crowdsourcing

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pages: 368 words: 96,825

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

3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Charles Lindbergh, cloud computing, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dematerialisation, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Exxon Valdez, fear of failure, Firefox, Galaxy Zoo, Google Glasses, Google Hangouts, gravity well, ImageNet competition, industrial robot, 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, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, 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, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, superconnector, technoutopianism, telepresence, telepresence robot, Turing test, urban renewal, web application, X Prize, Y Combinator, zero-sum game

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. Crowdsourcing Tasks Tasks are work. Crowdsourcing tasks means getting someone somewhere to do the work for you.

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. Crowdsortium was formed by a group of industry practitioners that have the mission of advancing the industry through best practices, education, data collection, and public dialogue. 4.

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. To qualify a work force, simply put out a few very simple and inexpensive requests to see how quickly and accurately the job gets done. For example, if you have one hundred images you need created, and a dozen crowdsourced workers to choose from, rather than choosing a single graphic artist immediately, consider taking a few of your images and asking a few freelancers to show you their style and speed.


pages: 502 words: 107,657

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

Albert Einstein, algorithmic trading, Amazon Mechanical Turk, 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, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, lifelogging, 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, speech recognition, statistical model, Steven Levy, 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

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. 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.

As he explains it, aspects of his work mapping the edges of glaciers from satellite photos could extend to mapping galaxies as well. Crowdsourcing Gone Wild Given the right set of conditions, the crowd will almost always outperform any number of employees. —Jeff Howe, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business The organizations I’ve worked with have mostly viewed the competition in business as a race that benefits from sharing, rather than a fight, where one’s gain can come only from another’s loss. The openness of crowdsourcing aligns with this philosophy. —Stein Kretsinger, Founding Executive of Advertising.com One small groundbreaking firm, Kaggle, has taken charge and leads the production of PA crowdsourcing. Kaggle has launched 53 PA competitions, including the essay-grading and dark matter ones mentioned above.

Mehul Patel, Kaggle, and Giles Pavey, “Predicting Retail Customer Behaviour,” Predictive Analytics World London Conference, December 1, 2011, London, UK. www.predictiveanalyticsworld.com/london/2011/agenda.php#day1–16a. Crowdsourcing in general, beyond analytics projects: Jeff Howe, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business (Three Rivers Press, 2008). Quote from Anthony Goldbloom about Kaggle’s crowdsourcing: Tanya Ha, “Lucrative Algorithms,” Catalyst Online, August 18, 2011. www.abc.net.au/catalyst/stories/3296837.htm. Regarding the shortage of analytics experts: James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers, “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey Global Institute, May 2011. www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation. More on Kaggle and crowdsourcing predictive analytics: Kaggle, “About Us: Our Team,” www.kaggle.com/about.


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, David Heinemeier Hansson, eurozone crisis, Firefox, Florence Nightingale: pie chart, game design, Google Earth, Hans Rosling, information asymmetry, Internet Archive, John Snow's cholera map, Julian Assange, linked data, moral hazard, MVC pattern, New Journalism, openstreetmap, Ronald Reagan, Ruby on Rails, Silicon Valley, social graph, SPARQL, text mining, web application, WikiLeaks

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. But in terms of generating data, we haven’t used crowdsourcing so much. Some of the crowdsourcing projects that we’ve done that have worked really well have been more like old-fashioned surveys.

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. Also you may get hold of material that is inaccessible or in a bad format and you aren’t able to do much with it. This is where crowdsourcing can help. One thing the Guardian has got is lots of readers, lots of pairs of eyes.

New communities around data journalism (photo by Heinze Havinga) After RegioHack, we noticed that journalists considered data journalism a viable addition to traditional journalism. 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. Most of the time, they began with a journalistic question.


pages: 523 words: 112,185

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, bioinformatics, computer vision, correlation does not imply causation, crowdsourcing, distributed generation, 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, 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, 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?

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?

Competitions cut out the messy stuff before you start building models—asking good questions, collecting and cleaning the data, etc.—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 net effect is a fairly high-quality compendium of all of human knowledge (more or less).


pages: 255 words: 76,495

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

business process, call centre, Clayton Christensen, cloud computing, commoditize, conceptual framework, corporate governance, crowdsourcing, glass ceiling, jimmy wales, Mark Zuckerberg, Metcalfe’s law, Network effects, pets.com, pre–internet, rolodex, semantic web, sentiment analysis, Silicon Valley, Silicon Valley startup, social graph, social web, software as a service, 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.

The Service Cloud allows vendors to monitor, aggregate, and search conversations customers are having about their products, and to incorporate crowdsourced solutions into their centralized knowledge based in Salesforce CRM. In addition, Get Satisfaction, Lithium, and FixYa are popular startups that specialize in crowdsourced support tools. Get Satisfaction is used by companies like Whole Foods, Adobe, and Apple to facilitate support-related conversations among customers and between customers and employees using voting to prioritize important issues (see Figure 4.11). Figure 4.11 Get Satisfaction helps alleviate companies’ support volume by tapping into customers to help answer each other’s questions. Salesforce.com hopes to tackle all three dimensions with Service Cloud, a set of technologies that augment traditional knowledge base solution data with crowdsourced knowledge. Service Cloud weaves together disparate silos of conversations across Facebook and Internet blogs and forums, and then relies on user voting and tagging to incorporate high-quality knowledge from the community into the corporate knowledge base.

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.


pages: 266 words: 87,411

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

Albert Einstein, Atul Gawande, Broken windows theory, call centre, Checklist Manifesto, clean water, clockwatching, cloud computing, crowdsourcing, Dava Sobel, delayed gratification, drone strike, Enrique Peñalosa, Erik Brynjolfsson, Ernest Rutherford, Exxon Valdez, fundamental attribution error, game design, 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, planetary scale, Ralph Waldo Emerson, RAND corporation, shareholder value, Silicon Valley, Skype, stem cell, Steve Jobs, Steve Wozniak, the scientific method, The Wisdom of Crowds, ultimatum game, urban renewal, War on Poverty

Even if not one single suggestion from the National Assemblies makes its way into the new constitution, or into government policy, the investment in crowdsourcing may yet pay off. Voters will feel they have been consulted, that their ideas count for something, and that they have a real stake in the process. “In recent years, politics felt like something that other people did to me,” says Dagur Jónsson, a schoolteacher in Reykjavik. “Now I feel like the government doesn’t just have to be some external thing that operates separately from the people. We can be the government.” That is sweet vindication to Gudjon Gudjonsson, the main man behind the Assemblies. He thinks crowdsourcing is the perfect tonic to reinvigorate electoral politics around the world. “In this new model of democracy, you crowdsource vision and values to give you a guiding light from the population,” he says.

Only a select few are invited to work on projects at Le Laboratoire or conduct research in those open-format labs at Columbia and Princeton. Crowdsourcing means taking a problem normally tackled by the few and putting it to the many. In the wrong hands it might only deliver a quick blast of publicity or some cheap market research. Used properly, however, the crowd can be a powerful ally in the battle to solve hard problems. You can ask the crowd to gather or mine data. You can invite it to test and judge solutions. Sometimes it pays to limit the interaction within the crowd to avoid groupthink. Just look at the catastrophic bubbles that inflate when everyone starts singing from the same hymn sheet in the financial markets. But sometimes the crowd does its best work when its members communicate and collaborate. One of the drivers behind Iceland’s experiment in crowdsourcing is Gudjon Mar Gudjonsson, a boyish, fortysomething entrepreneur with a string of high-tech companies and patents on his CV.

The event was so popular that other, smaller Assemblies were convened around the country. 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. Everyone uses first names, and the atmosphere is relaxed though expectant.


pages: 677 words: 206,548

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, 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, Brian Krebs, business process, butterfly effect, call centre, Charles Lindbergh, Chelsea Manning, cloud computing, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, Dean Kamen, disintermediation, don't be evil, double helix, Downton Abbey, drone strike, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, global pandemic, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, 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, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, lifelogging, litecoin, low earth orbit, M-Pesa, 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, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, off grid, offshore financial centre, optical character recognition, Parag Khanna, pattern recognition, peer-to-peer, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, RAND corporation, ransomware, Ray Kurzweil, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Ross Ulbricht, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, supply-chain management, technological singularity, telepresence, telepresence robot, Tesla Model S, The Future of Employment, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Wall-E, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, Westphalian system, WikiLeaks, Y Combinator, 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. YouTube is replete with great examples of apparent strangers suddenly breaking out into song, whether at Heathrow Airport or Times Square.

Open-source warfare and crowdsourced crime must be met with open-source security and crowdsourced public safety. Fortunately, there are a few bright spots where this new paradigm of public safety is beginning to shine. Organizations such as Crisis Commons and Ushahidi are reinventing disaster relief and saving lives by coordinating citizen response to public emergencies, including during the Haiti earthquake and the terrorist attack at the Westgate Mall in Nairobi. Citizens in Mexico, a country racked by fifty thousand narcotics-related murders from 2006 to 2012, are using tools such as Google Maps to crowdsource reporting on the cartels, their activities, and their whereabouts. In eastern Europe, the Organized Crime and Corruption Reporting Project, comprising journalists and citizens, crowdsources sophisticated multinational investigations to reveal which dictators, crooked officials, terrorists, and organized crime groups are moving and laundering their massive ill-gotten gains around the globe.

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. When other protest participants launched Sukey on their mobiles, they knew which areas contained riot police and were shown interactive compasses advising them how to avoid the cops (green pointed to safe areas, red to police danger zones).


pages: 369 words: 80,355

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, Amazon Mechanical Turk, Berlin Wall, Black Swan, book scanning, Cass Sunstein, commoditize, corporate social responsibility, crowdsourcing, Danny Hillis, David Brooks, Debian, double entry bookkeeping, double helix, en.wikipedia.org, Exxon Valdez, Fall of the Berlin Wall, future of journalism, Galaxy Zoo, Hacker Ethic, Haight Ashbury, hive mind, Howard Rheingold, invention of the telegraph, jimmy wales, Johannes Kepler, John Harrison: Longitude, Kevin Kelly, linked data, Netflix Prize, New Journalism, Nicholas Carr, Norbert Wiener, openstreetmap, P = NP, Pluto: dwarf planet, profit motive, Ralph Waldo Emerson, RAND corporation, Ray Kurzweil, Republic of Letters, RFID, Richard Feynman, Ronald Reagan, semantic web, slashdot, social graph, Steven Pinker, Stewart Brand, technological singularity, Ted Nelson, 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.

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.

Harris, In the Shadow of Slavery: African Americans in New York City, 1626–1863 (University of Chicago Press: Chicago, 2004). 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?


pages: 313 words: 91,098

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, crowdsourcing, Dmitri Mendeleev, Elon Musk, Ethereum, Flynn Effect, Hernando de Soto, hindsight bias, hive mind, indoor plumbing, Isaac Newton, John von Neumann, libertarian paternalism, Mahatma Gandhi, Mark Zuckerberg, meta analysis, meta-analysis, obamacare, prediction markets, randomized controlled trial, Ray Kurzweil, Richard Feynman, Richard Thaler, Rodney Brooks, Rosa Parks, 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

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. 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.

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. Crowdsourcing creates intelligent machines, but not through AI wizardry. Their intelligence doesn’t come from a deep understanding of the best way to reason or through immense computing power. Their intelligence derives from making use of the community. Waze guides you through traffic by integrating the reports of thousands of individuals who know a lot about the traffic conditions where they happen to be located. The advance here is not in intelligence in the conventional sense. It is in the power of connecting people. One of the big problems facing entrepreneurs using crowdsourcing is how to incentivize experts to contribute.

Contributing to the community of knowledge is in our collaborative nature. 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. Sometimes expertise is only apparent.


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

3D printing, Albert Michelson, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, bitcoin, Black Swan, blockchain, Burning Man, buy low sell high, Claude Shannon: information theory, cloud computing, Computer Numeric Control, conceptual framework, crowdsourcing, cryptocurrency, data acquisition, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, frictionless, game design, Gerolamo Cardano, informal economy, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, Nate Silver, Network effects, neurotypical, Oculus Rift, pattern recognition, peer-to-peer, pirate software, pre–internet, prisoner's dilemma, Productivity paradox, 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, technological singularity, technoutopianism, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, universal basic income, unpaid internship, uranium enrichment, urban planning, WikiLeaks

., “Critical Structure of a Monometric Retroviral Protease Solved by Folding Game Players,” Nature Structural and Molecular Biology 18 (2011): 1175–77, http://www.nature.com/nsmb/journal/v18/n10/full/nsmb.2119.html; “Mason Pfizer Monkey Virus,” Microbe Wiki, http://microbewiki.kenyon.edu/index.php/Mason_pfizer_monkey_virus. 2 “Solve Puzzles for Science,” Foldit, accessed June 1, 2016, http://fold.it/portal/. 3 Ewan Callaway, “Video Gamers Take on Protein Modellers,” Nature Newsblog, accessed June 1, 2016, http://blogs.nature.com/news/2011/09/tk.html. 4 “Welcome to Eterna!,” http://eterna.cmu.edu/eterna_page.php?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.

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. When it works—and contrary to the initial hype, it’s hardly a digital age panacea—crowdsourcing exhibits an almost magical efficacy. Institutions and companies like NASA, the LEGO Group, and Samsung have integrated public contributions into the core of how they do business. In the process they’ve remade the boundary that has traditionally separated the producers of a thing from the consumers of that thing.

In June 2006, Jeff wrote an article for Wired magazine entitled “The Rise of Crowdsourcing.” 6 Drawing evidence from industries like stock photography and customer support, the article proposed that a radical new form of economic production had sprung from the fertile soil of open-source software, Wikipedia, and the dramatic decline in the price of technological tools ranging from digital cameras to benchtop laboratory equipment. “Hobbyists, part-timers, and dabblers suddenly have a market for their efforts, as smart companies… discover ways to tap the latent talent of the crowd,” Jeff wrote. “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.


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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, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Ben Horowitz, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, 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, means of production, 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, prediction markets, profit motive, publish or perish, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, subscription business, supply-chain management, TaskRabbit, telepresence, telepresence robot, Tony Hsieh, transaction costs, Travis Kalanick, Tyler Cowen: Great Stagnation, uber lyft, urban planning, 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]. Next, implementing Engagement, Local Motors undertook its first competition for a car design.

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.

We have referenced Quirky several times throughout this book and will now focus on its MTP, which is “Make Invention Accessible.” General Electric early on saw the huge potential of the new crowdsourced model of product development. It subsequently partnered with Quirky in 2012 on incentive competition [Engagement], whereby the Quirky community was tasked with dreaming up innovative everyday products. The submissions would then be put to a community vote, with the winning invention manufactured by GE. Out of a total of 1,500 submissions, the Quirky community selected the Milkmaid, a smart container that alerts users when milk begins to spoil or run low, as the top product. Each subsequent phase of the Milkmaid’s production, including product design, name, tagline and even price, was crowdsourced as well [Crowd], resulting in a total of 2,530 contributions from the Quirky community for a single product.


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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, game design, information retrieval, iterative process, natural language processing, pattern recognition, performance metric, sentiment analysis, social web, speech recognition, statistical model, text mining

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. Bretonnel Cohen. 2011. “Amazon Mechanical Turk: Gold Mine or Coal Mine?” Computational Linguistics 37(2):413–420. Kittur, E.H. Chi, and B. Suh. 2008. “Crowdsourcing user studies with Mechanical Turk.” In Proceedings of CHI ’08: The 26th Annual SIGCHI Conference on Human Factors in Computing Systems.

This is why it is important to have well-defined guidelines and good IAA scores; if a task is clear, then it can be reproduced later by other groups of people. 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.

In this chapter we look toward the future of annotation projects and ML algorithms, and show you some ways that the field of Natural Language Processing (NLP) is changing, as well as how those changes can help (or hurt) your own annotation and ML projects. Crowdsourcing Annotation As you have learned from working your way through the MATTER cycle, annotation is an expensive and time-consuming task. Therefore, you want to maximize the utility of your corpus to make the most of the time and energy you put into your task. One way that people have tried to ameliorate the cost of large annotation projects is to use crowdsourcing—by making the task available to a large group of (usually untrained) people, it becomes both cheaper and faster to obtain annotated data, because the annotation is no longer being done by a handful of selected annotators, but rather by large groups of people. If the concept of crowdsourcing seems strange, think about asking your friends on Facebook to recommend a restaurant, or consider what happens when a famous person uses Twitter to ask her followers for a piece of information, or their preferences for an upcoming event.


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

1960s counterculture, 4chan, A Pattern Language, Airbnb, Amazon Web Services, anti-communist, Apple II, Bay Area Rapid Transit, Burning Man, business process, call centre, carbon footprint, charter city, chief data officer, clean water, cleantech, cloud computing, computer age, congestion charging, connected car, crack epidemic, crowdsourcing, DARPA: Urban Challenge, data acquisition, Deng Xiaoping, digital map, Donald Davies, East Village, Edward Glaeser, game design, garden city movement, Geoffrey West, Santa Fe Institute, George Gilder, ghettoisation, global supply chain, Grace Hopper, Haight Ashbury, Hedy Lamarr / George Antheil, hive mind, Howard Rheingold, interchangeable parts, Internet Archive, Internet of things, Jacquard loom, Jane Jacobs, jitney, John Snow's cholera map, Joi Ito, Khan Academy, Kibera, Kickstarter, knowledge worker, load shedding, M-Pesa, Mark Zuckerberg, megacity, mobile money, mutually assured destruction, new economy, New Urbanism, Norbert Wiener, Occupy movement, off grid, openstreetmap, packet switching, Panopticon Jeremy Bentham, Parag Khanna, patent troll, Pearl River Delta, place-making, planetary scale, popular electronics, RFC: Request For Comment, RFID, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart grid, smart meter, social graph, social software, social web, 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, 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

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. 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.

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. In the slums of the developing world’s megacities, where those responsibilities were hardly acknowledged to begin with, crowdsourced alternatives may allow governments to free themselves from the obligation to equalize services in the future.

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. In a sense, it is the architecture of total civic participation in urban regeneration that Patrick Geddes could only dream of. But as much as crowdsourcing can augment capacity, government needs to ensure that critical public services are delivered to everyone and on time. What happens when helping one part of a crowd hurts another, for instance in traffic avoidance? Do you reward one set of users by revealing secret but limited-capacity, clog-free routes around jams?


Bulletproof Problem Solving by Charles Conn, Robert McLean

active transport: walking or cycling, Airbnb, Amazon Mechanical Turk, asset allocation, availability heuristic, Bayesian statistics, Black Swan, blockchain, business process, call centre, carbon footprint, cloud computing, correlation does not imply causation, Credit Default Swap, crowdsourcing, David Brooks, Donald Trump, Elon Musk, endowment effect, future of work, Hyperloop, Innovator's Dilemma, inventory management, iterative process, loss aversion, meta analysis, 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, smart contracts, stem cell, the rule of 72, the scientific method, The Signal and the Noise by Nate Silver, time value of money, 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.

We start with simple data analysis and then move on to multiple regression, Bayesian statistics, simulations, constructed experiments, natural experiments, machine learning, crowd‐sourced problem solving, and finish up with another big gun for competitive settings, game theory. Of course each of these tools could warrant a textbook on their own, so this is necessarily only an introduction to the power and applications of each technique. Summary of Case Studies Data visualization: London air quality Multivariate regression: Understanding obesity Bayesian statistics: Space Shuttle Challenger disaster Constructed experiments: RCTs and A|B testing Natural experiments: Voter prejudice Simulations: Climate change example Machine learning: Sleep apnea, bus routing, and shark spotting Crowd‐sourcing algorithms Game theory: Intellectual property and serving in tennis It is a reasonable amount of effort to work through these, but bear with us—these case studies will give you a solid sense of which advanced tool to use in a variety of problem settings.

But many business decisions lack the sheer amounts of high‐quality data required to employ learning algorithms.13 Some business and life decisions require subjective intuitions about past events and future issues unavailable in data, or involve guessing the responses of other players. 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. Often problem solving competitions are presented at hackathons, daylong events where programmers and analysts compete for the best solution given the data available to solve the problem at hand.


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

Affordable Care Act / Obamacare, Berlin Wall, bitcoin, blood diamonds, Bretton Woods, Brian Krebs, British Empire, butter production in bangladesh, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, corporate social responsibility, creative destruction, crowdsourcing, digital map, Edward Snowden, en.wikipedia.org, failed state, Fall of the Berlin Wall, feminist movement, Filter Bubble, Firefox, Francis Fukuyama: the end of history, Google Earth, Howard Rheingold, income inequality, informal economy, Internet of things, 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, packet switching, pension reform, prediction markets, sentiment analysis, Silicon Valley, Skype, spectrum auction, statistical model, Stuxnet, trade route, 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. Ushahidi isn’t the only platform, though it is one of the most popular because of its crowd-sourced content and community of volunteers.

Modern political life is rife with examples of how people have used social media to catch dictators off guard and engage their neighbors with political questions. Ushahidi, the mapping platform for crowd-sourced knowledge, has a good record of problem solving. It may be one of the largest and most high profile of such providers of connective action, but it’s not the only one. Uchaguzi is a platform built specifically for monitoring the Kenyan election in 2013.27 In neighboring Nigeria, researchers find that the number and location of electoral fraud reports is highly correlated with voter turnout.28 This means that social media are starting to generate statistically valid snapshots of what’s happening on the ground—even in countries as chaotic as Nigeria. Still, connective action doesn’t just happen through crowd-sourced maps. Indian Kanoon, an online, searchable database on Indian law, has opened up a whole swath of data to the average person.29 Many Indians are proud of living in the largest democracy in the world, but it is difficult for average citizens to understand Indian law.

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. With this pernicious structural flaw, how much faith should we have that China’s rival information infrastructure will stay rivalrous?


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

23andMe, 3D printing, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, 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, 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, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, death of newspapers, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, full employment, future of work, 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, lifelogging, lump of labour, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, optical character recognition, Paul Samuelson, personalized medicine, 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, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, transaction costs, Turing test, Watson beat the top human players on Jeopardy!, WikiLeaks, 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.

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.

Today this kind of shared online platform can be set up in minutes, using inexpensive off-the-shelf software. 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. Again, this is highly collaborative. A load is shared: a problem, or a piece of work, or a sum of money is subdivided and the burden is distributed across a community.


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, discrete time, disruptive innovation, George Gilder, Google Earth, Infrastructure as a Service, Internet Archive, Internet of things, invisible hand, knowledge economy, late capitalism, lifelogging, linked data, longitudinal study, Masdar, means of production, Nate Silver, natural language processing, openstreetmap, pattern recognition, platform as a service, recommendation engine, RFID, semantic web, sentiment analysis, 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, 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). 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.

Moreover, there are concerns about the quality and consistency of content and metadata created across diversely skilled/motivated individuals, and how to provide documented degrees of reliability and generate a sense of trustworthiness (Dodge and Kitchin 2013). This has led some to posit that ‘amateur’, crowdsourced labour is best expended on data verification and correction, not creation (Carr 2007). The example Carr highlights is Wikipedia, which although popular and extensive, has grown in a haphazard way that matches the selective interests of participants, and has incomplete, sometimes poorly written, trivial and highly contested articles which undermine its authority and usability. Carr contends that ‘if Wikipedia weren’t free, it is unlikely its readers would be so forgiving of its failings’ (2007: 4). OpenStreetMap can suffer from a lack of coverage in some areas where there are few volunteers. There are also concerns as to the sustainability of volunteered crowdsourced labour, with Carr (2007) arguing that the connections that bind a virtual crowd together are often superficial, lacking depth and obligatory commitment, are liable to dispersion, and are reliant on a small core group to keep the project going and provide the bulk of the labour.

Table 2.4 lists over thirty such reasons divided into three dimensions – direct/indirect, near-term/long-term, public/private – as defined by Beagrie et al. (2010). 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). Moreover, by preserving data over time it becomes possible to track trends and patterns, and the longer the record, the greater the ability to build models and simulations and have confidence in the conclusions drawn (Lauriault et al. 2007).


pages: 247 words: 63,208

The Open Organization: Igniting Passion and Performance by Jim Whitehurst

Airbnb, cloud computing, crowdsourcing, en.wikipedia.org, Google Hangouts, Infrastructure as a Service, job satisfaction, market design, Network effects, new economy, place-making, platform as a service, post-materialism, profit motive, risk tolerance, shareholder value, side project, Silicon Valley, Skype, Snapchat, Steve Jobs, subscription business, The Wisdom of Crowds, Tony Hsieh

This book reveals the secrets of how an open organization really works by taking an insider’s in-depth look into one of the premier open organizations in the world, Red Hat (a software company with a value of more than $10 billion where I am the CEO), along with illustrative examples of other companies operating this way too, such as Whole Foods, Pixar, Zappos, Starbucks, W. L. Gore, and others. 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. Work is distributed in a network-like fashion and people are held accountable, all without a formal hierarchy.

There’s even a company called InnoCentive that firms can hire to help them use the power of the crowd. 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.

