history can emerge that are partly author-guided and partly crowdsourced.) I also want to thank Alice Mayhew and Amanda Urban, who have been my editor and agent for thirty years, and the team at Simon & Schuster: Carolyn Reidy, Jonathan Karp, Jonathan Cox, Julia Prosser, Jackie Seow, Irene Kheradi, Judith Hoover, Ruth Lee-Mui, and Jonathan Evans.

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This chapter also draws on five well-reported and insightful books about how the counterculture helped to shape the personal computer revolution: Steven Levy, Hackers (Anchor/Doubleday, 1984; locations refer to the twenty-fifth anniversary reissue, O’Reilly, 2010); Paul Freiberger and Michael Swaine, Fire in the Valley (Osborne, 1984); John Markoff, What the Dormouse Said (Viking, 2005, locations refer to the Kindle edition); Fred Turner, From Counterculture to Cyberculture (University of Chicago, 2006); Theodore Roszak, From Satori to Silicon Valley (Don’t Call It Frisco Press, 1986). 4. Liza Loop post on my crowdsourced draft on Medium and email to me, 2013. 5. Lee Felsenstein post on my crowdsourced draft on Medium, 2013. See also, “More Than Just Digital Quilting,” Economist, Dec. 3, 2011; Victoria Sherrow, Huskings, Quiltings, and Barn Raisings: Work-Play Parties in Early America (Walker, 1992). 6. Posters and programs for the acid tests, in Phil Lesh, “The Acid Test Chronicles,” http://www.postertrip.com/public/5586.cfm; Tom Wolfe, The Electric Kool-Aid Acid Test (Farrar, Straus and Giroux, 1987), 251 and passim. 7.

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These patent disputes were the forerunner of a major issue of the digital era: Should intellectual property be shared freely and placed whenever possible into the public domain and open-source commons? That course, largely followed by the developers of the Internet and the Web, can spur innovation through the rapid dissemination and crowd-sourced improvement of ideas. Or should intellectual property rights be protected and inventors allowed to profit from their proprietary ideas and innovations? That path, largely followed in the computer hardware, electronics, and semiconductor industries, can provide the financial incentives and capital investment that encourages innovation and rewards risks.

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity by Douglas Rushkoff

activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, bank run, banking crisis, barriers to entry, benefit corporation, bitcoin, blockchain, Burning Man, business process, buy and hold, buy low sell high, California gold rush, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, centralized clearinghouse, citizen journalism, clean water, cloud computing, collaborative economy, collective bargaining, colonial exploitation, Community Supported Agriculture, corporate personhood, corporate raider, creative destruction, crowdsourcing, cryptocurrency, data science, deep learning, disintermediation, diversified portfolio, Dutch auction, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fiat currency, Firefox, Flash crash, full employment, future of work, gamification, Garrett Hardin, gentrification, gig economy, Gini coefficient, global supply chain, global village, Google bus, Howard Rheingold, IBM and the Holocaust, impulse control, income inequality, independent contractor, index fund, iterative process, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joseph Schumpeter, Kickstarter, Large Hadron Collider, loss aversion, low interest rates, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, market fundamentalism, Marshall McLuhan, means of production, medical bankruptcy, minimum viable product, Mitch Kapor, Naomi Klein, Network effects, new economy, Norbert Wiener, Oculus Rift, passive investing, payday loans, peer-to-peer lending, Peter Thiel, post-industrial society, power law, profit motive, quantitative easing, race to the bottom, recommendation engine, reserve currency, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Reagan, Russell Brand, Satoshi Nakamoto, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Snapchat, social graph, software patent, Steve Jobs, stock buybacks, TaskRabbit, the Cathedral and the Bazaar, The Future of Employment, the long tail, trade route, Tragedy of the Commons, transportation-network company, Turing test, Uber and Lyft, Uber for X, uber lyft, unpaid internship, Vitalik Buterin, warehouse robotics, Wayback Machine, Y Combinator, young professional, zero-sum game, Zipcar

Some business futurists envision digital workers succeeding by working on the Internet itself.* Just as Wikipedia marshaled the talents of thousands of individual people working online to create an encyclopedia, corporations are beginning to use what are called crowdsourcing platforms to engage online workers in a multitude of freelance tasks. Again, such opportunities are touted by their proponents as part of the digital revolution. The CEO of the CrowdFlower crowdsourcing platform, Lukas Biewald, explains that these platforms are “bringing opportunities to people who never would have had them before, and we operate in a truly egalitarian fashion, where anyone who wants to can do microtasks, no matter their gender, nationality, or socio-economic status, and can do so in a way that is entirely of their choosing and unique to them.”40 Crowdsourcing platforms, such as Amazon Mechanical Turk, pay people to perform tiny, repetitive tasks that computers just can’t handle yet.

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The CEO of the CrowdFlower crowdsourcing platform, Lukas Biewald, explains that these platforms are “bringing opportunities to people who never would have had them before, and we operate in a truly egalitarian fashion, where anyone who wants to can do microtasks, no matter their gender, nationality, or socio-economic status, and can do so in a way that is entirely of their choosing and unique to them.”40 Crowdsourcing platforms, such as Amazon Mechanical Turk, pay people to perform tiny, repetitive tasks that computers just can’t handle yet. Workers log into one of the platforms from home or an Internet café and then choose from a series of tasks on offer. They might be paid three cents each time they identify the subject of a photo, transcribe a sentence from a video lecture, or list the items in a scanned receipt. These are invariably mundane tasks—the sorts of data entry that wouldn’t even exist were so many business processes not already tied to computer databases, and ones that will certainly be carried out by computers themselves sooner than later.

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For employers, it’s a perfect realization of the industrial ideal: anyone can request work, do so anonymously, never meet the employee, and reject the results without ever paying. The labor force isn’t simply replaceable; it’s in constant flux, perpetually changing and responsible for its own training and care. As digital labor scholar and activist Trebor Scholz has pointed out,41 in crowdsourcing there’s no minimum wage, no labor regulation, no governmental jurisdiction. Although 18 percent of workers on Amazon Mechanical Turks are full-time laborers, most of them make less than two dollars an hour. Amazon argues that the platform is all about choice and empowerment, that workers can “vote with their feet” against bad labor practices.

Places of the Heart: The Psychogeography of Everyday Life by Colin Ellard

Apollo 11, augmented reality, Benoit Mandelbrot, Berlin Wall, Broken windows theory, Buckminster Fuller, carbon footprint, classic study, cognitive load, commoditize, crowdsourcing, data science, Dunbar number, Frank Gehry, gentrification, Google Glasses, Guggenheim Bilbao, haute couture, Howard Rheingold, Internet of things, Jaron Lanier, Lewis Mumford, mandelbrot fractal, Marshall McLuhan, Masdar, mass immigration, megastructure, mirror neurons, Mondo 2000, more computing power than Apollo, Oculus Rift, overview effect, Peter Eisenman, RFID, Richard Florida, risk tolerance, sentiment analysis, Skinner box, smart cities, starchitect, TED Talk, the built environment, theory of mind, time dilation, urban decay, urban planning, urban sprawl, Victor Gruen

If imaginative content is to help to engage us with space rather than turn us away from it, then it must be somehow embedded in our everyday uses of space and technology. At Yahoo Labs in Barcelona, Daniele Quercia’s group has developed a GPS application that can find routes that are not based solely on a shortest-distance-to-destination algorithm, but instead on a series of aesthetic variables.10 To build this application, Quercia’s group first collected crowd-sourced data on the aesthetic values of urban viewpoints. To do this, they simply made available large numbers of images of urban locations and invited participants to rate the images for beauty, happiness, and quietness. Based on this feedback, the group went on to try to define the visual properties of urban scenes that were most likely to elicit strong impressions of those three aesthetic properties among viewers, and they applied their findings to an even larger set of images from the photo-sharing application Flickr.

