cloud computing

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The Art of Scalability: Scalable Web Architecture, Processes, and Organizations for the Modern Enterprise by Martin L. Abbott, Michael T. Fisher

always be closing, anti-pattern, barriers to entry, Bernie Madoff, business climate, business continuity plan, business intelligence, business process, call centre, cloud computing, combinatorial explosion, commoditize, Computer Numeric Control, conceptual framework, database schema, discounted cash flows, en.wikipedia.org, fault tolerance, finite state, friendly fire, hiring and firing, Infrastructure as a Service, inventory management, new economy, packet switching, performance metric, platform as a service, Ponzi scheme, RFC: Request For Comment, risk tolerance, Rubik’s Cube, Search for Extraterrestrial Intelligence, SETI@home, shareholder value, Silicon Valley, six sigma, software as a service, the scientific method, transaction costs, Vilfredo Pareto, web application, Y2K

—Sun Tzu In the previous chapter, we covered the history, characteristics, and comparison of cloud computing and grid computing. We also discussed how important they were to scalability. In this chapter, we are going to cover the benefits and drawbacks of cloud computing. After we’ve covered this in sufficient detail, we are going to discuss where we think cloud computing makes the most sense in different companies. Lastly, we are going to cover how we recommend you think through the decision of whether to use a cloud computing service for various environments. This will provide you with examples of how you can use cloud computing in your scaling efforts as well as give you a framework for making the decision to use it or not. There is a lot of excitement about cloud computing services and rightly so. Cloud computing is a significant breakthrough in computing infrastructure and holds the possibility of changing dramatically how many products and services are offered.

Decision Steps The following are steps to help make a decision about whether to introduce cloud computing into your infrastructure: 1. Determine the goals or purpose of the change. 2. Create alternative designs for how to use cloud computing. 3. Place weights on all the pros and cons that you can come up with for cloud computing. 4. Rank or score the alternatives using the pros and cons. 5. Tally scores for each alternative by multiplying the score by the weight and summing. This decision matrix process will help you make data driven decisions about which cloud computing alternative implementation is best for you. C ONCLUSION The most likely question with regard to introducing cloud computing into your infrastructure is not whether to do it but rather when and how is the right way to do it. Cloud computing is not going away and in fact is likely to be the preferred but not only infrastructure model of the future.

This most important advancement is the advent of cloud computing. Although most people think of this as a very new technology innovation, the reality is that this has taken well over a decade to become a reality. In this chapter, we are going to cover the history that led up to the launch of cloud computing, provide an overview of both cloud and grid computing, discuss the common characteristics of clouds, and finish the chapter with a comparison of grid and cloud computing. Cloud computing is important to scalability because it offers the promise of cheap, on-demand storage and compute capacity. As we will discuss in this chapter, this has many advantages, and a few disadvantages, to physical hardware scaling. Grid computing, similar to cloud computing, although utilized differently, offers a method of scaling for computationally intensive applications.


pages: 525 words: 142,027

CIOs at Work by Ed Yourdon

8-hour work day, Apple's 1984 Super Bowl advert, business intelligence, business process, call centre, cloud computing, crowdsourcing, distributed generation, Donald Knuth, Flash crash, Googley, Grace Hopper, Infrastructure as a Service, Innovator's Dilemma, inventory management, Julian Assange, knowledge worker, Mark Zuckerberg, Nicholas Carr, rolodex, shareholder value, Silicon Valley, six sigma, Skype, smart grid, smart meter, software as a service, Steve Ballmer, Steve Jobs, Steven Levy, the new new thing, the scientific method, WikiLeaks, Y2K, Zipcar

Are you spending a lot of time worrying and thinking about things like virtualization and cloud computing and so forth? Strassmann: Oh absolutely. Yourdon: Or are there, if you’re looking further into the future, are there other things? Strassmann: Yes. By the way, my course, that I’m starting … has 13 lectures. And they are three-hour lectures, and two of those lectures are on virtualization and cloud computing. Yourdon: Okay, well, that obviously gives it some great significance. Strassmann: And the significance is really driven by the economics, the shifting economics of how do you equip an enterprise with information technology that’s long-lasting? And so you have to go towards cloud computing. Yourdon: The less exotic sort of form or sister of that is the virtualization approach, which also seems like a clear-cut economic issue for any large organization.

So those technologies are big ones in terms of your vision of the future. What I see of virtualization is that it’s no longer a leading-edge or early adopter stuff. It’s becoming mainstream. Strassmann: Yes, it is. Yourdon: Cloud computing is a little further out. Strassmann: Well, it depends. You know, I have a list of cloud computing companies. I don’t know if you’ve looked at the list. They’re global now. I looked at companies that provide servers that have over 100,000 servers in one building. Yourdon: Wow. Strassmann: So a huge amount of business is now being channeled to cloud computing. One of the intriguing things is that many of the startups are experiments—in other words, if you are in a given corporation, and you want to experiment with something, and they don’t want to let it into the protected area, you just go out to Amazon and you buy yourself a server, for 25 cents a minute.

And our sweet spot is certainly central to where most IT organizations are, and I think that’s a part of it as well. Yourdon: I’ve obviously been following many of the things that Microsoft has been doing in the marketplace, and I would imagine you can just give me a general answer on a lot of these topical issues, like cloud computing and so on, you’ve got or Microsoft has got white papers or position papers. Do those tend to come from your group or are they influenced by your group? Scott: Well, it is really collaboration. Just like the dogfooding and things I was describing earlier. For example, cloud computing group, we have played a key role in helping shape the product from an architecture perspective and from the perspective of, “here is what CIOs are going to look for in terms of capability, and manageability, and how they think about the business case for the cloud and so on.”


pages: 307 words: 17,123

Behind the cloud: the untold story of how Salesforce.com went from idea to billion-dollar company--and revolutionized an industry by Marc Benioff, Carlye Adler

Albert Einstein, Apple's 1984 Super Bowl advert, barriers to entry, Bay Area Rapid Transit, business continuity plan, call centre, carbon footprint, Clayton Christensen, cloud computing, corporate social responsibility, crowdsourcing, iterative process, Maui Hawaii, Nicholas Carr, platform as a service, Silicon Valley, software as a service, Steve Ballmer, Steve Jobs

Because salesforce.com offers employees an opportunity to make a difference, not just earn a paycheck, it’s known as one of the best places to work. Its original application has become the number-one hosted CRM service, and the company has established itself as the leader in the Software-as-a-Service (SaaS) industry it pioneered. And, through relentless focus, creativity, and passion, salesforce.com inspired an enterprise cloud computing industry. In short, the new and unconventional ideas that salesforce.com has evangelized have changed the way we do business and changed the world. There has been a profound shift toward cloud computing in the past few years. Nearly every major public and private cloud is powered by Dell, and we are ecstatic to be running today’s most exciting companies, including salesforce.com, Facebook, Microsoft, and many others. What motivates me most about this new way of computing is its potential for mass innovation.

For me, launching salesforce.com was a way to respond to new directions and new opportunities that I could not pursue from inside an established corporation. It was a license to do things differently. From the very beginning, salesforce.com set out to build a new technology model (on-demand, or delivered over the Internet—now called cloud computing), a new sales model (subscription based), and a new philanthropic model (integrated into the corporation). Ten years later, we had succeeded on all of these fronts. We also had surpassed my expectations by creating the first $1 billion cloud computing company and spawning a new $46 billion industry, of which we are the market leader. Read on to learn how we became one of the world’s fastest-growing software companies and about the tremendous fun we’ve had along the way. You’ll travel with us as we have our big entrepreneurial epiphany, as we turn a simple idea into a start-up company, and as we develop innovative technology and sell it through unconventional strategies.

Today, more than ever before, companies do not need to build technology from scratch; you can build on Internet-based platforms and tap into distribution centers, data centers, and unlimited computing power. The cloud computing model saves time and capital. All companies benefit when they can afford to focus on innovation rather than infrastructure. Consider Appirio, a software and services company that runs its entire business in the cloud. It has exploded from zero to one hundred fifty people in twenty-three states and in three countries in two-and-a-half years—and spent less than one-third of what a company of its size spends on IT. ‘‘Because of the cloud we were able to save money and be more innovative in how we work,’’ says cofounder and marketing chief Narinder 109 BEHIND THE CLOUD Singh. ‘‘We’re like a next-generation IBM without the baggage of hardware.’’ Play #56: Embrace Transparency and Build Trust One of the biggest issues for any cloud computing company is ensuring reliability of the service.


pages: 398 words: 86,855

Bad Data Handbook by Q. Ethan McCallum

Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, Chuck Templeton: OpenTable:, cloud computing, cognitive dissonance, combinatorial explosion, commoditize, conceptual framework, database schema, DevOps, en.wikipedia.org, Firefox, Flash crash, Gini coefficient, illegal immigration, iterative process, labor-force participation, loose coupling, natural language processing, Netflix Prize, quantitative trading / quantitative finance, recommendation engine, selection bias, sentiment analysis, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application

Goldstein’s career retrospective explains how dirty data will give your classical statistics training a harsh reality check. Data Storage and Infrastructure How you store your data weighs heavily in how you can analyze it. Bobby Norton explains how to spot a graph data structure that’s trapped in a relational database in Crouching Table, Hidden Network (Chapter 13). Cloud computing’s scalability and flexibility make it an attractive choice for the demands of large-scale data analysis, but it’s not without its faults. In Myths of Cloud Computing (Chapter 14), Steve Francia dissects some of those assumptions so you don’t have to find out the hard way. We debate using relational databases over NoSQL products, Mongo over Couch, or one Hadoop-based storage over another. Tim McNamara’s When Databases Attack: A Guide for When to Stick to Files (Chapter 12) offers another, simpler option for storage.

Computer Networks and ISDN Systems 30: 107–117 Chapter 14. Myths of Cloud Computing Steve Francia Myths are an important and natural part of the emergence of any new technology, product, or idea as identified by the hype cycle. Like any myth, technology myths originate in a variety of ways, each revealing intriguing aspects of the human psyche. Some myths come from early adopters, whose naive excitement and need to defend their higher risk decision introduce hopeful, yet mistaken myths. Others come from vendors who, with eagerness, over-promise to their customers. By picking apart some of the more prominent myths surrounding the cloud, we gain better understanding of not only this technology, but hopefully the broader ability to discern truth from hype. Introduction to the Cloud In some ways, cloud computing myths are easily among the most pervasive of all technology myths.

For the purposes of this text, we will be using the term “the cloud” to refer to virtualized nodes on elastic demand as provided by vendors like Amazon’s EC2, Rackspace, Microsoft Azurel, Joyent, and more. Even with this somewhat restricting definition, there are significant differences between the different vendors. The Cloud and Big Data You may be wondering what cloud computing has to do with big data. A significant percentage of companies today are using cloud computing and that number is increasing daily. While some positions exist where a data scientist can leave things completely to an infrastructure team, in many jobs they may be responsible for the infrastructure. In a startup, it’s quite likely, at least to some degree. In all jobs, some knowledge and awareness of infrastructure strengths and best practices would benefit the diligent data scientist.


pages: 757 words: 193,541

The Practice of Cloud System Administration: DevOps and SRE Practices for Web Services, Volume 2 by Thomas A. Limoncelli, Strata R. Chalup, Christina J. Hogan

active measures, Amazon Web Services, anti-pattern, barriers to entry, business process, cloud computing, commoditize, continuous integration, correlation coefficient, database schema, Debian, defense in depth, delayed gratification, DevOps, domain-specific language, en.wikipedia.org, fault tolerance, finite state, Firefox, Google Glasses, information asymmetry, Infrastructure as a Service, intermodal, Internet of things, job automation, job satisfaction, Kickstarter, load shedding, longitudinal study, loose coupling, Malcom McLean invented shipping containers, Marc Andreessen, place-making, platform as a service, premature optimization, recommendation engine, revision control, risk tolerance, side project, Silicon Valley, software as a service, sorting algorithm, standardized shipping container, statistical model, Steven Levy, supply-chain management, Toyota Production System, web application, Yogi Berra

Now imagine trying to start a new web site that sells books when your competition gets its infrastructure “for free.” These are the economic aspirations that drive the supplier side of cloud computing. In the cloud computing era, the scale provides economics that make the cost a new order less expensive. This frees up enough headroom to price the service at less than customers could do it themselves and delivers additional profit that subsidizes the provider’s infrastructure. Anything done to improve the efficiency of operations either adds to the service provider’s profitability or enables it to offer services at a lower cost than the competition. To understand the consumer demand for cloud computing, we need to look at the costs associated with small-scale computing systems. At a small scale one cannot take advantage of the economics of distributed computing.

Possibly more important is that at the end of the campaign, the servers can be “given back” to the cloud provider. Doing that the old way with physical hardware would be impractical. Another non-cost advantage for many companies is that cloud computing enabled other departments to make an end-run around IT departments that had become recalcitrant or difficult to deal with. The ability to get the computing resources they need by clicking a mouse, instead of spending months of arguing with an uncooperative and underfunded IT department, is appealing. We are ashamed to admit that this is true but it is often cited as a reason people adopt cloud computing services. Scaling and High Availability Meeting the new requirements of scaling and high availability in the cloud computing era requires new paradigms. Lower latency is achieved primarily through faster storage technology and faster ways to move information around.