It’s a one-and-done kind of result and not the basis for any kind of sustainable competitive advantage. 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.


pages: 270 words: 79,992

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, Any sufficiently advanced technology is indistinguishable from magic, Apple's 1984 Super Bowl advert, barriers to entry, Berlin Wall, big-box store, bitcoin, business climate, call centre, Cass Sunstein, centralized clearinghouse, Chelsea Manning, citizen journalism, cloud computing, collaborative consumption, collaborative editing, commoditize, creative destruction, crony capitalism, cross-subsidies, crowdsourcing, David Brooks, death of newspapers, disruptive innovation, Donald Trump, Douglas Engelbart, Douglas Engelbart, en.wikipedia.org, Exxon Valdez, Fall of the Berlin Wall, Filter Bubble, Firefox, global supply chain, Google Chrome, Gordon Gekko, Hacker Ethic, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, Lean Startup, Mark Zuckerberg, minimum viable product, Mitch Kapor, Mohammed Bouazizi, Mother of all demos, Narrative Science, new economy, Occupy movement, old-boy network, peer-to-peer, period drama, Peter Thiel, pirate software, publication bias, Robert Metcalfe, Ronald Reagan, Ronald Reagan: Tear down this wall, sharing economy, Silicon Valley, Skype, social web, Steve Jobs, Steve Wozniak, Stewart Brand, Stuxnet, Ted Nelson, Telecommunications Act of 1996, telemarketer, 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?

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.

It’s part of the “free agent” world, where radical connectivity and cloud computing allow individuals to operate outside the bounds of a normal nine-to-five job at a big company—with all the uncertainty and opportunity that comes with freelancing. How far can we take crowdsourcing? “Giffgaff” is a Scottish English word that means “mutual giving,” which is a pretty good way to describe the British mobile phone company giffgaff. Its customers are its sales team. Its customers are its technical support team. In fact, giffgaff uses customers to do as much as possible, compensating them with a virtual currency, “payback,” that can be redeemed for mobile phone minutes, cash, and other rewards. By distributing key expenses out to the “crowd”—in this case, the customers—this mobile phone network in the U.K. keeps costs down and establishes itself as a competitive choice. Open Source Takes on the Big Guys Crowdsourcing has its roots in open-source programming, which first took hold during the 1960s.


pages: 491 words: 77,650

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

3D printing, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Andrei Shleifer, autonomous vehicles, barriers to entry, call centre, cashless society, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, disruptive innovation, Donald Trump, Erik Brynjolfsson, full employment, future of work, George Akerlof, gig economy, global supply chain, hiring and firing, income inequality, information asymmetry, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, low skilled workers, Lyft, 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, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Singh, software as a service, Steve Jobs, TaskRabbit, 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, working-age population

This commitment was emphatically restated in the Declaration of Philadelphia in 1944. 12. 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.

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. In cities around the world, consumers can hail Ubers instead of traditional taxis, order their food through Deliveroo, request handyman assistance from TaskRabbit, and out- source small digital tasks on Amazon’s Mechanical Turk (MTurk).

Indeed, even the descriptions in this chapter will have to resort to generaliza- tions and are by no means future-proof: changes to the business model require little more than a software update. * * * 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.


pages: 230 words: 61,702

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, crowdsourcing, Edward Snowden, Firefox, Google Glasses, hive mind, income inequality, Internet of things, John von Neumann, meta analysis, meta-analysis, Nate Silver, new economy, 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, WikiLeaks

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. An organization throws out a problem, and those who want (or those granted access to the relevant site or network) contribute solutions, and see what sticks. 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.)

“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. This may seem reasonable if you think of such laborers as amateurs—doing such work in their “spare time.”

Weinberger, Too Big to Know, 23. 15. Sosa, Reflective Knowledge, chs. 7 and 8.. 16. Pritchard, Epistemic Luck, 225. Chapter 7: Who Gets to Know 1. Lawrence M. Sanger, “Who Says We Know: On the New Politics of Knowledge,” Edge 208 (April 25, 2007): http://edge.org/3rd_culture/sanger07/sanger07_index.html%3E. Accessed August 25, 2015. 2. Brabham, Crowdsourcing, xix. 3. Jeppesen and Lakhani, “Marginality and Problem-Solving Effectiveness.” 4. Brabham, Crowdsourcing, 21. 5. Rifkin, The Zero Marginal Cost Society, 18. 6. Ibid., 19. See also 179–80. 7. Fricker rightly distinguishes epistemic inequality from what she calls epistemic injustice: Epistemic Injustice, 1–2. But the two are related, as noted below. 8. Frank LaRue, Special Rapporteur on the promotion and protection of the right to freedom of opinion and expression, Report to the Human Rights Council of the United Nations General Assembly, May 16, 2011.


pages: 400 words: 88,647

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, Bretton Woods, business climate, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cloud computing, collaborative consumption, collaborative economy, Computer Numeric Control, connected car, corporate social responsibility, creative destruction, crowdsourcing, disruptive innovation, Elon Musk, financial exclusion, financial innovation, 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, low cost carrier, M-Pesa, Mahatma Gandhi, megacity, minimum viable product, more computing power than Apollo, new economy, payday loans, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, precision agriculture, race to the bottom, reshoring, risk tolerance, Ronald Coase, 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, TaskRabbit, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, transaction costs, Travis Kalanick, unbanked and underbanked, underbanked, women in the workforce, 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.

Co-create value with prosumers. 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.

An important aspect of the company’s business model is that the inventors themselves stand to make money from the process. 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. Increasingly, inventors do not even need access to companies like Quirky. Armed with a laptop, high-speed broadband and design software, individuals can manufacture one-off products and make a profit.


pages: 382 words: 120,064

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

3D printing, additive manufacturing, Airbus A320, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, asset-backed security, augmented reality, barriers to entry, 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, M-Pesa, Mark Zuckerberg, mass affluent, Metcalfe’s law, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, 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, Tim Cook: Apple, transaction costs, underbanked, US Airways Flight 1549, web application

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. 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.

Figure 8.8: DBS Remix branch, a result of crowdsourced input (Credit: DBS) Not to be outdone in the supercompetitive market of Singapore, OCBC then launched its own initiative to crowdsource not just a new branch design, but a whole new Y-Gen-focused brand initiative. The result was FRANK by OCBC. Frank (or #frankbyocbc) has been phenomenally successful in building a “cult”’ brand following within their intended Y-Gen segment. Figure 8.9 and 8.10: FRANK (Credit: OCBC) If you want to engage customers, why not let them choose the direction for a selection of new products, branch design or engagement approaches. What have you got to lose? A good best practice for this is, not surprisingly, from outside of financial services. A great crowdsourcing platform is also one of the hottest conferences on innovation on the planet—known as SXSW—but more formally as South-by-South-West.

Regardless of where social media is taking us, credibility is built only through dialogue and open communication with the crowd. 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.


pages: 327 words: 103,336

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

active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, business cycle, butterfly effect, 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, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, 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, 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 intelligence, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, 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.

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.

“Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design.” 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. Johnson, and John Sprague. 2004. Political Disagreement: The Survival of Disagreement with Communication Networks.


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

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, housing crisis, income inequality, microcredit, Mohammed Bouazizi, Occupy movement, offshore financial centre, Port of Oakland, social software, statistical model, We are the 99%, web application, WikiLeaks, World Values Survey, young professional, zero-sum game

[Online] Available at: <http://www.VoxEU.org/index.php?q=node/7077> [Accessed on January 9, 2012]. 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. New Left Review, 65: 5–29.

Available at: <http://ijoc.org/ojs/index.php/ijoc/article/view/1174/606>. 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].

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.


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

"side hustle", 3D printing, 4chan, Affordable Care Act / Obamacare, Airbnb, augmented reality, autonomous vehicles, battle of ideas, Benjamin Mako Hill, bitcoin, blockchain, British Empire, Chris Wanstrath, Columbine, Corn Laws, crowdsourcing, David Attenborough, Donald Trump, Elon Musk, Ferguson, Missouri, future of work, game design, gig economy, hiring and firing, IKEA effect, income inequality, informal economy, job satisfaction, Jony Ive, Kibera, Kickstarter, Lean Startup, Lyft, Mark Zuckerberg, Minecraft, Network effects, new economy, Nicholas Carr, obamacare, Occupy movement, profit motive, race to the bottom, ride hailing / ride sharing, rolodex, Saturday Night Live, sharing economy, Silicon Valley, six sigma, Snapchat, social web, TaskRabbit, the scientific method, transaction costs, Travis Kalanick, Uber and Lyft, uber lyft, upwardly mobile, web application, WikiLeaks

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.

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.

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.” You only got one shot to get that right, and you never knew how it would turn out.


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

"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, 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, British Empire, business cycle, business intelligence, business process, call centre, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, digital map, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, 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, global village, 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, 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, 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, pattern recognition, Paul Samuelson, payday loans, post-work, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, 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 Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen: Great Stagnation, Vernor Vinge, 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. In fact, it’s given rise to a large new crop of companies, often grouped together as the ‘peer economy.’ Peer economy companies satisfy their customers’ requests by crowdsourcing them. Some of the graphs you see in this book, for example, were generated or improved by people we’d never met before.

Cragin said that, “Though I hadn’t worked in the area of solar physics as such, I had thought a lot about the theory of magnetic reconnection.”22 This was evidently the right theory for the job, because Cragin’s approach enabled prediction of SPEs eight hours in advance with 85 percent accuracy, and twenty-four hours in advance with 75 percent accuracy. 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. They also found that people whose expertise was far away from the apparent domain of the problem were more likely to submit winning solutions.

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. It knew that the redesign would mean generating many candidates for each of several attributes—bottle shape, embossments, label color, label placement, cap design, and so on—then settling on the right combination of all of these.


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

barriers to entry, business process, Clayton Christensen, creative destruction, crowdsourcing, Dean Kamen, disruptive innovation, financial independence, game design, global supply chain, Jeff Bezos, Kickstarter, Mark Zuckerberg, Minecraft, Rubik’s Cube, Silicon Valley, Steve Jobs, The Wisdom of Crowds, Wall-E

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. For more conventional companies, however, crowdsourcing remains a conundrum, and a scary one at that.

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. And who could argue with them?

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.


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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, John von Neumann, market bubble, money market fund, pattern recognition, Paul Samuelson, Ponzi scheme, prediction markets, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Robert Shiller, Rubik’s Cube, statistical model, Steven Pinker, transaction costs

• Pick a random password that you can convert into a memorable nonsense phrase. 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). On the one hand, this could indicate that we’ve achieved a consumer Valhalla in which the things we rate are, on the whole, pretty good.

Overall, 7 was in “the unique position of being … the ‘oddest’ digit.” A crowd-sourced rating is neither a randomness experiment nor a study in first impulses. The participants are asked to translate their feelings about a product into a number or a position on a scale. This is not as straightforward as it might seem. Is that gastropub a 3 or a 4? Is that Dan Brown book a 2 (the characterization was awful) or a 5 (it did keep me gripped)? The raters are making up numbers that correspond to complicated and mixed emotions — or no emotions at all. You can think of the Yale experiment as a focus group in search of a product. Having no reason to rate high or low, the subjects gave whatever number came to mind. There is an element of that in any crowd-sourced rating. Some of the raters will be indifferent or have such mixed feelings that any response is defensible.

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

Affordable Care Act / Obamacare, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, basic income, 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, collaborative consumption, collective bargaining, computer vision, corporate social responsibility, crowdsourcing, data is the new oil, deindustrialization, deskilling, don't be evil, Donald Trump, Elon Musk, employer provided health coverage, en.wikipedia.org, equal pay for equal work, Erik Brynjolfsson, financial independence, Frank Levy and Richard Murnane: The New Division of Labor, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, industrial robot, informal economy, information asymmetry, Jeff Bezos, job automation, knowledge economy, low skilled workers, low-wage service sector, market friction, Mars Rover, natural language processing, new economy, passive income, pattern recognition, post-materialism, post-work, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, 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, The Future of Employment, The Nature of the Firm, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, women in the workforce, Works Progress Administration, Y Combinator

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. These people power modern AI systems, websites, and apps that we all use and take for granted.

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. Uber uses CrowdFlower’s API to pay someone to review the results of Ayesha’s work, and, if it passes muster, it will process Uber’s payment to her within minutes.

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.


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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, 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, disintermediation, disruptive innovation, don't be evil, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, information asymmetry, interchangeable parts, Internet of things, Isaac Newton, job automation, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, Lyft, Mark Zuckerberg, Marshall McLuhan, meta analysis, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, RAND corporation, randomized controlled trial, 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, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize

K. 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. L. Fox, “Snowden and His Accomplices,” Wall Street Journal, April 14, 2014, http://online.wsj.com/news/articles/SB10001424052702303603904579495391321958008. 2.

Collectively, via the social online health network PatientsLikeMe, a rapid crowdsourced clinical trial took form, albeit without controls (patients not receiving sodium chlorite) but showing no sign of efficacy. In fact, taking sodium chlorite was associated with an adverse effect.102,103 Using clever algorithms, PatientsLikeMe can simulate a randomized trial within their database and had already demonstrated that another candidate drug for ALS, lithium bicarbonate, was ineffective using this method. That finding was subsequently validated by a traditional, expensive, and time-consuming randomized trial. Although open is not a guarantee of good outcomes, social online networks of “ePatients” with like conditions do have real advantages over classic clinical trials. A major one is that they provide unique, real-world, relevant crowdsourcing information.

., “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. L. Scanlon, “Genentech and PatientsLikeMe Enter Patient-Centric Research Collaboration,” PatientsLikeMe, April 7, 2014, http://news.patientslikeme.com/print/node/470. 106.


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

airport security, Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, Bayesian statistics, big-box store, business process, call centre, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, delayed gratification, Donald Trump, en.wikipedia.org, epigenetics, Eratosthenes, Exxon Valdez, framing effect, friendly fire, fundamental attribution error, Golden Gate Park, Google Glasses, haute cuisine, impulse control, index card, indoor plumbing, information retrieval, invention of writing, iterative process, jimmy wales, job satisfaction, Kickstarter, life extension, longitudinal study, meta analysis, 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, shared worldview, 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, Turing test, ultimatum game, zero-sum game

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. Crowdsourcing is just one example of organizing our social world—our social networks—to harness the energy, expertise, and physical presence of many individuals for the benefit of all.

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. 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.

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. Now, with TripAdvisor, Yelp, Angie’s List, and others of their ilk, ordinary people are empowered to write reviews about their own experiences. This cuts both ways. In the best cases, we are able to learn from the experiences of hundreds of people about whether this motel is clean and quiet, or that restaurant is greasy and has small portions.


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

agricultural Revolution, Airbnb, Albert Einstein, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, Asperger Syndrome, Baxter: Rethink Robotics, bitcoin, blockchain, business process, call centre, clean water, creative destruction, crowdsourcing, Donald Trump, Douglas Engelbart, Douglas Engelbart, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental economics, Exxon Valdez, future of work, Galaxy Zoo, gig economy, happiness index / gross national happiness, 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, Marshall McLuhan, Occupy movement, Pareto efficiency, pattern recognition, prediction markets, price mechanism, Ray Kurzweil, Rodney Brooks, Ronald Coase, 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, transaction costs, Travis Kalanick, Uber for X, uber lyft, Vernor Vinge, Vilfredo Pareto, Watson beat the top human players on Jeopardy!

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. 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.

Shai Wininger, “The Secret Behind Lemonade’s Instant Insurance,” Lemonade, November 23, 2016, https://stories.lemonade.com/the-secret-behind-lemonades-instant-insurance-3129537d661. 6. Wikipedia, s.v. “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. Reprinted by permission. 9. Simple English Wikipedia, accessed October 21, 2017, https://simple.wikipedia.org/wiki/Main_Page. 10.

SUMMARY The theory we’ve developed in this chapter brings together knowledge about group decision making from many different disciplines to help us • understand which types of superminds are likely to be most common in different situations and • decide which kinds of superminds are most likely to help us achieve our goals. This systematic approach to comparing superminds is one of the most important benefits of looking at the world from the perspective of superminds. Part IV How Can Superminds Create More Intelligently? CHAPTER 12 Bigger Is (Often) Smarter In November 2009, my colleagues and I launched a new online platform called Climate CoLab.1 The goal of this platform is to crowdsource the process of finding solutions for one of the most important problems facing humanity today: global climate change. On the day we launched, only a few dozen people were registered members, mostly people we already knew. By the end of December 2009, we had 193 members who had created 20 proposals for different approaches to the climate-change problem. Over the years, the Climate CoLab community has continued to grow, sometimes doubling or tripling in a year.


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

4chan, Albert Einstein, AltaVista, Andrew Keen, augmented reality, Burning Man, Carrington event, cognitive dissonance, crowdsourcing, dematerialisation, en.wikipedia.org, 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, lifelogging, Loebner Prize, low earth orbit, Marshall McLuhan, McMansion, moral panic, Nicholas Carr, pattern recognition, pre–internet, Republic of Letters, Silicon Valley, Skype, Snapchat, social web, Steve Jobs, the medium is the message, The Wisdom of Crowds, 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.

And that everybody in the world is going to respond to it like the mean-spirited person that created it.” 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?” (Indeed, Facebook has well over one billion users whose actions it must manage.)

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. Even the algorithms that choose which companies should hawk things at me when I search for “Erica Feldman” are in on the mass delusion.


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

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

In the Saudi case, banned porn websites carry a message that explains in detail the reasons for the ban, referencing a Duke Law Journal article on pornography written by the American legal scholar Cass Sunstein and a 1,960-page study conducted by the U.S. attorney general’s Commission on Pornography in 1986. (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. Found a porn site?

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. After all, anyone can text in deliberately erroneous reports to accuse their opponents of wrongdoing or, even worse, to sow panic in their ranks (remember the Nigerian SMS that said that all food was poisoned?).

Craig, G. A. “The Professional Diplomat and His Problems, 1919-1939.” 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. “Chinese Student in U.S. Is Caught in Confrontation.” New York Times, April 17, 2008. Doppelt, G.


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

A Declaration of the Independence of Cyberspace, bike sharing scheme, 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, dematerialisation, digital map, distributed ledger, fault tolerance, fiat currency, Filter Bubble, floating exchange rates, global value chain, Google Earth, hive mind, Internet of things, Kickstarter, knowledge economy, lifelogging, linked data, loose coupling, new economy, New Urbanism, Nicholas Carr, open economy, openstreetmap, packet switching, pattern recognition, performance metric, place-making, 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, TaskRabbit, text mining, The Chicago School, The Death and Life of Great American Cities, 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. 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.

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.

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). Conclusion Crime data are a good example of ‘capta’ – data that have been selectively harvested from a broad pool of available data. They rely upon human ‘sensors’ for their recording and interpretation, and reporting and recording are shaped by bureaucratic demands and priorities.


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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, en.wikipedia.org, Firefox, informal economy, jimmy wales, Kickstarter, late capitalism, loose coupling, Marshall McLuhan, means of production, Naomi Klein, Network effects, optical character recognition, packet switching, postnationalism / post nation state, prediction markets, Richard Stallman, semantic web, Silicon Valley, slashdot, Slavoj Žižek, stealth mode startup, technoutopianism, the medium is the message, The Wisdom of Crowds, web application, WikiLeaks

Glossary: Architecture 100 From Splicing Code to Assembling Social Solidarities We can already see early examples of these approaches outside of Free So ware. 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.

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.

Glossary: Tyranny Among us. 105 29. Free vs. Gratis Labor What makes a collaboration “open”? 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. Meanwhile, everyday people are happy to help. Why? Howe says people do it for fun and for the “cred”, otherwise known as the “emerging reputation economy.”


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

Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, basic income, bitcoin, blockchain, business cycle, call centre, cognitive dissonance, collective bargaining, crowdsourcing, David Attenborough, Donald Trump, East Village, Elon Musk, financial independence, future of work, game design, gig economy, income inequality, information asymmetry, Jeff Bezos, job automation, law of one price, Lyft, Mark Zuckerberg, market clearing, minimum wage unemployment, new economy, payday loans, post-work, profit maximization, QR code, race to the bottom, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, TaskRabbit, 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.

Go one layer bigger, and you’d see the IBO (the small business that hired Gary). 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.”

The second was to find some way outside of those laws to organize independent contractors. 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. The International Association of Machinists and Aerospace Workers union in New York City solved this problem by striking a deal with Uber in which Uber officials agreed to hand over contact information for its New York City drivers.


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

Airbnb, Amazon Web Services, Andy Kessler, banking crisis, barriers to entry, basic income, Benevolent Dictator For Life (BDFL), bitcoin, blockchain, Burning Man, business climate, call centre, car-free, cloud computing, collaborative consumption, collaborative economy, collective bargaining, commoditize, congestion charging, creative destruction, crowdsourcing, cryptocurrency, decarbonisation, different worldview, do-ocracy, don't be evil, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, Ferguson, Missouri, Firefox, frictionless, Gini coefficient, hive mind, income inequality, index fund, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jane Jacobs, Jeff Bezos, jimmy wales, job satisfaction, Kickstarter, Lean Startup, Lyft, means of production, megacity, Minecraft, minimum viable product, Network effects, new economy, Oculus Rift, openstreetmap, optical character recognition, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, 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 Nature of the Firm, transaction costs, Turing test, turn-by-turn navigation, Uber and Lyft, uber lyft, Zipcar

I understood clearly that we were on a path that was going to break a hundred-year-old industry. 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. There is one structure that underlies all these—excess capacity + a platform for participation + diverse peers—and it is fundamentally changing the way we work, build businesses, and shape economies.

Packaged products are shipped off to five global warehouses and distribution centers, which in turn send those boxes on to 35,000 retail locations.26 As the company has grown, Quirky has been able to partner with both physical and online retailers to sell its products. 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.

We could have averted the thousands of horrific deaths, decimated families, countless hours of suffering and worrying, and billions of dollars in medical aid if we had listened to the right person at the right time. The U.S. 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.


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Curation Nation by Rosenbaum, Steven

Amazon Mechanical Turk, Andrew Keen, barriers to entry, citizen journalism, cognitive dissonance, commoditize, creative destruction, crowdsourcing, disintermediation, en.wikipedia.org, future of journalism, Jason Scott: textfiles.com, means of production, 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

No longer is the owner of the distribution system the king of the castle. Today, curation is king. 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.]

Pepsi has taken seriously the change that Garfield is shouting from the rooftops. 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. Frank Cooper, Pepsi’s chief consumer engagement officer, explains the company’s social media initiatives this way: “We want to become a catalyst in the culture rather than act like a big brand announcing something.”

And we believe social media and digital engagement can fuel, extend, and inform these efforts.” 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. Instead, they can say, “No, I think I’ll change it and take control of the brand.”


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

Airbnb, barriers to entry, basic income, Bernie Sanders, bitcoin, call centre, centre right, creative destruction, crowdsourcing, disintermediation, disruptive innovation, Donald Trump, Edward Snowden, feminist movement, gig economy, industrial robot, Jaron Lanier, Jeff Bezos, 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, technoutopianism, Thomas L Friedman, universal basic income, 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.

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). Rather, we are faced with a more ambivalent trend, which may be described as ‘distributed centralisation’, to express the way which the opening at the party’s bottom is accompanied by an increasing concentration of power in the hands of the charismatic party leader, whom I describe as the ‘hyperleader’, and his or her immediate entourage.

Chapter 6 looks at the architecture of participatory platforms and the way in which they have come to constitute the organisational backbone of these formations. 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.


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

Airbnb, altcoin, asset-backed security, autonomous vehicles, barriers to entry, bitcoin, blockchain, Blythe Masters, Bretton Woods, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, cloud computing, cognitive dissonance, commoditize, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, crowdsourcing, cryptocurrency, disintermediation, disruptive innovation, distributed ledger, Donald Trump, double entry bookkeeping, 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, Galaxy Zoo, George Gilder, glass ceiling, Google bus, Hernando de Soto, income inequality, informal economy, information asymmetry, 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, 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, ransomware, Ray Kurzweil, renewable energy credits, rent-seeking, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, seigniorage, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, smart grid, social graph, social intelligence, social software, standardized shipping container, Stephen Hawking, Steve Jobs, Steve Wozniak, Stewart Brand, supply-chain management, TaskRabbit, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, The Wisdom of Crowds, transaction costs, Turing complete, Turing test, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, unorthodox policies, wealth creators, X Prize, Y2K, Zipcar

Augur’s style of prediction making could engage citizens in making small choices that contribute to national discussions of policy, eventually shaping the future of their own democracy. Blockchain Judiciary The blockchain can also transform our judiciary. 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. The process57 starts with the reporting online of a suspected civil or criminal wrongdoing (e.g., a public official suspected of receiving bribes) and inviting potential witnesses to provide evidence, and combining information from multiple sources.

“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. DISTRIBUTED AUTONOMOUS ENTERPRISES We now suggest you buckle up in your Star Trek captain’s seat for a moment.

Interview with Eduardo Robles Elvira, September 10, 2015. 52. http://cointelegraph.com/news/111599/blockchain_technology_smart_contracts_and_p2p_law. 53. 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. See full report and recommendations here, including a description of models worldwide: www.judiciary.gov.uk/reviews/online-dispute-resolution/. 60. http://blog.counter-strike.net/index.php/overwatch/. 61.


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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, 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, drone strike, Elon Musk, failed state, fear of failure, Filter Bubble, Google Earth, Google Glasses, hive mind, income inequality, information trail, invention of the printing press, job automation, John Markoff, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, means of production, MITM: man-in-the-middle, mobile money, mutually assured destruction, Naomi Klein, Nelson Mandela, offshore financial centre, Parag Khanna, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Singer: altruism, 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, The Wisdom of Crowds, upwardly mobile, Whole Earth Catalog, WikiLeaks, young professional, zero day

Through diligent crowd-sourced detective work, the perpetrator was soon tracked to a small town in northeastern China, and after her name, phone number and employer were made public, she fled, as did her cameraman. It’s not just computers that can find needles in haystacks, apparently; locating this woman among more than one billion Chinese—through only the clues in the video—took just six days. This kind of mob behavior can veer into unpredictable chaos, but that does not mean attempts to harness its collective power for good should be abandoned. 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.