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Second, many applications will allow you to contribute your own thoughts and feelings to the accumulated database of location-coded information. If you eat at a restaurant and enjoy yourself (or otherwise), you can rate the experience, write some words, and even contribute a photograph of your meal if you are so inclined. By donating information to a crowd-sourced repository of reviews, you are adding great value to the data that are generally available to users of these applications, the vast majority of which are provided free. Again, it seems as though one might have to have a severely dyspeptic disposition to find fault with a tool that allows the user access to such a treasure trove of valuable information at no cost other than the price of a phone and a data plan.

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Though such data may be messy and lack the clinical archival quality that can be acquired using formal experiments of the kind that I’ve described in this book, they will constitute useful starting points for the analysis of existing patterns, debate, and dialog with the governing bodies that dictate what can be built and where it can go. Such crowd-sourced, grassroots efforts have the great advantage that they help to make citizens themselves active participants in the processes that eventually lead to built designs. None of this is to suggest that making ordinary citizens active participants in the processes that lead to new buildings will be an easy thing—anyone who has attended a city council meeting to voice an opinion about a proposed development will know that to suggest that stakeholders are always poised to listen to the public is the attitude of a naïve Pollyanna.

Free Ride by Robert Levine

A Declaration of the Independence of Cyberspace, Anne Wojcicki, book scanning, borderless world, Buckminster Fuller, citizen journalism, commoditize, company town, correlation does not imply causation, creative destruction, crowdsourcing, death of newspapers, Edward Lloyd's coffeehouse, Electric Kool-Aid Acid Test, Firefox, future of journalism, Googley, Hacker Ethic, informal economy, Jaron Lanier, John Gilmore, John Perry Barlow, Joi Ito, Julian Assange, Justin.tv, Kevin Kelly, linear programming, Marc Andreessen, Mitch Kapor, moral panic, offshore financial centre, pets.com, publish or perish, race to the bottom, Saturday Night Live, Silicon Valley, Silicon Valley startup, Skype, spectrum auction, Steve Jobs, Steven Levy, Stewart Brand, subscription business, Telecommunications Act of 1996, the long tail, Whole Earth Catalog, WikiLeaks

The main idea behind much of this thinking is that there are other ways to create the kind of artistic work we enjoy now without laws to protect it, either by funding it in ways that don’t involve the media business or by using the reach of the Internet to harness volunteerism. Could art be “crowdsourced,” created by scattered people working together online, much as Wikipedia is? While online collaboration has intriguing possibilities, it seems to work better as a way of creating tools—like software or encyclopedias—rather than art. As ambitious as it is, Wikipedia depends on facts that can be sourced from other expensively produced publications, and experiments with crowdsourced original reporting have been underwhelming compared with professional work. A system based on these arrangements would also create a new generation of media gatekeepers.

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Due to bad decisions that had nothing to do with technology, many newspaper chains were saddled with enormous debt run up by parent companies that overspent to buy publications and gain market share. And they faced a post-boomer generation of readers who have not picked up the habits of paying for news or reading it on a printed page. Conventional wisdom says that newspaper journalism must be reinvented for a changing world—blogged, Twittered, or somehow crowdsourced. It’s a tempting prescription—innovative, optimistic, and forward-looking—but it doesn’t get to the heart of the problem. Despite complaints about the media, newspaper journalism now reaches a larger audience than ever before, both directly and on various sites that summarize it. How dissatisfied can readers really be?

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As the media executive and news business blogger Alan Mutter pointed out, U.S. newspapers now spend $4.4 billion a year on reporting, while nonprofit journalism institutions raised only $144 million over the last four.42 “The finding about that in general is that the content is great and the funding model is very unstable,” says Nicholas Lemann, dean of the Columbia University Graduate School of Journalism. “Then there are these experiments in crowdsourcing and other forms of social production, and my view is that they haven’t really delivered the goods.” Although nonprofit groups like the Knight Foundation have become enamored with citizen journalism projects, their track record has been uneven at best. A 2010 study by the Pew Research Center’s Project for Excellence in Journalism that examined the news “ecosystem” of Baltimore over the course of a week found that traditional media produced 95 percent of the stories with new information and that newspapers were responsible for most of them.43 (It also found that 80 percent of all stories contained no new information at all, which is damning for old and new media alike.)

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future by Kevin Kelly

A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, Albert Einstein, Alvin Toffler, Amazon Web Services, augmented reality, bank run, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, book scanning, Brewster Kahle, Burning Man, cloud computing, commoditize, computer age, Computer Lib, connected car, crowdsourcing, dark matter, data science, deep learning, DeepMind, dematerialisation, Downton Abbey, driverless car, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, Gabriella Coleman, game design, Geoffrey Hinton, Google Glasses, hive mind, Howard Rheingold, index card, indoor plumbing, industrial robot, Internet Archive, Internet of things, invention of movable type, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Perry Barlow, Kevin Kelly, Kickstarter, lifelogging, linked data, Lyft, M-Pesa, machine readable, machine translation, Marc Andreessen, Marshall McLuhan, Mary Meeker, means of production, megacity, Minecraft, Mitch Kapor, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, off-the-grid, old-boy network, peer-to-peer, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, Project Xanadu, recommendation engine, RFID, ride hailing / ride sharing, robo advisor, Rodney Brooks, self-driving car, sharing economy, Silicon Valley, slashdot, Snapchat, social graph, social web, software is eating the world, speech recognition, Stephen Hawking, Steven Levy, Ted Nelson, TED Talk, The future is already here, the long tail, the scientific method, transport as a service, two-sided market, Uber for X, uber lyft, value engineering, Watson beat the top human players on Jeopardy!, WeWork, Whole Earth Review, Yochai Benkler, yottabyte, zero-sum game

As of 2015, these two largest peer-to-peer lending companies have facilitated more than 200,000 loans worth more than $10 billion. Innovation itself can be crowdsourced. The Fortune 500 company General Electric was concerned that its own engineers could not keep up with the rapid pace of invention around them, so it launched the platform Quirky. Anyone could submit online an idea for a great new GE product. Once a week, the GE staff voted on the best idea that week and would set to work making it real. If an idea became a product, it would earn money for the idea maker. To date GE has launched over 400 new products from this crowdsourced method. One example is the Egg Minder, an egg holder in your refrigerator that sends you a text when it’s time to reorder your eggs.

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But by far the most potent future role for crowdsharing is in fan base equity. Rather than invest into a product, supporters invest into a company. The idea is to allow fans of a company to purchase shares in the company. This is exactly what you do when you buy shares of stock on the stock market. You are part of a crowdsourced ownership. Each of your shares is some tiny fraction of the whole enterprise, and the collected money raised by public shares is used to grow the business. Ideally, the company is raising money from its own customers, although in reality big pension and hedge funds are the bulk buyers. Heavy regulation and intense government oversight of public companies offer some guarantee to the average stock buyer, making it so anyone with a bank account can buy stock.