If a site is down, by definition, it is not fast. The most visible cloud-scale services are web sites. However, there is a huge ecosystem of invisible internet-accessible services that are not accessed with a browser. For example, smartphone apps use API calls to access cloud-based services. For the remainder of this book we will tend to use the term “distributed computing” rather than “cloud computing.” Cloud computing is a marketing term that means different things to different people. Distributed computing describes an architecture where applications and services are provided using many machines rather than one. This is a book of fundamental principles and practices that are timeless. Therefore we don’t make recommendations about which specific products or technologies to use. We could provide a comparison of the top five most popular web servers or NoSQL databases or continuous build systems.


pages: 291 words: 77,596

Total Recall: How the E-Memory Revolution Will Change Everything by Gordon Bell, Jim Gemmell

airport security, Albert Einstein, book scanning, cloud computing, conceptual framework, Douglas Engelbart, full text search, information retrieval, invention of writing, inventory management, Isaac Newton, John Markoff, lifelogging, Menlo Park, optical character recognition, pattern recognition, performance metric, RAND corporation, RFID, semantic web, Silicon Valley, Skype, social web, statistical model, Stephen Hawking, Steve Ballmer, Ted Nelson, telepresence, Turing test, Vannevar Bush, web application

Here you can find references to printed publications, Web sites, people, products, conferences, and research labs. The section is arranged by chapter, and the order of material follows the order of the chapter as much as possible. 1. THE VISION Ray Ozzie is quoted from personal correspondence with the Authors. Other references on cloud computing: Hayes, B. 2008. “Cloud Computing.” ACM, Communications of the ACM 51, Issue 7 ( July). Gruman, Galen, and Eric Knorr. 2008. “What Cloud Computing Really Means. InfoWorld (April 7). Martin, Richard, and J. Nicholas Hoover. 2008. “Guide to Cloud Computing.” Information Week (June 21). Amazon Elastic Compute Cloud (Amazon EC2). http://aws.amazon.com/ec2Microsoft Azure Services Platform. http://www.microsoft.com/azure Science fiction that grapples with e-memories: Sawyer, Robert J. 2003. Hominids. New York: Macmillan.

See also files-and-folders organization; organization of data cell phones. See also smartphones and call logs and children and cloud computing and contact information and e-textbooks and electronic reminders and Gilmore and health data and lifelogging and memex as memory aid and miniaturization and physiological data and proactive health advisors and public surveillance and Total Recall technology CellScope cemeteries, digital Centers for Disease Control and Prevention Central Intelligence Agency (CIA) Chalmers, David chat chatbots Cheng, Allen children cholera cholesterol monitoring chronic illnesses classification of data. See also organization of data clothing cloud computing and CARPE and contact information and data storage described and Internet connection clutter. See also paperless environment coaching Cognitive Assistant that Learns and Organizes (CALO) cognitive computers cognitive impairment Cognitive Machines Cohen, Harold collaboration collections collective welfare command structures communism compact discs.

See also biometric sensors improvised explosive devices (IEDs) In Search of Memory: The Emergence of a New Scientific Mind (Kandel) indexing inductive charging industrial revolution Infinite Memory Multifunction Machine (IM3) Information Age inheritance instant messaging and cloud computing and cyber twins and note taking and smartphones and total data collection institutional memory instruction manuals insurance insurgency Intel Intellectual Ventures interfaces International Technology Roadmap for Semiconductors Internet. See also World Wide Web and cloud computing and data backup services and gossip and higher learning and implementation of Total Recall and information availability and the Millennial Generation and social values and unified communications inventory management IOgear iPhone Iraq War iTunes J Jaimes, Alexandro Jim Gray Endowed Chair in Computer Systems Joe Bill Jones, William JPEG files.


pages: 391 words: 71,600

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone by Satya Nadella, Greg Shaw, Jill Tracie Nichols

"Robert Solow", 3D printing, Amazon Web Services, anti-globalists, artificial general intelligence, augmented reality, autonomous vehicles, basic income, Bretton Woods, business process, cashless society, charter city, cloud computing, complexity theory, computer age, computer vision, corporate social responsibility, crowdsourcing, Deng Xiaoping, Donald Trump, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, Mars Rover, Minecraft, Mother of all demos, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, telepresence, telerobotics, The Rise and Fall of American Growth, Tim Cook: Apple, trade liberalization, two-sided market, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, young professional, zero-sum game

If you hold it too tightly, you will crush the bird; hold it too loosely, and it will fly away. That bird symbolizes trust in a time of transition to a digital world. But today we’re in a confused state in which that bird is in a precarious place. And a great deal is at stake. The United States is a beacon for democracy. It is also a technology powerhouse that is leading the wave in cloud computing, but the Snowden case broke a crucial ingredient in cloud computing—trust. How could we be an American cloud computing company, asking the world to trust us, when the NSA is using commercial services to spy on people up to and including heads of state? As tech companies, we have to design trust into everything we do. But policymakers also have an important role. Trust is not only dependent on our technology but also the legal framework that governs it.

Hospitals, schools, businesses, and researchers rely on what’s referred to as the “public cloud”—an array of large-scale, privacy-protected computers and data services accessible over a public network. Cloud computing makes it possible to analyze vast quantities of data to produce specific insights and intelligence, converting guesswork and speculation into predictive power. It has the power to transform lives, companies, and societies. Traveling the globe as CEO, I’ve seen example after example of this interplay between empathy and technology. Both in the state where I was born and the state in which I now live, schools use the power of cloud computing to analyze large amounts of data to uncover insights that can improve dropout rates. In Andhra Pradesh in India, and in Tacoma, Washington, too many kids drop out of school.

YouTube video, 18:27. Posted June 2, 2016. https://www.youtube.com/watch?v=hLMiuN8uSsk. Erlanger, Steven. “‘Brexit’: Explaining Britain’s Vote on European Union Membership.” New York Times, October 27, 2016. http://www.nytimes.com/interactive/2016/world/europe/britain-european-union-brexit.html?_r=0. Hardy, Quentin. “Cloud Computing Brings Sprawling Centers, but Few Jobs, to Small Towns.” New York Times, August 26, 2016. http://www.nytimes.com/2016/08/27/technology/cloud-computing-brings-sprawling-centers-but-few-jobs-to-small-towns.html. Acemoglu, Daron, and Pascual Restrepo. “The Race Between Man and Machine: Implications of Technology for Growth, Factor Shares and Employment.” Unpublished manuscript, December 2015. https://pdfs.semanticscholar.org/4159/521bb401c139b440264049ce0af522033b5c.pdf?


pages: 532 words: 139,706

Googled: The End of the World as We Know It by Ken Auletta

23andMe, AltaVista, Anne Wojcicki, Apple's 1984 Super Bowl advert, Ben Horowitz, bioinformatics, Burning Man, carbon footprint, citizen journalism, Clayton Christensen, cloud computing, Colonization of Mars, commoditize, corporate social responsibility, creative destruction, death of newspapers, disintermediation, don't be evil, facts on the ground, Firefox, Frank Gehry, Google Earth, hypertext link, Innovator's Dilemma, Internet Archive, invention of the telephone, Jeff Bezos, jimmy wales, John Markoff, Kevin Kelly, knowledge worker, Long Term Capital Management, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Network effects, new economy, Nicholas Carr, PageRank, Paul Buchheit, Peter Thiel, Ralph Waldo Emerson, Richard Feynman, Sand Hill Road, Saturday Night Live, semantic web, sharing economy, Silicon Valley, Skype, slashdot, social graph, spectrum auction, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, strikebreaker, telemarketer, the scientific method, The Wisdom of Crowds, Upton Sinclair, X Prize, yield management, zero-sum game

Google also introduced other services: Gmail, Google News, Google Earth, Google Maps, Google Video, Picasa for sharing digital photographs, Google Books to search every book ever published, Orkut, a social network site, or additional “cloud computing” applications such as Desktop or Docs. By 2008, Mel Karmazin was no longer alone in questioning Google’s intentions. Nor were those intentions obscure. In the disclosure documents it filed with the SEC in 2008, Google declared, “We began as a technology company, and have evolved into a software, technology, internet, advertising and media company all rolled into one.” When Google adds mobile phones and a full menu of software applications to its cloud computing, and if it figures out a way to monetize YouTube, Eric Schmidt told me, he thinks it is conceivable that Google can become the first media company to generate one hundred billion dollars in revenues.

“First place, you’re not going to get there with small little advertising deals. You need these big initiatives ... the number one big one right in front of us is television. Big market, well monetized, easily automatable. Second one is ... mobile.” The third was “enterprise,” by which he meant web-based services—“cloud computing”—offering various software applications and IT services for corporate customers, organizations, and individual consumers. Brave words, but throughout 2008 Schmidt’s company made no money from its mobile or YouTube or cloud-computing efforts. Google did not let up. It was still talking to cable companies, Schmidt said, about partnering to target advertising for cable’s digital set-top boxes, and for Android to become the operating system for cable mobile phones—should cable decide to enter the thriving wireless market.

And if the cable companies let Google in the door and grant them access to its data, “you can never build an alternative because Google’s will always be that much more efficient.” Cloud computing was another new Google initiative. Like other corporate giants with massive data centers and servers—IBM, Amazon, Oracle—Google was intent on launching its “cloud” of servers. The cloud would allow a user to access data stored in the Google server from anywhere; it would reduce corporate costs because companies could outsource their data centers; and it would subvert more expensive boxed software sold by Microsoft and spur the development of inexpensive netbooks whose applications are stored in the cloud. Because all these software applications can function on a browser, escaping the dominance of Microsoft’s operating system, in the future, said Christophe Bisciglia, the twenty-eight-year-old chief of cloud computing, “The browser becomes the operating system.


Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist

3D printing, additive manufacturing, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business process, chief data officer, cloud computing, connected car, cyber-physical system, deindustrialization, DevOps, digital twin, fault tolerance, global value chain, Google Glasses, hiring and firing, industrial robot, inflight wifi, Infrastructure as a Service, Internet of things, inventory management, job automation, low cost airline, low skilled workers, microservices, millennium bug, pattern recognition, peer-to-peer, platform as a service, pre–internet, race to the bottom, RFID, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, trade route, undersea cable, web application, WebRTC, Y2K

Along with SDN, network concepts fog can address outstanding issues with vehicular networks such as long latency, irregular connections, and 51 52 Chapter 3 |TheTechnical and Business Innovators of the Industrial Internet high packet loss by supplementing vehicle-vehicle communications with vehicle-infrastructure communication and ultimately unified control. • Fog computing addresses many of the severe problems cloud computing has with network latency and congestion over the Internet; however, it cannot completely replace cloud computing which will always have a place due to its ability to store Big Data and perform analytics on massive quantities of data. As Big Data analytics is a major part of the IIoT and then the cloud, computing will also remain highly relevant to the overall architecture. Big Data and Analytics Big Data describes data that is just too large to be managed by traditional databases and processing tools. These large data structures can be and usually are made up of a combination of structured and non-structured data from a variety of sources such as text, forms, web blogs, comments, video, photographs, telemetry, GPS trails, IM chats, news feeds, and so on.

Amazon’s vision of the cloud was on hyper-provisioning; in so much as they built massive data centers with hyper-capacity in order to meet their web-scale requirements. Amazon then took the business initiative to rent spare capacity to other businesses, in the form of leasing compute, and storage resources on an as-used basis. The cloud model has proved to be hugely successful. Microsoft and Google followed Amazon’s lead, as did several others such as IBM, HP, and Oracle. In essence, cloud computing is still following Amazon’s early pay-as-you-use formula, which makes cloud computing financially attractive to SMEs (small to medium enterprises), as the costs of running a data center and dedicated infrastructure both IT and networks can be crippling. Consequently, many cash-strapped businesses, for example start-ups, elected to move their development and application platforms to the cloud, as they only paid for the resources they used.

Therefore the question industrial business leaders often ask is, “why would connecting my M2M architecture to the Internet provide me with greater value?” What Is the Industrial Internet? To explain why businesses should adopt the Industrial Internet, we need to first consider what the IIoT actual is all about. The Industrial Internet provides a way to get better visibility and insight into the company’s operations and assets through integration of machine sensors, middleware, software, and backend cloud compute and storage systems. Therefore, it provides a method of transforming business operational processes by using as feedback the results gained from interrogating large data sets through advanced analytics. The business gains are achieved through operational efficiency gains and accelerated productivity, which results in reduced unplanned downtime and optimized efficiency, and thereby profits. Although the technologies and techniques used in existing machine-to-machine (M2M) technologies in today's industrial environments may look similar to the IIoT, the scale of operation is vastly different.


pages: 1,237 words: 227,370

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

active measures, Amazon Web Services, bitcoin, blockchain, business intelligence, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, database schema, DevOps, distributed ledger, Donald Knuth, Edward Snowden, Ethereum, ethereum blockchain, fault tolerance, finite state, Flash crash, full text search, general-purpose programming language, informal economy, information retrieval, Infrastructure as a Service, Internet of things, iterative process, John von Neumann, Kubernetes, loose coupling, Marc Andreessen, microservices, natural language processing, Network effects, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, statistical model, undersea cable, web application, WebSocket, wikimedia commons