The introduction of mobile phones is far more transformative than most people in modern countries realize. 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.

The more welcomed whistle-blowers of the future will be the ones who follow the example of people like Alexei Navalny, a Russian blogger and anticorruption activist, who enjoys much sympathy from many in the West. 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.


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

3D printing, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, augmented reality, Automated Insights, Berlin Wall, big data - Walmart - Pop Tarts, Buckminster Fuller, call centre, carbon footprint, Cass Sunstein, choice architecture, citizen journalism, cloud computing, cognitive bias, creative destruction, crowdsourcing, data acquisition, Dava Sobel, disintermediation, East Village, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, Firefox, Francis Fukuyama: the end of history, frictionless, future of journalism, game design, Gary Taubes, Google Glasses, 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, lone genius, Louis Pasteur, 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, Ray Kurzweil, recommendation engine, Richard Thaler, Ronald Coase, Rosa Parks, self-driving car, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, Slavoj Žižek, smart meter, social graph, social web, stakhanovite, Steve Jobs, Steven Levy, Stuxnet, technoutopianism, 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, urban decay, urban planning, urban sprawl, Vannevar Bush, WikiLeaks

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.

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. 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? Granted, today it’s much easier and cheaper to do such things, but a revolution in knowledge gathering it isn’t—not if we want the word “revolution” to retain any meaning at all. This message, however, is lost on our Internet pundits, who think that “the Internet” has fundamentally altered how knowledge is produced—nay, it has even altered what counts as knowledge.


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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, creative destruction, crowdsourcing, en.wikipedia.org, Firefox, game design, ghettoisation, Howard Rheingold, hypertext link, if you build it, they will come, Merlin Mann, Nate Silver, Network effects, Potemkin village, recommendation engine, RFC: Request For Comment, semantic web, SETI@home, Skype, slashdot, social graph, social software, social web, source of truth, stealth mode startup, Stewart Brand, telepresence, The Wisdom of Crowds, web application

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! Some commons-based peer production efforts are less self-conscious on the part of the users, and emerge more as a function of distributed coordinate behavior, like del.icio.us or Flickr.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. A Beautiful Day in the Neighborhood 14. One of Us, One of Us.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Relationships Find People Adding Friends Circles of Connections Publicize Relationships Unfriending The Ex-Boyfriend Anti-Pattern Groups Further Reading Download at WoweBook.Com 353 355 361 369 371 373 375 376 381 Contents xiii 15.

To Leisa Reichelt, pioneer of open design processes and sharp theorist, for expanding on her ambient intimacy coinage. 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.


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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, John Markoff, Kickstarter, knowledge worker, Silicon Valley, skunkworks, social intelligence, 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. Salesforce.com has permanently changed how it innovates.

Salesforce.com has permanently changed how it innovates. Del Monte thinks very differently about creating new products now. 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.

It received 148,000 “points,” which puts it at the top of the list of suggestions. 8. 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. The blog “all.things.fadra” by Fadra Nally is visible at http://allthingsfadra.com.


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

affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, 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, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Gödel, Escher, Bach, hindsight bias, housing crisis, Ignaz Semmelweis: hand washing, illegal immigration, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, lateral thinking, loss aversion, Louis Pasteur, Lyft, mail merge, Mark Zuckerberg, meta analysis, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nuclear winter, offshore financial centre, p-value, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, 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, 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, survivorship bias, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, Vilfredo Pareto, wikimedia commons

It is additionally likely that people close to you, such as those within your organization, share similar cultural traits, and therefore you should look beyond your normal contacts and venture outside your organization to get as much lateral and divergent thinking as you can. 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).

Almost eight hundred people participated, each individually guessing, and the average weight guessed was 1,197 pounds—exactly the weight of the ox, to the pound! 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.

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. For example, a price of $0.59 would represent a 59 percent probability that the candidate would 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, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, longitudinal study, Mars Rover, natural language processing, openstreetmap, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social graph, SPARQL, speech recognition, statistical model, supply-chain management, text mining, Vernor Vinge, web application

He teaches undergraduate organic chemistry courses with most content freely available on public blogs, wikis, games, and audio and video podcasts. 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.

(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.

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?


pages: 167 words: 50,652

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, 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. If a group comes with an impressive display of crowdsourcing support, this can demonstrate credibility. If members of the group have relevant educational credentials, this can demonstrate credibility.

Is there good reason to believe that the optimal system would allow no investments outside of the decision-making processes of councils and federations? 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.

This would, in effect, be simply a long-term pre-order of the product, although operating outside of the mechanism of the IFB. 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.


pages: 186 words: 49,595

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

citizen journalism, crowdsourcing, 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

Unlike the homegrown skits that went viral as people voluntarily passed them from phone to phone, the pro-US messages generally traveled unidirectionally from the embassy to the targeted phone. 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. We are not present. And the difficulty is that to be present is very labor-intensive.

Step 4 of the AYM manual urges: Use the press: Figure out your outreach strategy: The more people hear your message, the more influential you’ll be. 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. He would soon mobilize members to help him build a more comprehensive database of bloggers, journalists, and television personalities with reach in Egypt, the region, and internationally.

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. Mansour recalls: I was very affected by Khaled Said.


pages: 239 words: 56,531

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, butterfly effect, computer age, 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, Fall of the Berlin Wall, Francis Fukuyama: the end of history, Frank Gehry, Grace Hopper, gravity well, Guggenheim Bilbao, Honoré de Balzac, Howard Rheingold, invention of movable type, Isaac Newton, Jacquard loom, Jane Jacobs, Jeff Bezos, John Markoff, John von Neumann, Kickstarter, Mark Zuckerberg, Marshall McLuhan, Mercator projection, Metcalfe’s law, Mother of all demos, mutually assured destruction, Nelson Mandela, Network effects, new economy, Norbert Wiener, PageRank, pattern recognition, peer-to-peer, planetary scale, plutocrats, Plutocrats, post-materialism, Potemkin village, RFID, Richard Feynman, Richard Stallman, Robert Metcalfe, Robert X Cringely, Schrödinger's Cat, Search for Extraterrestrial Intelligence, SETI@home, Silicon Valley, Skype, social software, spaced repetition, Steve Ballmer, Steve Jobs, Steve Wozniak, Ted Nelson, the built environment, 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

I am indebted to Lessig’s work of over a decade defining, defending, and promoting Creative Commons, open-source culture, and the remix economy: Remix: Making Art and Commerce Thrive in the Hybrid Economy (New York: Penguin Press, 2008); Code v2 and Other Laws of Cyberspace (New York: Basic Books, 2007); Free Culture: How Big Media Uses Technology and the Law to Lock Down Creativity (New York: Penguin Press, 2004); The Future of Ideas: The Fate of the Commons in a Connected World (New York: Random House, 2001); Code and Other Laws of Cyberspace (New York: Basic Books, 1999). 13 . 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 . James Joyce, Ulysses (1922; repr., Oxford: Oxford University Press, 1998), 182. 16 .

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. The Creative Commons movement is trying to ensure that those who want to “write” with the culture machine—by this, I mean make and distribute motion pieces, new music, filmic fictions, digitally modeled fabrications, ubiquitous information environments, photo blogs, and the list goes on—will be able to have access to the contemporary raw materials of creativity.


pages: 294 words: 80,084

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

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

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. 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.

A year later, before the invasion by Russian military, the state of Georgia saw a massive cyberattack paralyze their banking system and disrupt cell-phone networks. 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.

We are entering a world where imagination is the only brake on biology, where dedicated individuals can create new life from scratch. Today, when a difficult problem is mentioned, a commonly heard refrain is, “There’s an app for that.” Sooner than you might believe, “applications” will be replaced by synthetically created “organisms” — as in, “There’s an org for that” — when we think about the solutions to many of our problems. Crowdsourcing the protection of the presidential genome, in light of this coming revolution, may prove to be the only way to protect the president. And in the process, the rest of us. The God of Sperm THE CONTROVERSIAL FUTURE OF BIRTH Most of the innovators we’ve covered in these pages emerged from beyond the mainstream. Whether it’s Dezso Molnar and his flying motorcycle or William Dobelle and his artificial vision implant or Craig Venter and his synthetic genome — all three are much more maverick outsider than cozy insider.


pages: 660 words: 141,595

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, 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, David Brooks, en.wikipedia.org, Erik Brynjolfsson, Gini coefficient, 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, speech recognition, Steve Jobs, supply-chain management, text mining, The Signal and the Noise by Nate Silver, Thomas Bayes, transaction costs, WikiLeaks

Through the book we have discussed in increasing complexity the notion of investing in data. 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. Micro-outsourcing is particularly relevant to data science, because it changes the economics, as well as the practicalities, of investing in data.[84] As one example, recall the requirements for applying supervised modeling.

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?

–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?


pages: 88 words: 22,980

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

Iceland had failed to insulate its government from cronyism and corruption. 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. With a process it is something that can scale. It’s like how Linux competed with Windows. … The process … can scale so clever people all over the world can participate.”

As Wes Boyd recounted in an interview for this book, We got blank stares for years and years and years from most of the professional political people. 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). Each time, the pattern has been the same: A surprising and unpredicted “open-source” energy, enabled by cheap and ubiquitous technology, shows us a part of us, We, the People, that conventional politics had forgotten or thought lost.

. … The process … can scale so clever people all over the world can participate.” 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.


pages: 720 words: 197,129

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, Apple II, augmented reality, back-to-the-land, beat the dealer, Bill Gates: Altair 8800, bitcoin, Bob Noyce, Buckminster Fuller, Byte Shop, c2.com, call centre, citizen journalism, Claude Shannon: information theory, Clayton Christensen, commoditize, computer age, crowdsourcing, cryptocurrency, Debian, desegregation, Donald Davies, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, El Camino Real, Electric Kool-Aid Acid Test, en.wikipedia.org, Firefox, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, Haight Ashbury, Howard Rheingold, Hush-A-Phone, HyperCard, hypertext link, index card, Internet Archive, Jacquard loom, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, Joseph-Marie Jacquard, Leonard Kleinrock, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, new economy, New Journalism, Norbert Wiener, Norman Macrae, packet switching, PageRank, Paul Terrell, pirate software, popular electronics, pre–internet, RAND corporation, Ray Kurzweil, 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, technological singularity, technoutopianism, Ted Nelson, The Coming Technological Singularity, The Nature of the Firm, The Wisdom of Crowds, Turing complete, Turing machine, Turing test, 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

That’s approximately 18,170 more draft readers than I’ve ever had in the past. Scores of readers posted comments, and hundreds sent me emails. 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. At the Aspen Institute, I am indebted to Pat Zindulka and Leah Bitounis, among many others.

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.

When Mauchly and Eckert tried to patent the architecture of a stored-program computer, they were stymied because (as both the Army’s lawyers and the courts eventually ruled) von Neumann’s report was deemed to be a “prior publication” of those ideas. 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. In the seventy years since von Neumann effectively placed his “Draft Report” on the EDVAC into the public domain, the trend for computers has been, with a few notable exceptions, toward a more proprietary approach.


pages: 313 words: 92,053

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

augmented reality, Benoit Mandelbrot, Berlin Wall, Broken windows theory, Buckminster Fuller, carbon footprint, commoditize, crowdsourcing, Frank Gehry, Google Glasses, Guggenheim Bilbao, haute couture, Howard Rheingold, Internet of things, Jaron Lanier, mandelbrot fractal, Marshall McLuhan, Masdar, mass immigration, megastructure, more computing power than Apollo, Oculus Rift, Peter Eisenman, RFID, Richard Florida, risk tolerance, sentiment analysis, smart cities, starchitect, the built environment, theory of mind, urban decay, urban planning, urban sprawl, Victor Gruen

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. When money is at stake, the game always gets rough. Nor am I suggesting that with crowd-sourced data gathering, the people can take matters of design entirely into their own hands.

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.

There’s no question that having this kind of information at your fingertips can be useful, especially if you don’t know the landscape very well. 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.


pages: 366 words: 94,209

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

activist fund / activist shareholder / activist investor, Airbnb, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, bank run, banking crisis, barriers to entry, 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, disintermediation, diversified portfolio, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fiat currency, Firefox, Flash crash, full employment, future of work, gig economy, Gini coefficient, global supply chain, global village, Google bus, Howard Rheingold, IBM and the Holocaust, impulse control, income inequality, index fund, iterative process, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joseph Schumpeter, Kickstarter, loss aversion, 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, profit motive, quantitative easing, race to the bottom, recommendation engine, reserve currency, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Snapchat, social graph, software patent, Steve Jobs, TaskRabbit, The Future of Employment, trade route, transportation-network company, Turing test, Uber and Lyft, Uber for X, uber lyft, unpaid internship, Y Combinator, young professional, zero-sum game, Zipcar

.* 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.

The worker is not eliminated; he’s just invisible. 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. But when even minimum-wage jobs aren’t available to many workers today, they are empowered to make only one choice or none at all.

No cash is risked, because the backers don’t pay until the total amount sought has been raised. The only risk is that the project is never completed, but the open market seems pretty good at evaluating competence: 98 percent of projects that meet just 60 percent of their funding goals are fully completed. Startups funded by venture capital do about the reverse, with more than 90 percent of fully funded enterprises failing.59 The same crowdsourcing dynamics that Upwork or 99designs* use to shift risk onto freelancers can also shift risk off the table altogether. The less risk, the less money is owed to the risk taker. So, used appropriately, the net disintermediates the funder, eliminates the need to abandon ongoing productivity in favor of a quick exit, spares the marketplace from having to pay back investors, keeps cash in circulation instead of being extracted, and gives regular people the opportunity to put their money toward what they want to see happen.


pages: 465 words: 109,653

Free Ride by Robert Levine

A Declaration of the Independence of Cyberspace, Anne Wojcicki, book scanning, borderless world, Buckminster Fuller, citizen journalism, commoditize, 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, 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, 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. In the meantime, free culture advocates take any chance they get to argue that the media business can adjust to a world in which laws against illegal copying are not enforced.

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? The product isn’t the problem. Online publications that Twitter on a 24-7 schedule face the same difficulties, and even the inventive start-up Politico has said it makes most of its revenue on the print edition it distributes in Washington, D.C.14 At most online start-ups, any interest in journalistic innovation seems to come from the desire to cut costs, which is why more of them are experimenting with “citizen journalism” than, say, professionally shot video.

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.)


pages: 371 words: 108,317

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

A Declaration of the Independence of Cyberspace, AI winter, Airbnb, Albert Einstein, 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, connected car, crowdsourcing, dark matter, dematerialisation, Downton Abbey, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, game design, 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, Kevin Kelly, Kickstarter, lifelogging, linked data, Lyft, M-Pesa, Marc Andreessen, Marshall McLuhan, means of production, megacity, Minecraft, Mitch Kapor, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, old-boy network, peer-to-peer, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, recommendation engine, RFID, ride hailing / ride sharing, 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, the scientific method, transport as a service, two-sided market, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Review, 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. Another popular version of crowdsourcing appears, at first, to be less about collaboration and more about competition.

A couple platforms (Flattr, Unglue) use fans to fund work that has already been released. 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.

No matter how many times the picture may be recopied, the credit comes back to me. Compared with last century, it’s really easy to make, say, an instructional video now because you can assemble the available parts (images, scenes, even layouts) from other excellent creators, and the micropayments for their work automatically flow back to them as a default. The electric car we are making will be crowdsourced, but unlike decades earlier, every engineer who contributes to the car, no matter how small her contribution, gets paid proportionally. I have a choice of 10,000 different co-ops I can contribute to. (Not many of my generation want to work for a corporation.) They offer different rates, varying benefits, but, most important, different sets of coworkers. I try to give my favorite co-ops a lot of time not because they pay more, but because I really enjoy working with the best folks—even though we’ve never met in real life.


pages: 523 words: 61,179

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

3D printing, AI winter, algorithmic trading, Amazon Mechanical Turk, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, digital twin, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, friendly AI, future of work, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, natural language processing, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Rodney Brooks, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, software as a service, speech recognition, telepresence, telepresence robot, text mining, the scientific method, uber lyft

For instance, Julie Shah, a robotics professor at MIT, has been developing robots that can shadow people in their jobs so that they can eventually perform certain tasks. 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. One client has retained Mighty AI to teach its machine-learning platform to extract intent and meaning from human conversations.

Think Like Waze To help illustrate the profound difference between the old process thinking and the new, consider the history of GPS navigation. 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.

., 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.


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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, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, blood diamonds, Burning Man, call centre, cashless society, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, crowdsourcing, cryptocurrency, Dean Kamen, delayed gratification, dematerialisation, digital twin, disruptive innovation, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, food miles, game design, Geoffrey West, Santa Fe Institute, gig economy, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, indoor plumbing, industrial robot, informal economy, 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, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, mass immigration, megacity, meta analysis, meta-analysis, microbiome, mobile money, multiplanetary species, Narrative Science, natural language processing, Network effects, new economy, New Urbanism, Oculus Rift, out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, Satoshi Nakamoto, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, technoutopianism, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

Fast Company, March 3, 2017, See: https://www.fastcompany.com/3068534/how-will-the-rise-of-crowdfunding-reshape-how-we-give-to-charity-2. 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

short film Sunspring: “Sunspring” (video). See: https://www.youtube.com/watch?v=LY7x2Ihqjmc. 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. See: https://en.wikipedia.org/wiki/Category:Video_games_with_user-generated_gameplay_content.

But if two gamblers can decide in advance what source to trust as an arbiter of results—say, the sports page of the New York Times—then they can build a blockchain contract that allows them to bet with one another, have the system settle the bet via the pages of the Times, then automatically move the money. 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. To figure out where this is all going, there’s one more property of the blockchain worth discussing—the fact that it can serve as a bridge between worlds.


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

Airbnb, barriers to entry, Bernie Madoff, bike sharing scheme, Buckminster Fuller, 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, George Akerlof, global village, hedonic treadmill, Hugh Fearnley-Whittingstall, information retrieval, iterative process, Kevin Kelly, Kickstarter, late fees, Mark Zuckerberg, market design, Menlo Park, Network effects, new economy, new new economy, out of africa, Parkinson's law, peer-to-peer, peer-to-peer lending, peer-to-peer rental, Ponzi scheme, pre–internet, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Robert Shiller, Robert Shiller, Ronald Coase, Search for Extraterrestrial Intelligence, SETI@home, Simon Kuznets, Skype, slashdot, smart grid, South of Market, San Francisco, Stewart Brand, The Nature of the Firm, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thorstein Veblen, Torches of Freedom, transaction costs, traveling salesman, ultimatum game, Victor Gruen, web of trust, women in the workforce, Zipcar

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). 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.

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___.” Gallop illustrates how it works with a simple example. “The blank would be filled with something like ‘plant a garden to feed the local homeless.’ ” On the IfWeRanTheWorld platform, the user and the community all help break down the goal into microactions that friends, family, neighbors, businesses, celebrities, or total strangers can all help complete.

There will be an explosion in services that enable you to repair, upgrade, and customize owned or secondhand products. Instead of automatically paying with cash for many products and services, we will offer to barter talents, skills, and ideas, and virtual social currencies will have become a normal way to exchange. The consumer preference for handmade or locally produced goods will become the norm. Neighborhood networks such as EveryBlock or NeighborGoods will explode and enable local crowdsourcing between residents on creative and social projects. There will be a whole ecosystem of apps and software for our phones and computers that will enable us to share any kind of product or service. A collaborative and sharing culture will be the culture. We believe we will look back and see this epoch as a time when we took a leap and re-created a sustainable system built to serve basic human needs—in particular, the needs for community, individual identity, recognition, and meaningful activity—rooted in age-old market principles and collaborative behaviors.


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

23andMe, 4chan, Airbnb, anti-communist, anti-globalists, augmented reality, Ayatollah Khomeini, bitcoin, blockchain, Boris Johnson, citizen journalism, cognitive dissonance, crowdsourcing, cryptocurrency, Donald Trump, Elon Musk, feminist movement, game design, glass ceiling, Google Earth, job satisfaction, Mark Zuckerberg, mass immigration, Menlo Park, Mikhail Gorbachev, Network effects, off grid, pattern recognition, pre–internet, QAnon, RAND corporation, ransomware, rising living standards, self-driving car, Silicon Valley, Skype, Snapchat, social intelligence, Steve Jobs, Transnistria, WikiLeaks, zero day

‘If you believe the banks are part of the Jewish world conspiracy nonsense, well, then there are only two ways to make financial transactions: it’s either cash or it’s bitcoin.’ 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). For example, Weev received $1.8 million cryptocurrency donations to his visible wallet address, which was tracked by Bambenek.

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’. By 2018, forty-nine patrons were supporting him financially with an average of US$532.

I could have lost my job and everything. Because that’s what happens: you get fired here if you are a Nazi.’ ‘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. It’s the weekend, this will be a waste of time,’ I add hastily. ‘Oh don’t be lame, this could be so much fun.’


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WikiLeaks and the Age of Transparency by Micah L. Sifry

1960s counterculture, Amazon Web Services, banking crisis, barriers to entry, Bernie Sanders, Buckminster Fuller, Chelsea Manning, citizen journalism, Climategate, crowdsourcing, Google Earth, Howard Rheingold, Internet Archive, Jacob Appelbaum, John Markoff, Julian Assange, Network effects, RAND corporation, school vouchers, Skype, social web, source of truth, Stewart Brand, web application, WikiLeaks

Chapter 6 1 President Barack Obama, “Transparency and Open Government,” January 21, 2009, www.whitehouse.gov/the_press_office/Transparency andOpenGovernment. 2 President Barack Obama, “Freedom of Information Act,” January 21, 2009, www.whitehouse.gov/the_press_office/FreedomofInformationAct. 3 William Branigin, “Democrats Take Majority in House; Pelosi Poised to Become Speaker,” The Washington Post, November 8, 2006, www.washingtonpost.com/wp-dyn/content/article/2006/11/07/ AR2006110700473.html. 4 A full list of participants in the Open House Project can be found here: www.theopenhouseproject.com/press/launch. 5 John Wonderlich, “Open House Project Retrospective,” The Open House Project, October 14, 2008, www.theopenhouseproject.com/2008/10/14/ open-house-project-retrospective. 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.

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. Call it crowd-scouring instead.

But we still won’t know what members of Congress are doing when they meet with lobbyists, nor will we know anything but the roughest range of their personal financial holdings in companies whose business they may oversee. 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. Tom Steinberg of mySociety was then asked to coauthor a study along with Ed Mayo, the head of the National Consumer Council, on the “power of information” to foster new kinds of citizen-to-citizen information sharing and collaboration.


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

"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, 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, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

As early as 2005, for example, two chess amateurs used a chess-playing software program to win a match against chess experts and individual chess-playing programs. 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.

“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.

In the last century the car became synonymous with the American ideal of freedom and independence. That era is now ending. What will replace it? 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.


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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, British Empire, call centre, Charles Lindbergh, crowdsourcing, Dava Sobel, digital map, don't be evil, Edmond Halley, Edward Snowden, Firefox, game design, Google Earth, Hedy Lamarr / George Antheil, Isaac Newton, job automation, John Harrison: Longitude, John Snow's cholera map, 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

The resulting traffic maps are far more comprehensive than the road reports from drive-time radio DJs. 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.

Patrick Meier, “The Past and Future of Crisis Mapping,” iRevolution, October 18, 2008, http://irevolution.net/2008/10/18/future-of-crisis-mapping/. 12. 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.

Still, Google’s careless handling of the matter had conferred new prestige upon a once obscure competitor. 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. Much of the increase came as Google licensed map data from government agencies in countries like Russia and China.


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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, cognitive dissonance, credit crunch, crowdsourcing, dark matter, Dava Sobel, David Brooks, Donald Davies, future of journalism, 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, meta-analysis, Naomi Klein, Nate Silver, Occupy movement, packet switching, peer-to-peer, Peter Thiel, planetary scale, pre–internet, RAND corporation, risk tolerance, shareholder value, Silicon Valley, Silicon Valley startup, social graph, Steve Jobs, Steven Pinker, Stewart Brand, The Death and Life of Great American Cities, Tim Cook: Apple, urban planning, US Airways Flight 1549, WikiLeaks, William Langewiesche, working poor, X Prize, your tax dollars at work

The figure of some $1.5 billion passing through crowdfunding sites in 2011 is from Forbes, http://www.forbes.com/sites/suwcharmananderson/2012/05/11/crowdfunding-raised-1-5bn-in-2011-set-to-double-in-2012/. II. 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. Journalism. The Pothole Paradox For more on the debate over the future of journalism, see my discussion with Paul Starr published in Prospect, “Are We on Track for a Golden Age of Serious Journalism?”

Systems based on pure information are clearly more amenable to the experimentation, decentralization, and diversity of peer networks than more material realms are. 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. This is how progress happens: some problem or unmet need is identified, imaginative new solutions are proposed, and eventually society decides to implement one (or more) of those solutions.

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. It doesn’t set its priorities from above; it doesn’t get bound up in official definitions or categories.