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One example is the Egg Minder, an egg holder in your refrigerator that sends you a text when it’s time to reorder your eggs. Another popular version of crowdsourcing appears, at first, to be less about collaboration and more about competition. A commercial need prompts a contest for the best solution. A company offers a payment prize to the best solution selected among a crowd of entrants. For instance, Netflix announced an award of $1 million to the programmers who could invent an algorithm that recommended movies 10 percent better than the algorithm they had. Forty thousand groups submitted very good solutions that improved the performance, but only one team achieved the goal and won the prize.

Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, circular economy, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, digital twin, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, fail fast, friendly AI, fulfillment center, future of work, Geoffrey Hinton, Hans Moravec, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, machine translation, Marc Benioff, natural language processing, Neal Stephenson, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Salesforce, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, Snow Crash, software as a service, speech recognition, tacit knowledge, telepresence, telepresence robot, text mining, the scientific method, uber lyft, warehouse automation, warehouse robotics

One goal is that the robots will make certain rudimentary decisions—interrupting one job to complete a more crucial task, and then returning to the original job—just as a human worker would. AI training doesn’t necessarily have to be done in-house. Like payroll, IT, and other functions, the training of AI systems can be crowdsourced or outsourced. One such third-party crowdsourcer called Mighty AI ingeniously uses crowdsourcing techniques to help train systems in vision recognition (for example, identifying lakes, mountains, and roads from photographs) and natural-language processing. The company has amassed copious amounts of training data that it can then deploy for different clients.

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The first online maps were largely just a digital version of their paper counterparts. But soon, GPS navigation devices changed how we used maps, giving us directions after entering a destination. Even that, though, was still a fairly static process. Now, mobile map apps like Waze are taking advantage of real-time user data—about drivers’ locations and speeds as well as crowd-sourced information about traffic jams, accidents, and other obstructions to create the perfect map in real time. All that data enables the system to update directions en route so that, if necessary, it can reroute drivers midcourse to minimize any possible delays. Whereas the old approach with GPS simply digitized a static paper-map route, Waze has combined AI algorithms and real-time data to create living, dynamic, optimized maps that can get people to their destinations as quickly as possible.

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., 192–193 Cefkin, Melissa, 113–114 Celetano, Domenick, 91 change pace of, 184–185 self-adaptive, 155–156 chatbots, 55–56, 91, 145–146 guardrails with, 168–169 job search, 198–199 training, 117 Cigna, 188 Cincinnati Children’s Hospital, 82 Clara, 196 Clark, Scott, 77 clinical trials, 80, 82 Cloudbreak, 28 cobots (collaborative robotics), 65, 148–150 Coca-Cola, 85–86, 98, 100 Colette, 146 collaboration, 1–2, 7–9, 10, 107 AI “winters” in, 25, 41 effectiveness of, 148–150 ergonomics and, 149–150 fusion skills for, 183–206 power of, 51–52 process reimagination and, 50–52 responsible normalizing of, 12, 189–191 with robotic arms, 21–23 commerce, integrated, 87–90 complaint processes, 47–48 compliance, 172 computer vision, 151 in Amazon Go, 161–165 definition of, 64 in mannequins, 89 context designers, 128 Cortana, 11, 96–97, 118–119, 145–146, 201 Coyne, Allie, 28 Crawford, Esther, 199 credit risk analysis, 123–124 crowdsourcing, 120–121 culture blended human/machine, 166–174 of experimentation, 161–165, 204–205 customer relationship management (CRM), 85–86 customer service, 85–101 brands and, 87 integrated commerce and, 87–90 natural-language assistants in, 55–56 customization. See personalization cybersecurity, 56–58, 59 Darktrace, 58 DARPA Cyber Grand Challenges, 57, 190 Dartmouth College conference, 40–41 dashboards, 169 data, 10 in AI training, 121–122 barriers to flow of, 176–177 customization and, 78–80 discovery with, 178 dynamic, real-time, 175–176 in enterprise processes, 59 exhaust, 15 in factories, 26–27, 29–30 leadership and, 180 in manufacturing, 38–39 in marketing and sales, 92, 98–99, 100 in R&D, 69–72 in reimagining processes, 154 on supply chains, 33–34 supply chains for, 12, 15 velocity of, 177–178 data hygienists, 121–122 data supply-chain officers, 179 data supply chains, 12, 15, 174–179 decision making, 109–110 about brands, 93–94 black box, 106, 125, 169 employee power to modify AI, 172–174 empowerment for, 15 explainers and, 123–126 transparency in, 213 Deep Armor, 58 deep learning, 63, 161–165 deep-learning algorithms, 125 DeepMind, 121 deep neural networks (DNN), 63 deep reinforcement learning, 21–22 demand planning, 33–34 Dennis, Jamie, 158 design at Airbus, 144 AI system, 128–129 Elbo Chair, 135–137 generative, 135–137, 139, 141 product/service, 74–77 Dickey, Roger, 52–54 digital twins, 10 at GE, 27, 29–30, 183–184, 194 disintermediation, brand, 94–95 distributed learning, 22 distribution, 19–39 Ditto Labs, 98 diversity, 52 Doctors Without Borders, 151 DoubleClick Search, 99 Dreamcatcher, 136–137, 141, 144 drones, 28, 150–151 drug interactions, 72–74 Ducati, 175 Echo, 92, 164–165 Echo Voyager, 28 Einstein, 85–86, 196 Elbo Chair, 136–137, 139 “Elephants Don’t Play Chess” (Brooks), 24 Elish, Madeleine Clare, 170–171 Ella, 198–199 embodied intelligence, 206 embodiment, 107, 139–140 in factories, 21–23 of intelligence, 206 interaction agents, 146–151 jobs with, 147–151 See also augmentation; missing middle empathy engines for health care, 97 training, 117–118, 132 employees agency of, 15, 172–174 amplification of, 138–139, 141–143 development of, 14 hiring, 51–52 job satisfaction in, 46–47 marketing and sales, 90, 92, 100–101 on-demand work and, 111 rehumanizing time and, 186–189 routine/repetitive work and, 26–27, 29–30, 46–47 training/retraining, 15 warehouse, 31–33 empowerment, 137 bot-based, 12, 195–196 in decision making, 15 of salespeople, 90, 92 workforce implications of, 137–138 enabling, 7 enterprise processes, 45–66 compliance, 47–48 determining which to change, 52–54 hiring and recruitment, 51–52 how much to change, 54–56 redefining industries with, 56–58 reimagining around people, 58–59 robotic process automation (RPA) in, 50–52 routine/repetitive, 46–47 ergonomics, 149–150 EstherBot, 199 ethical, moral, legal issues, 14–15, 108 Amazon Echo and, 164–165 explainers and, 123–126 in marketing and sales, 90, 100 moral crumple zones and, 169–172 privacy, 90 in R&D, 83 in research, 78–79 ethics compliance managers, 79, 129–130, 132–133 European Union, 124 Ewing, Robyn, 119 exhaust data, 15 definition of, 122 experimentation, 12, 14 cultures of, 161–165 in enterprise processes, 59 leadership and, 180 learning from, 71 in manufacturing, 39 in marketing and sales, 100 in process reimagining, 160–165 in R&D, 83 in reimagining processes, 154 testing and, 74–77 expert systems, 25, 41 definition of, 64 explainability strategists, 126 explaining outcomes, 107, 114–115, 179 black-box concerns and, 106, 125, 169 jobs in, 122–126 sustaining and, 130 See also missing middle extended intelligence, 206 extended reality, 66 Facebook, 78, 79, 95, 177–178 facial recognition, 65, 90 factories, 10 data flow in, 26–27, 29–30 embodiment in, 140 job losses and gains in, 19, 20 robotic arms in, 21–26 self-aware, 19–39 supply chains and, 33–34 third wave in, 38–39 traditional assembly lines and, 1–2, 4 warehouse management and, 30–33 failure, learning from, 71 fairness, 129–130 falling rule list algorithms, 124–125 Fanuc, 21–22, 128 feedback, 171–172 feedforward neural networks (FNN), 63 Feigenbaum, Ed, 41 financial trading, 167 first wave of business transformation, 5 Fletcher, Seth, 49 food production, 34–37 ForAllSecure, 57 forecasts, 33–34 Fortescue Metals Group, 28 Fraunhofer Institute of Material Flow and Logistics (IML), 26 fusion skills, 12, 181, 183–206, 210 bot-based empowerment, 12, 195–196 developing, 15–16 holistic melding, 12, 197, 200–201 intelligent interrogation, 12, 185, 193–195 judgment integration, 12, 191–193 potential of, 209 reciprocal apprenticing, 12, 201–202 rehumanizing time, 12, 186–189 relentless reimagining, 12, 203–205 responsible normalizing, 12, 189–191 training/retraining for, 211–213 Future of Work survey, 184–185 Garage, Capital One, 205 Gaudin, Sharon, 99 GE.