, Glossaryabstractions for, Consistency and Consensus formalization in consensus, Fault-Tolerant Consensus-Limitations of consensususe of replication, Single-leader replication and consensus human fault tolerance, Philosophy of batch process outputs in batch processing, Bringing related data together in the same place, Philosophy of batch process outputs, Fault tolerance, Fault tolerance in log-based systems, Applying end-to-end thinking in data systems, Timeliness and Integrity-Correctness of dataflow systems in stream processing, Fault Tolerance-Rebuilding state after a failureatomic commit, Atomic commit revisited idempotence, Idempotence maintaining derived state, Maintaining derived state microbatching and checkpointing, Microbatching and checkpointing rebuilding state after a failure, Rebuilding state after a failure of distributed transactions, XA transactions-Limitations of distributed transactions transaction atomicity, Atomicity, Atomic Commit and Two-Phase Commit (2PC)-Exactly-once message processing faults, ReliabilityByzantine faults, Byzantine Faults-Weak forms of lying failures versus, Reliability handled by transactions, Transactions handling in supercomputers and cloud computing, Cloud Computing and Supercomputing hardware, Hardware Faults in batch processing versus distributed databases, Designing for frequent faults in distributed systems, Faults and Partial Failures-Cloud Computing and Supercomputing introducing deliberately, Reliability, Network Faults in Practice network faults, Network Faults in Practice-Detecting Faultsasymmetric faults, The Truth Is Defined by the Majority detecting, Detecting Faults tolerance of, in multi-leader replication, Multi-datacenter operation software errors, Software Errors tolerating (see fault tolerance) federated databases, The meta-database of everything fence (CPU instruction), Linearizability and network delays fencing (preventing split brain), Leader failure: Failover, The leader and the lock-Fencing tokensgenerating fencing tokens, Using total order broadcast, Membership and Coordination Services properties of fencing tokens, Correctness of an algorithm stream processors writing to databases, Idempotence, Exactly-once execution of an operation Fibre Channel (networks), MapReduce and Distributed Filesystems field tags (Thrift and Protocol Buffers), Thrift and Protocol Buffers-Field tags and schema evolution file descriptors (Unix), A uniform interface financial data, Advantages of immutable events Firebase (database), API support for change streams Flink (processing framework), Dataflow engines-Discussion of materializationdataflow APIs, High-Level APIs and Languages fault tolerance, Fault tolerance, Microbatching and checkpointing, Rebuilding state after a failure Gelly API (graph processing), The Pregel processing model integration of batch and stream processing, Batch and Stream Processing, Unifying batch and stream processing machine learning, Specialization for different domains query optimizer, The move toward declarative query languages stream processing, Stream analytics flow control, Network congestion and queueing, Messaging Systems, Glossary FLP result (on consensus), Distributed Transactions and Consensus FlumeJava (dataflow library), MapReduce workflows, High-Level APIs and Languages followers, Leaders and Followers, Glossary(see also leader-based replication) foreign keys, Comparison to document databases, Reduce-Side Joins and Grouping forward compatibility, Encoding and Evolution forward decay (algorithm), Describing Performance Fossil (version control system), Limitations of immutabilityshunning (deleting data), Limitations of immutability FoundationDB (database)serializable transactions, Serializable Snapshot Isolation (SSI), Performance of serializable snapshot isolation, Limitations of distributed transactions fractal trees, B-tree optimizations full table scans, Reduce-Side Joins and Grouping full-text search, Glossaryand fuzzy indexes, Full-text search and fuzzy indexes building search indexes, Building search indexes Lucene storage engine, Making an LSM-tree out of SSTables functional reactive programming (FRP), Designing Applications Around Dataflow functional requirements, Summary futures (asynchronous operations), Current directions for RPC fuzzy search (see similarity search) G garbage collectionimmutability and, Limitations of immutability process pauses for, Describing Performance, Process Pauses-Limiting the impact of garbage collection, The Truth Is Defined by the Majority(see also process pauses) genome analysis, Summary, Specialization for different domains geographically distributed datacenters, Distributed Data, Reading Your Own Writes, Unreliable Networks, The limits of total ordering geospatial indexes, Multi-column indexes Giraph (graph processing), The Pregel processing model Git (version control system), Custom conflict resolution logic, The causal order is not a total order, Limitations of immutability GitHub, postmortems, Leader failure: Failover, Leader failure: Failover, Mapping system models to the real world global indexes (see term-partitioned indexes) GlusterFS (distributed filesystem), MapReduce and Distributed Filesystems GNU Coreutils (Linux), Sorting versus in-memory aggregation GoldenGate (change data capture), Trigger-based replication, Multi-datacenter operation, Implementing change data capture(see also Oracle) GoogleBigtable (database)data model (see Bigtable data model) partitioning scheme, Partitioning, Partitioning by Key Range storage layout, Making an LSM-tree out of SSTables Chubby (lock service), Membership and Coordination Services Cloud Dataflow (stream processor), Stream analytics, Atomic commit revisited, Unifying batch and stream processing(see also Beam) Cloud Pub/Sub (messaging), Message brokers compared to databases, Using logs for message storage Docs (collaborative editor), Collaborative editing Dremel (query engine), The divergence between OLTP databases and data warehouses, Column-Oriented Storage FlumeJava (dataflow library), MapReduce workflows, High-Level APIs and Languages GFS (distributed file system), MapReduce and Distributed Filesystems gRPC (RPC framework), Current directions for RPC MapReduce (batch processing), Batch Processing(see also MapReduce) building search indexes, Building search indexes task preemption, Designing for frequent faults Pregel (graph processing), The Pregel processing model Spanner (see Spanner) TrueTime (clock API), Clock readings have a confidence interval gossip protocol, Request Routing government use of data, Data as assets and power GPS (Global Positioning System)use for clock synchronization, Unreliable Clocks, Clock Synchronization and Accuracy, Clock readings have a confidence interval, Synchronized clocks for global snapshots GraphChi (graph processing), Parallel execution graphs, Glossaryas data models, Graph-Like Data Models-The Foundation: Datalogexample of graph-structured data, Graph-Like Data Models property graphs, Property Graphs RDF and triple-stores, Triple-Stores and SPARQL-The SPARQL query language versus the network model, The SPARQL query language processing and analysis, Graphs and Iterative Processing-Parallel executionfault tolerance, Fault tolerance Pregel processing model, The Pregel processing model query languagesCypher, The Cypher Query Language Datalog, The Foundation: Datalog-The Foundation: Datalog recursive SQL queries, Graph Queries in SQL SPARQL, The SPARQL query language-The SPARQL query language Gremlin (graph query language), Graph-Like Data Models grep (Unix tool), Simple Log Analysis GROUP BY clause (SQL), GROUP BY grouping records in MapReduce, GROUP BYhandling skew, Handling skew H Hadoop (data infrastructure)comparison to distributed databases, Batch Processing comparison to MPP databases, Comparing Hadoop to Distributed Databases-Designing for frequent faults comparison to Unix, Philosophy of batch process outputs-Philosophy of batch process outputs, Unbundling Databases diverse processing models in ecosystem, Diversity of processing models HDFS distributed filesystem (see HDFS) higher-level tools, MapReduce workflows join algorithms, Reduce-Side Joins and Grouping-MapReduce workflows with map-side joins(see also MapReduce) MapReduce (see MapReduce) YARN (see YARN) happens-before relationship, Ordering and Causalitycapturing, Capturing the happens-before relationship concurrency and, The “happens-before” relationship and concurrency hard disksaccess patterns, Advantages of LSM-trees detecting corruption, The end-to-end argument, Don’t just blindly trust what they promise faults in, Hardware Faults, Durability sequential write throughput, Hash Indexes, Disk space usage hardware faults, Hardware Faults hash indexes, Hash Indexes-Hash Indexesbroadcast hash joins, Broadcast hash joins partitioned hash joins, Partitioned hash joins hash partitioning, Partitioning by Hash of Key-Partitioning by Hash of Key, Summaryconsistent hashing, Partitioning by Hash of Key problems with hash mod N, How not to do it: hash mod N range queries, Partitioning by Hash of Key suitable hash functions, Partitioning by Hash of Key with fixed number of partitions, Fixed number of partitions HAWQ (database), Specialization for different domains HBase (database)bug due to lack of fencing, The leader and the lock bulk loading, Key-value stores as batch process output column-family data model, Data locality for queries, Column Compression dynamic partitioning, Dynamic partitioning key-range partitioning, Partitioning by Key Range log-structured storage, Making an LSM-tree out of SSTables request routing, Request Routing size-tiered compaction, Performance optimizations use of HDFS, Diversity of processing models use of ZooKeeper, Membership and Coordination Services HDFS (Hadoop Distributed File System), MapReduce and Distributed Filesystems-MapReduce and Distributed Filesystems(see also distributed filesystems) checking data integrity, Don’t just blindly trust what they promise decoupling from query engines, Diversity of processing models indiscriminately dumping data into, Diversity of storage metadata about datasets, MapReduce workflows with map-side joins NameNode, MapReduce and Distributed Filesystems use by Flink, Rebuilding state after a failure use by HBase, Dynamic partitioning use by MapReduce, MapReduce workflows HdrHistogram (numerical library), Describing Performance head (Unix tool), Simple Log Analysis head vertex (property graphs), Property Graphs head-of-line blocking, Describing Performance heap files (databases), Storing values within the index Helix (cluster manager), Request Routing heterogeneous distributed transactions, Distributed Transactions in Practice, Limitations of distributed transactions heuristic decisions (in 2PC), Recovering from coordinator failure Hibernate (object-relational mapper), The Object-Relational Mismatch hierarchical model, Are Document Databases Repeating History?

As we shall see, you may not even know whether something succeeded or not, as the time it takes for a message to travel across a network is also nondeterministic! This nondeterminism and possibility of partial failures is what makes distributed systems hard to work with [5]. Cloud Computing and Supercomputing There is a spectrum of philosophies on how to build large-scale computing systems: At one end of the scale is the field of high-performance computing (HPC). Supercomputers with thousands of CPUs are typically used for computationally intensive scientific computing tasks, such as weather forecasting or molecular dynamics (simulating the movement of atoms and molecules). At the other extreme is cloud computing, which is not very well defined [6] but is often associated with multi-tenant datacenters, commodity computers connected with an IP network (often Ethernet), elastic/on-demand resource allocation, and metered billing.

database as cache of transaction log, State, Streams, and Immutability in CPUs, Memory bandwidth and vectorized processing, Linearizability and network delays, The move toward declarative query languages invalidation and maintenance, Keeping Systems in Sync, Maintaining materialized views linearizability, Linearizability CAP theorem, The CAP theorem-The CAP theorem, Glossary Cascading (batch processing), Beyond MapReduce, High-Level APIs and Languageshash joins, Broadcast hash joins workflows, MapReduce workflows cascading failures, Software Errors, Operations: Automatic or Manual Rebalancing, Timeouts and Unbounded Delays Cascalog (batch processing), The Foundation: Datalog Cassandra (database)column-family data model, Data locality for queries, Column Compression compaction strategy, Performance optimizations compound primary key, Partitioning by Hash of Key gossip protocol, Request Routing hash partitioning, Partitioning by Hash of Key-Partitioning by Hash of Key last-write-wins conflict resolution, Last write wins (discarding concurrent writes), Timestamps for ordering events leaderless replication, Leaderless Replication linearizability, lack of, Linearizability and quorums log-structured storage, Making an LSM-tree out of SSTables multi-datacenter support, Multi-datacenter operation partitioning scheme, Partitioning proportionally to nodes secondary indexes, Partitioning Secondary Indexes by Document sloppy quorums, Sloppy Quorums and Hinted Handoff cat (Unix tool), Simple Log Analysis causal context, Version vectors(see also causal dependencies) causal dependencies, The “happens-before” relationship and concurrency-Version vectorscapturing, Version vectors, Capturing causal dependencies, Ordering events to capture causality, Reads are events tooby total ordering, The limits of total ordering causal ordering, Ordering and Causality in transactions, Decisions based on an outdated premise sending message to friends (example), Ordering events to capture causality causality, Glossarycausal ordering, Ordering and Causality-Capturing causal dependencieslinearizability and, Linearizability is stronger than causal consistency total order consistent with, Sequence Number Ordering, Lamport timestamps consistency with, Sequence Number Ordering-Lamport timestamps consistent snapshots, Ordering and Causality happens-before relationship, The “happens-before” relationship and concurrency in serializable transactions, Decisions based on an outdated premise-Detecting writes that affect prior reads mismatch with clocks, Timestamps for ordering events ordering events to capture, Ordering events to capture causality violations of, Consistent Prefix Reads, Multi-Leader Replication Topologies, Timestamps for ordering events, Ordering and Causality with synchronized clocks, Synchronized clocks for global snapshots CEP (see complex event processing) certificate transparency, Tools for auditable data systems chain replication, Synchronous Versus Asynchronous Replicationlinearizable reads, Implementing linearizable storage using total order broadcast change data capture, Logical (row-based) log replication, Change Data CaptureAPI support for change streams, API support for change streams comparison to event sourcing, Event Sourcing implementing, Implementing change data capture initial snapshot, Initial snapshot log compaction, Log compaction changelogs, State, Streams, and Immutabilitychange data capture, Change Data Capture for operator state, Rebuilding state after a failure generating with triggers, Implementing change data capture in stream joins, Stream-table join (stream enrichment) log compaction, Log compaction maintaining derived state, Databases and Streams Chaos Monkey, Reliability, Network Faults in Practice checkpointingin batch processors, Fault tolerance, Fault tolerance in high-performance computing, Cloud Computing and Supercomputing in stream processors, Microbatching and checkpointing, Multi-partition request processing chronicle data model, Event Sourcing circuit-switched networks, Synchronous Versus Asynchronous Networks circular buffers, Disk space usage circular replication topologies, Multi-Leader Replication Topologies clickstream data, analysis of, Example: analysis of user activity events clientscalling services, Dataflow Through Services: REST and RPC pushing state changes to, Pushing state changes to clients request routing, Request Routing stateful and offline-capable, Clients with offline operation, Stateful, offline-capable clients clocks, Unreliable Clocks-Limiting the impact of garbage collectionatomic (caesium) clocks, Clock readings have a confidence interval, Synchronized clocks for global snapshots confidence interval, Clock readings have a confidence interval-Synchronized clocks for global snapshots for global snapshots, Synchronized clocks for global snapshots logical (see logical clocks) skew, Relying on Synchronized Clocks-Clock readings have a confidence interval, Implementing Linearizable Systems slewing, Monotonic clocks synchronization and accuracy, Clock Synchronization and Accuracy-Clock Synchronization and Accuracy synchronization using GPS, Unreliable Clocks, Clock Synchronization and Accuracy, Clock readings have a confidence interval, Synchronized clocks for global snapshots time-of-day versus monotonic clocks, Monotonic Versus Time-of-Day Clocks timestamping events, Whose clock are you using, anyway? cloud computing, Distributed Data, Cloud Computing and Supercomputingneed for service discovery, Service discovery network glitches, Network Faults in Practice shared resources, Network congestion and queueing single-machine reliability, Hardware Faults Cloudera Impala (see Impala) clustered indexes, Storing values within the index CODASYL model, The network model(see also network model) code generationwith Avro, Code generation and dynamically typed languages with Thrift and Protocol Buffers, Thrift and Protocol Buffers with WSDL, Web services collaborative editingmulti-leader replication and, Collaborative editing column families (Bigtable), Data locality for queries, Column Compression column-oriented storage, Column-Oriented Storage-Writing to Column-Oriented Storagecolumn compression, Column Compression distinction between column families and, Column Compression in batch processors, The move toward declarative query languages Parquet, Column-Oriented Storage, Archival storage, Philosophy of batch process outputs sort order in, Sort Order in Column Storage-Several different sort orders vectorized processing, Memory bandwidth and vectorized processing, The move toward declarative query languages writing to, Writing to Column-Oriented Storage comma-separated values (see CSV) command query responsibility segregation (CQRS), Deriving several views from the same event log commands (event sourcing), Commands and events commits (transactions), Transactionsatomic commit, Atomic Commit and Two-Phase Commit (2PC)-From single-node to distributed atomic commit(see also atomicity; transactions) read committed isolation, Read Committed three-phase commit (3PC), Three-phase commit two-phase commit (2PC), Introduction to two-phase commit-Coordinator failure commutative operations, Conflict resolution and replication compactionof changelogs, Log compaction(see also log compaction) for stream operator state, Rebuilding state after a failure of log-structured storage, Hash Indexesissues with, Downsides of LSM-trees size-tiered and leveled approaches, Performance optimizations CompactProtocol encoding (Thrift), Thrift and Protocol Buffers compare-and-set operations, Compare-and-set, What Makes a System Linearizable?