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Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman

23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, basic income, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, commoditize, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, drone strike, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, lifelogging, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, mass incarceration, meta analysis, meta-analysis, Minecraft, move fast and break things, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social intelligence, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, universal basic income, unpaid internship, women in the workforce, Y Combinator, Zipcar

June 19, 2012. peasantmuse.com/2012/06/from-data-self-to-data-serf.html. 260 “This digital labor”: Scholz, “Why Does Digital Labor Matter Now?,”2. 260 “the micro-division of labor”: Andrew Ross. “In Search of the Lost Paycheck.” In Digital Labor: The Internet as Playground and Factory. Trebor Scholz, ed. New York: Routledge, 2013, 20. 260 Apps similar to Twitch: Rachel Metz. “The Next Frontier in Crowdsourcing: Your Smartphone.” MIT Technology Review. March 12, 2014. technologyreview.com/news/525481/thenext-frontier-in-crowdsourcing-your-smartphone. 262 Wall Street Journal praise for BuzzFeed: Farhad Manjoo. “BuzzFeed’s Brazen, Nutty, Growth Plan.” Wall Street Journal. Oct. 14, 2013. wsj.com/article/SB10001424052702304500404579129590411867328.html. 262 “digital volunteers”: Curt Hopkins. “The Smithsonian Is Outsourcing Transcription . . . to You.” Daily Dot.

(Sometimes rival networks fight back, as when Facebook bought Instagram—which was immensely popular with Twitter users—and later disabled the ability for Instagram photos to appear in the Twitter timeline and some Twitter apps.) Facebook has achieved a similar effect by encouraging third-party sites, from online magazines to Spotify, to use its Facebook Connect tool for user log-ins and, in the case of crowdsourced service sites such as Airbnb, as a kind of ersatz background check. Your Facebook identity becomes the proof of who you are and your reliability. This may make it more difficult for non-Facebook users to access these services, but it also means that instead of having to sign up for an account to read Foreign Policy or use the Tinder dating app, a Facebook member can just join through his or her Facebook account.

Even so, companies remain extraordinarily reliant on these reviews. A 2011 Harvard Business School study found that, on Yelp, “an extra star is worth an extra 5 to 9 percent in revenue.” The result of all this reviewing has been the atrophying of the critical culture, with professional critics seen as dispensable, nothing more than recommendation engines who can be replaced with algorithms and free, crowdsourced reviews. (Even so, some prominent cultural critics remain, though with less influence than they used to hold, and a smattering of publications, from the actuarially precise Consumer Reports to the liberal humanist New York Review of Books, continue to thrive.) It’s also expanded the idea of what should be reviewed, with everything now potentially susceptible to, if not a star rating, then the kind of up-or-down judgment we perform all the time when we choose to like things.


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

Airbnb, 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, Google Glasses, Google X / Alphabet X, information asymmetry, Internet of things, inventory management, Jean Tirole, Jeff Bezos, John Markoff, Just-in-time delivery, Khan Academy, Kickstarter, late fees, 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, selection bias, self-driving car, shareholder value, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, special economic zone, Stephen Hawking, Steve Jobs, Steven Levy, Thomas L Friedman, transaction costs, two-sided market, ubercab, WikiLeaks, winner-take-all economy, zero-sum game

“People need to believe either that their contributions will make a difference—as with NASA—or that they’ll get discovered,” Anil Dash told me. “But crowdsourced news reporting tends to be like giving people homework: ‘Tell us what happened at the city council meeting.’ It turns out, most people don’t want to go to those meetings, and they can sniff out very quickly when you’re just trying to get them to do your homework for you.” Sharability has costs, too. After the Boston Marathon bombings in 2013, a false rumor that a Brown University student was a suspect spread like wildfire. Harmful triggers can also spread. As with vitriol and vandals, the challenge is not only to create positive connections but also to prevent negative ones. Focusing merely on contributions at the expense of connections is the first error commonly made in crowdsourcing. There’s a second, more basic, one: thinking that merely “opening up” to crowds will generate content.

We’d just seen a friend, Ben Malbon, who decided to crowdsource a new logo for BBH Labs. They used the open platform crowdspring, on which the buyer would continually provide scoring feedback to submissions that were visible to all, a platform feature designed to allow buyers to shape crowd work in a preferred direction. The industry reaction was predictable: “We are making money, don’t blow a hole in this.” Alex and I looked at each other and said, “Oh, this is kind of cool.” So we decided to use crowdspring for the Brammo work. CPB put out a simple brief for Brammo’s work on crowdspring. They also offered a prize—$10,000 for the winning submission. “It was ten times more than anything that had been offered on the crowdspring platform. And this was the first crowdsourcing work ever offered by the ad industry on behalf of a client.”

In television, content and distribution companies alike got distracted: seduced by near-term licensing revenues that made streaming providers stronger in the end, seduced by the lure of higher prices that ultimately created consumer backlash, and seduced by megamergers that were struck down and that made usage-based pricing harder to implement. Seduced by the trees, they had forgotten about the forest. 7 CROWDS Networks can be used for content creation, not just consumption. Crowdsourcing, user-generated content, and user contribution networks have become commonplace terms. But what that means for the future of content businesses is a matter of fierce debate. While many observers scoff at the content increasingly emerging from crowds, others believe it will ultimately replace traditionally created material. Karim Lakhani studies crowds. He’s been at it for more than a decade, first at MIT and now at Harvard Business School.


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Facebook: The Inside Story by Steven Levy

active measures, Airbnb, Airbus A320, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, augmented reality, Ben Horowitz, blockchain, Burning Man, business intelligence, cloud computing, computer vision, crowdsourcing, cryptocurrency, don't be evil, Donald Trump, East Village, Edward Snowden, El Camino Real, Elon Musk, Firefox, Frank Gehry, glass ceiling, indoor plumbing, Jeff Bezos, John Markoff, Jony Ive, Kevin Kelly, Kickstarter, Lyft, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, move fast and break things, natural language processing, Network effects, Oculus Rift, PageRank, Paul Buchheit, paypal mafia, Peter Thiel, pets.com, post-work, Ray Kurzweil, recommendation engine, Robert Mercer, Robert Metcalfe, rolodex, Sam Altman, Sand Hill Road, self-driving car, sexual politics, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, slashdot, Snapchat, social graph, social software, South of Market, San Francisco, Startup school, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, Tim Cook: Apple, web application, WikiLeaks, women in the workforce, Y Combinator, Y2K

One engineer on the team who had been a huge fan of the Reddit website suggested that Facebook adopt the Up and Down buttons that Redditors used to sort content. The crowdsourcing approach ran counter to accepted wisdom. The tried-and-true method was known as the 80/20 rule, where resources were concentrated, and professional help sought, on the key languages known as FIGSCJK—French, Italian, German, Spanish, Chinese, Japanese, and Korean. That would get you the vast bulk of the online audience. “As a company we’ve always had the mission of connecting the world,” says Olivan. “And the 80/20 rule is not going to cut it. We really wanted to make sure it was available for everyone.” To do that Facebook needed to improve its crowdsourcing tools so those professionals weren’t needed. The idea was to have Facebook everywhere. Even in many languages that no one at Facebook was familiar with. Crowdsourcing would only go so far, though, and Facebook had to accept that it needed professional translators for what were deemed the most important languages, which they calculated by a country’s gross domestic product.

It checked its logs to identify people who were using the English version of Facebook in some foreign country. Those chosen—by algorithm of course—would see a message on top of their News Feed asking if they’d like to help to translate Facebook. These unpaid helpers (another benefit of crowdsourcing) would do a first draft, setting up a scaffolding that identified the pitfalls of moving Facebook to the individual language. After that first step, Facebook might open up the process of translating the terminology to everyone. Sometimes it would prompt people for hints: a native speaker might get a pop-up saying, “Hey do you speak this language, can you help us with these ads?” Sometimes the crowdsourcing would be used to refine a machine translation. The hurdle was getting good translations. In order to be put into production, a translated version of Facebook needed verification that the translation was accurate and nuanced, not to mention avoiding any ugly American faux pas.

Even though the company eventually struck a deal with the media giant Bertelsmann, music executives and investors never forgot its association with copyright infringement. That, and the fat target it presented for copyright litigation, ensured its demise. Parker didn’t make a dime from it, but managed to emerge with excellent connections in the music world. His next act was Plaxo, a start-up that tried to crowdsource everyone’s contact lists. (It fulfilled Andrew Weinreich’s 1997 vision about a great global networked Rolodex, when he launched sixdegrees.) Napster had been viral because of great word of mouth, but Plaxo had virality built in. With a click of a button, new users would bombard those on their contact list with requests to upload their addresses and phone numbers to Plaxo. Those targeted would often become furious at the multiple solicitations in their inboxes.


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Messing With the Enemy: Surviving in a Social Media World of Hackers, Terrorists, Russians, and Fake News by Clint Watts

4chan, active measures, Affordable Care Act / Obamacare, barriers to entry, Berlin Wall, Bernie Sanders, Chelsea Manning, Climatic Research Unit, crowdsourcing, Daniel Kahneman / Amos Tversky, Donald Trump, drone strike, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, failed state, Fall of the Berlin Wall, Filter Bubble, global pandemic, Google Earth, illegal immigration, Internet of things, Julian Assange, loss aversion, Mark Zuckerberg, Mikhail Gorbachev, mobile money, mutually assured destruction, obamacare, Occupy movement, offshore financial centre, pre–internet, side project, Silicon Valley, Snapchat, The Wisdom of Crowds, Turing test, University of East Anglia, Valery Gerasimov, WikiLeaks, zero day

The prediction, however, was actually designed as a vehicle for crowdsourcing an important question. What would al-Qaeda and the world of terrorism be if bin Laden were no more? I used my New Year’s bin Laden prediction to provoke the audience to answer my “Post–bin Laden” survey. My attempts at crowdsourcing this survey failed miserably. Rather than yielding great wisdom or important insights from experts, the results instead returned a pattern of answers of no consequence. “Nothing will change” and “It doesn’t matter” became patent answers from the best thinkers in the field, regardless of the question. My crowds weren’t wise; they seemed a bit lazy and dumb. Even more, on Twitter, they were hypercritical. Reviewers of my analysis couldn’t wait to tell me my prediction was wrong or that my questions were stupid. Crowdsourcing the future wasn’t working as planned.

The advantages the U.S. intelligence community had once enjoyed were slipping away in the open-source world. A few years before, in 2004, American journalist James Surowiecki had published a book called The Wisdom of Crowds, which described how the internet provided a vehicle for crowds to make smarter decisions than even the smartest person in the crowd, working alone, could make. Collective intelligence mined from the internet through crowdsourcing proved effective in three types of decisions: coordination challenges, where groups work together to determine an optimal solution, such as the best way to get to work or travel overseas; cognition calculations, where people involved in a market compete to provide the right answer, such as guessing the winner of an election; and cooperation networks, where a central system collects information and the crowd then controls behavior and enforces compliance—think Wikipedia as an example.

The internet lowered the barriers for users to contribute their data and opinions in each of these situations and significantly decreased the costs of collecting such information. The advent of the iPhone propelled this phenomenon even further with social media apps rapidly capturing user experiences. Amazon provided millions of product reviews, Yelp’s app created restaurant recommendations for any neighborhood, and Rotten Tomatoes guided people to the better movies available in theaters. As I began to further develop my blog, selectwisdom.com, in 2010, I thought crowdsourcing might be a way to harness counterterrorism analysis in the same way it had helped terrorists march forward online. I started the blog off slow. I’d write a short post on whatever al-Qaeda affiliate had pulled off an attack that week. I’d profile those groups that I knew better than others. I’d showcase the great research of those who didn’t get enough attention for their work, predominantly those outside of Washington, D.C.’s thought bubble.


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The Purpose Economy: How Your Desire for Impact, Personal Growth and Community Is Changing the World by Aaron Hurst

Airbnb, Atul Gawande, barriers to entry, big-box store, business process, call centre, carbon footprint, citizen journalism, commoditize, corporate social responsibility, crowdsourcing, disintermediation, Elon Musk, Firefox, glass ceiling, greed is good, housing crisis, informal economy, Jane Jacobs, jimmy wales, Khan Academy, Kickstarter, Lean Startup, longitudinal study, means of production, Mitch Kapor, new economy, pattern recognition, Peter Singer: altruism, Peter Thiel, QR code, Ray Oldenburg, remote working, Ronald Reagan, selection bias, sharing economy, Silicon Valley, Silicon Valley startup, Steve Jobs, TaskRabbit, Tony Hsieh, too big to fail, underbanked, women in the workforce, young professional, Zipcar

Five Levers in Action: Nine Mini-Case Studies You can find these levers in action across industries and sectors. While Taproot used all five, different organizations moved markets by using different levers. 1. Mosaic: Solar Energy Mosaic, an online portal for solar energy investment and crowdsourced finance, utilized disruptive energy and financing sector technologies to address the public perception of solar’s high investment costs. Publicly subsidizing solar investments has been tricky from a policy perspective, but Mosaic’s decision to crowdsource its financing and “green” investment was not only a clever marketing tool, but another way to challenge perceptions of the business viability of solar energy. To date, $5.6 million has been invested through the Mosaic platform. 2. OMEGA: Biofuels The OMEGA (Offshore Membrane Enclosures for Growing Algae) Project used new research and data by scientists and engineers to create a disruptive technology in the form of algae.

While this worked as a controlled pilot project, the next step is to understand the commercial viability of offshore OMEGA systems for a variety of uses, including biofuels production, wastewater treatment, and carbon sequestration. 3. Kickstarter: Crowdsourced Investment Kickstarter, an online platform for crowdfunding independent creative projects, launched in 2009 using secure online fundraising platforms (itself a recent disruptive technology). The platform recognized and filled a gap for creative entrepreneurs, designers, and other freelancers wanting to maintain creative control over their projects. The founders like to highlight this process as being rooted in the time of Mozart or Mark Twain, who solicited money from their communities and gave that community one of their finished products. Even so, Kickstarter’s inventive use of technology to harness the power of the creative community has enabled a crowd-sourced $789 million for 53,672 different projects, and in the process, Kickstarter has become one of the most influential bright spots in and beyond the tech world. 4.

LinkedIn, then, was part of this second wave, as it evolved past sites like Monster.com to allow us to assess professionals through a network of relationships between users. Google, too, was built on this 2.0 Internet, moving beyond searching web pages to allow us to search the network of links between users. Letters became emails (online letters), and emails became tweets (networked letters). Meetings became online discussion forums and eventually crowdsourcing. Social media is at the heart of Internet 2.0. By helping move people from consumers to creators online, social media drove the web’s next generation to emerge. It sparked our collective imagination in thinking about how technology could be leveraged for self-expression, community building, and service. And as our lives become more public, we are increasingly conscious of “personal brands.” So many people now have windows into our activities, network, and points of view, and this new level of transparency has created new ways to display our aspirational selves.


Speaking Code: Coding as Aesthetic and Political Expression by Geoff Cox, Alex McLean

4chan, Amazon Mechanical Turk, augmented reality, bash_history, bitcoin, cloud computing, computer age, computer vision, crowdsourcing, dematerialisation, Donald Knuth, Douglas Hofstadter, en.wikipedia.org, Everything should be made as simple as possible, finite state, Gödel, Escher, Bach, Jacques de Vaucanson, Larry Wall, late capitalism, means of production, natural language processing, new economy, Norbert Wiener, Occupy movement, packet switching, peer-to-peer, Richard Stallman, Ronald Coase, Slavoj Žižek, social software, social web, software studies, speech recognition, stem cell, Stewart Brand, The Nature of the Firm, Turing machine, Turing test, Vilfredo Pareto, We are Anonymous. We are Legion, We are the 99%, WikiLeaks

At the same time, and for obvious reasons, labor relations are downplayed through carefully chosen language, describing “requesters” rather than employers, perhaps to avoid questions of labor value and subsequent antagonisms. Even more depressing is that so-called “Turkers” in the US use this “crowdsourcing marketplace” as a form of entertainment. As Trebor Scholz puts it, “The biggest trick that Mechanical Turk ever pulled off was to make Code Working 49 Figure 2.4 Aaron Koblin, The Sheep Market (2006). Image courtesy of Aaron Koblin. workers believe that what they do is not really work. That, at least, is somewhat suggested by the slogan on MTurk’s coffee mugs: ‘Why work if you can turk?’”38 Artists have also enthusiastically responded with experiments in crowdsourcing, such as Aaron Koblin’s The Sheep Market (2006), a collection of 10,000 sheep made by workers using Mechanical Turk, each paid US$0.02 to “draw a sheep facing to the left.”39 Whether effective irony or not, such examples reflect how labor is becoming ever more informational and communicative, leading to a situation in which all activities seem to have been turned into production.40 The Sheep Market is also a good example of how capital tries to capture the creative and communicative capacity of the socialized labor force and turn it into information that can be marketized.

Happiness is exemplified by the public performance of songs. “For Marx, the privileged example of really free working—happiness itself—is ‘composition,’ the construction of the communist score . . . communism whose song will free the space in which it resonates, and spreads.”118 A further example is Aaron Koblin and Daniel Massey’s artwork Bicycle Built for Two Thousand (2009), using Amazon’s Mechanical Turk crowdsourcing web service as mentioned earlier in the chapter. This time over two thousand recorded voices are collected and assembled into the song “Daisy Bell,” the same song that was used in the first example of musical speech synthesis in 1962 (which made the IBM 7094 the first computer to sing a song).119 Yet the comparison between the computer-synthesized vocals and the one created with distributed humans is not very encouraging, as both have been effectively synthesized.

Marx, Grundrisse: Foundations of the Critique of Political Economy (Rough Draft). 37. Amazon.com, Mechanical Turk (available at http://www.mturk.com/). 38. Trebor Scholz, “On MTurk, Some Examples of Exploitation” (2009; available at http:// www.collectivate.net/journalisms/). 39. Aaron Koblin, The Sheep Market (2006; available at http://www.aaronkoblin.com/work/ thesheepmarket/). It cannot go unnoticed that Koblin is both an artist known for his innovative uses of crowdsourcing and also currently Creative Director of the Data Arts Team at Google Creative Lab. His biography is available at http://www.aaronkoblin.com/info.html. 40. Antonio Negri, Marx beyond Marx: Lessons on the Grundrisse, ed. Jim Fleming, trans. Harry Cleaver, Michael Ryan, and Maurizio Viano (New York: Autonomedia/Pluto, 1991), 77. 41. Lazzarato, “Immaterial Labor,” 132–146. 42. Franco “Bifo” Berardi, The Soul at Work: From Alienation to Autonomy, trans.


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Fewer, Better Things: The Hidden Wisdom of Objects by Glenn Adamson

big-box store, blood diamonds, blue-collar work, Buckminster Fuller, carbon footprint, crowdsourcing, dematerialisation, dumpster diving, haute couture, informal economy, Jacquard loom, Joseph-Marie Jacquard, Mason jar, race to the bottom, trade route, white flight

From the days of the Silk Road, the overland trading route by which textiles and other goods traveled between the Mediterranean and the Far East, to the glory days of the China trade in the seventeenth and eighteenth centuries, when porcelains from Asia flooded into Europe, to today’s online web browsers, objects are ideal global emissaries. Needless to say, there is a big difference between handling an object and seeing a picture of it on-screen. Yet there is already tremendous value in the V&A’s online provision and that of other institutions. And as massive as these recent efforts have been, there can be little doubt that they are only a first step. As we know from the example of Wikipedia, crowdsourcing can be highly effective when it comes to making knowledge available to the public. One can easily imagine a time soon when museums will adopt moderated “wiki” structures that will allow online readers to add content to catalog records. Already, institutions are exploring collaborative partnerships in which aggregated and hyperlinked search engines can connect collections to one another, raising the possibility of universal object databases for research and discovery.1 In the future, these and other currently unimaginable initiatives will certainly be put in place.

NOTES Introduction:  ENGAGING WITH THE OBJECTS AROUND US   1   Andy Tope, “The Children of Technology, a New Species,” Fox Gazette (November 22, 2011).   2   William Morris, “Art and the Beauty of the Earth,” 1881. Chapter 2:  A FEW WORDS ON CRAFT   1   On the question of craft’s presumed inferiority from the perspective of fine art, see Glenn Adamson, Thinking Through Craft (Oxford: Berg, 2008), and Glenn Adamson and Julia Bryan-Wilson, Art in the Making: Artists and Their Materials from the Studio to Crowdsourcing (New York: Thames and Hudson, 2016).   2   Hugh Aldersey-Williams, Periodic Tales (New York: Viking, 2011), p. 12.   3   Thomas Heatherwick and Maisie Rowe, Thomas Heatherwick: Making (London: Monacelli, 2015).   4   Andrew O’Hagan, “Imaginary Spaces: Es Devlin and the Psychology of the Stage,” New Yorker (March 28, 2016).   5   Andrew Bolton, ed., Alexander McQueen: Savage Beauty (New York: Metropolitan Museum of Art, 2011).

Chapter 13:  THE CONTACT ZONE   1   “Architectural Terra Cotta a Big Factor in New Building,” New York Times (May 14, 1911). See also Susan Tunick’s fine book Terra-Cotta Skyline: New York’s Architectural Ornament (Princeton: Princeton Architectural Press, 2007).   2   For more on fabricators like Milgo/Bufkin, see Glenn Adamson and Julia Bryan-Wilson, Art in the Making: Artists and Their Materials from the Studio to Crowdsourcing (London: Thames and Hudson, 2016).   3   One report suggests that over five million dollars’ worth of American flags are imported from China every year, along with over 90 percent of fireworks used in the United States. See “The Fourth of July, Made in China,” Vox (July 3, 2017).   4   A. C. Grayling, The Meaning of Things: Applying Philosophy to Life (London: Weidenfeld and Nicolson, 2001), p. 184.


pages: 579 words: 160,351

Breaking News: The Remaking of Journalism and Why It Matters Now by Alan Rusbridger

accounting loophole / creative accounting, Airbnb, banking crisis, Bernie Sanders, Boris Johnson, centre right, Chelsea Manning, citizen journalism, cross-subsidies, crowdsourcing, David Attenborough, David Brooks, death of newspapers, Donald Trump, Doomsday Book, Double Irish / Dutch Sandwich, Downton Abbey, Edward Snowden, Etonian, Filter Bubble, forensic accounting, Frank Gehry, future of journalism, G4S, high net worth, invention of movable type, invention of the printing press, Jeff Bezos, jimmy wales, Julian Assange, Mark Zuckerberg, Menlo Park, natural language processing, New Journalism, offshore financial centre, oil shale / tar sands, open borders, packet switching, Panopticon Jeremy Bentham, pre–internet, ransomware, recommendation engine, Ruby on Rails, sexual politics, Silicon Valley, Skype, Snapchat, social web, Socratic dialogue, sovereign wealth fund, speech recognition, Steve Jobs, The Wisdom of Crowds, Tim Cook: Apple, traveling salesman, upwardly mobile, WikiLeaks

In the volume of space devoted to the massacre you can feel the editor of the Times, Thomas Barnes, grappling with how anyone could establish the truth. Would people naturally trust the word of one reporter over that of the magistrates? Would readers be more convinced if there were multiple accounts broadly corroborating one version? In addition to its own reporting the paper went in for two techniques that became routine in the early twenty-first century – aggregation and crowdsourcing. The aggregation took the form of excerpts from other local papers’ reports of the day. The crowdsourcing came from a petition and from numerous ‘private letters’ similar to Taylor’s. They painted a confusing picture, but the accumulation of evidence overwhelmingly demonstrated that the crowd had behaved peacefully and there was no possible justification for the violence meted out. Taylor understood the importance of facts – and also predicted that the facts of the day would be contested, and litigated, for months, if not years.

Possibly she was right, but it was too late in the day to go into reverse. In time, a number of web start-ups saw ‘slow’ as a business model and attempted to do exactly what Emily was groping towards. If ‘follow my leader’ wouldn’t work as a leadership style, what would? We devised three or four hypothetical situations which we were likely to face – a high-profile sports event on a Saturday, a big breaking news story on a Thursday night, and so on – and crowdsourced solutions among editorial employees. For instance, they might consider a major prison report being published on, say, a Tuesday. The desk might want the home affairs correspondent on the Guardian to write a snap story, with the most important news line; they would then want a gut of the report, some analysis and maybe some audio or video. Comment might commission a piece. The newspaper would want a front-page story with reaction for later in the day.

We had used ‘open’ techniques in June 2009 at the height of the concern over the sums British MPs were claiming by way of expenses. When the entire database was released it consisted of 700,000 individual documents within 5,500 PDF files covering all 646 members of parliament. It would have taken weeks for a team of reporters to work their way through the material. So we asked the readers to help: we created a basic crowdsourcing app whereby readers could download any documents, comment on them and highlight anything that caught their eye. This act of sifting and commenting was of incalculable help to the reporters – and was enthusiastically embraced by numerous readers. Once again, pro-am worked to our advantage. We did it again on tax – with complex corporate structures that would have taken days to understand. Sometimes we just threw a question out to the readers: we don’t have the immediate knowledge to hand to interpret this offshore arrangement, but we know some of you will.


pages: 81 words: 24,626

The Internet of Garbage by Sarah Jeong

4chan, Brian Krebs, crowdsourcing, John Markoff, Kickstarter, Network effects, Silicon Valley

You are lower than shit and deserve to be hurt, maimed, killed, and finally, graced with my piss on your rotting corpse a thousand times over.” Quinn was doxed—her personal information, including her address and Social Security number, was published. She moved. The harassment continued—and with some patient investigation, Quinn was able to document Gjoni egging on her harassers from behind the scenes. What Gjoni was doing was both complicated and simple, old and new. He had managed to crowdsource domestic abuse. Kathy Sierra After the previous examples, Kathy Sierra’s story will begin to sound redundant. But what happened to Sierra, an author and tech blogger, was in 2007, long before this current wave of interest in gendered harassment. At first she only received messages, messages that read just like the kinds received by Quinn, Criado-Perez, Sarkeesian, and Hess. “Fuck off you boring slut … i hope someone slits your throat and chums down your gob.”