Tomorrow's Lawyers: An Introduction to Your Future by Richard Susskind

business intelligence, business process, business process outsourcing, call centre, Clayton Christensen, cloud computing, commoditize, crowdsourcing, data science, disruptive innovation, global supply chain, information retrieval, invention of the wheel, power law, pre–internet, Ray Kurzweil, Silicon Valley, Skype, speech recognition, supply-chain management, telepresence, Watson beat the top human players on Jeopardy!

Many tasks do not require the expertise and cost of qualified lawyers and can be taken on by other skilled and knowledgeable individuals within the legal sector. TABLE 4.3. Sources of legal service * * * in-sourcing de-lawyering relocating off-shoring outsourcing subcontracting co-sourcing near-shoring leasing home-sourcing open-sourcing crowd-sourcing computerizing solo-sourcing no-sourcing * * * Relocating involves an organization moving some of its legal work to less costly locations, but still within countries in which the main business already has a presence. An illustration here is the US-based international law firm Orrick, which has a global operations centre based in Wheeling, West Virginia.

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Open-sourcing is the provision, at no charge, of all sorts of legal materials (standard documents, guidelines, procedures, opinions, case studies, practical experience, and more) on publicly accessible websites. This is likely to be most effective if organized in the form of a wiki (an online resource that any users can edit and add to). Crowd-sourcing involves harnessing the collective talents of large groups of individuals who make some of their time available to undertake certain categories of legal task. On one approach, for instance, a legal problem might be broadcast to a large, unknown group of volunteers. And these volunteers—the crowd—respond with their proposed legal solutions.

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INDEX AI-based problem-solving, 49 Access to justice dispute resolution, 85–86 ignorance of the law, 85 online legal services, 87–91 plight of non-lawyers, 86–87 unresolved problems, 84–85 Woolf reforms, 85 Accountancy firms, 122–123 Advocacy, 58–59 Alternative fee arrangements hourly billing, 15–17 strategies for success, 18–19 Apprenticeship model, 143 Banks co-sourcing, 70 collaboration strategy, 21–22 new range of employers, 126–127 Benevolent custodians, 163–164 Bespoke legal work, 23–26 Big data, 48–49 Businesses see Law firms Clementi Report (2004), 6–7 Closed legal communities, 45 Co-sourcing, 35, 69–70 Collaboration closed legal communities, 45 in-house legal departments, 70–71 strategies for success, 5, 21–22 Commoditization of legal work bespoke legal work compared, 23–24 five stage evolution, 25–28 importance, 5 open-sourcing, 44 outcome of efficiency strategy, 20 Communities of legal experience, 89 Computer-assisted transcripts (CAT), 97 Computerizing, 37 Consumer branding, 7–8 Consumer choice, 6 Courts see also Judges computer-assisted transcripts (CAT), 97 document display systems, 98 e-filing, 97 electronic presentation of evidence (EPE), 98 need for improvement, 96–97 virtual courts fair trial concerns, 102–105 meaning, 99–100 online dispute resolution (ODR), 100–102 Crowd-sourcing, 36–37 Cybersettle, 101 De-lawyering, 33–34 Decomposing of work applicability, 30 document review, 31 legal process analysts, 114 litigation, 31 move to efficiency, 29–30 outcome of efficiency strategy, 21 project management, 31–32 sourcing of work, 37–38 transactions, 33 Denial stage, 77–79 Disruption stage, 81–83 Disruptive technologies AI-based problem-solving, 49 big data, 48–49 closed legal communities, 45 document assembly, 41 e-learning, 43 electronic legal marketplace, 42–43 embedded legal knowledge, 46–47 intelligent legal search, 47–48 online dispute resolution (ODR), 47 online legal guidance, 43–44 online legal services, 91 open-sourcing, 44–45 overview, 39–41 relentless connectivity, 42 timescale for change, 82 workflow and project management, 46 Documents court display systems, 98 decomposing of work, 31 disruptive technologies, 41 evolution of legal services, 27 in-house knowledge management, 64–65 intelligent legal search, 47–48 Drivers of change information technology claims to be over-hyped, 10–11 emerging new applications, 12–13 growth in processing power, 11–12 Moore’s Law, 10–11 need to connect social use and working lives, 13–14 pervasive influence, 10 liberalization Clementi Report (2004), 6–7 consumer branding, 7 law as a reserved business, 5–6 need for consumer choice, 6 new entrepreneurial spirit, 7–9 new ownership rules, 7 ripple effect around the work, 9–10 more-for-less challenge, 4–5 overview, 3 E-filing, 97 E-learning disruptive technologies, 43 new form of training, 143–144 online lectures, 144–145 simulation-based training, 145–146 E-working courts, 94–97 judiciary, 94 Efficiency decomposing of work, 29–30 key question for future of law firms, 53–54 strategies for success, 19–21 Electronic presentation of evidence (EPE), 98 Electronic legal marketplace, 42–43 Embedded legal knowledge, 46–47 Enhanced practitioners, 110–111 Entrepreneurial spirit, 7–9 Expert trusted advisers, 109–110 Fair trial, 102–105 Fees alternative fee arrangements ‘hourly billing’, 15–17 differentiation of law firms, 66–67 key question for future of law firms, 53 key questions for junior partners, 59–61 more-for-less challenge, 4–5 strategies for success alternative fee arrangements, 18–19 charging less, 15–18 Firms see Law firms General Counsel collaboration approach, 70–71 future strategies, 72 more-for-less challenge, 68–70 risk management, 62–64 Global elites emerging cadre, 5–56 need for alternative structures, 56–57 return of accountancy firms, 122–123 High street retailers, 126–127 Holes or power drills?