pages: 411 words: 98,128

Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It by Brian Dumaine

activist fund / activist shareholder / activist investor, AI winter, Airbnb, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Black Swan, call centre, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, Donald Trump, Elon Musk, Erik Brynjolfsson, future of work, gig economy, Google Glasses, Google X / Alphabet X, income inequality, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Lyft, Marc Andreessen, Mark Zuckerberg, money market fund, natural language processing, pets.com, plutocrats, Plutocrats, race to the bottom, ride hailing / ride sharing, Sand Hill Road, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, Tim Cook: Apple, too big to fail, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, wealth creators, web application, Whole Earth Catalog

As Amazon invents something to please its customers, as it pushes the AI flywheel a little harder, it often ends up creating a product or a service that becomes a business of its own. This is what has enabled Bezos to enter one new industry after another, from cloud computing to media to consumer electronics. It’s what has many in the business world worried—and they should be—that the Amazon AI flywheel could come crashing through their industry. Over its two decades of existence, Amazon invested billions in making its site the most intuitive and dependable online shopping destination. The company then took some of the programming talent and computer expertise that it used to build up its online business and created its cloud service, AWS. Cloud computing lets businesses and individuals use the Internet to store, manage, and process data on large server farms instead of on a local server or a personal computer, and it’s one of the fastest-growing sectors of the tech industry.

In early 2019, he was the richest man in the world with a net worth of $160 billion, and he remained in that top spot even after giving his ex-wife, MacKenzie, a quarter of their jointly owned Amazon stock (worth at the time $38 billion) in a divorce settlement. The company he founded controlled, as of 2019, nearly 40 percent of all online retailing in the U.S. and is one of the largest e-tailers in Europe. Amazon has expanded its Prime membership program to seventeen countries, and the number of people who have signed up for the service globally has hit more than 150 million. Bezos built Amazon Web Services (AWS) into the world’s largest cloud computing company, and Prime Video into a streaming media giant nipping at the heels of Netflix, and he’s the driving force behind the Echo, a smart speaker with Alexa inside that sold nearly 50 million units in its first few years of existence. Throughout the 2010s, this profitable company grew at an average rate of 25 percent a year—an astounding performance for such a large corporation (as of 2018, it had $233 billion in annual revenues).

At the same time the company has been a spawning ground for start-ups. As of 2019, millions of independent businesses—1 million in the U.S. alone—from 130 countries sold 58 percent of all items on the company’s Marketplace platform. Worldwide, Amazon says that the small businesses selling on its site have added, as of 2018, 1.6 million jobs. Amazon also helps small businesses in other ways. Its cloud computing service, AWS, has brought the power of big-corporate computer systems to entrepreneurs at a reasonable price. Its Alexa AI voice software has created a huge opportunity for app developers and smart-appliance makers. Yet all this comes at a cost. Amazon employs hundreds of thousands in their vast global warehouse network, and these jobs are hard, demeaning, and non-union. As bad as that situation is, those workers have to worry that they’ll be replaced by robots who can do their tasks more quickly and cheaply.


pages: 222 words: 54,506

One Click: Jeff Bezos and the Rise of Amazon.com by Richard L. Brandt

Amazon Web Services, automated trading system, big-box store, call centre, cloud computing, Dynabook, Elon Musk, inventory management, Jeff Bezos, Kevin Kelly, Kickstarter, Marc Andreessen, new economy, science of happiness, search inside the book, Silicon Valley, Silicon Valley startup, skunkworks, software patent, Steve Jobs, Stewart Brand, Tony Hsieh, Whole Earth Catalog, Y2K

About two years after the launch of Web services, Amazon boasted sixty-five thousand developers using the program and sending some ten million queries a day to Amazon’s servers.That’s a lot of new customers. Further, the offering began to look a lot like the phenomenon now known as cloud computing—tapping into a program sitting on a Web server somewhere rather than one sitting on your own desktop. Another company using the service, for example, Monsoon of Portland, Oregon, offered software that companies could use to tap into Amazon’s software to simplify their own inventory management. “Web 1.0 was making the Internet for people; Web 2.0 is making the Internet better for computers,” Bezos predicted in a speech at the Web 2.0 conference in San Francisco in 2004. Amazon became known as one of the most innovative companies in cloud computing. From its initial start in 2002, Amazon Web Services offerings just kept expanding. It can distribute content for other companies (such as Netflix) from its own computers and networks.

That’s less than 2 percent of overall sales. But it has higher profit margins than the retail businesses—up to 23 percent operating margins compared to 5 percent in the rest of the business. During the company’s shareholder meeting in May 2010, Bezos saved most of his ever-present enthusiasm for a discussion of cloud computing services. “It has the potential to be as big as our retail business,” he said. In his opinion, Amazon can do a better job than most competitors in the business. Cloud computing, he said, is “a very large area right now [and] it’s done in our opinion in a very inefficient way. Whenever something big is done inefficiently that creates an opportunity.” That’s an astounding claim, since Web Services provides less than 2 percent of revenues today. But Bezos is now on a 1999-style rush to build up the business and maintain an early lead advantage.

Netflix can’t afford (at least not yet) to buy all the computing power needed to load up films instantly and stream them to thousands of customers at any moment. So it rents computers from Amazon’s vast store at pennies per minute to handle the tasks, tapping into just as much computer power as it needs at any given moment. It’s all part of a surprising business from the online retailing company, called Amazon Web Services, which is part of a larger trend known as cloud computing. Services like this bring in half a billion dollars annually in revenues to Amazon. Buying companies is a relatively easy way for a stock-rich company to expand its business. But sometimes a great executive will stumble upon an unexpected new idea, or one of his employees may come up with something. The key is the ability to look beyond the current conventional wisdom and embrace a radical new idea.


pages: 294 words: 81,292

Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

AI winter, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, Thomas Bayes, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day

First, as we learned from Kurzweil’s LOAR, computer speed and capacity double in as little as a year, every year. That means whatever hardware requirements an AGI system requires today should be satisfied on average by half the hardware, and cost, a year from now. Second, the accessibility of cloud computing. Cloud computing permits users to rent computing power and capacity over the Internet. Vendors like Amazon, Google, and Rackspace offer users a choice of processor speeds, operating systems, and storage space. Computer power has become a service instead of a hardware investment. Anyone with a credit card and some know-how can rent a virtual supercomputer. On Amazon’s EC2 cloud computing service, for instance, a vendor called Cycle Computing created a 30,000-processor cluster they named Nekomata (Japanese for Monster Cat). Every eight processors of its 30,000 came with seven gigabytes of RAM (about as much random access memory as a PC has), for a total of 26.7 terabytes of RAM and two petabytes of disk space (that’s equal to forty million, four-drawer filing cabinets full of text).

And like any information technology, market forces and innovation fuel it. One important innovation for cybercrime is cloud computing—selling computing as a service, not a product. As we’ve discussed, cloud services like those offered by Amazon, Rackspace, and Google allow users to rent processors, operating systems, and storage by the hour, over the Internet. Users can pile on as many processors as their project needs, within reason, without attracting attention. Clouds give anyone with a credit card access to a virtual supercomputer. Cloud computing has been a runaway success, and by 2015 is expected to generate $55 billion in revenue worldwide. But, it’s created new tools for crooks. In 2009 a criminal network used Amazon’s Elastic Cloud Computing Service (EC2) as a command center for Zeus, one of the largest botnets ever. Zeus stole some $70 million from customers of corporations, including Amazon, Bank of America, and anti-malware giants Symantec and McAfee.

In 2011, botnet victims increased 654 percent: Schwartz, Mathew, “Botnet Victims Increased 654 percent in 2011,” InformationWeek, February 18, 2011, http://www.informationweek.com/news/security/attacks/229218944?cid=RSSfeed_IWK_All (accessed July 11, 2012). a one trillion-dollar industry: Symantec, “What is Cybercrime?” last modified 2012, http://us.norton.com/cybercrime/definition.jsp (accessed July 11, 2012). Cloud computing has been a runaway success: Malik, Om, “How Big is Amazon’s Cloud Computing Business? Find Out,” GIGAOM, August 11, 2010, http://gigaom.com/cloud/amazon-web-services-revenues/ (accessed June 4, 2011). Zeus stole some $70 million: Ragan, Steve, “ZBot data dump discovered with over 74,000 FTP credentials,” The Tech Herald, June 29, 2009, http://www.thetechherald.com/articles/ZBot-data-dump-discovered-with-over-74-000-FTP-credentials/6514/ (accessed June 4, 2011). 21.3 percent overall, comes from Shaoxing: Melanson, Donald, “Symantec names Shaoxing, China, as world’s malware capital,” Engadget, March 29, 2010, http://www.engadget.com/2010/03/29/symantec-names-shaoxing-china-worlds-malware-capital (accessed June 4, 2011).


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

By paying it you not only create new money, but you strengthen property values around your own house, effectively making a little of the money that your neighbors create when they get mortgages. This sort of result is already calculated today, but the payments don’t flow. The extra work for the microprocessors in the cloud computers would be trivial, considering the expected course of Moore’s Law, and the extra payments would expand the economy for everyone, including the cloud computing companies. Economic expansion ought to more than pay for the extra trouble. Would the correlation be valid? Well, this would be business and not science. Honestly, as I explained earlier, I am super-skeptical of algorithms of this kind. It’s incredibly hard to design experiments that separate the influence of such algorithms from their predictive veracity.

If we continue on the present path, benefits will instead flow mostly to the tenders of the top computers that route data about surgery, essentially by spying on doctors and patients. The Beach at the Edge of Moore’s Law A heavenly idea comes up a lot in what might be called Silicon Valley metaphysics. We anticipate immortality through mechanization. A common claim in utopian technology culture is that people—well, perhaps not everyone—will be uploaded into cloud computing servers* later in this century, perhaps in a decade or two, to become immortal in Virtual Reality. Or, if we are to remain physical, we will be surrounded by a world animated with robotic technology. We will float from joy to joy, even the poorest among us living like a sybaritic magician. We will not have to call forth what we wish from the world, for we will be so well modeled by statistics in the computing clouds that the dust will know what we want.