There’s a difference between info buried in small font in a dense book of which only a few thousand copies exist in a relatively small geographic location versus blasting this data out online where anyone with a net connection anywhere in the world can access it.” The context in which the publicly information gets posted matters. When the dox is posted “before a pre-existing hostile audience,” the likelihood that malicious action follows from it is much higher. Katherine Cross calls it “crowdsourcing harassment.” In the words of Kathy Sierra: “That’s the thing—it’s not so much the big REVEAL, it’s the context in which that reveal happens—where someone is hoping to whip others up into a froth and that at least some of them will be just angry and/or unbalanced enough to actually take action. The troll doesn’t have to get his hands dirty knowing someone else will do it for him.” Kathy Sierra was fully doxed, meaning that her Social Security number was posted.

When sorted by investment, harassing behavior is defined according to the investment that the harasser makes in their efforts. • Sustained Hounding This is, more or less, stalking—a person acting solo that doggedly goes after one or more individuals, whether by just sending them horrible messages, obsessively posting about their personal lives, or by even sending them physical mail or physically following them around. • Sustained Orchestration Orchestration is crowdsourced abuse—the recruitment of others into harassing someone in a sustained campaign. Orchestration may happen simply by posting dox, or by postings that incite an audience to go after someone for whatever reason. • Low-Level Mobbing This is the behavior of those who are recruited into a sustained campaign, but never themselves become orchestrators of the ongoing campaign. They amplify the harassment, but may not themselves obsess over the targets.


pages: 743 words: 201,651

Free Speech: Ten Principles for a Connected World by Timothy Garton Ash

A Declaration of the Independence of Cyberspace, activist lawyer, Affordable Care Act / Obamacare, Andrew Keen, Apple II, Ayatollah Khomeini, battle of ideas, Berlin Wall, bitcoin, British Empire, Cass Sunstein, Chelsea Manning, citizen journalism, Clapham omnibus, colonial rule, crowdsourcing, David Attenborough, don't be evil, Donald Davies, Douglas Engelbart, Edward Snowden, Etonian, European colonialism, eurozone crisis, failed state, Fall of the Berlin Wall, Ferguson, Missouri, Filter Bubble, financial independence, Firefox, Galaxy Zoo, George Santayana, global village, index card, Internet Archive, invention of movable type, invention of writing, Jaron Lanier, jimmy wales, John Markoff, Julian Assange, Mark Zuckerberg, Marshall McLuhan, mass immigration, megacity, mutually assured destruction, national security letter, Nelson Mandela, Netflix Prize, Nicholas Carr, obamacare, Peace of Westphalia, Peter Thiel, pre–internet, profit motive, RAND corporation, Ray Kurzweil, Ronald Reagan, semantic web, Silicon Valley, Simon Singh, Snapchat, social graph, Stephen Hawking, Steve Jobs, Steve Wozniak, The Death and Life of Great American Cities, The Wisdom of Crowds, Turing test, We are Anonymous. We are Legion, WikiLeaks, World Values Survey, Yom Kippur War

In the best case, what in the online world are usually called ‘community standards’ are exactly that—the self-determined standards of a self-governing community. (Freespeechdebate.com raises an interesting question in this respect: how freely can we speak about free speech? Our community standards explain our approach and the reasoning behind it.184) A book by Jeff Howe, who seems to have coined the term ‘crowdsourcing’, concludes with ten pieces of advice, one of which is ‘pick the right crowd’.185 This is a plain but useful truth. Although the number of potential members is huge, the active membership of such crowds typically is not. Howe quotes an estimate that the optimal user base for crowdsourcing is around 5,000 people. Interestingly, that is roughly the size of the crowd you might have got together in person at a typical assembly in ancient Athens.186 As we shall see when we look more closely at free speech and knowledge, under principle 3, a few thousand very active editors have been the key to the success of Wikipedia.

Delete this Video other wise you will see that your Family Dead Bodies’ (contributed by one Anmad Sarwar) may have had something to do with that decision; see https://www.youtube.com/user/DarthF3TT/discussion 197. David Kirkpatrick, ‘A Deadly Mix in Benghazi’, New York Times, 28 December 2013, http://www.nytimes.com/projects/2013/benghazi/#/?chapt=0, 198. our map is based on the interactive map prepared by John Hudson of The Wire, embedded in http://perma.cc/QVL3-YES9 and accessible directly at http://perma.cc/ATR9-VJ8C. The daily evolving, crowdsourced Wikipedia pages documenting the affair once again demonstrated the online encyclopaedia’s value as a crowdsourced aggregator of useful information on recent events. That information was not always reliable, but with more than 200 footnotes, many of them containing links, the reader could check it out 199. Raza S. Hassan, ‘Two Cops Among 17 Killed in Karachi’, Dawn, 22 September 2012, http://perma.cc/D6VP-TUDW 200. quoted in Washington Post, 17 September 2012; watch online at http://www.youtube.com/watch?

‘Each of them by himself may not be of a good quality; but when they all come together it is possible that they may surpass—collectively and as a body, although not individually—the quality of the few best . . . For when there are many, each has his share of goodness and practical wisdom’.182 The legal philosopher Jeremy Waldron calls this the ‘doctrine of the wisdom of the many’.183 Twenty-five hundred years ago, that was the original ‘crowdsourcing’. The internet offers us opportunities to create our own self-governing online communities and draw on the ‘wisdom of crowds’ across frontiers. Within the technical, legal and political outer limits set by the big cats and dogs, we can establish communities where we say: ‘we wish to conduct this debate by certain rules. If you don’t want to live by those rules, go somewhere else’. In the best case, what in the online world are usually called ‘community standards’ are exactly that—the self-determined standards of a self-governing community.


pages: 299 words: 91,839

What Would Google Do? by Jeff Jarvis

23andMe, Amazon Mechanical Turk, Amazon Web Services, Anne Wojcicki, barriers to entry, Berlin Wall, business process, call centre, cashless society, citizen journalism, clean water, commoditize, connected car, credit crunch, crowdsourcing, death of newspapers, different worldview, disintermediation, diversified portfolio, don't be evil, fear of failure, Firefox, future of journalism, G4S, Google Earth, Googley, Howard Rheingold, informal economy, inventory management, Jeff Bezos, jimmy wales, Kevin Kelly, Mark Zuckerberg, moral hazard, Network effects, new economy, Nicholas Carr, old-boy network, PageRank, peer-to-peer lending, post scarcity, prediction markets, pre–internet, Ronald Coase, search inside the book, Silicon Valley, Skype, social graph, social software, social web, spectrum auction, speech recognition, Steve Jobs, the medium is the message, The Nature of the Firm, the payments system, The Wisdom of Crowds, transaction costs, web of trust, WikiLeaks, Y Combinator, Zipcar

Instead, these tools enable any business to build a new relationship with customers. Not every customer will want a personal relationship; most will eat and run. Following Wikipedia’s 1 percent rule, it takes only a small proportion of customers to get involved and contribute great value. Restaurants are even being crowdsourced. Trend-tracker Springwise reported that a restaurant called Instructables, where customers will make all decisions, is launching in Amsterdam. The Washington Post reported on the creation of an eatery called Elements, whose owners claim it is America’s first crowdsourced restaurant. Its volunteers collaborate on concept, design, and logo. The crowd will share 10 percent of the restaurant’s profits based on the depth of their involvement. As a fan of sizzling burgers and steaming burritos, I am less than enthralled with Elements’ concept: a “sustainable vegetarian/raw foods restaurant” (in the online discussion, there was talk of adding kosher and gluten-free to the mission with round-the-clock breakfast featuring salads and green smoothies).

Why not just ask the question and give everyone the means to answer? Your worst diner could be your best friend. The more layers of data you have, the more you learn, the more useful your advice can be: People who like this also like that. Or here are the popular dishes among runners (a proxy for the health-minded) or people who order expensive wines (a proxy for good taste, perhaps). If you know about your crowd’s taste in wine, why not crowdsource the job of sommelier? Have customers rate and describe every bottle. Show which wines were ordered with which dishes and what made diners happy. If this collection of data were valuable in one restaurant, it would be exponentially more valuable across many. Thinking openly, why not compile and link information from many establishments so diners can learn which wines go best with many kinds of spicy dishes?

Fashion is top-down—or it was. Just as the internet democratizes news and entertainment, it is opening up style. A darling of the open fashion movement is Threadless, a T-shirt company that invites users to submit designs, which are voted on, Digg-like, by the community. Winning designers receive $2,000 plus a $500 credit and $500 every time a design is reprinted. They become the Versaces of the crowdsourced runway. Just as in entertainment, we are learning that the public wants to create and leave its mark. A smart response is to create a platform to make that possible. CafePress.com and Zazzle provide the means for anyone to make and sell designs on T-shirts, mugs, bumper stickers, even underwear, getting a cut of every on-demand order. Threadbanger, a weekly internet video show, teaches viewers how to make cool do-it-yourself fashion with young designers.


pages: 389 words: 87,758

No Ordinary Disruption: The Four Global Forces Breaking All the Trends by Richard Dobbs, James Manyika

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, access to a mobile phone, additive manufacturing, Airbnb, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, autonomous vehicles, Bakken shale, barriers to entry, business cycle, business intelligence, Carmen Reinhart, central bank independence, cloud computing, corporate governance, creative destruction, crowdsourcing, demographic dividend, deskilling, disintermediation, disruptive innovation, distributed generation, Erik Brynjolfsson, financial innovation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Gini coefficient, global supply chain, global village, hydraulic fracturing, illegal immigration, income inequality, index fund, industrial robot, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, inventory management, job automation, Just-in-time delivery, Kenneth Rogoff, Kickstarter, knowledge worker, labor-force participation, low skilled workers, Lyft, M-Pesa, mass immigration, megacity, mobile money, Mohammed Bouazizi, Network effects, new economy, New Urbanism, oil shale / tar sands, oil shock, old age dependency ratio, openstreetmap, peer-to-peer lending, pension reform, private sector deleveraging, purchasing power parity, quantitative easing, recommendation engine, Report Card for America’s Infrastructure, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, sovereign wealth fund, spinning jenny, stem cell, Steve Jobs, supply-chain management, TaskRabbit, The Great Moderation, trade route, transaction costs, Travis Kalanick, uber lyft, urban sprawl, Watson beat the top human players on Jeopardy!, working-age population, Zipcar

By connecting real-time data from aircraft, passenger engineers, maintenance groups, operations staff, and suppliers, Boeing believes it can help its customers—airlines—maximize efficiency, profitability, and environmental performance.59 And as firms like Boeing and Airbus push toward increased digital tracking of individual parts and components, companies such as Fujitsu and IBM are becoming part of the aerospace ecosystem through their RFID and other automated intelligence tracking products and services. Companies are also relying on digital platforms to reach potential partners, to connect customers, suppliers, and financiers and to crowd-source ideas. Etsy, an online global marketplace in which independent artisans sell a wide range of products, connects thirty-million buyers and sellers and exemplifies the twenty-first-century digital ecosystem. The company recently partnered with Kiva to help crowd-source funding for its artisans. In addition to providing a digital portal that links buyers to sellers, Etsy provides entrepreneurial education and connects designers to suppliers. In 2013, the Etsy community generated more than $1.35 billion in total sales, a 50 percent increase from 2012.60 Pharmaceutical company AstraZeneca launched a digital open innovation platform in 2014 and aims to connect with researchers and academics at the UK Medical Research Council, the US National Institutes of Health, and similar organizations in Sweden, Germany, Taiwan, and Canada, among others.61 Consumer packaged goods companies, including Unilever and Procter & Gamble (P&G), often engage customers in new product development.

Upgrading to premium membership—monthly prices start at $59.99 per month for the Business Plus account—affords the user greater insight into who has been looking at his or her profile, the ability to send more messages to potential leads, and the use of more advanced search filters.60 A third model is monetization of big data, either through innovative business-to-business offerings (for example, crowd-sourcing business intelligence or outsourced data science services) or through developing more relevant products, services, or content for which consumers are willing to pay. LinkedIn, for example, makes 20 percent of its revenue from subscriptions, 30 percent from marketing, and 50 percent from talent solutions, a core part of which is selling targeted talent intelligence and tools to recruiters.61 You will have to keep experimenting in order to capture more consumer surplus for your business.

These are often of particular interest for smaller companies that do not have access to more traditional capital sources such as public markets and bank loans. Peer-to-peer lending and fund-raising platforms such as Kiva and Kickstarter know no national borders. Kiva, a web-based platform that allows users to lend money to people around the world, has reached over 1.2 million lenders, intermediating more than $600 million in loans.62 Since its founding in 2009, Kickstarter, a crowd-sourcing platform for creative projects—from movie documentaries to board games—has coordinated $1.3 billion in pledges from more than 6.9 million people.63 Among the notable projects funded on Kickstarter was the Veronica Mars movie, a sequel to the television show, which raised $5.7 million from more than ninety thousand “backers.”64 Alipay, the payment processing company launched in China by e-commerce giant Alibaba, has a unit that provides financing to small businesses.65 Exploit New Commercial Opportunities Companies with access to privileged sources of capital will have a clear competitive advantage.


pages: 317 words: 98,745

Black Code: Inside the Battle for Cyberspace by Ronald J. Deibert

4chan, Any sufficiently advanced technology is indistinguishable from magic, Brian Krebs, call centre, citizen journalism, cloud computing, connected car, corporate social responsibility, crowdsourcing, cuban missile crisis, data acquisition, failed state, Firefox, global supply chain, global village, Google Hangouts, Hacker Ethic, informal economy, invention of writing, Iridium satellite, jimmy wales, John Markoff, Kibera, Kickstarter, knowledge economy, low earth orbit, Marshall McLuhan, MITM: man-in-the-middle, mobile money, mutually assured destruction, Naomi Klein, new economy, Occupy movement, Panopticon Jeremy Bentham, planetary scale, rent-seeking, Ronald Reagan, Ronald Reagan: Tear down this wall, Silicon Valley, Silicon Valley startup, Skype, smart grid, South China Sea, Steven Levy, Stuxnet, Ted Kaczynski, the medium is the message, Turing test, undersea cable, We are Anonymous. We are Legion, WikiLeaks, zero day

This ability to identify large-scale patterns can lead to new opportunities for humanitarian aid and development assistance, even in the most impoverished and dangerous of environments. In Haiti, for example, researchers used mobile-data patterns to monitor the movement of refugees and health risks following the massive hurricanes that slammed into that small island country in 2010. Crowd-sourcing data through the Ushahidi platform – a free and open-source software tool developed for information collection, visualization, and interactive mapping after the 2008 Kenyan election – is used to monitor elections, conflicts, and numerous other issues around the world. The LRA Crisis Tracker uses crowd-sourced data plotted on Ushahidi from radios distributed to local communities and other means to monitor atrocities undertaken by the Lord’s Resistance Army (LRA), responsible for one of the most ruthless insurgencies in Africa. Each LRA-related incident is plotted on a map by type – civilian death, injury, abduction, looting – and once consolidated, the map shows the movements of the LRA across the region, and the scope, scale, and frequency of its actions.

Each LRA-related incident is plotted on a map by type – civilian death, injury, abduction, looting – and once consolidated, the map shows the movements of the LRA across the region, and the scope, scale, and frequency of its actions. Incidents captured by cellphone cameras are linked to specific events on the website as corresponding evidence. In Kibera, Nairobi, Kenya’s largest slum, an experiment in crowd-sourcing data may revolutionize access to basic health care and sanitary services. Conditions in Kibera are dire: most residents are illegal squatters, and local officials regularly withhold basic services, including electricity, sewage treatment, and garbage collection. The most important commodity, water, is extremely scarce – turned on and off by capricious officials, and grossly overpriced by private dealers.

(At the time, WikiLeaks noted: “The material shows how a private intelligence agency works, and how they target individuals for their corporate and government clients.”) Typically these are posted to sites like Pastebin, a resource primarily used to share bits of computer code but repurposed for Anonymous-style disclosures of data and announcements of successful attacks. Most Anonymous DDOS attacks employ a crowd-sourced piling-on against targeted websites, using their preferred Low Orbit Ion Cannon (LOIC), a DDOS attack application that sympathetic users are encouraged to download and employ against a chosen victim. When used in numbers (i.e., in a “distributed” way), the LOIC makes repeated requests to servers from so many users that the servers are overwhelmed, taking them offline for a period of time. In cases where financial firms and retailers are involved, the DDOS attacks can result in significant losses of revenue.


pages: 424 words: 114,905

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, augmented reality, autonomous vehicles, bioinformatics, blockchain, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, conceptual framework, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, David Brooks, digital twin, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fault tolerance, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, microbiome, natural language processing, new economy, Nicholas Carr, nudge unit, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, speech recognition, Stephen Hawking, text mining, the scientific method, Tim Cook: Apple, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

When I was at the Cleveland Clinic at the turn of the millennium, we started an online service called MyConsult, which has now provided tens of thousands of second opinions, often leading to disagreement with the original diagnosis. Doctors hoping to facilitate diagnostic accuracy can crowdsource data with their peers and find help with diagnostic work. This isn’t exactly System 2 thinking, but taking advantage of the reflexive input and experience of multiple specialists. In recent years several smartphone apps for doctors have cropped up, including Figure One, HealthTap, and DocCHIRP. Figure One, for example, is pretty popular for sharing medical images to get a quick diagnosis from peers. My team at Scripps recently published data from what is currently the most widely used doctor crowdsourcing app, Medscape Consult.24 Within two years of launch, the app was used by a steadily growing population of 37,000 physicians, representing more than two hundred countries and many specialties, with rapid turnaround on requests for help; interestingly, the average age of users was more than sixty years.

He was unsure of the diagnosis of rheumatoid arthritis, so he posted on the HumanDx app “35F with pain and joint stiffness in L/R hands X 6 months, suspected rheumatoid arthritis.” He also uploaded a picture of her inflamed hands. Within hours, multiple rheumatologists confirmed the diagnosis. Human Dx intends to recruit at least 100,000 doctors by 2022 and increase the use of natural-language-processing algorithms to direct the key data to the appropriate specialists, combining AI tools with doctor crowdsourcing. An alternative model for crowdsourcing to improve diagnosis incorporates citizen science. Developed by CrowdMed, the platform sets up a financially incentivized competition among doctors and lay people to crack difficult diagnostic cases. The use of non-clinicians for this purpose is quite novel and has already led to unexpected outcomes: as Jared Heyman, the company’s founder and CEO told me, the lay participants have a higher rate of accurate diagnosis than the participating doctors do.

It turns out all Watson did to beat humans in the game show was to essentially ingest Wikipedia, from which more than 95 percent of the show’s questions were sourced. Gleaning information from biomedical literature is not like making sense of Wikipedia entries. A computer reading a scientific paper requires human oversight to pick out key words and findings. Indeed, Andrew Su, a member of our group at Scripps Research, has a big project called Mark2Cure using web-based crowdsourcing, with participants drawn from outside the scientific community, to do this work. Volunteers (we call them citizen scientists) mine and annotate the biomedical literature, as represented by the more than 20 million articles in PubMed, a research database run by the National Institutes of Health. No software today has the natural-language-processing capabilities to achieve this vital function.


pages: 397 words: 110,130

Smarter Than You Think: How Technology Is Changing Our Minds for the Better by Clive Thompson

4chan, A Declaration of the Independence of Cyberspace, augmented reality, barriers to entry, Benjamin Mako Hill, butterfly effect, citizen journalism, Claude Shannon: information theory, conceptual framework, corporate governance, crowdsourcing, Deng Xiaoping, discovery of penicillin, disruptive innovation, Douglas Engelbart, Douglas Engelbart, drone strike, Edward Glaeser, Edward Thorp, en.wikipedia.org, experimental subject, Filter Bubble, Freestyle chess, Galaxy Zoo, Google Earth, Google Glasses, Gunnar Myrdal, Henri Poincaré, hindsight bias, hive mind, Howard Rheingold, information retrieval, iterative process, jimmy wales, Kevin Kelly, Khan Academy, knowledge worker, lifelogging, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Netflix Prize, Nicholas Carr, Panopticon Jeremy Bentham, patent troll, pattern recognition, pre–internet, Richard Feynman, Ronald Coase, Ronald Reagan, Rubik’s Cube, sentiment analysis, Silicon Valley, Skype, Snapchat, Socratic dialogue, spaced repetition, superconnector, telepresence, telepresence robot, The Nature of the Firm, the scientific method, The Wisdom of Crowds, theory of mind, transaction costs, Vannevar Bush, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize, éminence grise

seeking justice for a black teenager: Kelly McBride, “Trayvon Martin Story Reveals New Tools of Media Power, Justice,” Poynter.org, March 23, 2012, accessed March 26, 2013, www.poynter.org/latest-news/making-sense-of-news/167660/trayvon-martin-story-a-study-in-the-new-tools-of-media-power-justice/; Miranda Leitsinger, “How One Man Helped Spark Online Protest in Trayvon Martin Case,” NBC News, March 29, 2012, accessed March 26, 2013, usnews.nbcnews.com/_news/2012/03/29/10907662-how-one-man-helped-spark-online-protest-in-trayvon-martin-case; An Xiao Mina, “A Tale of Two Memes: the Powerful Connection Between Trayvon Martin and Chen Guangcheng,” The Atlantic, July 12, 2012, accessed March 26, 2013, www.theatlantic.com/technology/archive/12/07/a-tale-of-two-memes-the-powerful-connection-between-trayvon-martin-and-chen-guangcheng/259604/. The Haitian earthquake of 2010: Jessica Heinzelman and Carol Waters, Crowdsourcing Crisis Information in Disaster-Affected Haiti (United States Institute of Peace, October 2010); Francesca Garrett, “We Are the Volunteers of Mission 4636,” Ushahidi blog, January 27, 2010, accessed March 26, 2013, blog.ushahidi.com/2010/01/27/mission-4636/. “When compared side by side”: Heinzelman and Waters, Crowdsourcing, 2. slower-moving civic issues: Silvia Vinas, “Colombia: #Yodigoaquiestoy, a Tool for Denouncing Child Labor,” trans. Thalia Rahme, Global Voices, July 22, 2013, accessed March 26, 2013, globalvoicesonline.org/2012/07/22/colombia-yodigoaquiestoy-a-tool-for-denouncing-child-labor/; Geoavalanche, accessed March 26, 2014, geoavalanche.org/incident/.

Soon there was a wiki with 15,789 pages, including dozens of walk-throughs—descriptions of quests—complete with maps and screenshots, and boards teeming with tips and strategies on managing your inventory. (“You can get an invincible companion dog to fight for you with another human companion at the same time by speaking to Lod at Falkreath to find his dog Barbas.”) To be sure, not all gamers use these collective documents. It’s fun to solve problems on your own, so research shows most players use crowdsourced documents sparingly, such as when they’re stuck or pressed for time. But when it comes to the biggest, most sprawling games—like World of Warcraft—the number of players who dip into crowd wisdom is huge: According to one estimate in 2008, about 50 percent of English-speaking World of Warcraft players were using the game’s wiki every month. Top players form “guilds” that play together, cooperating to tackle the most difficult monsters.

Though each microcontribution is a small grain of sand, when you get thousands or millions you quickly build a beach. Microcontributions also diversify the knowledge pool. If anyone who’s interested can briefly help out, almost everyone does, and soon the project is tapping into broad expertise: “The small contributions help the collaboration rapidly explore a much broader range of ideas than would otherwise be the case,” as the author Michael Nielsen notes in Reinventing Discovery, an investigation of crowdsourced science. Tahrir Supplies leveraged microcontributions brilliantly. Because Abulhassan made it so easy for activists to contribute news—all they needed to do was text, tweet at, or e-mail the central team—thousands did, and together they amassed a more complete picture of the situation than the central team could have achieved on its own. We see this pattern at Wikipedia, too. Because it’s so easy to add to a Wikipedia article—hit “edit” and boom, you’re a contributor—the scale of microcontributions is vast, well into the hundreds of millions.


pages: 237 words: 74,109

Uncanny Valley: A Memoir by Anna Wiener

autonomous vehicles, back-to-the-land, basic income, blockchain, Burning Man, call centre, charter city, cloud computing, cognitive bias, cognitive dissonance, commoditize, crowdsourcing, cryptocurrency, Extropian, future of work, Golden Gate Park, housing crisis, Jane Jacobs, job automation, knowledge worker, Lean Startup, means of production, medical residency, new economy, New Urbanism, passive income, pull request, rent control, ride hailing / ride sharing, Sand Hill Road, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, social web, South of Market, San Francisco, special economic zone, technoutopianism, telepresence, telepresence robot, union organizing, universal basic income, unpaid internship, urban planning, urban renewal, women in the workforce, Y2K, young professional

I liked examining someone else’s product selections, judging their clutter. I wasn’t thinking about how the home-sharing platform might also be driving up rents, displacing residents, or undermining the very authenticity that it purported to sell. Mostly, the fact that it functioned, and nobody had murdered me, seemed like a miracle. I had given myself a few days to get adjusted before starting the job. In the mornings, I bought coffee at a laundromat, consulted a crowdsourced reviewing app to find something to eat, and returned to my bedroom to spend the rest of the day reading technical documentation for the analytics software and panicking. The documentation was indecipherable to me. I didn’t know what an API was, or how to use one. I didn’t know how I would possibly provide technical support to engineers—I couldn’t even fake it. The night before my first day of work, too unmoored and overwhelmed to sleep, I scrolled through previous guests’ reviews of my room and realized that the apartment was owned by one of the founders of the home-sharing platform.