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives by Peter H. Diamandis, Steven Kotler

Ada Lovelace, additive manufacturing, Airbnb, Albert Einstein, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, barriers to entry, Big Tech, biodiversity loss, bitcoin, blockchain, blood diamond, Boston Dynamics, Burning Man, call centre, cashless society, Charles Babbage, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, CRISPR, crowdsourcing, cryptocurrency, data science, Dean Kamen, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital twin, disruptive innovation, Donald Shoup, driverless car, Easter island, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, fake news, food miles, Ford Model T, fulfillment center, game design, Geoffrey West, Santa Fe Institute, gig economy, gigafactory, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, impact investing, indoor plumbing, industrial robot, informal economy, initial coin offering, intentional community, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the telegraph, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kickstarter, Kiva Systems, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, Masayoshi Son, mass immigration, megacity, meta-analysis, microbiome, microdosing, mobile money, multiplanetary species, Narrative Science, natural language processing, Neal Stephenson, Neil Armstrong, Network effects, new economy, New Urbanism, Nick Bostrom, Oculus Rift, One Laptop per Child (OLPC), out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, planned obsolescence, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, robo advisor, Satoshi Nakamoto, Second Machine Age, self-driving car, Sidewalk Labs, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, SoftBank, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, tech billionaire, technoutopianism, TED Talk, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Vision Fund, VTOL, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

Kickstarter: See: https://www.kickstarter.com/help/stats. Pebble Time: John McDermott, “Pebble ‘Smartwatch’ Funding Soars on Kickstarter,” Inc., April 20, 2012. See: https://www.inc.com/john-mcdermott/pebble-smartwatch-funding-sets-kickstarter-record.html. $300 billion: Massolution/Crowdsourcing.org, 2015 CF Crowdfunding Industry Report. See: http://reports.crowdsourcing.org/index.php?route=product/product&product_id=54. crowdfunding as “potentially the most disruptive of all the new models of finance.”: The Future of Finance, the Socialization of Finance (Goldman Sachs Report, March 2015). See: https://www.planet-fintech.com/downloads/The-future-of-Finance-the-Socialization-of-Finance-Golman-Sachs-march-2015_t18796.html

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upcoming thriller Morgan: “BM Creates First Movie Trailer by AI [HD] | 20th Century FOX,” August 31, 2016. See: https://www.youtube.com/watch?v=gJEzuYynaiw. an AI that creates Choose Your Own Adventure–style stories for video games: Matthew Guzdial, “Crowdsourcing Open Interactive Narrative.” See: https://www.cc.gatech.edu/~riedl/pubs/guzdial-fdg15.pdf. See also: “Artificial Intelligence System for Crowdsourcing Interactive Fiction” (video), September 1, 2016. See: https://www.youtube.com/watch?time_continue=1&v=znqw17aOrCs. From Passive to Active Video games with user-generated gameplay content: “Category: Video Games with User-Generated Gameplay Content,” Wikipedia.

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It’s a smart contract because it executes itself, without need for human involvement. And it’s for all of these reasons that the tech is exploding. As of 2018, major financial firms like J.P. Morgan, Goldman Sachs, and Bank of America are rolling out crypto-strategies at scale. Initial coin offerings, or ICOs, are blockchain’s version of crowdsourcing (which we’ll explore in depth in Chapter Four) are also exploding, with a market value of almost $10 billion as of 2018. In total, what began less than a decade ago with the sale of two pizzas, will, according to Gartner, Inc., grow to $176 billion by 2025, and could exceed $3.1 trillion in 2030.

What's Mine Is Yours: How Collaborative Consumption Is Changing the Way We Live by Rachel Botsman, Roo Rogers

"World Economic Forum" Davos, Abraham Maslow, Airbnb, Apollo 13, barriers to entry, behavioural economics, Bernie Madoff, bike sharing, Buckminster Fuller, business logic, buy and hold, carbon footprint, Cass Sunstein, collaborative consumption, collaborative economy, commoditize, Community Supported Agriculture, credit crunch, crowdsourcing, dematerialisation, disintermediation, en.wikipedia.org, experimental economics, Ford Model T, Garrett Hardin, George Akerlof, global village, hedonic treadmill, Hugh Fearnley-Whittingstall, information retrieval, intentional community, iterative process, Kevin Kelly, Kickstarter, late fees, Mark Zuckerberg, market design, Menlo Park, Network effects, new economy, new new economy, out of africa, Paradox of Choice, Parkinson's law, peer-to-peer, peer-to-peer lending, peer-to-peer rental, planned obsolescence, Ponzi scheme, pre–internet, public intellectual, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Robert Shiller, Ronald Coase, Search for Extraterrestrial Intelligence, SETI@home, Simon Kuznets, Skype, slashdot, smart grid, South of Market, San Francisco, Stewart Brand, systems thinking, TED Talk, the long tail, The Nature of the Firm, The Spirit Level, the strength of weak ties, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thorstein Veblen, Torches of Freedom, Tragedy of the Commons, transaction costs, traveling salesman, ultimatum game, Victor Gruen, web of trust, women in the workforce, work culture , Yochai Benkler, Zipcar

Open, collaborative projects appealed to a participant’s need for individuality and autonomy and at the same time provided a sense of belonging, a community. The collective power of physically dispersed yet virtually connected individuals only became stronger and more apparent through the 2000s. Crowdsourcing, a concept coined by Jeff Howe as the “act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call,” became an important business idea. It is now a well-documented phenomenon applied to the creation of collective repositories of content (Wikipedia), products (the T-shirt company Threadless, where all the designs are created by the user community), new business ideas (Procter & Gamble’s Connect & Develop), and even problems such as climate change (MIT’s Climate Collaboratorium).

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It is now a well-documented phenomenon applied to the creation of collective repositories of content (Wikipedia), products (the T-shirt company Threadless, where all the designs are created by the user community), new business ideas (Procter & Gamble’s Connect & Develop), and even problems such as climate change (MIT’s Climate Collaboratorium). What the success of crowdsourcing has shown is that as people move from hyper-individualistic consumer behaviors, a “me mind-set,” to a “we mind-set,” an empowering dynamic emerges. Specifically, online networks bring people together again, making them more willing to leverage the old rule of thumb: There is power in numbers. And impacts of online sharing and collaboration are not confined to the virtual world.

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More than 2.5 million people around the world respond to some kind of Meetup invitation every month and more than two thousand local groups, from stay-at-home moms to small business owners to walking clubs, get together face-to-face each day.42 “We are using the Internet to get off the Internet and form a twenty-first-century civil society,” Heiferman commented.43 In 2006, a couple of years after leaving BBH, Gallop found herself thinking, “How could I take all the good intentions most of us have on a daily basis, our single biggest pool of untapped natural resource, and transform them into shared actions?” Like Chris Hughes on Obama’s campaign, Gallop also knew she had to make her initiative fun and avoid the “yawn factor” by adding a healthy dose of what she refers to as “competitive collaboration.” She launched IfWeRanTheWorld.com early in 2010. It’s essentially a crowdsourcing project based on the similar principles of microfunding sites such as Kiva or Kickstarter. People are motivated to do big things by taking all easy steps, microactions that in Gallop’s words “can bring about great leaps.” When you arrive at the site, you are asked to complete the statement, “If I ran the world, I would___.”