These nanopayments will add up, and lead to a new social contract in which people are motivated to contribute to an information economy in ever more substantial ways. This is an idea that takes capitalism more seriously than it has been taken before. 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 . . .


pages: 587 words: 117,894

Cybersecurity: What Everyone Needs to Know by P. W. Singer, Allan Friedman

4chan, A Declaration of the Independence of Cyberspace, Apple's 1984 Super Bowl advert, barriers to entry, Berlin Wall, bitcoin, blood diamonds, borderless world, Brian Krebs, business continuity plan, Chelsea Manning, cloud computing, crowdsourcing, cuban missile crisis, data acquisition, do-ocracy, drone strike, Edward Snowden, energy security, failed state, Fall of the Berlin Wall, fault tolerance, global supply chain, Google Earth, Internet of things, invention of the telegraph, John Markoff, Julian Assange, Khan Academy, M-Pesa, MITM: man-in-the-middle, mutually assured destruction, Network effects, packet switching, Peace of Westphalia, pre–internet, profit motive, RAND corporation, ransomware, RFC: Request For Comment, risk tolerance, rolodex, Silicon Valley, Skype, smart grid, Steve Jobs, Stuxnet, uranium enrichment, We are Anonymous. We are Legion, web application, WikiLeaks, zero day, zero-sum game

John Nasibett once said Brian Monger, “Knowing Who Your Market Is and What They Want,” SmartaMarketing, November 11, 2012, http://smartamarketing.wordpress.com/2012/11/11/knowing-who-your-market-is-and-what-they-want/. 40 to 80 percent Ray, “Cloud Computing Economics: 40–80 percent Savings in the Cloud,” CloudTweaks, April 9, 2011, http://www.cloudtweaks.com/2011/04/cloud-computing-economics-40-80-savings-in-the-cloud/. General Martin Dempsey General Martin Dempsey, “Defending the Nation at Network Speed,” remarks at the Brookings Institution, Washington, DC, June 27, 2013. $149 billion in 2014 Transparency Market Research, “Cloud Computing Services Market-Global Industry Size, Market Share, Trends, Analysis and Forecasts, 2012–2018,” http://www.transparencymarketresearch.com/cloud-computing-services-market.html, accessed August 11, 2013. a Brookings report explored Allan A. Friedman and Darrell M. West, “Privacy and Security in Cloud Computing,” Issues in Technology Innovation, no. 3, the Brookings Institution (October 26, 2010), http://www.brookings.edu/research/papers/2010/10/26-cloud-computing-friedman-west.

It’s yet another illustration of how the Internet isn’t ungoverned, but rather is self-governed in strange and fascinating ways. Security Risk or Human Right? Foreign Policy and the Internet Cloud computing, the concept of delivering computing resources remotely over a network, is both a multibillion-dollar industry and a growing field that many believe is key to the future of the online world (as we’ll explore later on). But for three days in 2011, the Dutch government threatened to undermine the new era of cloud computing, all in the name of human rights. Taking issue with American laws that gave the US government access to any data stored on computers controlled by American companies, the Dutch Minister of Safety and Justice threatened to deny any American firm the ability to offer cloud-computing services to the Dutch government in 2011. Yet if no country was willing to let its data be held by a foreign company for fear of government surveillance, the transformative power of cloud computing to store and distribute data globally would be severely undermined.

Centers for Disease Control and Prevention (CDC): A public agency that coordinates research, communications, and information sharing for public health in the United States. certificate authority (CA): A trusted organization that produces signed digital “certificates” that explicitly tie an entity to a public key. This allows asymmetric cryptography users to trust that they are communicating with the right party. cloud computing: A shift in control of computing resources from the individual or organization to a shared resource run by a third party. By pooling network-enabled resources, cloud computing enables mobility, scalability, flexibility, and efficiency, but increases the dependency on the cloud provider. computer emergency response team (CERT): Organizations located around the world that serve as hubs of cybersecurity technical expertise, collaboration, and security information dissemination. Many governments have their own national CERTs, as do an increasing number of industrial sectors and large organizations.


pages: 260 words: 67,823

Always Day One: How the Tech Titans Plan to Stay on Top Forever by Alex Kantrowitz

accounting loophole / creative accounting, Albert Einstein, AltaVista, Amazon Web Services, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, Firefox, Google Chrome, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Peter Thiel, QR code, ride hailing / ride sharing, self-driving car, Silicon Valley, Skype, Snapchat, Steve Ballmer, Steve Jobs, Steve Wozniak, Tim Cook: Apple, uber lyft, wealth creators, zero-sum game

By 2008, Server & Tools was a $13 billion business that had turned in twenty-four consecutive quarters of double-digit growth, making up 20 percent of Microsoft’s total revenue. Some Server & Tools customers created programs they sold to others, and many developed applications for internal use. When internet speeds grew faster, companies started to host internal applications (such as email servers) externally and build software for use on the web browser as opposed to desktop (aka cloud computing). Seeing this early move to the cloud, Microsoft had to decide whether to support it and to what extent. Cloud computing, though promising, was a threat to Microsoft’s Windows business. If software went to the cloud, people wouldn’t need Windows. They’d be able to access applications on any operating system, whether it was Windows, Apple’s macOS, or Google’s ChromeOS. And they wouldn’t need Microsoft’s pricey internal servers. For the asset milkers, pivoting the lucrative Server & Tools division while undermining Windows would be disastrous.

It’s the name of a key building, it’s the title of the company’s blog, and it’s a recurring theme in Bezos’s annual letter to shareholders. And though it’s tempting to read it as an order to work ceaselessly, particularly at the notoriously hard-charging Amazon, its meaning runs deeper. “Day One” at Amazon is code for inventing like a startup, with little regard for legacy. It’s an acknowledgment that competitors today can create new products at record speeds—thanks to advances in artificial intelligence and cloud computing especially—so you might as well build for the future, even at the present’s expense. It’s a departure from how corporate giants like GM and Exxon once ruled our economy: by developing core advantages, hunkering down, and defending them at all costs. Getting fat on existing businesses is no longer an option. In the 1920s, the average life expectancy of a Fortune 500 company was sixty-seven years.

Starting as an online directory, the company reinvented itself with the News Feed, and it’s reinventing today by moving from broadcast sharing to intimate sharing: giving the News Feed over to Facebook Groups—a series of smaller networks—and treating messaging as a first-class citizen. In the most fickle of all industries, social media, Facebook still leads. Until recently, it seemed like Microsoft’s inventing days were over. The company was so attached to Windows it almost let the future pass it by. But with a leadership change from Steve Ballmer to Satya Nadella, the company returned to Day One and embraced cloud computing, a threat to desktop operating systems like Windows, and became the world’s most valuable company once again. Apple under Steve Jobs developed the iPhone, a device that rendered desktop computers like the Mac and portable music players like the iPod less relevant but also set the company up for years of success. Today, Apple is having its Windows moment. It must leave iPhone orthodoxy behind and reinvent itself again to compete in the age of voice computing.


pages: 302 words: 82,233

Beautiful security by Andy Oram, John Viega

Albert Einstein, Amazon Web Services, business intelligence, business process, call centre, cloud computing, corporate governance, credit crunch, crowdsourcing, defense in depth, Donald Davies, en.wikipedia.org, fault tolerance, Firefox, loose coupling, Marc Andreessen, market design, MITM: man-in-the-middle, Monroe Doctrine, new economy, Nicholas Carr, Nick Leeson, Norbert Wiener, optical character recognition, packet switching, peer-to-peer, performance metric, pirate software, Robert Bork, Search for Extraterrestrial Intelligence, security theater, SETI@home, Silicon Valley, Skype, software as a service, statistical model, Steven Levy, The Wisdom of Crowds, Upton Sinclair, web application, web of trust, zero day, Zimmermann PGP

This chapter represents just my perspective—maybe my bias—but my team’s performance depends on how closely the future measures up to the thoughts in this chapter! Cloud Computing and Web Services: The Single Machine Is Here Civilization advances by extending the number of important operations which we can perform without thinking of them. —Alfred North Whitehead An Introduction to Mathematics (1911) Today, much is being made of “cloud computing” in the press. For at least the past five years, the computer industry has also expressed a lot of excitement about web services, which can range from Software as a Service (SaaS) to various web-based APIs and service-oriented architecture (SOA, pronounced “so-ah”). Cloud computing is really nothing more than the abstraction of computing infrastructure (be it storage, processing power, or application hosting) from the hardware system or users.

They care about the functionality of the system instead of the infrastructure that makes it possible, in the same way that average telephone users don’t care which exchanges they are routed through or what type of cable the signal travels over in order to talk to their nanas. But even though cloud computing is a natural extension of other kinds of online services and hosting services, it’s an extremely important development in the history of the global network. Cloud computing democratizes the availability of computing power to software creators from virtually all backgrounds, giving them supercomputers on-demand that can power ideas into reality. Some may say this is a return to the old days when all users could schedule time on the mainframe and that cloud computing is nothing new, but that’s hardly the point. The point is that this very day, supercomputers are available to anyone who has access to the Internet. Web services are standards-based architectures that expose resources (typically discrete pieces of application functionality) independently of the infrastructure that powers them.

Google is renowned for building megalithic data centers across the world; Microsoft is investing heavily in a cloud operating system called Azure, along with gigantic data center infrastructures to host software and services; and Amazon has started renting out parts of the infrastructure that they built as part of their own bid to dominate the online retailing space. Clouds and Web Services to the Rescue The question security professionals should be asking is not “Can cloud computing and web services be made secure?” but “How can we apply security to this new approach?” Even more cleverly, we should think: “How can we embrace this paradigm to our advantage?” The good news is that applying security to web services and cloud computing is not as hard as people may think. What at first seems like a daunting task just requires a change of paradigm. The assumption that the company providing you with a service also has to guarantee your security is just not valid. To show you how readily you can see the new services as a boon to security instead of a threat, let me focus on a real-world scenario.


pages: 329 words: 95,309

Digital Bank: Strategies for Launching or Becoming a Digital Bank by Chris Skinner

algorithmic trading, AltaVista, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, bank run, Basel III, bitcoin, business cycle, business intelligence, business process, business process outsourcing, buy and hold, call centre, cashless society, clean water, cloud computing, corporate social responsibility, credit crunch, crowdsourcing, cryptocurrency, demand response, disintermediation, don't be evil, en.wikipedia.org, fault tolerance, fiat currency, financial innovation, Google Glasses, high net worth, informal economy, Infrastructure as a Service, Internet of things, Jeff Bezos, Kevin Kelly, Kickstarter, M-Pesa, margin call, mass affluent, MITM: man-in-the-middle, mobile money, Mohammed Bouazizi, new economy, Northern Rock, Occupy movement, Pingit, platform as a service, Ponzi scheme, prediction markets, pre–internet, QR code, quantitative easing, ransomware, reserve currency, RFID, Satoshi Nakamoto, Silicon Valley, smart cities, social intelligence, software as a service, Steve Jobs, strong AI, Stuxnet, trade route, unbanked and underbanked, underbanked, upwardly mobile, We are the 99%, web application, WikiLeaks, Y2K

This will be the new bank model and the new bank order post this crisis will weed out those who get the follow the free model and provide real value, versus those transactional banks that are just processors. Banking as a Service Banking as a Service (BaaS) is the new model of banking, and is based upon cloud computing structures of Digital Banking. Bearing in mind that the bank has moved from integrated to modular, this is the new way of working where bank processes are apps and bank processing are APIs (Application Program Interfaces). A slightly confusing and technical discussion, so let’s start with the idea of cloud computing in banking. Cloud Computing is a wide and diverse operation that has gained a panacea status of being all things to all people. It’s Salesforce.com, Azure, Exalogic, Amazon and more. Put in “Cloud Computing” to Google, who also provide clouds, and you get sponsored adverts from HP, Intel, Siemens and more all talking about clouds. It’s Software as a Service, Platform as a Service, and Infrastructure as a Service.

The customer can then setup the way in which their payments account works for them, in the same way you would set up your iPhone. It’s totally flexible and unique to them. This can only be achieved through collaboration and partnership however and we, as a company, develop these capabilities and then ensure that they conform to the rules, which is the regulations to maintain our banking licence. Within these partnerships, do you use cloud computing? Our technology people philosophically think cloud is potentially relevant but, in fact, we are not using cloud computing today for four main reasons. First of all we feel that it is not that secure for financial servicing and we are careful about, and possibly distrust, the use of cloud. We prefer to know where the data is stored and security is crucial to a bank, so this is why we stay out of any cloud discussion today. Second, we do not feel it is necessary to use such services today.

It’s not something where we are putting our bank services in a cloud computing operation like Amazon. So it depends how you define cloud. Yes. There are very many ways of talking about cloud, and I am referring here to placing my operating systems and bank data in the cloud. I would never place my bank data in the cloud, but outsourcing is different. Do you not think cloud is just like outsourcing? We are using outsourced services, but the difference between outsourced relationships and cloud is that we use outsourcing for very specific reasons. For example, if I outsource the running of my machines I know who the companies are, I know where they are, my auditors have approved them and they are working according to the requirements of a German banking regulator. We don’t have that today with cloud computing. The quality and security standards are not there yet, and a typical cloud provider is not able to give those to us.


pages: 257 words: 72,251

Nothing to Hide: The False Tradeoff Between Privacy and Security by Daniel J. Solove

Albert Einstein, cloud computing, Columbine, hindsight bias, illegal immigration, invention of the telephone, Marshall McLuhan, national security letter, security theater, the medium is the message, traffic fines, urban planning

Despite the fact only these companies have the information, and despite the fact that they don’t share it with anybody, you lack a reasonable expectation of privacy in the information according to the third party doctrine.3 Cloud Computing For quite a long time, we’ve been accustomed to having all our electronic documents and software stored on our own computers. A recent trend is to store them remotely and access them via the Internet. 105 Constitutional Rights An example is GoogleDocs. It allows you to store wordprocessing and spreadsheet documents to Google’s servers, where you can jointly edit them with other people you’re collaborating with. Another example is Apple’s MobileMe, where you can back up the information on your iPhone—your photos, documents, contacts, and other personal data. Microsoft’s SkyDrive lets you store your personal documents for free. This allows you to back up many of the important files on your computer. The promise of cloud computing is that your documents can be much safer and your software can be always up to date.

Since people’s documents are no longer stored on their home computers but reside instead with third parties, the shift to cloud computing will effectively remove Fourth Amendment protection from their documents.4 Collusion and Compulsion There are times when companies readily cooperate with the government and will turn over your information. This happened after September 11. Government agencies went to the airlines and demanded that they surrender their customer records. Despite the fact the airlines had promised never to share their information with others, they readily handed it over.5 But in many instances, companies would rather not give your information to the government. They want you to trust them. Suppose you’re uncertain about using a cloud computing service. The company might want to point out that it respects your privacy and will never share your information with anyone without your consent.