They dressed for work as if embarking on an alpine expedition: high-performance down jackets and foul-weather shells, backpacks with decorative carabiners. They looked ready to gather kindling and build a lean-to, not make sales calls and open pull-requests from climate-controlled open-plan offices. They looked in costume to LARP their weekend selves. The culture these inhabitants sought and fostered was lifestyle. They engaged with their new home by rating it. Crowdsourced reviewing apps provided opportunities to assign anything a grade: dim sum, playgrounds, hiking trails. Founders went out to eat and confirmed that the food tasted exactly how other reviewers promised it would; they posted redundant photographs of plated appetizers and meticulous restaurant-scapes. They pursued authenticity without realizing that the most authentic thing about the city was, at this moment in time, them.

I donated a little to a reproductive-health-care nonprofit. I donated a little to a local organization that provided mobile toilets and showers to homeless people in my neighborhood. I bought a vibrator with a USB port, because it made me feel more technical. I enrolled in a gym with a saltwater pool that I knew I’d never have time to swim in, and booked an appointment with a hypnotherapist recommended by a crowdsourced reviewing platform. I spent two hundred dollars on a single session, hoping to stop biting my nails, during which I accidentally fell asleep and had an unerotic dream about the founder of the social network everyone hated. The rest of my money went straight into a savings account. Okay okay okay, I reassured myself, hiding in the server room on bad days, reviewing my bank balance. Escape hatch


pages: 25 words: 5,789

Data for the Public Good by Alex Howard

23andMe, Atul Gawande, Cass Sunstein, cloud computing, crowdsourcing, Hernando de Soto, Internet of things, Kickstarter, lifelogging, Network effects, openstreetmap, Silicon Valley, slashdot, social intelligence, social software, social web, web application

There are a growing number of data journalism efforts around the world, from New York Times interactive features to the award-winning investigative work of ProPublica. Here are just a few promising examples: Spending Stories, from the Open Knowledge Foundation, is designed to add context to news stories based upon government data by connecting stories to the data used. Poderopedia is trying to bring more transparency to Chile, using data visualizations that draw upon a database of editorial and crowdsourced data. The State Decoded is working to make the law more user-friendly. Public Laboratory is a tool kit and online community for grassroots data gathering and research that builds upon the success of Grassroots Mapping. Internews and its local partner Nai Mediawatch launched a new website that shows incidents of violence against journalists in Afghanistan. Open Aid and Development The World Bank has been taking unprecedented steps to make its data more open and usable to everyone.

The challenge is for the men and women entrusted with coordinating response to identify signals in the noise. First responders and crisis managers are using a growing suite of tools for gathering information and sharing crucial messages internally and with the public. Structured social data and geospatial mapping suggest one direction where these tools are evolving in the field. A web application from ESRI deployed during historic floods in Australia demonstrated how crowdsourced social intelligence provided by Ushahidi can enable emergency social data to be integrated into crisis response in a meaningful way. The Australian flooding web app includes the ability to toggle layers from OpenStreetMap, satellite imagery, and topography, and then filter by time or report type. By adding structured social data, the web app provides geospatial information system (GIS) operators with valuable situational awareness that goes beyond standard reporting, including the locations of property damage, roads affected, hazards, evacuations and power outages.


pages: 271 words: 52,814

Blockchain: Blueprint for a New Economy by Melanie Swan

23andMe, Airbnb, altcoin, Amazon Web Services, asset allocation, banking crisis, basic income, bioinformatics, bitcoin, blockchain, capital controls, cellular automata, central bank independence, clean water, cloud computing, collaborative editing, Conway's Game of Life, crowdsourcing, cryptocurrency, disintermediation, Edward Snowden, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, financial innovation, Firefox, friendly AI, Hernando de Soto, intangible asset, Internet Archive, Internet of things, Khan Academy, Kickstarter, lifelogging, litecoin, Lyft, M-Pesa, microbiome, Network effects, new economy, peer-to-peer, peer-to-peer lending, peer-to-peer model, personalized medicine, post scarcity, prediction markets, QR code, ride hailing / ride sharing, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, SETI@home, sharing economy, Skype, smart cities, smart contracts, smart grid, software as a service, technological singularity, Turing complete, uber lyft, unbanked and underbanked, underbanked, web application, WikiLeaks

“Hacker Dreams Up Crypto Passport Using the Tech Behind Bitcoin.” Wired, October 30, 2014. http://www.wired.com/2014/10/world_passport/; Ellis, C. “World Citizenship Project Features in Wired Magazine.” Blog post, November 1, 2014. http://chrisellis.me/world-citizenship-project-features-in-wired-magazine/. 113 De Soto, H. The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else. New York: Basic Books, 2003. 114 Swan, M. “Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public Health Research Ecosystem.” J Med Internet Res 14, no. 2 (2012): e46. 115 Mishra, P. “Inside India’s Aadhar, the World’s Biggest Biometrics Database.” TechCrunch, December 6, 2013. http://techcrunch.com/2013/12/06/inside-indias-aadhar-the-worlds-biggest-biometrics-database/. 116 Deitz, J. “Decentralized Governance Whitepaper.”

Existing Customers Can Access Their Results Here Until January 1st 2015.” http://en.wikipedia.org/wiki/DeCODE_genetics. 143 Castillo, M. “23andMe to Only Provide Ancestry, Raw Genetics Data During FDA Review.” CBS News, December 6, 2013. http://www.cbsnews.com/news/23andme-to-still-provide-ancestry-raw-genetics-data-during-fda-review/. 144 Swan, M. “Health 2050: The Realization of Personalized Medicine Through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen.” J Pers Med 2, no. 3 (2012): 93–118. 145 ———. “Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet Med 12, no. 5 (2010): 279–88; Kido, T. et al. “Systematic Evaluation of Personal Genome Services for Japanese Individuals.” Nature: Journal of Human Genetics 58 (2013):734–41. 146 Tamblyn, T. “Backup Your DNA Using Bitcoins.”

“Nearly $2 Million Worth of Vericoin Stolen from MintPal, Hard Fork Implemented.” Digital Currency Magnates, July 15, 2014. http://dcmagnates.com/nearly-2-million-worth-of-vericoin-stolen-from-mintpal-hard-fork-considered/. 189 Greenberg, A. “Hacker Hijacks Storage Devices, Mines $620,000 in Dogecoin.” Wired, June 17, 2014. http://www.wired.com/2014/06/hacker-hijacks-storage-devices-mines-620000-in-dogecoin/. 190 Swan, M. “Scaling Crowdsourced Health Studies: The Emergence of a New Form of Contract Research Organization.” Pers Med. 9, no. 2 (2012): 223–34. 191 Reitman, R. “Beware the BitLicense: New York’s Virtual Currency Regulations Invade Privacy and Hamper Innovation.” Electronic Frontier Foundation, October 15, 2014. https://www.eff.org/deeplinks/2014/10/beware-bitlicense-new-yorks-virtual-currency-regulations-invade-privacy-and-hamper. 192 Santori, M.


pages: 216 words: 61,061

Without Their Permission: How the 21st Century Will Be Made, Not Managed by Alexis Ohanian

Airbnb, barriers to entry, carbon-based life, cloud computing, crowdsourcing, en.wikipedia.org, Hans Rosling, hiring and firing, Internet Archive, Justin.tv, Kickstarter, Marc Andreessen, Mark Zuckerberg, means of production, Menlo Park, minimum viable product, Occupy movement, Paul Graham, Silicon Valley, Skype, slashdot, social web, software is eating the world, Startup school, Tony Hsieh, unpaid internship, Y Combinator

And they were doing it with the same bullish ambitions in spite of tremendous hardships, such as a thirty-year dictatorship that had been recently overthrown by a haphazard collective of revolutionaries with the hope of crowdsourcing a constitution and holding Egypt’s first free and fair election in decades. They had no hesitation. They were as fearless as any other entrepreneurs you’d meet, only they had legitimate fears in a country that was figuratively and literally rebuilding. Taking the agony out of travel also turns out to be the basis for a viable business in many contexts, including on the congested streets of Cairo. That’s what five cousins—Aly Rafea, Mohamed Rafea, Gamal Sadek, Mostafa Beltagy, and Yehia Ismail—are doing with Bey2ollak (it’s Egyptian slang, used when telling someone about something you’ve heard), a mobile application (iOS, Android, BlackBerry, Windows, and even Nokia!) that allows commuters to crowdsource more effective routes to avoid the city’s notoriously awful traffic.

Whether they were donating twenty dollars or twenty thousand dollars, they wanted to know that their contributions were making a concrete, measurable difference in the lives of students and teachers. Soon, Charles was hard at work developing a novel way to tap this potentially tremendous resource. Charles took a simple pencil sketch of his idea to a computer programmer who had recently emigrated from Poland. For two thousand dollars, the programmer built Charles a working version of DonorsChoose.org. This was in 2000, years before anyone was using phrases like “crowdsourcing” or “social media.” The first version of the website was markedly low-tech. Charles used a manual credit-card reader, like the ones you find in grocery stores, to process donations. It wasn’t pretty or fast, but it worked. The term “minimum viable product” hadn’t been coined yet, but that’s exactly what Charles put together. The first projects were posted on the site by Charles and a few of his fellow teachers whom he’d bribed with baked goods from his mother—roasted pears with spices, apricot glaze, and slices of orange rind (remember that part about treating your first customers like gold?).

Literally every single person we met—once we explained the half red and half blue bus that had INTERNET 2012 written on it—was excited about our campaign. Not only did we have a special bus, but the lead car that escorted us on this road trip was built by Local Motors. What’s remarkable about these guys isn’t just that the car was built entirely in the USA. It’s that the design of the car was crowdsourced entirely online. That’s right: people from all over the world contributed designs for a working car that eventually rolled off the assembly line. As a result, the production costs from idea to vroom12 were a fraction of what it’d cost a traditional auto manufacturer. Also, the entire process happened either in cyberspace or in the United States of America. The idea for Local Motors came to John “Jay” Rogers while he was a marine serving in Iraq.


pages: 561 words: 157,589

WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly

4chan, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, blockchain, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, corporate governance, corporate raider, creative destruction, crowdsourcing, Danny Hillis, data acquisition, deskilling, DevOps, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, full employment, future of work, George Akerlof, gig economy, glass ceiling, Google Glasses, Gordon Gekko, gravity well, greed is good, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, move fast and break things, Network effects, new economy, Nicholas Carr, obamacare, Oculus Rift, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, strong AI, TaskRabbit, telepresence, the built environment, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar

Lawrence Wilkinson, one of the cofounders of Global Business Network, the company that pioneered a technique called scenario planning, whom I met in 2005, introduced me to a wonderful phrase that captured how my mind works: “news from the future.” So, for example, consider how the “Harnessing Collective Intelligence” vector became clear to us: 1. In the late 1980s and early 1990s, we were exposed to the “barn raising” style of collaborative software development of the early Unix community—what we later came to call open source software. 2. In developing our first books, we practiced a version of this kind of crowdsourcing ourselves. In 1987, I wrote a book called Managing UUCP and Usenet, which described how to use a program called the Unix-to-Unix Copy Program (UUCP) to connect to Usenet, a distributed dial-up precursor to today’s social web. It was on Usenet that the world’s software developers conversed about their work, shared tips and advice, and, increasingly, talked about everything from sex to politics.

In 1992, trying to create a print book that emulated the link style of the World Wide Web, I designed and coauthored a book called Unix Power Tools, which wove together tips and tricks harvested from hundreds of Internet contributors into a hyperlinked web of short articles, each of which could be read independently because it also contained links to additional articles providing tutorial and background information that my coauthors Jerry Peek and Mike Loukides and I felt was needed to make sense of the crowdsourced lessons. 3. In 1992 and 1993, as we turned “the Whole Internet Catalog” into GNN, the Global Network Navigator, every day we sought out the best of the new sites joining the World Wide Web, curating them into a rich catalog of experiences created, as if by magic, by a distributed network of people pursuing their own passions. 4. We watched the early search engines, starting with Web-crawler in 1994, automatically collect links not just to the best websites, but to every website.

Not only that, but there was further human intent signaled by the “anchor text”—the words in the source document that hyperlinked to another one. Google found a gold mine of data, and never looked back. I still remember a blog post by Robert Scoble in which he gleefully demonstrated how human contribution was central to search engines. “I just discovered a new restaurant in Seattle. Its website isn’t in Google. But it will be tomorrow, because I just linked to it!” 5. In 1995, we saw how eBay and Craigslist brought crowdsourcing to products and services, and began to realize that the magical aggregation of millions of people into new kinds of services wasn’t limited to “content,” but could also be used in the physical world. 6. We watched how Amazon ran rings around Barnes & Noble and Borders in online bookselling by applying the same principles that Google used to make a better search engine to more effective e-commerce.


pages: 533

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population

Where it is undertaken without topdown control, this kind of activity has been called commons-based peer production or open-source production.50 Where there’s more central direction and control, it tends to be called crowdsourcing. In the digital lifeworld it will be possible, using commons-based peer production or crowdsourcing, to invite the citizenry directly to help set the political agenda, devise policies, and draft and refine legislation. Advocates of this sort of democracy, or variants of it, have called it wiki-government, collaborative democracy, and crowdocracy.51 I refer to it as Wiki Democracy. Small experiments in Wiki Democracy have already been tried with some success. As long ago as 2007, New Zealand gave citizens the chance to participate in writing the new Policing Act using a wiki.52 In Brazil, about a third of the final text of the Youth Statute Bill was crowdsourced from young Brazilians and the Internet Civil Rights Bill received hundreds of contributions on the e-Democracia Wikilegis platform.53 These were carefully planned exercises within closely confined parameters.

And the disciplinary implications are perhaps even more imposing: one can imagine humans not just refraining from crime, but from perfectly legal things like going to the house of a friend who happens to have a criminal record, which might lead a system to predict, by association, that they will go on to commit crimes themselves. Rateable In the last few years, you may have used an online platform that ‘crowdsources’ ratings for certain goods—movies, Uber drivers, hotels, restaurants, dry cleaners, and so forth. You may not have ­suspected that, in due course, you might be the subject of other people’s ratings. We’re already ranked and scored in various ways—from credit scores that determine whether we can secure finance to ‘health scores’ compiled from information gathered about us online.45 OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 140 FUTURE POLITICS In the digital lifeworld it will be possible for each person to bear a holistic personal rating compiled of scores awarded by friends, ­colleagues, businesses, and acquaintances on whatever measures are considered socially valuable: trustworthiness, reliability, attractiveness, charm, intelligence, strength, fitness, and so forth.

The result was the holy grail of political campaigning: a large-scale shift in public opinion. This new approach to data-based campaigning has been called the ‘engineering of consent’37 and more ominously, the ‘weaponized AI propaganda machine’.38 Second, the internet has changed the relationship between government and citizens, enabling them to work together to solve public policy problems. Online consultations, open government, e-petitions, e-rulemaking,39 crowdsourcing of ideas (as in Estonia and Finland),40 hackathons, and participatory budgeting (as in Paris, where residents propose and vote on items of public spending)41 are all new ways of coming up with ideas, subjecting policy to scrutiny and refinement, bringing private-sector resources to bear on big problems, and increasing the efficiency and legitimacy of government.42 The notion of e-government is underpinned by one question, posed by Beth Simone Noveck: ‘If we can develop the algorithms and platforms to target consumers, can we not also target citizens for the far worthier purpose of undertaking public service?’


pages: 421 words: 110,406

Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You by Sangeet Paul Choudary, Marshall W. van Alstyne, Geoffrey G. Parker

3D printing, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, Apple's 1984 Super Bowl advert, autonomous vehicles, barriers to entry, big data - Walmart - Pop Tarts, bitcoin, blockchain, business cycle, business process, buy low sell high, chief data officer, Chuck Templeton: OpenTable:, clean water, cloud computing, connected car, corporate governance, crowdsourcing, data acquisition, data is the new oil, digital map, discounted cash flows, disintermediation, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, Haber-Bosch Process, High speed trading, information asymmetry, Internet of things, inventory management, invisible hand, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, Khan Academy, Kickstarter, Lean Startup, Lyft, Marc Andreessen, market design, Metcalfe’s law, multi-sided market, Network effects, new economy, payday loans, peer-to-peer lending, Peter Thiel, pets.com, pre–internet, price mechanism, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Satoshi Nakamoto, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Skype, smart contracts, smart grid, Snapchat, software is eating the world, Steve Jobs, TaskRabbit, The Chicago School, the payments system, Tim Cook: Apple, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, winner-take-all economy, zero-sum game, Zipcar

And Airbnb, the world’s largest accommodation provider, owns no real estate.”3 The community provides these resources. Strategy has moved from controlling unique internal resources and erecting competitive barriers to orchestrating external resources and engaging vibrant communities. And innovation is no longer the province of in-house experts and research and development labs, but is produced through crowdsourcing and the contribution of ideas by independent participants in the platform. External resources don’t completely replace internal resources—more often they serve as a complement. But platform firms emphasize ecosystem governance more than product optimization, and persuasion of outside partners more than control of internal employees. THE PLATFORM REVOLUTION: HOW WILL YOU RESPOND? As you’ll see in this book, the rise of the platform is driving transformations in almost every corner of the economy and of society as a whole, from education, media, and the professions to health care, energy, and government.

The underlying principle: Give fast, open feedback when applying laws that define good behavior, but give slow, opaque feedback when applying laws that punish bad behavior. Norms. One of the greatest assets any platform—indeed, any business—can have is a dedicated community. This doesn’t happen by accident. Vibrant communities are nurtured by skilled platform managers in order to develop norms, cultures, and expectations that generate lasting sources of value. iStockphoto, today one of the world’s largest markets for crowdsourced photographs, was originally founded by Bruce Livingstone to sell CD-ROM collections of images by direct mail. As that business tanked, Bruce and his partners hated the thought that their work might go to waste. So they began giving away their images online.25 Within months, they were discovered by thousands of people who not only downloaded images but asked to share their own images as well.

Platform-based students for whom a conventional credential is important can often make special arrangements to receive one—for example, at Coursera, college credit is a “premium service” you pay extra for. The platform-based unbundling of educational activities is separating the teaching of specific skills from reliance on vast, multipurpose institutions like traditional universities. Duolingo uses a crowdsourcing platform to teach foreign languages. Its founder, Luis von Ahn, is a computer scientist who never studied language instruction. After reading the most respected books on the topic, he performed comparative tests of the leading theories using the crowds that visited his website and an evolving set of testing tools to measure the results. Today, more people are using Duolingo to learn a language than all the students in high school in the U.S. combined.2 Duolingo separates language teaching from traditional educational institutions.


pages: 378 words: 94,468

Drugs 2.0: The Web Revolution That's Changing How the World Gets High by Mike Power

air freight, Alexander Shulgin, banking crisis, bitcoin, blockchain, Buckminster Fuller, Burning Man, cloud computing, credit crunch, crowdsourcing, death of newspapers, Donald Davies, double helix, Douglas Engelbart, Electric Kool-Aid Acid Test, fiat currency, Firefox, Fractional reserve banking, frictionless, Haight Ashbury, John Bercow, John Markoff, Kevin Kelly, Leonard Kleinrock, means of production, Menlo Park, moral panic, Mother of all demos, Network effects, nuclear paranoia, packet switching, pattern recognition, PIHKAL and TIHKAL, pre–internet, QR code, RAND corporation, Satoshi Nakamoto, selective serotonin reuptake inhibitor (SSRI), sexual politics, Skype, Stephen Hawking, Steve Jobs, Stewart Brand, trade route, Whole Earth Catalog, Zimmermann PGP

The site, which was set up in 2000, offers a fascinating window into a hidden culture, and plots geographically and publicly the quality of drugs across Europe in real time, while most official reports lag by at least a year. The site’s servers are located in the Netherlands, thanks to the country’s lenient and liberal drug laws. It has 29,345 user-generated reports on pills sold as Ecstasy, and gets around 15,000 unique visitors each day. It is a self-managing community, with moderators keeping an eye on threads to ensure that dealers do not use the service to advertise their products. ‘Like any site that crowdsources user reviews there is always the danger of people gaming the system, but we find that good information always drives out bad,’ the site’s administrator told me by email. To those who argue that the site encourages drug use, he offers the baldly factual response: ‘Studies on the influence of drug information, and particularly pill testing, have shown that the more information people have about what is in pills the less likely they are to take them.’

The sample was tested by John Ramsey at St George’s, and he said it was indeed excellent quality. The story was broadcast and published on the BBC’s website. Andrew Lewman audibly facepalms as he relates the story over the telephone. ‘What better advert could they have given? Not only does this illegal site sell rare drugs, it sells very high-quality product.’ But you didn’t need to trust the BBC. The forums at the site offered crowdsourced proof of the best vendors and worst scammers. In June 2012, reviews for the best LSD vendor ran to eighty-one pages, with 50,000 views, heroin to twenty-two pages with 8,000 views. Cocaine vendors were reviewed in a 292-page behemoth with over 90,000 views, and MDMA ran in at 129 pages with over 60,000 views. One vendor said dealing drugs on the site wasn’t without its moral problems. ‘The prospect of a twelve-year-old loaded to the gills on my MDMA is not a pleasant one.

Torrents are shared downloads, so when a user fires up their bit-torrent client and downloads, say, a film using a torrent file from an illegal site such as the Pirate Bay, they actually download millions of chunks of the file from a swarm of users at once, rather than one file from one central server. The torrent software on the downloaders’ machines then assembles the pieces of data into a film or music file. Nakamoto’s elegant solution to the double-spend dilemma was to create what he called a ‘block chain’, a distributed, or shared ledger of all transfers of coins from one person to another. Crowdsourced, decentralized, massively distributed cryptographic cash had arrived. Users, known as miners, donate processor time to maintain and update the block chain, which records all transactions between users, and in the process also ‘dig’ for new coins. Miners’ computers send evidence of those transactions to the network, racing each other to solve these irreversible crypotographic puzzles that contain several transactions.


pages: 233 words: 64,702

China's Disruptors: How Alibaba, Xiaomi, Tencent, and Other Companies Are Changing the Rules of Business by Edward Tse

3D printing, Airbnb, Airbus A320, Asian financial crisis, barriers to entry, bilateral investment treaty, business process, capital controls, commoditize, conceptual framework, corporate governance, creative destruction, crowdsourcing, currency manipulation / currency intervention, David Graeber, Deng Xiaoping, disruptive innovation, experimental economics, global supply chain, global value chain, high net worth, industrial robot, Joseph Schumpeter, Lyft, money market fund, offshore financial centre, Pearl River Delta, reshoring, rising living standards, risk tolerance, Silicon Valley, Skype, Snapchat, sovereign wealth fund, special economic zone, speech recognition, Steve Jobs, thinkpad, trade route, wealth creators, working-age population

its valuation up to $40–$50 billion: See Bloomberg News, “Xiaomi Said to Seek Funding at About $50 Billion Valuation,” November 4, 2014, available at http://www.bloomberg.com/news/2014-11-03/xiaomi-said-to-seek-funding-at-valuation-of-about-50-billion.html (accessed November 5, 2014). Lei told TechHive Web site journalist Michael Kan: See Michael Kan, “China’s Xiaomi Takes Crowdsourced Phone Development Model Abroad,” TechHive, May 8, 2013, at http://www.techhive.com/article/2038184/chinas-xiaomi-takes-crowdsourced -phone-development-model-abroad.html (accessed September 9, 2014). Companies wanting to sell into these markets have released an extraordinary number of products: See Edward Tse, The China Strategy (New York: Basic Books, 2010), pages 27–28. tourism, where the 3 billion trips Chinese now take annually are expected to double in number by 2020: For the tourism forecast, see Lily Kuo, “Five Trends That Could Make China the World’s Largest Consumer Market by 2015,” Quartz.com, March 19, 2013, available at http://qz.com/64610/five-trends-that-could-make-china -the-worlds-largest-consumer-market-by-2015/, and for the aircraft forecast, see Tom Mitchell, “Boeing Tips China to Overtake US as Biggest Aviation Market,” Financial Times, September 4, 2014, available at http://www.ft.com/intl/cms/s/0/57d76fa2-3418-11e4-8832-00144feabdc0.html#axzz3D0EvfkQR (both accessed September 11, 2014).

Its $25 billion initial public offering in 2014 was the world’s largest to date Pony Ma, whose Shenzhen-based company, Tencent, dominates online games and messaging in China, and is becoming a major rival to Alibaba in e-commerce Robin Li, the founder of China’s leading search engine and social network company, Baidu, which provides more than 60 percent of Chinese search engine activity (Baidu’s influence, along with that of Alibaba and Tencent, is such that many commentators refer to this trio of China’s three most prominent Internet companies as the BATs) Ren Zhengfei, the founder of Huawei, China’s largest privately owned exporter and the world’s leading manufacturer of mobile and fixed-line telecom-network equipment Yang Yuanqing, who, as chief executive of Lenovo, has built the company into the world’s number-one seller of personal computers and a top five seller of smartphones Lei Jun, a serial entrepreneur whose latest business, Xiaomi, has turned China’s smartphone market on its head and become a global rival to Samsung. He uses innovative crowdsourcing techniques to determine the direction of development of his products and sell them with almost no outlay for marketing Yu Gang, a former Dell executive whose Yihaodian online supermarket, with annual revenues that have risen to nearly $2 billion in just five years, is transforming how urban Chinese buy their daily necessities Li Shufu, the founder of Geely Auto, China’s most successful privately owned carmaker, and one of the most prominent global automakers, thanks to his takeover of Volvo in 2011 Xu Lianjie, a former farmer who has fended off competition from Procter & Gamble, Kimberly-Clark and other Western consumer-goods firms to build Hengan International, China’s leading maker of tissues, diapers, and sanitary napkins Diane Wang, who, after working for Microsoft and Cisco, served as CEO of Joyo.com, an online bookstore launched by Xiaomi’s Lei Jun, and then—when that business was sold to Amazon—launched her own global business-to-business Web site, DHgate.com Chen Haibin, the owner of a chain of medical laboratories pioneering private company involvement in improving standards and choice in China’s largely publicly run health-care system Wang Jingbo, who, since founding Noah Wealth Management in 2005, has signed up more than 50,000 of China’s richest people to establish the country’s leading private wealth management company Zhang Yue, a maverick from central China’s Hunan Province who, having created a globally successful coolant-free air-conditioning business, now wants to build environmentally sustainable cities using prefabricated modules; to demonstrate the viability of his products and ideas, he is currently seeking permission to erect the world’s highest building in Changsha, Hunan’s capital Not all Chinese entrepreneurs are successful, of course.