Going Dark: The Secret Social Lives of Extremists by Julia Ebner

23andMe, 4chan, Airbnb, anti-communist, anti-globalists, augmented reality, Ayatollah Khomeini, Bellingcat, Big Tech, bitcoin, blockchain, Boris Johnson, Cambridge Analytica, citizen journalism, cognitive dissonance, Comet Ping Pong, crisis actor, crowdsourcing, cryptocurrency, deepfake, disinformation, Donald Trump, Dunning–Kruger effect, Elon Musk, fake news, false flag, feminist movement, game design, gamification, glass ceiling, Google Earth, Greta Thunberg, information security, job satisfaction, Mark Zuckerberg, mass immigration, Menlo Park, Mikhail Gorbachev, Network effects, off grid, OpenAI, Overton Window, pattern recognition, pre–internet, QAnon, RAND corporation, ransomware, rising living standards, self-driving car, Silicon Valley, Skype, Snapchat, social intelligence, Social Justice Warrior, SQL injection, Steve Bannon, Steve Jobs, Transnistria, WikiLeaks, zero day

Against this background, it is unsurprising that American white nationalist Richard Spencer labelled bitcoin the ‘currency of the alt-right’ long before the bitcoin craze started. After prominent alt-right figures were banned from mainstream crowdsourcing platforms such as Patreon and GoFundMe, and blocked by online payment providers such as PayPal, Apple Pay and Google Pay, some switched to Hatreon. The alternative crowdsourcing platform was used to fund anti-democratic projects such as the maintenance of the world’s biggest neo-Nazi platforms Daily Stormer and Stormfront and hacking activities of the white supremacist Weev (see pp. 217–26).

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After I got out of prison on a costly appeal that left my life in shambles, I fled the United States as a political refugee and became a crowdfunded blogger dedicated to racial issues.15 Then the shutoffs started, as he would say.16 In 2014 his became the first account ever to be suspended from the crowdsourcing platform Patreon. Soon afterwards his Gratipay was also shut down. He claims he lost his bank accounts as well as his PayPal and dozens of brokerages. ‘They did their best to keep me away from any source of income,’ he would say.17 In one of his livestream AMAs (Ask Me Anything) that I joined, he told us that he was ‘the world’s most censored man’. Wherever the hacker was, he kept finding new ways to continue his stunts and to receive funds. For a while Weev fundraised on the alt-right crowdsourcing site Hatreon, advertising his activities as ‘fascist polemic, trolls, hacks, poetry reading, lulz’.

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‘Hope not Hate, what’s that?’ I repeat stupidly. Hope not Hate is the UK’s most prominent anti-racist organisation and prevented the GI team from carrying out their ‘Defend Europe’ mission in the summer of 2017. Their researchers revealed the criminal past of the C Star’s owner and convinced the crowd-sourcing platform Patreon to block the payment services of the white nationalist activists.17 ‘All right, you know what? Let’s go to their office – it’s just a ten-minute walk from here,’ Thomas says. ‘No!’ I gasp. I bite my tongue so hard that I can feel it start bleeding. ‘I mean, I think that’s a bad idea.

WikiLeaks and the Age of Transparency by Micah L. Sifry

1960s counterculture, Amazon Web Services, Andy Carvin, banking crisis, barriers to entry, Bernie Sanders, Buckminster Fuller, Chelsea Manning, citizen journalism, Climategate, crowdsourcing, digital divide, digital rights, Evgeny Morozov, Gabriella Coleman, Google Earth, Howard Rheingold, Internet Archive, Jacob Appelbaum, John Markoff, John Perry Barlow, Julian Assange, Network effects, RAND corporation, school vouchers, Skype, social web, source of truth, Stewart Brand, the Cathedral and the Bazaar, web application, WikiLeaks, Yochai Benkler

It’s also worth noting that press credentialing for citizen journalists and bulk access to congressional floor and committee video were two recommendations that the project made the least progress on. 6 Micah L. Sifry, “Obama as Crowdsourcer; Organizing the Country for Change and Accountability,” techPresident.com, February 8, 2009, http://techpresident.com/blog-entry/obama-crowdsourcer-organizingcountry-change-and-accountability. 7 Clint Hendler, “Obama on Recovery.gov,” Columbia Journalism Review, February 9, 2009, www.cjr.org/the_kicker/obama_on_recoverygov.php. 8 Edward Luce and Tom Braithwaite, “US stimulus tsar to unleash 1m inspector-generals,” The Financial Times, August 20, 2009, www.ft.com/ cms/s/0/e731fd52-8db0-11de-93df-00144feabdc0.html#axzz1 AZfFLMQT. 9 Earl Devaney, “Chairman’s Corner,” Recovery.gov, March 22, 2010, www. recovery.gov/News/chairman/Pages/march222010.aspx. 10 Clay Johnson, “Recovery.gov: Stop with the Data Defense, Start with the Conversation,” March 30, 2010, http://sunlightlabs.com/blog/2010/ recoverygov-stop-data-defense-start-conversation. 11 Earl Devaney, “Chairman’s Corner,” Recovery.gov, October 27, 2010, www.recovery.gov/News/chairman/Pages/2010Oct27.aspx 197 WIKILEAKS AND THE AGE OF TRANSPARENCY 12 13 14 15 16 17 18 19 20 21 22 23 198 Becky Hogge, “Open Data Study,” Open Society Foundations Information Program, May 2010, www.soros.org/initiatives/information/focus/ communication/ar ticles_publications/publications/open-datastudy-20100519.

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Eric Raymond, one of the earliest evangelists for the open source software movement, famously wrote that the ethos of the first developer of Linux, Linus Torvalds, was “given enough eyeballs, all bugs are shallow.”11 In technical terms, this means that if you have enough early testers of your software, and you make the source code visible, the community as a whole can easily help spot problems and help solve them. The same notion has come to apply, again and again, to the transparency movement. If you make a problem visible, or put it out on the web in a form that lots of people can swarm around, often 75 WIKILEAKS AND THE AGE OF TRANSPARENCY a solution will be found. “Crowdsourcing” is the term often used to describe this process, though I think it’s somewhat of a misnomer. We aren’t outsourcing a job that used to belong to professionals (such as investigative journalists) and giving it to a crowd to do; we’re inviting lots of civic watchdogs to add their eyeballs and time to the process of making government more transparent and accountable.

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Transparency and participatory politics has run through a similarly checkered path in the United Kingdom in recent years. For a time, it appeared that the U.K.’s Labour government was inching close to embracing a much more people-driven vision of open government, akin to Obama’s “eyes and ears” notion of crowd-sourcing. First, in early 2007, the prime minister’s office (then under Tony Blair) hired mySociety.org to build a tool enabling the public to create and sign petitions to his office directly from the 10 Downing Street website.23 Millions of citizens swarmed in, and some of the top petitions forced the government to make actual policy changes, like dropping plans for a new vehicle tax.

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots by John Markoff

A Declaration of the Independence of Cyberspace, AI winter, airport security, Andy Rubin, Apollo 11, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Baxter: Rethink Robotics, Bill Atkinson, Bill Duvall, bioinformatics, Boston Dynamics, Brewster Kahle, Burning Man, call centre, cellular automata, Charles Babbage, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, cognitive load, collective bargaining, computer age, Computer Lib, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deep learning, DeepMind, deskilling, Do you want to sell sugared water for the rest of your life?, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, driverless car, dual-use technology, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, factory automation, Fairchild Semiconductor, Fillmore Auditorium, San Francisco, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, General Magic , Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, Hans Moravec, haute couture, Herbert Marcuse, hive mind, hype cycle, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Ivan Sutherland, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, Jeff Hawkins, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, Kaizen: continuous improvement, Kevin Kelly, Kiva Systems, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, military-industrial complex, Mitch Kapor, Mother of all demos, natural language processing, Neil Armstrong, new economy, Norbert Wiener, PageRank, PalmPilot, pattern recognition, Philippa Foot, pre–internet, RAND corporation, Ray Kurzweil, reality distortion field, Recombinant DNA, Richard Stallman, Robert Gordon, Robert Solow, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, Seymour Hersh, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Strategic Defense Initiative, strong AI, superintelligent machines, tech worker, technological singularity, Ted Nelson, TED Talk, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Tony Fadell, trolley problem, Turing test, Vannevar Bush, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

Horvitz is continuing to deepen the human-machine interaction by researching ways to couple machine learning and computerized decision-making with human intelligence. For example, his researchers have worked closely with the designers of the crowd-sourced citizen science tool called Galaxy Zoo, harnessing armies of human Web surfers to categorize images of galaxies. Crowd-sourced labor is becoming a significant resource in scientific research: professional scientists can enlist amateurs, who often need to do little more than play elaborate games that exploit human perception, in order to help scientists map tricky problems like protein folding.19 In a number of documented cases teams of human experts have exceeded the capability of some of the most powerful supercomputers.