Leis, 255 F.3d 325, 336 (6th Cir. 2001) (holding that people “lack a Fourth Amendment privacy interest in their [Internet service] subscriber information because they communicate[] it to the systems operators”); see also United States v. Kennedy, 81 F. Supp. 2d 1103, 1110 (D. Kan. 2000); United States v. Hambrick, 55 F. Supp. 2d 504, 508 (W.D. Va. 1999). 4. For further discussion about cloud computing and privacy, see Nicole A. Ozer & Chris Conley, Cloud Computing: Storm Warning for Privacy? (2010) (report for the ACLU of Northern California), available at http:// ssrn.com/abstract=1611820. 5. See In re Jet Blue Airways Corp. Privacy Litigation, 379 F. Supp. 2d 299, 305 (E.D.N.Y. 2005); Dyer v. Northwest Airlines Corp., 334 F. Supp. 2d 1196, 1197, 1199 (D.N.D. 2004). 6. Protecting Your Personal Information, U.S. Census 2010, http://2010. census.gov/2010census/privacy/index.php (last visited Aug. 17, 2010). 7.


pages: 458 words: 135,206

CTOs at Work by Scott Donaldson, Stanley Siegel, Gary Donaldson

Amazon Web Services, bioinformatics, business intelligence, business process, call centre, centre right, cloud computing, computer vision, connected car, crowdsourcing, data acquisition, distributed generation, domain-specific language, glass ceiling, orbital mechanics / astrodynamics, pattern recognition, Pluto: dwarf planet, QR code, Richard Feynman, Ruby on Rails, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, thinkpad, web application, zero day, zero-sum game

The second one is we decided about a year and a half ago to change the world in terms of how IT works. And not a lot of companies do that. And I sometimes joke, “We're either going to succeed beyond our wildest expectations or go down in a giant ball of flames, but it's not going to be anything in between.” We decided to change the world. It's about cloud computing and managing cloud computing. The realization I came to is 98% of what you need for cloud computing is IT management and security. Historically, if you look at the way IT has worked, it has been application platforms, software development tools, pre-built applications, but if I'm using the Amazon relational database I don't need a database platform. I need to manage the Amazon RDS (relational database service). S. Donaldson: Right. Ferguson: If you look at Salesforce.com, people don't actually write Salesforce.com applications.

But they don't want to be controlled by a commercial vendor who can go out of business, can be purchased, merged, acquired, situations like that. Siegel: So given what you just said, how does the rush to cloud computing influence how you handle your customers? Cherches: Cloud computing is a very big, emerging technology right now. In the old days, literally five years ago, I would say to someone, “It will take us two weeks, three weeks, or even a month to get the new server from the manufacturer, then configure the system, then patch it, then provision it on a network, then…” Customers (and our internal developers) won't take that answer anymore. They want something in days at most. Virtualization and cloud computing are important technologies. Customers still want custom solutions, but that custom solution can now live in the cloud. If they are very successful, they can scale the solution quickly.

Index 3D technology David Kuttler, 302 Jerry Krill, 83–85 64-bit architectures, 272–274, 281 A acquisitions, William Ballard, 279–280 Adobe, reverse image searching and, 246 AEP (American Electric Power), 128–129 Aguru Images, Craig Miller, 53–54, 56–59 algae, Craig Miller, 69, 76 Alm, Al, 50 Alving, Amy, 1–20 Amazon EC2, 36 Amazon Web Services (AWS), 245 American Electric Power (AEP), 128–129 American Public Media, 163 Amperion, 128 APL (Applied Physics Laboratory) global technical outreach, 90 Jerry Krill, 82 application development, Jeff Tolnar, 145 architecture technical review, 26 Arduinos, Craig Miller, 77 asymmetric warfare, Jerry Krill's comments on, 92 AWS (Amazon Web Services), 245 B Ballard, William, CTO of Gerson Lehrman Group, Inc, 261–283 Beyster, Bob, 51–52 Bilger, Mark, 23 Black, David, 262 Bloore, Paul, 239–260 Bodine, Greg, 30 BPLG (BPL Global), 127–128, 132 breakout engagements, Gerson Lehrman, 265 budgeting, Jeff Tolnar, 146–148 bundling hardware and software, Jeff Tolnar, 135 Burke, Thomas, 177 business development personnel, Gerson Lehrman, 266 C CA Open Space, 39 callable interface, 41 career lessons Darko Hrelic, 206–207, 211 Jan-Erik de Boer, 236 Jerry Krill, 91 Paul Bloore, 253 Wesley Kaplow, 110 career path Amy Alving, 1–6 Darko Hrelic, 205–206 Dmitry Cherches, 181–185 Jan-Erik de Boer, 237 Jeff Tolnar, 128 Marty Garrison, 152 Paul Bloore, 240–242 Tom Loveland, 176–178 Wesley Kaplow, 105–110 CATEX, Craig Miller, 51 Cherches, Dimitri background, 173 of Mind Over Machines, 173–204 ChoicePoint, 158 CIO responsibilities, of William Ballard, 268 CIO role, versus CTO role, 282 CIOs, Jeff Tolnar, 130–131 cloud computing, 35 Amy Alving, 16–17 Craig Miller, 65 David Kuttler, 302 Dmitry Cherches, 186–188, 200 Gerson Lehrman, 277 Jeff Tolnar, 142, 147–148 Marty Garrison, 160, 167 CloudShield, 13 cluster analysis, Craig Miller, 64 CommonCrawl group, 257 communication activities, of William Ballard, 269 communication, importance of, 30 competition, evaluating Amy Alving, 12–13 Darko Hrelic, 211–212, 217 Gerson Lehrman, 265 Jerry Krill, 101–102 Paul Bloore, 244–245 computing cloud computing, 35 Amy Alving, 16–17 Craig Miller, 65 David Kuttler, 302 Dmitry Cherches, 186–188, 200 Gerson Lehrman, 277 Jeff Tolnar, 142, 147–148 Marty Garrison, 160, 167 distributed computing, 242 mobile computing, 39–40 Darko Hrelic, 209–210 impact on Gerson Lehrman, 276 Jerry Krill, 96 Marty Garrison, 168 Paul Bloore, 254 Wesley Kaplow, 123 Comverge, 137 conference calls, ZipDx, 277–278 consulting, by Gerson Lehrman, 265 continuous deployment cycle, at Gerson Lehrman, 281 corporate strategy, Rick Mosca, 191–194 councils, Gerson Lehrman, 264 creativity, role in career path Jerry Krill, 85–87 Paul Bloore, 241 Wesley Kaplow, 116 Crinks, Bert, 115–116 CTO responsibilities, of William Ballard, 267–271, 282–283 Cuomo, Jerry, 25 customizing software, William Ballard's advice regarding, 270–272 cyanobacteria, Craig Miller, 79 cyber security Amy Alving, 18–19 CloudShield, 13 Darko Hrelic, 214–215 Dmitry Cherches, 201 Gerson Lehrman, 276 Springer, 235 Wesley Kaplow, 123 D Dannelly, Doug, 69 DARPA (Defense Advanced Research Projects Agency), Amy Alving, 5–6 Darwin, Charles, 70 data mining, real-time, 281–282 databases Jeff Tolnar, 141 used at Gerson Lehrman, 272 David Kuttler, 296 DAVID system, 165 de Boer, Jan-Erik, 219–237 Debevec, Paul, 57 decision-making, Jeff Tolnar, 133–134 Defense Advanced Research Projects Agency (DARPA), Amy Alving, 5–6 Delman, Debra, 161 Demand Media, 261–262 Department of Energy (DoE), 54–55, 64 deployment cycle, at Gerson Lehrman, 281 DER (distributed energy resources), 139, 142–149 DEs (Distinguished Engineers), 25 development methodology, Paul Bloore, 256–257 development resources, Jeff Tolnar, 139–140 DiData, Craig Miller, 53, 61 digital watermarking, 243 Dimension Data, Craig Miller, 52 Distinguished Engineers (DEs), 25 distributed computing, 242 distributed energy resources (DER), 139, 142–149 Dobson, David, 36 DoE (Department of Energy), 54–55, 64 domain expertise, 36 E economics of consulting with Gerson Lehrman, 265 William Ballard's advice regarding, 270–271, 279 electric co-ops, Craig Miller, 54–55 electrical transformers, Craig Miller, 68 employees, Gerson Lehrman, 265–266 Endhiran movie, Craig Miller, 58–59 energy policy, Craig Miller, 50 engineering offices, Gerson Lehrman, 266 engineers, cost of, 270, 279 ETL (extraction, translation, load), 203 evaluating competition Amy Alving, 12–13 Darko Hrelic, 211–212, 217 Gerson Lehrman, 265 Jerry Krill, 101–102 Paul Bloore, 244–245 expert network space, Gerson Lehrman, 265 expertise, use of, 274–275 external organizations, affiliations of Gerson Lehrman with, 269 extinction, Craig Miller, 78–79 extraction, translation, load (ETL), 203 F FDA, David Kuttler, 300–301 Ferguson, Don, interview with, 21–47 Fernandez, Raul, 52 ferrofluid, 241 First of a Kind project, 23 Folderauer, Ken, 114 fractal architecture, 273–274 free space optics, 84, 95 frequency variations, Craig Miller, 62–63 functional programming, 278 fundraising, Jeff Tolnar, 146 G Garrison, Marty, interview with, 151 Gayal, Amoosh, 24 Gelpin, Tim, 89 G.I.


pages: 484 words: 104,873

Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford

"Robert Solow", 3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, debt deflation, deskilling, disruptive innovation, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, Goldman Sachs: Vampire Squid, Gunnar Myrdal, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kenneth Arrow, Khan Academy, knowledge worker, labor-force participation, liquidity trap, low skilled workers, low-wage service sector, Lyft, manufacturing employment, Marc Andreessen, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, Paul Samuelson, performance metric, Peter Thiel, plutocrats, Plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Rodney Brooks, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, The Future of Employment, Thomas L Friedman, too big to fail, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce

IBM envisions the rapid emergence of an entire ecosystem of smart, natural language applications—all carrying the “Powered by Watson” label.26 The migration of leading-edge artificial intelligence capability into the cloud is almost certain to be a powerful driver of white-collar automation. Cloud computing has become the focus of intense competition among major information technology companies, including Amazon, Google, and Microsoft. Google, for example, offers developers a cloud-based machine learning application as well as a large-scale compute engine that lets developers solve huge, computationally intensive problems by running programs on massive supercomputer-like networks of servers. Amazon is the industry leader in providing cloud computing services. Cycle Computing, a small company that specializes in large-scale computing, was able to solve a complex problem that would have taken over 260 years on a single computer in just 18 hours by utilizing tens of thousands of the computers that power Amazon’s cloud service.

At the same time, entrepreneurs are already finding ways to use the same cloud-based building blocks to create affordable automation products geared toward small or medium-sized businesses. Cloud computing has already had a significant impact on information technology jobs. During the 1990’s tech boom, huge numbers of well-paying jobs were created as businesses and organizations of all sizes needed IT professionals to administer and install personal computers, networks, and software. By the first decade of the twenty-first century, however, the trend began to shift as companies were increasingly outsourcing many of their information technology functions to huge, centralized computing hubs. The massive facilities that host cloud computing services benefit from enormous economies of scale, and the administrative functions that once kept armies of skilled IT workers busy are now highly automated.

Jet aircraft were still essentially similar to the designs of the 1970s; however, they now had “fly by wire” systems, in which computers moved the control surfaces in response to the pilots’ inputs, as well as increased flight automation. In the years following 2000, information technology continued its acceleration and productivity rose as businesses got better at taking full advantage of all the new innovations. Many of those good jobs created in the 1990s began to disappear as corporations automated or offshored jobs, or began to outsource their IT departments to centralized “cloud” computing services. Throughout the economy, computers and machines were increasingly replacing workers rather than making them more valuable, and wage increases fell far short of growth in productivity. Both the share of national income going to labor and the labor force participation rate declined dramatically. The job market continued to polarize, and jobless recoveries became the norm. Jet aircraft still used the same basic designs and propulsion systems as in the 1970s, but computer-aided design and simulation had resulted in many incremental improvements in areas such as fuel efficiency.


pages: 666 words: 181,495

In the Plex: How Google Thinks, Works, and Shapes Our Lives by Steven Levy

23andMe, AltaVista, Anne Wojcicki, Apple's 1984 Super Bowl advert, autonomous vehicles, book scanning, Brewster Kahle, Burning Man, business process, clean water, cloud computing, crowdsourcing, Dean Kamen, discounted cash flows, don't be evil, Donald Knuth, Douglas Engelbart, Douglas Engelbart, El Camino Real, fault tolerance, Firefox, Gerard Salton, Gerard Salton, Google bus, Google Chrome, Google Earth, Googley, HyperCard, hypertext link, IBM and the Holocaust, informal economy, information retrieval, Internet Archive, Jeff Bezos, John Markoff, Kevin Kelly, Kickstarter, Mark Zuckerberg, Menlo Park, one-China policy, optical character recognition, PageRank, Paul Buchheit, Potemkin village, prediction markets, recommendation engine, risk tolerance, Rubik’s Cube, Sand Hill Road, Saturday Night Live, search inside the book, second-price auction, selection bias, Silicon Valley, skunkworks, Skype, slashdot, social graph, social software, social web, spectrum auction, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Ted Nelson, telemarketer, trade route, traveling salesman, turn-by-turn navigation, undersea cable, Vannevar Bush, web application, WikiLeaks, Y Combinator

“When people said that it should be canceled, I told them it’s really the foundation for a lot of other products. It just seemed obvious that the way things were going, all information was going to be online.” People would quickly identify that concept as a core value of “cloud computing.” The term came from the phenomenon where data—even private, proprietary information once stored on one’s own computer—would be accessed via the Internet, no matter where you were. As far as the user was concerned, information lived in a huge data cloud, and you pulled it down and sent it back up without regard to its actual location. The term originally wasn’t popular at Google. “Internally, we thought of ‘cloud computing’ as a marketing term,” says Urs Hölzle. (“Marketing” being pejorative in this context.) “Technically speaking, it’s cluster computing that you do.” (At Google, people refer to a “cluster” as a large number of servers—well into the thousands—usually representing the minimum number of machines needed to serve search results from a query.)