Shortages of skilled and experienced staff have long been a problem for Chinese companies, exacerbated by the country’s growth rate. In many key areas, businesses cope through adopting guerrilla strategies as a way of coping. Take marketing, where a lack of experience at running traditional promotional campaigns forces companies to develop their own offbeat ways of attracting and retaining new customers. An example of this is Xiaomi’s use of crowdsourcing to get input on ways of improving its phones and to create a buzz for each new release. Coping with wage pressures. Companies also need to innovate to overcome China’s shrinking labor force and fast-rising costs. Fifteen years ago, Chinese workers were among the cheapest in the world, with average monthly wages of less than $100, one-third the rate of Mexico’s. By 2013, that average had risen to $700, on a par with Malaysia’s, and more than one-third higher than Mexico’s.


pages: 237 words: 67,154

Ours to Hack and to Own: The Rise of Platform Cooperativism, a New Vision for the Future of Work and a Fairer Internet by Trebor Scholz, Nathan Schneider

1960s counterculture, activist fund / activist shareholder / activist investor, Airbnb, Amazon Mechanical Turk, barriers to entry, basic income, bitcoin, blockchain, Build a better mousetrap, Burning Man, capital controls, citizen journalism, collaborative economy, collaborative editing, collective bargaining, commoditize, conceptual framework, crowdsourcing, cryptocurrency, Debian, deskilling, disintermediation, distributed ledger, Ethereum, ethereum blockchain, future of work, gig economy, Google bus, hiring and firing, income inequality, information asymmetry, Internet of things, Jacob Appelbaum, Jeff Bezos, job automation, Julian Assange, Kickstarter, lake wobegon effect, low skilled workers, Lyft, Mark Zuckerberg, means of production, minimum viable product, moral hazard, Network effects, new economy, offshore financial centre, openstreetmap, peer-to-peer, post-work, profit maximization, race to the bottom, ride hailing / ride sharing, SETI@home, shareholder value, sharing economy, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, Snapchat, TaskRabbit, technoutopianism, transaction costs, Travis Kalanick, Uber for X, uber lyft, union organizing, universal basic income, Whole Earth Catalog, WikiLeaks, women in the workforce, Zipcar

There is also a money-management app for mobiles that combines cash and loan requests, again simplifying the lives of very low-income people who need to cash their paychecks before payday, and can avoid the high interest rates charged by so called “payday sharks.” A very different type of app from the aforementioned is Panoply (presented by Robert Morris): an online intervention that replaces a health professional with a crowd-sourced response to individuals with anxiety and depression. What I find significant here is that it has the added effect of mobilizing a network of people, which may be one step in a larger trajectory of support that can also become a local neighborhood network. Panoply coordinates support from crowd workers and unpaid volunteers. It incorporates recent advances in crowdsourcing and human computation, enabling timely feedback and quality vetting. Crowds are recruited to help users think more flexibly and objectively about stressful events. Another useful tool seeks to develop new ways of working together online.

These events started in 2009 and one of the recent ones was Platform Cooperativism in 2015. Initially, at these events, discussions focused on the Italian Workerists, immaterial labor, and “playbor.” Artists like Burak Arikan, Alex Rivera, Stephanie Rothenberg, and Dmytri Kleiner played pioneering roles in alerting the public to these issues. Later, debates became more concerned with “crowd fleecing,” the exploitation of thousands of invisible workers in crowdsourcing systems like Amazon Mechanical Turk or content moderation farms in the Philippines. Over the past few years, the search for concrete alternatives for a better future of work has become more dynamic. The theory of platform cooperativism has two main tenets: communal ownership and democratic governance. It is bringing together 135 years of worker self-management, the roughly 170 years of the cooperative movement, and commons-based peer production with the compensated digital economy.

Each of these seeks to enable members of online networks to carry on direct, sustained, and somewhat complicated discussions, and then to clarify group sentiment and reach decisions that participants see as binding, legitimate, and meaningful. NETWORKS OF PEER PRODUCERS In a natural extension of such capacities, open value networks, or OVNs, are attempts to enable bounded networks of participants to carry out crowdfunding, crowdsourcing of knowledge, and co-budgeting among their identifiable participants. OVNs such as Enspiral and Sensorica have been described as an “operating system for a new kind of organization” and a “pilot project for the new economy.” These enterprises consist of digital platforms that facilitate new modes of decentralized and self-organized social governance, production, and livelihoods among members of distinct communities.


pages: 565 words: 151,129

The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism by Jeremy Rifkin

"Robert Solow", 3D printing, active measures, additive manufacturing, Airbnb, autonomous vehicles, back-to-the-land, big-box store, bioinformatics, bitcoin, business process, Chris Urmson, clean water, cleantech, cloud computing, collaborative consumption, collaborative economy, Community Supported Agriculture, Computer Numeric Control, computer vision, crowdsourcing, demographic transition, distributed generation, en.wikipedia.org, Frederick Winslow Taylor, global supply chain, global village, Hacker Ethic, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Julian Assange, Kickstarter, knowledge worker, longitudinal study, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, market design, mass immigration, means of production, meta analysis, meta-analysis, natural language processing, new economy, New Urbanism, nuclear winter, Occupy movement, off grid, oil shale / tar sands, pattern recognition, peer-to-peer, peer-to-peer lending, personalized medicine, phenotype, planetary scale, price discrimination, profit motive, QR code, RAND corporation, randomized controlled trial, Ray Kurzweil, RFID, Richard Stallman, risk/return, Ronald Coase, search inside the book, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, smart grid, smart meter, social web, software as a service, spectrum auction, Steve Jobs, Stewart Brand, the built environment, The Nature of the Firm, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, transaction costs, urban planning, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog, Whole Earth Review, WikiLeaks, working poor, zero-sum game, Zipcar

Interdisciplinary and multicultural studies prepare students to become comfortable entertaining different perspectives and more adept at searching out synergies between phenomena. The idea of learning as an autonomous private experience and the notion of knowledge as an acquisition to be treated as a form of exclusive property made sense in a capitalist environment that defined human behavior in similar terms. In the Collaborative Age, learning is regarded as a crowdsourcing process and knowledge is treated as a publically shared good, available to all, mirroring the emerging definition of human behavior as deeply social and interactive in nature. The shift from a more authoritarian style of learning to a more lateral learning environment better prepares today’s students to work, live, and flourish in tomorrow’s collaborative economy. The new collaborative pedagogy is being applied and practiced in schools and communities around the world.

Universities that participate in edX augment their study groups by asking their own alumni to volunteer as online mentors and discussion group leaders. Harvard professor Gregory Nagy recruited ten of his former teaching fellows to help serve as online study group facilitators in the MOOC based on his popular course, Concepts of the Ancient Greek Hero.12 Upon graduating the Coursera and edX courses, the students receive a certificate of completion. The crowdsourcing approach to learning online is designed to foster a distributed, collaborative, peer-to-peer learning experience on the Commons—the kind that prepares students for the coming era. By February 2013, Coursera had approximately 2.7 million students from 196 countries enrolled in hundreds of courses.13 EdX’s first course, in 2012, had an enrollment of 155,000 students. Anant Agarwal, edX’s president and formerly the director of MIT’s artificial-intelligence laboratory, noted that enrollment in the first virtual course nearly equaled the total number of MIT alumni in the university’s 150 years of existence.

A similar software program developed by Epagogix analyzes movie scripts to project box office hits for the film industry.36 Its success in identifying winners has made algorithm assessment standard fare in the industry. In the future, these kinds of forecasting tools will eliminate the need to hire pricey marketing agents to conduct expensive focus-group encounters and other marketing-research initiatives, the accuracy of which might pale against the crowdsourcing accuracy of Big Data filtered by algorithms. Big Data and algorithms are even being used to create copy for sports stories that are chatty, chock full of information, and engaging. The Big Ten Network uses algorithms to create original pieces posted just seconds after games, eliminating human copywriters.37 Artificial intelligence took a big leap into the future in 2011 when an IBM computer, Watson—named after IBM’s past chairman—took on Ken Jennings, who held the record of 74 wins on the popular TV show Jeopardy, and defeated him.


pages: 138 words: 40,787

The Silent Intelligence: The Internet of Things by Daniel Kellmereit, Daniel Obodovski

Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, connected car, crowdsourcing, data acquisition, en.wikipedia.org, Erik Brynjolfsson, first square of the chessboard, first square of the chessboard / second half of the chessboard, Freestyle chess, Google X / Alphabet X, Internet of things, lifelogging, Metcalfe’s law, Network effects, Paul Graham, Ray Kurzweil, RFID, Robert Metcalfe, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, web application, Y Combinator, yield management

People can start asking, “What can you do to make it a better place: more efficient, safer, cleaner, etc.? What are the new ways to think about energy management, the removal chain, and pollution?” If local governments don’t necessarily need to focus on execution and operations so much anymore, but more on governing, that is also a different situation. If, instead of having to design the management system for the transport network for each city top down, you crowd-source it or take it from another city with relevant data — have the solution come out from start-ups, individuals, NGOs, corporations, you name it — then the city government takes a new role. It would ensure that minorities get as much service as majorities do, that budgets are allocated correctly, that the experts and planners use their expertise for the public good. There’s room for this new citizenship.

Volvo, for example, has successfully demonstrated road trains as part of the EU’s SARTRE (Safe Road Trains for the Environment) Project, which has several cars following one another in a platoon formation; the lead car has a professional driver taking responsibility for the platoon, while following vehicles operate in a semi-autonomous mode, reducing the distance between the vehicles, and reducing drag and fuel consumption, while getting to their destination faster.23 You may be familiar with the crowd-sourced navigation application Waze, which is one of the most accurate personal navigation applications today because it uses real-time traffic and construction information provided by users. For example, it can get you from Palo Alto to San Francisco during rush hour within five minutes of the ETA. But it still requires physical user input. What if we take this concept a notch further and imagine cars automatically populating an application like this with real-time data like speed, acceleration, weather conditions, and more?

Again, Assaf explains: We were also giving people feedback on their phone about distances they traveled and surrounding data. We also embedded air-quality sensors that measure CO, NO2, relative humidity, and temperature. All the data is collected by the sensors on the wheel and is channeled through the phone to the cloud. With open APIs, anybody can write apps to the wheel. In Copenhagen, as people ride around, each bike populates its own part of a real-time map. You get this very nice crowd-sourced and crowd-sensed map of real-time weather, pollution, and so on. Steve Hudson of Omnilink started his journey into the world of personal tracking devices in 2004 with a location-based platform to monitor and manage criminals who are assigned to an alternative program instead of being incarcerated. It’s well known that jails are overcrowded, but there is also a higher probability of re-offending after jail time, and the costs of keeping people in jail are too high.


The History and Uncertain Future of Handwriting by Anne Trubek

computer age, crowdsourcing, Internet Archive, invention of the printing press, lateral thinking, Norman Mailer, QWERTY keyboard, Ralph Waldo Emerson, Steven Pinker, Whole Earth Catalog

Another set of librarians and scholars has been working to solve this gap in access and knowledge by entering the texts of handwritten manuscripts, not just the images of them, into their databases. To enter the entire text of an eleventh-century Spanish manuscript, for instance, requires people who can read the words on those pages written in various and often difficult-to-decipher scripts. It also requires many hours, so some libraries and universities have asked the public to help them out by transcribing handwriting from their couches at home. They are literally crowdsourcing handwriting. The National Archives’ pilot program, launched in 2011, was called “Transcription Pilot Project.” More than three hundred documents were loaded onto the archives’ website, including “letters to a civil war spy, various laws and acts, presidential records, suffrage petitions, indictments, and fugitive slave case files,” and the public was invited to “help the National Archives make historical documents more accessible [and] help the next person discover and use that record.”

The documents, a small sample of those contained in the archive, were organized into three levels of difficulty—beginner, intermediate, and advanced—and they provided explanations and links to those who wanted to learn basic paleography. However, the pilot program ended very quickly, because citizens rapidly and accurately transcribed all the documents as they were loaded; even the “advanced” documents were easy for some. The same thing happened when the New York Public Library launched its “What’s on the Menu?” crowdsourced transcription program, in which menus from nineteenth- and early twentieth-century restaurants were scanned and uploaded in 2011. In just weeks, the public accurately transcribed all 9,000 menus the library had scanned. Since then, the library has digitized additional menus, and as of the end of 2015, more than 17,000 of the 45,000 in their collection had been transcribed by the public.2 A third project, Transcribe Bentham, is digitizing the Bentham Papers’ archive of more than 70,000 items handwritten by Jeremy Bentham.

“Most of the work has been done by a core of 25 volunteers,” says Causer, “though 440 people have transcribed something.” Other, similar programs include the “Transcribe the Renaissance,” a one-day “transcribathon” held in conjunction with the University of Pennsylvania and the Folger Shakespeare Library 3 and the Smithsonian Digital Volunteers’ Transcription Center.4 With the success of these initial crowdsourcing programs, the door can be opened to all sorts of handwriting that has been previously inaccessible. In that lies tremendous potential for future reclamation of the ephemeral past we do not want to lose. Libraries and institutions are developing tools and guidelines to enable individuals to digitize family documents; the National Archives has several guides. Some commercial options are available to have these documents transcribed as well, and scholars are working on additional transcription programs for anyone with a private manuscript collection.


pages: 510 words: 120,048

Who Owns the Future? by Jaron Lanier

3D printing, 4chan, Affordable Care Act / Obamacare, Airbnb, augmented reality, automated trading system, barriers to entry, bitcoin, book scanning, Burning Man, call centre, carbon footprint, cloud computing, commoditize, computer age, crowdsourcing, David Brooks, David Graeber, delayed gratification, digital Maoism, Douglas Engelbart, en.wikipedia.org, Everything should be made as simple as possible, facts on the ground, Filter Bubble, financial deregulation, Fractional reserve banking, Francis Fukuyama: the end of history, George Akerlof, global supply chain, global village, Haight Ashbury, hive mind, if you build it, they will come, income inequality, informal economy, information asymmetry, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kodak vs Instagram, life extension, Long Term Capital Management, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Metcalfe’s law, moral hazard, mutually assured destruction, Network effects, new economy, Norbert Wiener, obamacare, packet switching, Panopticon Jeremy Bentham, Peter Thiel, place-making, plutocrats, Plutocrats, Ponzi scheme, post-oil, pre–internet, race to the bottom, Ray Kurzweil, rent-seeking, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart meter, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, Ted Nelson, The Market for Lemons, Thomas Malthus, too big to fail, trickle-down economics, Turing test, Vannevar Bush, WikiLeaks, zero-sum game

A market economy should not just be about “businesses,” but about everyone who contributes value. I could just as well frame my argument in the language of barter and sharing. Leveraging cloud computing to make barter more efficient, comprehensive, and fair would ultimately lead to a similar design to what I am proposing. The usual Manichaean portrayal of the digital world is “new versus old.” Crowdsourcing is “new,” for instance, while salaries and pensions are “old.” This book proposes pushing what is “new” all the way instead of part of the way. We need not shy away. Big Talk, I Know . . . Am I making a Swiftian modest proposal, or am I presenting a plan on the level? It’s a little of both. I hope to widen the way people think about digital information and human progress. We need a palate cleansing, a broadening of horizons.

Automated milling machines and similar devices are already ubiquitous for shaping parts, such as forms for molds; robotic arms to assemble components are not as common, but still present in certain applications, such as assembling parts of large items like cars and big TVs. Detail work (like fitting touchscreens into the frame of a tablet) is still mostly done by hand, but that might change soon. At first manufacturing robots will be expensive, and there will be plenty of well-paying jobs created to operate them, but eventually they will become cheap and the data to operate them might then be crowdsourced, sending manufacturing down the same road traveled by the recorded music industry. A current academic and hobbyist craze is known as “3D printing.” A 3D printer looks a little like a microwave oven. Through the glass door, you can watch roaming robotic nozzles deposit various materials under software control in an incremental way to form a product as if by magic. You download a design from the ’net, as if you were downloading a movie file, send it to your 3D printer, and come back after a while.

Instead of just driving prices down, it turns consumers into a priori funders of innovation. But at an Amazon-like scale there would inevitably be an even bigger wave of tricksters, scammers, and the clueless to be dealt with. Kickstarter continues to produce some wonderful success stories and a huge ocean of doomed or befuddled proposals. Maybe the site will enter into an endless game with scammers and the clueless, as it scales up, and render itself irrelevant. Or it might adopt crowdsourced voting or automatic filters to keep out crap, only to find that crap is smart and happy to jump through hoops to get through. Or maybe Kickstarter will become more expensive to use, and less naïvely “democratic,” because human editors will block useless proposals. Maybe it will learn to take on at least a little risk to go with the benefits. Whatever happens, success will be dependent on finding some imperfect but survivable compromise.


pages: 377 words: 97,144

Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World by James D. Miller

23andMe, affirmative action, Albert Einstein, artificial general intelligence, Asperger Syndrome, barriers to entry, brain emulation, cloud computing, cognitive bias, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Brooks, David Ricardo: comparative advantage, Deng Xiaoping, en.wikipedia.org, feminist movement, Flynn Effect, friendly AI, hive mind, impulse control, indoor plumbing, invention of agriculture, Isaac Newton, John von Neumann, knowledge worker, Long Term Capital Management, low skilled workers, Netflix Prize, neurotypical, Norman Macrae, pattern recognition, Peter Thiel, phenotype, placebo effect, prisoner's dilemma, profit maximization, Ray Kurzweil, recommendation engine, reversible computing, Richard Feynman, Rodney Brooks, Silicon Valley, Singularitarianism, Skype, statistical model, Stephen Hawking, Steve Jobs, supervolcano, technological singularity, The Coming Technological Singularity, the scientific method, Thomas Malthus, transaction costs, Turing test, twin studies, Vernor Vinge, Von Neumann architecture

For example, if 90 percent of people who had some unusual allele or brain microstructure enjoyed a certain cat video, then the AI recommender would suggest the video to all other viewers who had that trait. 12.Amenable to Crowdsourcing—Netflix, the rent-by-mail and streaming video distributor, offered (and eventually paid) a $1 million prize to whichever group improved its recommendation system the most, so long as at least one group improved the system by at least 10 percent. This “crowdsourcing,” which occurs when a problem is thrown open to anyone, helps a company by allowing them to draw on the talents of strangers, while only paying the strangers if they help the firm. This kind of crowdsourcing works only if, as with a video recommendation system, there is an easy and objective way of measuring progress toward the crowdsourced goal. 13.Potential Improvement All the Way Up to Superhuman Artificial General Intelligence—A recommendation AI could slowly morph into a content creator.

See also amphetamines; amphetamines (“speed”); sleep deprivation Adderall (See under Adderall) author considers them safe and they significantly increase IQ, memory and mental staying power, 124, 211 author’s use of, 104–5, 112 benefits of, 155 as bio-enhancements for human self-improvement, 109 brain-power heightened by, 155 dangerous but effective, 124–25 doctors’ intelligence improved by, 158 drugs enhance excitement levels of different jobs, 159 drugs make life tolerable, 159 drugs reduce chronic unemployment, 159 drug studies, warnings about, 110–12 drugs would boost income of unskilled laborers, 159 economic inequality among nations, greatly reduces, 125 government-licensed occupations and, 158 hourly wages boosted by, 155 income increased by factor of 100, 125 investment banking and, 158–59 lawyers and, 158 militaries and, 109 modafinil, 104 opposition to, 109–10 parents concern that their children have, 211–12 pharmaceutical companies and, 108–9 Prisoners’ Dilemma of drug use and risks, 160–62 professors and, 158 regulated drugs, 123 Ritalin, xv, 104–5 safe and effective, 123–24 SAT tests and, 162–63 side-effects, 112 US ban on, 123–25 cognitive-enhancing technologies, 173 cognitive skills, human, 77 cognitive skills of hunter-gatherers, 76 cognitive training, 114 cognitive traits, farming-friendly, 76 Cohen, Gene, 113 cold-blooded killer, 93 Cold War, 127 community, short-term—oriented, 80 comparative advantage, 135–37 computational science, 185 computer(s) with brilliance of von Neumann, xiii chess, 132–33 Moore’s Law and computer chips, 5 nanotubes, 3-D molecular, 4 performance, 5 simulation, 45 simulation of chimpanzee’s brain, 177 Singularity-enabling of, 6 computing hardware, 7 construction workers, 179–80 consumer, hyper-rational, 42 consumer preference rankings, 42 consumption equality, 167–69 convergent evolution, 202 Coulson, Andrew, 172 crowdsourcing, 20 cruise missiles, hypersonic stealth, 127 cryocrastination, 219–20 cryonics, 213–21 Cryonics Institute, 214–15 cryonics patient, first, 220 crystallized intelligence, 115 culture software, 79 cystic fibrosis, 83–84 D Daily Princetonian, 86 DARPA. See Defense Department research agency (DARPA) Darwin, Charles, 91 dating market power of men, 194 debt-ridden countries, 177 Deep Blue (IBM’s supercomputer), 4, 132 Defense Department research agency (DARPA), 121 de Grey, Aubrey, 35 dementia, 108, 116, 149.


pages: 407 words: 103,501

The Digital Divide: Arguments for and Against Facebook, Google, Texting, and the Age of Social Netwo Rking by Mark Bauerlein

Amazon Mechanical Turk, Andrew Keen, business cycle, centre right, citizen journalism, collaborative editing, computer age, computer vision, corporate governance, crowdsourcing, David Brooks, disintermediation, Frederick Winslow Taylor, Howard Rheingold, invention of movable type, invention of the steam engine, invention of the telephone, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, late fees, Mark Zuckerberg, Marshall McLuhan, means of production, meta analysis, meta-analysis, moral panic, Network effects, new economy, Nicholas Carr, PageRank, peer-to-peer, pets.com, Results Only Work Environment, Saturday Night Live, search engine result page, semantic web, Silicon Valley, slashdot, social graph, social web, software as a service, speech recognition, Steve Jobs, Stewart Brand, technology bubble, Ted Nelson, The Wisdom of Crowds, Thorstein Veblen, web application

. >>> redefining collective intelligence: new sensory input To understand where the Web is going, it helps to return to one of the fundamental ideas underlying Web 2.0, namely that successful network applications are systems for harnessing collective intelligence. Many people now understand this idea in the sense of “crowdsourcing”—namely, that a large group of people can create a collective work whose value far exceeds that provided by any of the individual participants. The Web as a whole is a marvel of crowdsourcing, as are marketplaces such as those on eBay and craigslist, mixed media collections such as YouTube and Flickr, and the vast personal lifestream collections on Twitter, MySpace, and Facebook. Many people also understand that applications can be constructed in such a way as to direct their users to perform specific tasks, like building an online encyclopedia (Wikipedia), annotating an online catalog (Amazon), adding data points onto a map (the many Web-mapping applications), or finding the most popular news stories (Digg, Twine).

All of a sudden, we’re not using search via a keyboard and a stilted search grammar, we’re talking to and with the Web. It’s getting smart enough to understand some things (such as where we are) without us having to tell it explicitly. And that’s just the beginning. And while some of the databases referenced by the application—such as the mapping of GPS coordinates to addresses—are “taught” to the application, others, such as the recognition of speech, are “learned” by processing large, crowdsourced data sets. Clearly, this is a “smarter” system than what we saw even a few years ago. Coordinating speech recognition and search, search results and location, is similar to the “hand-eye” coordination the baby gradually acquires. The Web is growing up, and we are all its collective parents. >>> cooperating data subsystems In our original Web 2.0 analysis, we posited that the future “Internet operating system” would consist of a series of interoperating data subsystems.

Lessig isn’t the only one singing 2.0’s praises who seems confused about fundamental terms. Jay Rosen, a professor of journalism at New York University, is maybe the most voluble booster of the “citizen journalism” that he believes fulfills the blogosphere’s social promise. Rosen has started a blog-based initiative called Assignment Zero, in which anyone, journalist or not, can file an “investigative” news article. Rosen called this “crowdsourcing” in an interview with The New York Times’s David Carr, who reported the story without expressing the slightest skepticism and without presenting an opposing view to Rosen’s. And there is an opposing point of view. In the world of Assignment Zero, if you are someone working for a politician with an ax to grind, you could use Assignment Zero to expose a pesky journalist. Or you could just go on the blog to take down someone who has rubbed you the wrong way.


pages: 56 words: 16,788

The New Kingmakers by Stephen O'Grady

AltaVista, Amazon Web Services, barriers to entry, cloud computing, correlation does not imply causation, crowdsourcing, David Heinemeier Hansson, DevOps, Jeff Bezos, Khan Academy, Kickstarter, Marc Andreessen, Mark Zuckerberg, Netflix Prize, Paul Graham, Ruby on Rails, Silicon Valley, Skype, software as a service, software is eating the world, Steve Ballmer, Steve Jobs, Tim Cook: Apple, Y Combinator

Unlike traditional venture capital, however, Kickstarter claims no ownership stake in funded projects—all rights are retained by the project owners. Though Kickstarter is by no means focused strictly on developers, they have been among the most impressive beneficiaries. Of the top projects by funds raised, the first three are video games. In March 2012, Double Fine Adventure set the record for Kickstarter projects, attracting $3.3 million in crowd-sourced financing. Number two on the list, Wasteland 2, raised just under $3 million, with third place Shadowrun Returns receiving $1.8 million. The Kickstarter model is less established than even seed-stage venture dollars, but it shows every sign of being a powerful funding option for developers moving forward. In little more than a decade, developers had gained access to free software, affordable hardware, powerful networking tools, and more entrepreneur-friendly financing options.