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“The Lumiere project: The Origins and Science Behind Microsoft’s Office Assistant,” Robotics Zeitgeist, 2009, http://robotzeitgeist.com/2009/08/lumiere-project-origins-and-science.html. 18.Lexi Krock, “The Final Eight Minutes,” Nova, WGBH, October 17, 2006, http://www.pbs.org/wgbh/nova/space/final-eight-minutes.html. 19.Jessica Marshall, “Victory for Crowdsourced Biomolecule Design,” Nature, January 22, 2012, http://www.nature.com/news/victory-for-crowdsourced-biomolecule-design-1.9872. 20.Sherry Turkle, Alone Together: Why We Expect More from Technology and Less from Each Other (New York: Basic Books, 2011), 101. 21.Miriam Steffens, “Slaves That Are Reflections of Ourselves,” Sydney Morning Herald, November 19, 2012, http://www.smh.com.au/small-business/growing/slaves-that-are-reflections-of-ourselves-20121118-29k63.html. 22.Daniel Crevier, AI: The Tumultuous History of the Search for Artificial Intelligence (New York: Basic Books, 1993), 58. 23.Ken Jennings, “My Puny Human Brain,” Slate, February 16, 2011. 7|TO THE RESCUE 1.Stuart Nathan, “Marc Raibert of Boston Dynamics,” Engineer, February, 22, 2010, http://www.theengineer.co.uk/in-depth/interviews/marc-raibert-of-boston-dynamics/1001065.article#ixzz2pevPQoYI. 2.Public Information Office, “Fact Sheet on ‘Simon,’” Columbia University, May 18, 1950, http://www.blinkenlights.com/classiccmp/berkeley/simonfaq.html. 3.Ivan E.

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It is significant that Google is instrumental in changing the metaphor. In one sense the company began as the quintessential intelligence augmentation, or IA, company. The PageRank algorithm Larry Page developed to improve Internet search results essentially mined human intelligence by using the crowd-sourced accumulation of human decisions about valuable information sources. Google initially began by collecting and organizing human knowledge and then making it available to humans as part of a glorified Memex, the original global information retrieval system first proposed by Vannevar Bush in the Atlantic Monthly in 1945.11 As the company has evolved, however, it has started to push heavily toward systems that replace rather than extend humans.

You've Been Played: How Corporations, Governments, and Schools Use Games to Control Us All by Adrian Hon

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", 4chan, Adam Curtis, Adrian Hon, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Astronomia nova, augmented reality, barriers to entry, Bellingcat, Big Tech, bitcoin, bread and circuses, British Empire, buy and hold, call centre, computer vision, conceptual framework, contact tracing, coronavirus, corporate governance, COVID-19, crowdsourcing, cryptocurrency, David Graeber, David Sedaris, deep learning, delayed gratification, democratizing finance, deplatforming, disinformation, disintermediation, Dogecoin, electronic logging device, Elon Musk, en.wikipedia.org, Ethereum, fake news, fiat currency, Filter Bubble, Frederick Winslow Taylor, fulfillment center, Galaxy Zoo, game design, gamification, George Floyd, gig economy, GitHub removed activity streaks, Google Glasses, Hacker News, Hans Moravec, Ian Bogost, independent contractor, index fund, informal economy, Jeff Bezos, job automation, jobs below the API, Johannes Kepler, Kevin Kelly, Kevin Roose, Kickstarter, Kiva Systems, knowledge worker, Lewis Mumford, lifelogging, linked data, lockdown, longitudinal study, loss aversion, LuLaRoe, Lyft, Marshall McLuhan, megaproject, meme stock, meta-analysis, Minecraft, moral panic, multilevel marketing, non-fungible token, Ocado, Oculus Rift, One Laptop per Child (OLPC), orbital mechanics / astrodynamics, Parler "social media", passive income, payment for order flow, prisoner's dilemma, QAnon, QR code, quantitative trading / quantitative finance, r/findbostonbombers, replication crisis, ride hailing / ride sharing, Robinhood: mobile stock trading app, Ronald Coase, Rubik’s Cube, Salesforce, Satoshi Nakamoto, scientific management, shareholder value, sharing economy, short selling, short squeeze, Silicon Valley, SimCity, Skinner box, spinning jenny, Stanford marshmallow experiment, Steve Jobs, Stewart Brand, TED Talk, The Nature of the Firm, the scientific method, TikTok, Tragedy of the Commons, transaction costs, Twitter Arab Spring, Tyler Cowen, Uber and Lyft, uber lyft, urban planning, warehouse robotics, Whole Earth Catalog, why are manhole covers round?, workplace surveillance

Urgent Evoke—the World Bank’s project, funded to a tune of half a million dollars—had 19,324 registered and 4,693 active players, with only 223 managing to complete one small task for each of the ten weeks of the game.33 Success isn’t conferred by player numbers or even player engagement alone, but as Professor David Waddington in the Department of Education at Concordia University put it, “Many of [Evoke’s] attempts at social innovation, albeit well meaning, seem neither realistic nor well thought out,” such as the curiously circular idea to renew Buffalo, New York, by crowdsourcing ideas from its community. Perhaps Urgent Evoke inspired its players into social innovation by other means, but there is little evidence this actually happened. In retrospect, it’s perplexing that anyone believed the world’s problems could be solved by games that had between them attracted barely a thousand committed players.

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Players can choose to be either good or evil… but we don’t think that games should give players this choice… and this must be altered.”37 As with its social credit scores, the attention paid to China’s gamified propaganda has distracted from similar efforts elsewhere in the world. In the case of Urgent Evoke, one of the utopian gamification projects described earlier, the propaganda was delivered at arm’s length by the World Bank. David Waddington, professor in the Department of Education at Concordia University, argues the educational ARG’s goal of crowdsourcing “innovative solutions” to food security, energy, poverty, and human rights was freighted with an underlying philosophy in which “government appears ineffective and powerless, while homegrown, market-based solutions are cheap, democratic, and transformative.”38 Waddington highlights a part of the story when catastrophic floods bring London to the verge of a cholera epidemic; one character persuades another to get involved, saying it’s a “unique opportunity to position yourself as the brand the world trusts for safe water.”

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We should, however, be more aware of how governments might use game mechanics in ways that might initially seem harmless but could lead to more troubling ends. The DARPA Network Challenge was a thrilling experience for the teams involved, but it also provided invaluable data for the Department of Defense on how online communities might be coordinated to cheaply crowdsource military intelligence. Incorporating game mechanics into strategic planning might also warp participants’ thinking in dangerous ways, as Jill Lepore has documented in her book If Then. In 1961, the Advanced Research Projects Agency (ARPA)—the predecessor of DARPA—began counterinsurgency research programs focusing on Vietnam and Thailand, under the name Project AGILE.