This program guaranteed that Google’s ideas would spread throughout the world and made it easier to implement cloud computing. Even though competitors would benefit, this wasn’t seen as a negative in Mountain View. If everyone adopted this new computing paradigm, people would always be just a click away from Google’s services—and Google’s ads. What was good for the cloud would be good for Google. 3 “They’re created by machines. And that is what makes us powerful.” In its earlier days, Google had taken pains not to draw the attention of the world’s biggest software company. But everyone knew that eventually the Silicon Valley search kings would wind up in a death cage match with Microsoft. With the development of Google’s cloud computing strategy, it became clear just how that would happen. Microsoft’s revenues flowed largely from two cash cows, both of which were monopolies.

The program worked in two steps—first by mapping the system (figuring out how the information was spread out and duplicated in various locations—basically an indexing process) and then by reducing the information to the transformed data requested. The key was that the programmers could control a massive number of machines, swapping and sharing their contents—a cluster’s worth or more—as if they were a single desktop computer. Ghemawat and Jeff Dean called their project MapReduce. “The engineers only have to think about the data,” says Christophe Bisciglia, a Google engineer who became an evangelist for cloud computing. “The system takes care of the parallelization. You don’t have to think about what machine the data is stored on or how to synchronize what happens when the machine fails or if there’s a bad record or any of that. I just think about the data and how I want to explore or transform the data, so I write code for that, and the system takes care of everything else.” What’s more, with MapReduce Google could easily build out its system—adding thousands more machines, allowing for much more storage and much faster results—without having to change the original code.


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

Now, extend that to personal medical data, financial information, important company documents, purchase history, favourite movies, music and so forth. How do we get access to this information on the move? Figure 10.1: All the major platforms see a silver lining in Cloud services For this reason, Google, IBM, Apple, Microsoft and a host of other players are making various bets on what is known as cloud computing. Cloud computing uses the Internet and central remote servers to maintain data and applications. It allows the use of applications without installation and allows users access to their personal data and files using any device that has Internet access. Cloud computing abstracts users from their applications and data by providing those facilities via a browser, effectively minimising storage requirements and leaving processing to the cloud rather than requiring heavy local processing capability. It does, however, rely heavily on bandwidth to get expeditious results.

Why transfer files constantly from one device to another, or sync our smartphone every few days, when the data is shared constantly via an online store of all our personal information, our private and public data and our identity and associated artefacts? If all our devices connect with the same data in the cloud, we need never sync or transfer a file between our devices ever again. The Players Apple has recently started to make a foray into cloud computing in a major way. It is building a $1-billion data centre in North Carolina, possibly the largest of any in the world.2 iCloud (previously MobileMe®) was the first of a series of online services based on cloud computing designed to create new revenue streams for the tech giant. iCloud is designed to connect all our devices and push information up and down to keep everything synced and up to date. iDisk, incorporated into iCloud, gives users 20GB of remote hard disk space for storing files that are too big to email, photo galleries, and such.

The questions remain as to what services work, and what revenue models will drive cloud computing. For corporations, the business case is simple: shifting to the cloud reduces infrastructure costs and moves platform and application costs to an OpEx (Operating Expense) model instead of CapEx (Capital Expense). In the current economic environment, this has to be promising. Distributed platform access and the benefit of data centres in the cloud also create more opportunities for more agile institution operations and different models such as telecommuting, homeshoring, portable or outreach branches and so forth. If you are sitting there reading this right now with some scepticism about the possibilities of the cloud as it pertains to banking, think about this. Arguably the most successful cloud computing service today, with close to one billion users, is Facebook.3 It is run almost completely through our browser or apps.


pages: 90 words: 17,297

Deploying OpenStack by Ken Pepple

Amazon Web Services, cloud computing, database schema, Infrastructure as a Service, Kickstarter, Ruby on Rails, web application, x509 certificate

While there, he co-authored two books “Consolidation in the Data Center: Simplifying IT Environments to Reduce Total Cost of Ownership” and “Migrating to the Solaris Operating System: The Discipline of UNIX-to-UNIX Migrations” for Prentice Hall PTR. Ken is also a frequent speaker, presenting at conferences including Gartner's Data Centre Summit, TOGAF China, IDC's Asia/Pacific Cloud Computing Conferences and JavaOne. Currently, Ken focused on building cloud computing infrastructure. As part of this work, he has designed clouds for service providers and written code for the OpenStack project. You can catch up on Ken's current work at his blog (http://ken.pepple.info/) or view his author page at Amazon (http://www.amazon.com/Ken-Pepple/e/B004​QQBWJW). Colophon The animal on the cover of Deploying OpenStack is a Tenrec.

., was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. O'Reilly Media * * * Preface This book is aimed at developers, technologists, and system administrators eager to understand and deploy cloud computing infrastructure projects based upon OpenStack software. It is intended to provide the reader with a solid understanding of the OpenStack project goals, details of specific OpenStack software components, general design decisions, and detailed steps to deploy OpenStack in a few controlled scenarios. Along the way, readers would also learn common pitfalls in architecting, deploying, and implementing their cloud.

The OpenStack Project The OpenStack project has been created with the audacious goal of being the ubiquitous software choice for building cloud infrastructures. In just over one year, it has gone from an idea to start collaborating to being the most talked-about project in open source. In this chapter, we will examine the project’s goals, history, and how you can participate in its future. What Is the OpenStack Project ? The OpenStack Project aims to create an open source cloud computing platform for public and private clouds aimed at scalability without complexity. Initially focusing on Infrastructure as a Service (IaaS) offerings, the project currently encompasses three components: OpenStack Compute: Software to orchestrate, manage, and offer virtual machines. The software for this is called “Nova.” OpenStack Object Store: Software for the redundant storage of static objects.


pages: 116 words: 31,356

Platform Capitalism by Nick Srnicek

3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, collaborative economy, collective bargaining, deindustrialization, deskilling, disintermediation, future of work, gig economy, Infrastructure as a Service, Internet of things, Jean Tirole, Jeff Bezos, knowledge economy, knowledge worker, liquidity trap, low skilled workers, Lyft, Mark Zuckerberg, means of production, mittelstand, multi-sided market, natural language processing, Network effects, new economy, Oculus Rift, offshore financial centre, pattern recognition, platform as a service, quantitative easing, RFID, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, software as a service, TaskRabbit, the built environment, total factor productivity, two-sided market, Uber and Lyft, Uber for X, uber lyft, unconventional monetary instruments, unorthodox policies, Zipcar

Likewise, the sophisticated analytical tools that Google has developed are now beginning to be rented out as part of its AWS competitor.37 Other businesses can now rent the ability to use pattern recognition algorithms and audio transcription services. In other words, Google is selling its machine-learning processes (and this is precisely where Google sees its advantage over its competitors in the cloud computing field). Microsoft, meanwhile, has built an artificial intelligence platform that gives businesses the software development tools to build their own bots (‘intelligence as a service’, in the contemporary lingo). And International Business Machines (IBM) is moving to make quantum cloud computing a reality.38 Cloud platforms ultimately enable the outsourcing of much of a company’s information technology (IT) department. This process pushes knowledge workers out and often enables the automation of their work as well. Data analysis, storage of customer information, maintenance of a company’s servers – all of this can be pushed to the cloud and provides the capitalist rationale for using these platforms.

These platforms already are strong revenue sources for the companies: Predix currently brings GE $5 billion and is expected to triple this revenue by 2020.49 Predictions are that the sector will be worth $225 billion by 2020 – more than both the consumer internet of things and enterprise cloud computing.50 Nevertheless, demonstrating the power of monopolies, GE continues to use AWS for its internal needs.51 Product Platforms Importantly, the preceding developments – particularly the internet of things and cloud computing – have enabled a new type of on-demand platform. They are two closely related but distinct business models: the product platform and the lean platform. Take, for example, Uber and Zipcar – both platforms designed for consumers who wish to rent some asset for a time. While they are similar in this respect, their business models are significantly different.

In the process of building a massive logistical network, Amazon Web Services (AWS) was developed as an internal platform, to handle the increasingly complex logistics of the company. Indeed, a common theme in the genesis of platforms is that they often emerge out of internal company needs. Amazon required ways to get new services up and running quickly, and the answer was to build up the basic infrastructure in a way that enabled new services to use it easily.35 It was quickly recognised that this could also be rented to other firms. In effect AWS rents out cloud computing services, which include on-demand services for servers, storage and computing power, software development tools and operating systems, and ready-made applications.36 The utility of this practice for other businesses is that they do not need to spend the time and money to build up their own hardware system, their own software development kit, or their own applications. They can simply rent these on an ‘as needed’ basis.


pages: 571 words: 105,054

Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, G4S, implied volatility, information asymmetry, latency arbitrage, margin call, market fragmentation, market microstructure, martingale, NP-complete, P = NP, p-value, paper trading, pattern recognition, performance metric, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, Silicon Valley, smart cities, smart meter, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, traveling salesman

For example, the nodes on dirac1 use a 24-core 2.2Ghz Intel processor, which is common to cloud computing systems. Currently, dirac1 does not contain GPUs. Figure 22.1 Schematic of the Magellan cluster (circa 2010), an example of HPC computer cluster The networking system consists of two parts: the InfiniBand network connecting the components within the cluster, and the switched network connection to the outside world. In this particular example, the outside connections are labeled “ESNet” and “ANI.” The InfiniBand network switches are also common in cloud computing systems. The storage system in Figure 1 includes both rotating disks and flash storage. This combination is also common. What is different is that a HPC system typically has its storage system concentrated outside of the computer nodes, while a typical cloud computing system has its storage system distributed among the compute nodes.

Typically, a distributed file system, such as the Google file system (Ghemawat, Gobioff, and Leung [2003]), is layered on top of a cloud computing system to make the storage accessible to all CPUs. In short, the current generation of HPC systems and cloud systems use pretty much the same commercial hardware components. Their differences are primarily in the arrangement of the storage systems and networking systems. Clearly, the difference in the storage system designs could affect the application performance. However, the virtualization layer of the cloud systems is likely the bigger cause of application performance difference. In the next section, we will discuss another factor that could have an even larger impact, namely software tools and libraries. Virtualization is generally used in the cloud computing environment to make the same hardware available to multiple users and to insulate one software environment from another.

This same capability is also at the core of many internet companies, for example, to match users with advertisers (Zeff and Aronson [1999], Yen et al. [2009]). However, the hardware and software used in science and in commerce are quite different. The HPC tools have some critical advantages that should be useful in a variety of business applications. Tools for scientists are typically built around high-performance computing (HPC) platforms, while the tools for commercial applications are built around cloud computing platforms. For the purpose of sifting through large volumes of data to find useful patterns, the two approaches have been shown to work well. However, the marquee application for HPC systems is large-scale simulation, such as weather models used for forecasting regional storms in the next few days (Asanovic et al. [2006]). In contrast, the commercial cloud was initially motivated by the need to process a large number of independent data objects concurrently (data parallel tasks).


pages: 286 words: 87,401

Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies by Reid Hoffman, Chris Yeh

activist fund / activist shareholder / activist investor, Airbnb, Amazon Web Services, autonomous vehicles, bitcoin, blockchain, Bob Noyce, business intelligence, Chuck Templeton: OpenTable:, cloud computing, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, database schema, discounted cash flows, Elon Musk, Firefox, forensic accounting, George Gilder, global pandemic, Google Hangouts, Google X / Alphabet X, hydraulic fracturing, Hyperloop, inventory management, Isaac Newton, Jeff Bezos, Joi Ito, Khan Academy, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, margin call, Mark Zuckerberg, minimum viable product, move fast and break things, move fast and break things, Network effects, Oculus Rift, oil shale / tar sands, Paul Buchheit, Paul Graham, Peter Thiel, pre–internet, recommendation engine, ride hailing / ride sharing, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart grid, social graph, software as a service, software is eating the world, speech recognition, stem cell, Steve Jobs, subscription business, Tesla Model S, thinkpad, transaction costs, transport as a service, Travis Kalanick, Uber for X, uber lyft, web application, winner-take-all economy, Y Combinator, yellow journalism

For example, in 1994, the same year that Jeff Bezos founded Amazon, the restaurateur Thomas Keller bought The French Laundry in Yountville, California, and turned it into one of the world’s greatest restaurants, winning a coveted three-star rating from the Michelin Guide. Today, Amazon has over 541,900 employees and is the market leader in online retail, ebooks, cloud computing, and more, while The French Laundry, with less than fifty staff members, in a single location, serving just sixty customers per day, is still one of the world’s most famous restaurants. Both Amazon and The French Laundry are great businesses, but they exist in fundamentally different worlds. Amazon’s business relies on massive scale and billions of dollars of infrastructure; The French Laundry relies on local ingredients of the highest quality, prepared by some of the most skilled cooks in the world. Scale is critical to e-commerce and cloud computing; scale is antithetical to world-class fine dining. It is as impossible to imagine Amazon as a small, independent bookstore as it is to imagine The French Laundry as a global restaurant chain, vying with McDonald’s for franchise supremacy.