On October 2, 2006, Netflix announced the Netflix Prize: The first team of non-employees that could best their in-house algorithm by 10% would claim $1,000,000. This prize had two major implications. First, it implied that the benefits of an improved algorithm would exceed one million dollars for Netflix, presumably through customer acquisition and improvements in retention. Second, it implied that crowd-sourcing had the potential to deliver better results than the organization could produce on its own. In this latter assumption, Netflix was proven correct. On October 8—just six days after the prize was announced—an independent team bested the Netflix algorithm, albeit by substantially less than ten percent. The 10% threshold was finally reached in 2009. In September of that year, Netflix announced that the team “BellKor’s Pragmatic Chaos”—composed of researchers from AT&T Labs, Pragmatic Theory, and Yahoo!


Bit by Bit: How P2P Is Freeing the World by Jeffrey Tucker

Affordable Care Act / Obamacare, Airbnb, airport security, altcoin, bank run, bitcoin, blockchain, business cycle, crowdsourcing, cryptocurrency, disintermediation, distributed ledger, Fractional reserve banking, George Gilder, Google Hangouts, informal economy, invisible hand, Kickstarter, litecoin, Lyft, obamacare, Occupy movement, peer-to-peer, peer-to-peer lending, QR code, ride hailing / ride sharing, Ross Ulbricht, Satoshi Nakamoto, sharing economy, Silicon Valley, Skype, TaskRabbit, the payments system, uber lyft

Marcus for his editorial eye, the team at Liberty.me for showing what progress means, all my teachers in the bitcoin space, Andreas Antonopoulos for radical and inspirational punditry, Laurie Rice for social media and intellectual mastery, Patrick Byrne for corporate leadership and his introduction, Stephan Kinsella for showing me about the magic of information economies, all the god-like brains from the past whose immortal writings have inspired me, and countless others who have added their wits and wisdom to my thinking on this topic. It’s a crowd-sourced book. It’s a crowd-sourced world. Always has been. I. Liberation Ms. Fereshteh Forough, scientist and philanthropist, grew up as a refugee in Iran. Today her work centers in Afghanistan. Her passion is the liberation of women from poverty and oppression in the developing world. Toward that end, in 2012 she established the Women’s Annex Foundation and opened clinics all over the country. Their goal was to take maximum advantage of new economic tools that would allow women in Afghanistan to acquire and use computer skills to become economically empowered and independent.

Even social networks are getting there, with Tsu.co proposing to pay users for the traffic they draw, sharing ad revenue with the actual content providers. Lending services have been one of the biggest surprises. Prosper allows people who need extra cash to find someone with spare cash to lend. The Lending Tree looks up parties to lending transactions too. The Funding Circle helps restructure student debt. Many crowd-sourced platforms such as Indiegogo and Kickstarter provide a meeting spot for entrepreneurs and investors. Ten years ago, there was an emerging hysteria about how “quants” — super-smart number crunchers with private knowledge — were ruling the financial space, rigging the game and grabbing all available profits for themselves. Today, the same and better knowledge is being democratized with such services as Kensho, which is bringing quant-style power to every investor and institution, essentially running a Google-style search feature for investments, giving the information it gets based on real-time experience.


pages: 238 words: 73,824

Makers by Chris Anderson

3D printing, Airbnb, Any sufficiently advanced technology is indistinguishable from magic, Apple II, autonomous vehicles, barriers to entry, Buckminster Fuller, Build a better mousetrap, business process, commoditize, Computer Numeric Control, crowdsourcing, dark matter, David Ricardo: comparative advantage, death of newspapers, dematerialisation, Elon Musk, factory automation, Firefox, future of work, global supply chain, global village, IKEA effect, industrial robot, interchangeable parts, Internet of things, inventory management, James Hargreaves, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, Kickstarter, Lean Startup, manufacturing employment, Mark Zuckerberg, means of production, Menlo Park, Network effects, private space industry, profit maximization, QR code, race to the bottom, Richard Feynman, Ronald Coase, Rubik’s Cube, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, slashdot, South of Market, San Francisco, spinning jenny, Startup school, stem cell, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, supply-chain management, The Nature of the Firm, The Wealth of Nations by Adam Smith, transaction costs, trickle-down economics, Whole Earth Catalog, X Prize, Y Combinator

The Chandler site is just the first “microfactory” of many the company plans to build across America, each with about forty employees. Each will manufacture cars created by the community, which helps build them. It’s a glimpse into a whole new way to design, engineer, and produce cars—and maybe lots of other things, too. Local Motors is a car company built on Maker principles. Its designs are crowdsourced, as is the selection of mostly off-the-shelf components. It doesn’t patent ideas—the point is to give them away so that others can build on them and make them even better, for the benefit of all. It holds almost no inventory, and purchases components and prepares kits only after buyers have made a down payment and reserved a build date. It started with a question: How would you build a car company on the Web?

It can work in units of millions and units of ones: yet another scale-free network, just like the Internet. The thirty-eight-year-old Rogers favors military-style flight suits, an echo of his time as a captain in the Marines, including action in Iraq, and he boasts both a Harvard MBA and a stint as an entrepreneur in China. While at Harvard, Rogers saw a presentation on Threadless, the open-design T-shirt company, which showed him the power of crowdsourcing. Cars are more complicated than T-shirts, but both are examples of “platforms” on which many people can display their talents and collectively innovate. And in both cases there are far more people who can design them than are currently paid to do so. In the automotive world, the majority of students who study car design don’t get jobs in the industry; instead they end up designing toothpaste tubes or kids’ toys.

Most other components are simply ordered from car parts suppliers such as Penske Automotive Group; the engines and transmissions can be bought straight from big car makers such as BMW and GM, who will sell to third parties. The axle of the Rally Fighter comes from a Ford F-150 truck; the fuel cap comes from a Mitsubishi Eclipse. This combination—have the pros handle the elements that are critical to performance, safety, and manufacturability while the community designs the parts that give the car its shape and style—allows crowdsourcing to work even for a product whose use has life-and-death implications. The final assembly is done by the customers themselves under an expert mechanic’s tutelage, as part of a “build experience” at the Chandler factory. At any given time, a half dozen Rally Fighters are being built in two rows facing each other. Each has a custom tool cabinet and a rack of parts next to it; the mechanic coach is always working with one team or another.


pages: 274 words: 73,344

Found in Translation: How Language Shapes Our Lives and Transforms the World by Nataly Kelly, Jost Zetzsche

airport security, Berlin Wall, Celtic Tiger, crowdsourcing, Donald Trump, glass ceiling, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, randomized controlled trial, Ray Kurzweil, Skype, speech recognition, Steve Jobs, the market place

Technical solutions for capturing and channeling the thousands of messages were quickly in place, but the majority of the messages were in Haitian Creole, a language unknown to most responders. The relatively few professional translators in the area were already completely overwhelmed by other responses and were unable to handle this onslaught of additional translation tasks. Enter Rob Munro, a linguist and graduate fellow at Stanford who had been developing methods for processing large volumes of SMS text messages in less-common languages. He’d also been working on crowd-sourcing projects. These two distinct specialties became the perfect combination for a new project called Mission 4636, which was named after the number of the free phone line the individuals used to communicate. Munro went about setting up a team for the task. In the first week alone, he assembled more than a thousand volunteers from a total of forty-nine different countries. An online chat room served as both the orientation venue for newly joining volunteers and as a platform for translators to communicate with each other and their coordinators.

It decided to engage the crowd, to allow its users to determine how they would like to see the site translated into their languages. Facebook users rallied in support of the cause. Within just a couple of weeks of starting the effort, they launched the first language, Spanish. Envision a tiny snowball at the top of the mountain. Users responded with positive feedback, so the company opened up its crowdsourcing platform to enable users to translate the site into French and German. Imagine the snowball beginning to roll down the mountain. The following year, 2008, was the year of internationalization at Facebook. “One can argue that translations contributed the most growth,” explains Ghassan Haddad, director of internationalization at Facebook.13 He notes that the number of users in Italy skyrocketed following the launch of the Italian language, jumping from 375,000 to 933,000 in just four months.

Facebook is currently available in seventy-seven languages, including U.S. English, and another thirty languages are in various phases of translation progress. The currently available languages represent over 90 percent of the world population and over 95 percent of people with access to the Internet. The company has continued to tap into its user base to produce the translated versions of its website. Facebook’s crowdsourced translation model has since served as an example for many hundreds of organizations throughout the world, including various nonprofits and charitable organizations. In fact, there are now entire companies that make money from setting up such online communities and paying professional translators to provide the translations through these portals. “Since our users are major stakeholders and partners in the translation, the launchability of additional languages depends greatly on their involvement,” Haddad observes.


pages: 366 words: 76,476

Dataclysm: Who We Are (When We Think No One's Looking) by Christian Rudder

4chan, Affordable Care Act / Obamacare, bitcoin, cloud computing, correlation does not imply causation, crowdsourcing, cuban missile crisis, Donald Trump, Edward Snowden, en.wikipedia.org, Frank Gehry, Howard Zinn, Jaron Lanier, John Markoff, John Snow's cholera map, lifelogging, Mahatma Gandhi, Mikhail Gorbachev, Nate Silver, Nelson Mandela, new economy, obamacare, Occupy movement, p-value, pre–internet, race to the bottom, selection bias, Snapchat, social graph, Solar eclipse in 1919, Steve Jobs, the scientific method

This sample is ≈5,000 people. 2 The study of beauty by traditional methods is especially susceptible to the problem of insufficiency. If your research topic is, say, wealth, you can very easily get a measure of someone’s net worth or income and then move on to the dependent trait you want to look at. But to study beauty, first you have to determine how good-looking your subjects are, which is a resource-intensive process. Beauty being so wildly subjective (as opposed to, say, hair color, where if you crowdsourced it, you might get slight variations—brown, brunette, chestnut—that are essentially synonymous), you get wide swings in opinion that can only be absorbed by sampling a large, diverse research set. As we’ve seen with WEIRDness earlier, that has not been a strength of past academic research. 8. It’s What’s Inside That Counts There used to be two ways to figure out what a person really thinks.

People used it to complain about anything they didn’t like (had a beef with), ignoring the brand: Remi Mitchison @RemiBee #HeresTheBeef when a chick see another chick doin better and has more than she does … so she wanna stunt and #GetThatAssBeatUp Jeremy Baumhower @jeremytheproduc #HeresTheBeef The drugs companies have already cured HIV and cancer, however it is far more profitable to keep people barely alive on drugs More recently, Mountain Dew ran a “Dub the Dew” contest, trying to ride the “crowdsourcing” wave to a cool new soda name and thinking maybe, if everything went just right and the metrics showed enough traction to get buy-in from the right influencers, they’d earn some brand ambassadors in the blogosphere. Reddit and 4chan got ahold of it, and “Hitler did nothing wrong” led the voting for a while, until at the last minute “Diabeetus” swooped in and the people’s voice was heard: Dub yourself, motherfucker.

As the New York Times reported last year: “Using data drawn from queries entered into Google, Microsoft and Yahoo search engines, scientists at Microsoft, Stanford and Columbia University have for the first time been able to detect evidence of unreported prescription drug side effects before they were found by the Food and Drug Administration’s warning system.” The researchers determined that paroxetine and pravastatin were causing hyperglycemia in patients. Here, the payoff for living a little less privately is to live a little more healthily. Every day, it seems, brings word of some new advance. Today, I found out that a site called geni.com is well on the way to creating a crowdsourced family tree for all mankind. If it works, the company will have made, essentially, a social network for our genetic material. The week before, two political scientists debunked the received wisdom that Republicans owe their House majority to district gerrymandering. The authors had modeled every possible election over every possible configuration of the United States and concluded, with the computer playing Candide, that our divided world is the best we can hope for.


pages: 229 words: 72,431

Shadow Work: The Unpaid, Unseen Jobs That Fill Your Day by Craig Lambert

airline deregulation, Asperger Syndrome, banking crisis, Barry Marshall: ulcers, big-box store, business cycle, carbon footprint, cashless society, Clayton Christensen, cognitive dissonance, collective bargaining, Community Supported Agriculture, corporate governance, crowdsourcing, disintermediation, disruptive innovation, financial independence, Galaxy Zoo, ghettoisation, gig economy, global village, helicopter parent, IKEA effect, industrial robot, informal economy, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Mark Zuckerberg, new economy, pattern recognition, plutocrats, Plutocrats, recommendation engine, Schrödinger's Cat, Silicon Valley, single-payer health, statistical model, Thorstein Veblen, Turing test, unpaid internship, Vanguard fund, Vilfredo Pareto, zero-sum game, Zipcar

The “Ikea effect” may also strengthen brand loyalty for products that consumers have helped design and build. In many sectors, customers will look for more personalized goods and services—having things their way. They’ll assemble end tables and add their own cream to coffee—but will also want input on the design of their clothing, furniture, and bicycles, leading to more individualized products. Crowdsourcing, which taps the creativity of groups, could become a platform for such populist participation. For example, crowdsourcing on the web enables customers to “vote” on potential T-shirt designs. This form of shadow work provides companies with free market research—and essentially guarantees a certain number of sales, as consumers are nearly certain to buy a product they have helped invent, or customized to their preferences. Disintermediation will also grow as producers sell directly to their customers, eliminating middlemen in venues ranging from farmers’ markets to warehouse stores like Costco.

It is, in fact, the top source of health information for both doctors and patients, according to a report from the IMS Institute for Healthcare Informatics that Julie Beck cited in The Atlantic in 2014. Fifty percent of physicians consult Wikipedia for information, particularly on specific diseases. Tuberculosis, Crohn’s disease, pneumonia, multiple sclerosis, and diabetes were the top five conditions for which Internet users tapped Wikipedia in 2013. Wikipedia’s crowdsourced medical entries tend to be lengthy and comprehensive. Its 6,000-word article on coronary heart disease, for example, cites scores of references and covers signs and symptoms, risk factors, diagnosis, prevention, treatment, epidemiology, and research. Wiki’s throng of lay editors constantly tweaks it. The IMS examined five popular Wikipedia entries (diabetes, multiple sclerosis, rheumatoid arthritis, breast and prostate cancer) and found them perpetually in flux: On average, editors changed the pages from sixteen to forty-six times monthly.

After a few years, companies phased out such painstaking support. They unplugged the telephone lines and instead directed customers to websites. There, users could consult lists of FAQs and email queries. The basic idea was to get the company’s staff off the hook and put the technical problem back in the customer’s lap. In the next phase, manufacturers began to delegate the job of solving user problems to fellow users: in essence, crowdsourcing tech support. Apple online “communities” at discussions.apple.com, for example, include forums for users of most kinds of Apple software, like iTunes and Apple Pay, as well as Apple hardware like iPhones, iPads, and Macintosh desktop computers. Users may join such forums and pose their questions about Apple products. Fellow users will step up and try to provide an answer online, of course at no charge.


pages: 302 words: 73,581

Platform Scale: How an Emerging Business Model Helps Startups Build Large Empires With Minimum Investment by Sangeet Paul Choudary

3D printing, Airbnb, Amazon Web Services, barriers to entry, bitcoin, blockchain, business process, Chuck Templeton: OpenTable:, Clayton Christensen, collaborative economy, commoditize, crowdsourcing, cryptocurrency, data acquisition, frictionless, game design, hive mind, Internet of things, invisible hand, Kickstarter, Lean Startup, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, means of production, multi-sided market, Network effects, new economy, Paul Graham, recommendation engine, ride hailing / ride sharing, shareholder value, sharing economy, Silicon Valley, Skype, Snapchat, social graph, social software, software as a service, software is eating the world, Spread Networks laid a new fibre optics cable between New York and Chicago, TaskRabbit, the payments system, too big to fail, transport as a service, two-sided market, Uber and Lyft, Uber for X, uber lyft, Wave and Pay

Industrial designers will sell directly to consumers in much the same way that graphic designers currently do on platforms like Threadless and 99Designs. Collaboration models in industrial design and assembly will become networked as well. g. Crowdsourcing and the Wikipedia of everything The coordination of production has traditionally required a supply chain of integrated, top-down processes and controls. Wikipedia reconfigured this linear process and allowed it to be managed cyclically on a network. Wikipedia allows anyone to contribute content to a self-policing/semi-autonomous editorial base that works together to create a constantly changing document on the platform. Similarly, Waze, an Israeli traffic prediction app, crowdsources driving information from multiple drivers while simultaneously using algorithms to determine authenticity before distributing traffic conditions to the wider community.

Platform Scale explains the design of a family of emerging digital business models that enables today’s startups to achieve rapid scale: the platform business model. The many manifestations of the platform business model - social media, the peer economy, cryptocurrencies, APIs and developer ecosystems, the Internet of things, crowdsourcing models, and many others - are becoming increasingly relevant. Yet, most new platform ideas fail because the business design and growth strategies involved in building platforms are not well understood. Platform Scale is a builder’s manual for anyone building a platform business today. It lays out a structured approach to designing and growing a platform business model and addresses the key factors that lead to the success and failure of these businesses.

Every time that users participate on these platforms, they contribute value in the form of content and data. As users participate more often, platforms scale faster. Airbnb and Uber fight employee-driven organizations with ecosystems. Apple and Android created ecosystems of developers around their respective platforms to disrupt an entire industry. Increasingly numbers of companies are turning to crowdsourcing to solve problems that they traditionally solved in-house. The user-employee distinction is probably least stark in the case of a host of labor platforms that try to be Uber for X. Platforms like Homejoy, MyClean, and SpoonRocket create ecosystems of contract workers who might as well be employees. While some of these platforms are still negotiating the regulatory structures governing these new business models, many others have already demonstrated the power of producer ecosystems to drive value creation in a networked age.


pages: 552 words: 168,518

MacroWikinomics: Rebooting Business and the World by Don Tapscott, Anthony D. Williams

accounting loophole / creative accounting, airport security, Andrew Keen, augmented reality, Ayatollah Khomeini, barriers to entry, Ben Horowitz, bioinformatics, Bretton Woods, business climate, business process, buy and hold, car-free, carbon footprint, Charles Lindbergh, citizen journalism, Clayton Christensen, clean water, Climategate, Climatic Research Unit, cloud computing, collaborative editing, collapse of Lehman Brothers, collateralized debt obligation, colonial rule, commoditize, corporate governance, corporate social responsibility, creative destruction, crowdsourcing, death of newspapers, demographic transition, disruptive innovation, distributed generation, don't be evil, en.wikipedia.org, energy security, energy transition, Exxon Valdez, failed state, fault tolerance, financial innovation, Galaxy Zoo, game design, global village, Google Earth, Hans Rosling, hive mind, Home mortgage interest deduction, information asymmetry, interchangeable parts, Internet of things, invention of movable type, Isaac Newton, James Watt: steam engine, Jaron Lanier, jimmy wales, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, Marc Andreessen, Marshall McLuhan, mass immigration, medical bankruptcy, megacity, mortgage tax deduction, Netflix Prize, new economy, Nicholas Carr, oil shock, old-boy network, online collectivism, open borders, open economy, pattern recognition, peer-to-peer lending, personalized medicine, Ray Kurzweil, RFID, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, scientific mainstream, shareholder value, Silicon Valley, Skype, smart grid, smart meter, social graph, social web, software patent, Steve Jobs, text mining, the scientific method, The Wisdom of Crowds, transaction costs, transfer pricing, University of East Anglia, urban sprawl, value at risk, WikiLeaks, X Prize, young professional, Zipcar

When disaster struck Haiti two years later, Ushahidi’s director of crisis mapping, Patrick Meier, sprang into action. Meier had been enjoying a quiet evening watching the news at his home in Boston. It was 7:00 p.m. when he first learned about the earthquake. By 7:20, he’d contacted a colleague in Atlanta. By 7:40, the two were setting up a dedicated site for Haiti on the Ushahidi platform. By 8:00, they were gathering intelligence from everywhere, in a global effort to crowdsource assistance for Haiti. Since the majority of incoming text messages were in Creole, they needed a translation service. And since most reports lacked sufficient location details, they needed a way to quickly identify the GPS coordinates so that incidents could be mapped as accurately as possible. So Meier reached out to dozens of Haitian communities for help, including the large diaspora in Boston.

Sean Wise, a management professor and VC himself, leads one of a growing number of outfits determined to prove that a form of community-powered venture capital can both filter the global wealth of opportunities and channel more intellectual horsepower into making each investment successful. And he’s not just talking about it; he’s doing it—cofounding a company, VenCorps, that uses mass collaboration at every stage of the process. “For Venture Capital 2.0 to succeed,” he says, “there will need to be exponentially more people involved.” Just as Goldcorp used mass collaboration to locate their drilling sites, or Wikipedia crowdsourced the publication of expert articles, or Threadless works with customers to design T-shirts, VenCorps is leveraging collaboration. Wise is deploying the power of mass collaboration, not just in the process of choosing which start-ups to fund, but to help grow those start-up ventures after the investment is made. “VenCorps is basically wikinomics applied to venture capital,” says Wise. The money being invested by VenCorps in small companies comes from their own fund, but the choice of where to invest it belongs to the VenCorps community.

To be clear, collaborative innovation is not about “everybody doing everything,” as critics such as Jaron Lanier have suggested. Nor is it a wholesale replacement for cutting-edge R&D or the art of a good marketing campaign. It’s not about putting product duds in the public domain and hoping that someone will turn them into gold. Nor is it about enticing smart and talented people to give away their valuable ideas for free. Sure, a number of companies have exploited so-called crowdsourcing to get marketing and other services on the cheap. But schemes like these are not sustainable and rarely provide the foundation for a dynamic and fertile business ecosystem. In the most successful instances of mass collaboration, companies carve out meaningful roles for contributors and allow community members to share in the ownership and fruits of their creations. They make their core products modular, reconfigurable, and editable.


pages: 343 words: 102,846

Trees on Mars: Our Obsession With the Future by Hal Niedzviecki

"Robert Solow", Ada Lovelace, agricultural Revolution, Airbnb, Albert Einstein, anti-communist, big data - Walmart - Pop Tarts, big-box store, business intelligence, Colonization of Mars, computer age, crowdsourcing, David Brooks, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, Google Glasses, hive mind, Howard Zinn, if you build it, they will come, income inequality, Internet of things, invention of movable type, Jaron Lanier, Jeff Bezos, job automation, John von Neumann, knowledge economy, Kodak vs Instagram, life extension, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Peter H. Diamandis: Planetary Resources, Peter Thiel, Pierre-Simon Laplace, Ponzi scheme, precariat, prediction markets, Ralph Nader, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, rising living standards, Ronald Reagan, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, TaskRabbit, technological singularity, technoutopianism, Ted Kaczynski, Thomas L Friedman, Uber and Lyft, uber lyft, working poor

Corporations lined up to sponsor further prizes including the 2007-2010 Progressive Insurance Automotive XPrize, the 2010-2011 Wendy Schmidt Oil Cleanup XCHALLENGE, and the ongoing 2007 Google Lunar XPrize. Since then, prizes are coming at a furious pace. Among them include three new ocean-related XPrizes to be launched before 2020, whose goals, apparently, will be determined by crowdsourcing public opinion. The message of the XPrize Foundation is as unambiguous as its crowdsourcing marketing exercises: this is how you get to the future. Ten years ago, we might have dismissed the XPrize as an outsized personal obsession, an outlier that doesn’t actually represent any kind of systemic change in how we think about future collectively and individually. After all, XPrize founder Diamandis is a pundit, speaker, TED Talk regular, and author of iconic Silicon Valley text Abundance: The Future is Better Than You Think.

So, yes, it seems to be working, not only for Tetlock and his forecasters but for a wide array of researchers and social experimenters who want to find ways to know what is going to happen before it happens, who want to turn what was formerly an abstract unknowable into controllable data. IARPA and Philip Tetlock’s network of collaborators aren’t the only group trying to incorporate big data and psychology and crowdsourcing to get us to the point where we can actually turn future into information. In January 2014, George Mason University in Virginia launched SciCast, billed as the “largest and most advanced science and technology prediction market in the world.” I’m not going to get into all the technical details of how SciCast works, but basically the idea is that anyone in the world can join up and start answering questions about what will happen in the realms of science and technology in the next six months to two years.

Similarly, back at Patrick Tucker’s World Future Conference I sit in a packed room and listen to a presentation on the construction of a global knowledge hub called the Millennium Project. This project features “a coherent and cumulative process that collects and assesses judgments from over 3,500 people” who are invited into the project via “U.N. Organizations, governments, corporations, NGOs, universities, and individuals from around the world.”11 Their goal, too, is the building of a kind of massive hive mind of crowdsourced pundits—a postmodern oracle to soothe the fragmentation of our age by converting the mystery of future into information. Sensing that interest in the psychology of prediction and future perception has never been greater, I again check in with social psychologist Cheryl Wakslak, who teaches and researches at the University of So