You Are Here: From the Compass to GPS, the History and Future of How We Find Ourselves by Hiawatha Bray

A Declaration of the Independence of Cyberspace, Albert Einstein, Big bang: deregulation of the City of London, bitcoin, Boeing 747, British Empire, call centre, Charles Lindbergh, crowdsourcing, Dava Sobel, digital map, don't be evil, Easter island, Edmond Halley, Edward Snowden, Firefox, game design, Google Earth, GPS: selective availability, Hedy Lamarr / George Antheil, Isaac Newton, job automation, John Harrison: Longitude, John Perry Barlow, John Snow's cholera map, Korean Air Lines Flight 007, license plate recognition, lone genius, openstreetmap, polynesian navigation, popular electronics, RAND corporation, RFID, Ronald Reagan, Silicon Valley, Steve Jobs, Steven Levy, Thales of Miletus, trade route, turn-by-turn navigation, uranium enrichment, urban planning, Zipcar

Old-school traffic reports relied on data collected by state and local highway departments. With their limited resources, these agencies could track conditions only on major highways and arterial streets. Google’s traffic maps, based on “crowdsourced” data from millions of vehicles, suffer from no such limitation. Users can get a traffic report on any street that attracts a sufficient number of Android-equipped drivers. In mid-2013 the company expanded its commitment to crowdsourced cartography with one of the biggest acquisitions in its history, the $966 million deal to purchase an Israeli company called Waze. Founded in 2007, Waze offered a software app for iPhones, Androids, and other mobile devices that monitors traffic conditions in much the same way as Google Maps.

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Clay Shirky, Cognitive Surplus: Creativity and Generosity in a Connected Age (New York: Penguin Press, 2010), 15–17. 13. Andrew Zolli and Ann Marie Healy, Resilience: Why Things Bounce Back (New York: Simon and Schuster, 2012), 172–190. 14. Jessica Heinzelman and Carol Waters, “Crowdsourcing Crisis Information in Disaster-Affected Haiti,” US Institute of Peace, September 29, 2010, www.usip.org/publications/crowdsourcing-crisis-information-in-disaster-affected-haiti. 15. Steve Coast, interview with the author, November 1, 2009. 16. Ibid., January 25, 2012. 17. Roberto Rocha, “A Map-Making Democracy,” Montreal Gazette, December 20, 2007. 18. Michael Cross, “OS Maps Finally Available to Not-for-Profit Organisations,” Guardian, December 13, 2007. 19.

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Its small but potent stable of major clients left no doubt that OSM was for real. By this point even Google had long realized that Coast was on to something. In 2008 the company began its own effort to recruit amateur mapmakers with the release of Google Map Maker, a tool that let anybody modify and correct Google’s online maps. Google had begun crowdsourcing its maps out of sheer necessity. As of 2008 the company still offered maps of just 22 countries and 20 million kilometers of roads—about 12.4 million miles. That sounds like a lot, but it is a mere fraction of the mappable earth. By 2012 Google Maps covered 187 countries and 42 million kilometers of roads, or 26 million miles.

Future Perfect: The Case for Progress in a Networked Age by Steven Johnson

Airbus A320, airport security, algorithmic trading, banking crisis, barriers to entry, Bernie Sanders, call centre, Captain Sullenberger Hudson, Cass Sunstein, Charles Lindbergh, classic study, cognitive dissonance, credit crunch, crowdsourcing, dark matter, Dava Sobel, David Brooks, Donald Davies, Evgeny Morozov, Fairchild Semiconductor, future of journalism, Great Leap Forward, high-speed rail, hive mind, Howard Rheingold, HyperCard, Jane Jacobs, John Gruber, John Harrison: Longitude, Joi Ito, Kevin Kelly, Kickstarter, lone genius, Mark Zuckerberg, mega-rich, meta-analysis, Naomi Klein, Nate Silver, Occupy movement, packet switching, peer-to-peer, Peter Thiel, planetary scale, pre–internet, private spaceflight, radical decentralization, RAND corporation, risk tolerance, seminal paper, shareholder value, Silicon Valley, Silicon Valley startup, social graph, SpaceShipOne, Steve Jobs, Steven Pinker, Stewart Brand, systems thinking, techno-determinism, The Death and Life of Great American Cities, the long tail, Tim Cook: Apple, urban planning, US Airways Flight 1549, WikiLeaks, William Langewiesche, working poor, X Prize, Yochai Benkler, your tax dollars at work

PEER NETWORKS AT WORK Communities. The Maple Syrup Event For more on 311 and other urban technology platforms, see my essay “What a Hundred Million Calls to 311 Reveal About New York,” in the November 2010 issue of Wired, and Vanessa Quirk’s essay “Can You Crowdsource a City?” (http://www.archdaily.com/233194/can-you-crowdsource-a-city/). A number of sites and mobile apps are innovating in the area of peer-network urbanism; an overview of many of them is available at the DIY City website, http://diycity.org/. Chris Anderson’s “Vanishing Point theory of news” was originally published at http://www.longtail.com/the_long_tail/2007/01/the_vanishing_p.html.

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It’s harder to get those kinds of groups to gather in a barn or a city hall than it is to assemble them virtually. Moving bits around is far easier than doing the—sometimes literal—heavy lifting of civic life: building reservoirs or highways or jet engines that don’t explode when birds fly into them. A skeptic might plausibly say, Sure, if you want to conduct an online poll, or crowdsource a city slogan, the Internet is a great leap forward. But if you want to do the real work, you need the older tools. But every material advance in human history—from the Great Wall to the Hoover Dam to the polio vaccine to the iPad—was ultimately the by-product of information transfer and decision making.

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A New York–based site called UncivilServants collects reports and photos of government workers abusing parking rules around the city and ranks the top offenders by department. (The worst abuser, by a wide margin, is the NYPD.) There’s even a new Australian urban reporting site called, memorably, It’s Buggered Mate. Taken together, all this adaptive, flexible urban reporting points the way toward a larger, and potentially revolutionary, development: the crowdsourced metropolis, the city of quants. — Systems such as 311 and its ilk are the peer-progressive response to the problem that all great cities invariably confront: the problem of figuring out where the problems are. In the language of Seeing Like a State, 311 makes the city legible from below.

The Content Trap: A Strategist's Guide to Digital Change by Bharat Anand

Airbnb, Alan Greenspan, An Inconvenient Truth, AOL-Time Warner, Benjamin Mako Hill, Bernie Sanders, Clayton Christensen, cloud computing, commoditize, correlation does not imply causation, creative destruction, crowdsourcing, death of newspapers, disruptive innovation, Donald Trump, driverless car, electricity market, Eyjafjallajökull, fulfillment center, gamification, Google Glasses, Google X / Alphabet X, information asymmetry, Internet of things, inventory management, Jean Tirole, Jeff Bezos, John Markoff, Just-in-time delivery, Kaizen: continuous improvement, Khan Academy, Kickstarter, late fees, managed futures, Mark Zuckerberg, market design, Minecraft, multi-sided market, Network effects, post-work, price discrimination, publish or perish, QR code, recommendation engine, ride hailing / ride sharing, Salesforce, selection bias, self-driving car, shareholder value, Shenzhen special economic zone , Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, special economic zone, Stephen Hawking, Steve Jobs, Steven Levy, Stuart Kauffman, the long tail, Thomas L Friedman, transaction costs, two-sided market, ubercab, vertical integration, WikiLeaks, winner-take-all economy, zero-sum game