AIRBNB Airbnb.com Airbnb is an online marketplace and hospitality service, enabling people to lease or rent short-term lodging including vacation rentals, apartment rentals, homestays, hostel beds, or hotel rooms. Founded August 2008, San Francisco, CA ALIBABA Alibaba.com The Alibaba Group is an e-commerce, retail, and technology conglomerate that provides consumer-to-consumer, business-to-consumer, and business-to-business services including electronic payments and cloud computing. Founded April 1999, Hangzhou, China AMAZON Amazon.com Amazon is an e-commerce company that also produces consumer electronics like the Kindle and Echo and is the world’s largest provider of cloud computing services. Founded July 1994, Seattle, WA APPLE Apple.com Apple designs, develops, and sells consumer electronics, computer software, and online services, such as the iPhone, iOS operating system, and Mac personal computers. Founded April 1976, Los Altos, CA CHARITY: WATER Charitywater.org Charity: Water is a not-for-profit organization that provides clean and safe drinking water to people in developing nations.

When a market is up for grabs, the risk isn’t inefficiency—the risk is playing it too safe. If you win, efficiency isn’t that important; if you lose, efficiency is completely irrelevant. Over the years, many have criticized Amazon for its risky strategy of consuming capital without delivering consistent profits, but Amazon is probably glad that its “inefficiency” helped it win several key markets—online retail, ebooks, and cloud computing, to name just a few. When you blitzscale, you deliberately make decisions and commit to them even though your confidence level is substantially lower than 100 percent. You accept the risk of making the wrong decision and willingly pay the cost of significant operating inefficiencies in exchange for the ability to move faster. These risks and costs are acceptable because the risk and cost of being too slow is even greater.


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

Huge companies like Netflix host their services on top of AWS. It is now a $12 billion a year business. Microsoft, Google, and many others have been playing catch-up in cloud computing, but they were late to the game. Amazon had one big advantage that Jeff explained to me not long after Amazon’s cloud computing offerings were formally introduced in 2006: “I started out as a retailer. That’s a really low-margin business. There’s no way this can be worse for me. Microsoft and Google have very high margins. This is always going to be a worse business for them.” By the time Microsoft and Google realized just how big a business cloud computing would be, they were far behind. SOFTWARE AS ORGANIZATIONAL STRUCTURE But perhaps the deepest insight about the nature of networked organizations comes from the way that Amazon structured itself internally to match the service-oriented design of its platform.

In late 2016, the company announced that it is developing convenience stores powered by Amazon Go and “Just Walk Out Shopping.” Simply enable the Amazon Go app, and machine vision and other algorithmic systems keep track of what you take off the shelf, and automatically debit your account. I had proposed something like this myself in 2009 or so, in a “Web 2.0” brainstorming session with Russ Daniels, chief technology officer for cloud computing strategy at Hewlett-Packard. HP was trying to figure out how to do something distinctive in the cloud computing business. I knew that HP had once owned Verifone, the point-of-sale payment equipment vendor, and that led me to suggest that the future would include smart shopping carts that could do paymentless checkout. Foursquare had just been launched, and its magical ability to detect where you were and offer a location for “check-in” made me think that it could also be used for “checkout.”

., 48–49, 51 Behr, Kevin, 122 Belenzon, Sharon, 246 beliefs, truth, and fake news, 210–14, 220–24 benefit corporations “B corps,” 292, 293 Berkeley Unix project, 6–7, 16 Berners-Lee, Tim, 99 Bersin, Josh, 111 Bessen, James, 345–47 Bezos, Jeff, 44, 71–75, 110–13, 114–15, 124, 366–67 Bharat, Krishna, 215 big data, 155–56, 163, 325, 326–27, 335–36 Blecharczyk, Nathan, 97–98 Blyth, Mark, 239 Boston, Massachusetts, 138–40 Bostrom, Nick, 234 Bouganim, Ron, 140 Bowling Alone (Putnam), 218–19 Boyd, John, 209 Bregman, Rutger, 307 Brin, David, 177, 179 Brin, Sergey, 132, 157, 160, 289–90 Browder, Josh, 332 Brown, John Seely, 341 Brynjolfsson, Erik, 303 Bucheit, Paul, 306–7, 308, 309 Buffett, Warren, 225, 242–43, 265, 272 “Building Global Community” (Zuckerberg), 218 Burdick, Brad, 126 Burgess, Mark, 114, 115 businesses declining R&D, 245–46 economic impact reports, 290–95 fitness function, 226, 239–41, 274, 352 limiting CEO salaries, 247 management, xxi, 153–54, 247, 279–80 and media content, 226–28 social conscience squashed, 240–41 startups, 41, 186, 247, 275, 279, 282–85, 316 stock price vs. long-term investment, 242–50 and tragedy of the commons, 249–50 uncertain job opportunities, 301–2 See also financial markets Business Insider, 211–12 business model mapping, 48–51, 57–61, 62–70 Cabulous, 56 Cadwalladr, Carole, 202–3, 214 Camp, Garrett, 54, 75 Car2go, 85 Carlsen, Magnus, 330 Carr, Nicholas, 64 Casey, Liam, 66 “Cathedral and the Bazaar” (Raymond), 8–9 central banks, xxi–xxii centralization and decentralization, 105–8 Central Park, New York, 132–33 Cerf, Vint, 107 Chan, Priscilla, 302–3 Chase, Robin, 84–85 Chastanet, Vidal, 371 Chesky, Brian, 97–98 chess and AI, 330 Chinese companies, 53 Chrapaty, Debra, 121 Christensen, Clayton, 24–25, 33–34, 315, 331, 351 Church, George, 328 Clark, Dave, 107 climate change, 300, 302, 360–63 Climate Corporation, 326 Cline, Craig, 29 “Clothesline Paradox, The” (Baer), 295–97 cloud computing, 35, 41, 53, 78, 84, 110–11, 119 Coase, Ronald, 89 Code for America, 138–44, 147, 148–49, 187, 222 Cohen, Stephen, 134 Cohler, Matt, 54 Collins, Jim, 352 combinatorial effects, 96–98 Common Gateway Interface (CGI), 81 communication, 44–45, 84, 90, 114, 115–19, 117 community. See social infrastructure competition, outcomes of, 104 computer hardware, 7, 11–12, 165, 167 computer industry, 5–17, 186–87, 301, 334–36, 343–44. See also cloud computing; software Concrete Economics (Cohen and DeLong), 134 consumer reviews, 34, 92, 182 Conte, Jack, 316–17 corporate raiders, 242–52, 249 corporations. See businesses Craigslist, 39, 97, 101–2 Creative Commons licenses, 180 creative economy, 312–19 “creep factor,” 178 Cronin, Beau, 236 crowdfunding, 39, 305, 316–17 Culkin, Father John, 163 customers, 58, 250–52, 264, 271, 357 Cutting, Doug, 325 cybercrime, 208–9 Dalio, Ray, 223 DARPA Cyber Grand Challenge, 209 “Darwin’s Bulldog” (Huxley), 44 data accessibility of, 110, 128–31 big data, 155–56, 163, 325, 326–27, 335–36 as collective intelligence, 32–35 data-driven regulatory systems, 175–76 failure to provide or utilize, 188, 189–90 hidden intelligence in web links, 39 increasing creativity with, 46 SEC’s EDGAR documents, 125–26 for self-driving cars, 32–33, 34–35 from sensors, xviii–xix, 33, 34–35, 40, 41, 85, 176–77, 326 from surveillance, 177–81 unreasonable effectiveness of, 154–63 data aggregators, 179–80, 236 data science, 156 Davison, Lang, 341 Dawkins, Richard, 44 decentralization and centralization, 105–8 DeepMind, 165, 167, 168–69, 235 de Havilland Comet commercial jet, 217–18 Dell, Michael, 12 DeLong, Brad, 134 Denmark, 268 DevOps, 121–23 Dickens, Charles, 346 Dickerson, Mikey, 118–19, 146–47, 148 DiGiammarino, Frank, 129 digital footprint, physical assets with, 66–67 Digital Millennium Copyright Act, 202 disease elimination jobs, 300, 302–3 disinformation.


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Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce by Natalie Berg, Miya Knights

3D printing, Airbnb, Amazon Web Services, augmented reality, Bernie Sanders, big-box store, business intelligence, cloud computing, Colonization of Mars, commoditize, computer vision, connected car, Donald Trump, Doomsday Clock, Elon Musk, gig economy, Internet of things, inventory management, invisible hand, Jeff Bezos, market fragmentation, new economy, pattern recognition, Ponzi scheme, pre–internet, QR code, race to the bottom, recommendation engine, remote working, sensor fusion, sharing economy, Skype, supply-chain management, TaskRabbit, trade route, underbanked, urban planning, white picket fence

Going back to 2002, with necessity truly being the mother of all invention, Amazon Web Services (AWS) was first born of the need for sufficient number-crunching capacity and standardized, automated computing infrastructures on which to run its retail marketplace. Capitalizing on advances in networking, storage, compute power and virtualization, Amazon began reselling its cloud computing capabilities as services in 2006. However, from 2014 to 2015, Amazon saw its stock price fall 20 per cent. During that intervening time shareholders would have been forgiven for wondering if the company would ever make a profit, and its dwindling share price reflected this. In relative terms, it was smaller than Walmart. Even Alibaba, which went public in the autumn of the same year also dwarfed Amazon’s 2014 market cap. In the meantime, though, Amazon had quietly been consolidating market share in meeting the fast-growing demand for cloud computing services. Then, in 2015, in what would be a pivotal year for the company, it first revealed just how profitable AWS had become, with margins to rival those of Starbucks, and investors started to see their Amazon stock start to rise in value.

And the future is excelling at WACD: What Amazon Can’t Do. The titan of 21st-century commerce, Amazon has grown from online bookseller to become one of the most valuable public companies in the world. At the time of writing, Amazon accounted for nearly half of US e-commerce sales.1 In 2010, the retailer employed 30,000 people. By 2018, that figure rose to 560,000.2 Amazon has become the undisputed market leader in everything from cloud computing to voice technology. It is the number one destination for product search ahead of Google3 and, by the time you’re reading this, Amazon will likely hold the title of largest US clothing retailer.4 In 2018, at the time of writing, Amazon was worth the equivalent of Walmart, Home Depot, Costco, CVS, Walgreens, Target, Kroger, Best Buy, Kohl’s, Macy’s, Nordstrom, JC Penney and Sears combined.5 Those cardboard boxes are certainly changing retail.

Capitalizing on the strength and trust of its brand, Amazon is now spreading its tentacles across entirely new industries. The mere whisper that Amazon might enter a sector is enough to send stocks tumbling. And it’s getting clearer by the day that Amazon is not satisfied with just being the retailer; it also wants to be the infrastructure. We believe that, by 2021, the majority of Amazon’s sales will be derived from services rather than products, as cloud computing, subscriptions, advertising and financial services grow in importance. But Amazon is at an inflection point. The king of e-commerce has recognized that, for all its conveniences, online-only is no longer enough. The convergence of physical and digital retail is accelerating. If Amazon wants to crack the grocery and fashion sectors, it needs stores. If Amazon wants to offset rising shipping and customer acquisition costs, it needs stores.


Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, bitcoin, business intelligence, business process, chief data officer, clean water, cloud computing, computer vision, continuous integration, crowdsourcing, data is the new oil, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, income inequality, Infrastructure as a Service, Internet of things, Masdar, microservices, Minecraft, platform as a service, ransomware, RFID, ride hailing / ride sharing, risk tolerance, self-driving car, smart cities, smart meter, software as a service, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Stuxnet, Thomas Bayes, Turing test, urban sprawl, zero-sum game

This has enabled customers to focus on areas where they want to have expertise, which in most cases is not running a data center. When we are talking about the cloud, it is important to be precise, since there are many vendors and services out there. The American National Institute of Standards and Technology (NIST) has provided the most common definition of what is meant by cloud computing. The definition is as follows:Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.1 There are five essential characteristics for something to be cloud:1.On-demand self-service – The user has to be able to provision the resources needed without the intervention of the vendor or anyone else. 2.Broad network access – The service has to be accessible through standard interfaces like a computer, tablet, smartphone, and so on. 3.Resource pooling – The computer resources are pooled across multiple customers in a multi-tenant model.

For some services, it is possible to specify country and state though. 4.Rapid elasticity – Capabilities can be rapidly provisioned and de-provisioned depending on need and are able to scale rapidly often automatically with demand. 5.Measured service – The use and billing is metered at some appropriate level of abstraction, like hour, bandwidth, CPU usage, and number of users, to provide transparency, control, and monitoring. While there are many ways that this can be implemented, they are good indicators of what is unique about cloud computing. If you compare with on premise, it is not on demand, not necessarily available anywhere outside the internal network. It is not very elastic, since it is necessary to order new servers if demand picks up. Another important point is that it is not metered, but a lot of up-front investments must be done that are sunk costs. This is why you often hear about a move from capital expenditure to operational expenditure, when it comes to cloud computing. It is not necessary to make capital investments in equipment before starting. Another important point of the NIST definition is the division into three types: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).Software as a Service – Is the ability of the consumer to access system features through a standard interface like a web browser.

Along with this, support for master data management is usually necessary for complex organizations with data spread out across multiple solutions. Regardless of the level of data governance and master data management, addressing data quality is also a frequent concern. Understanding and addressing these different forces is crucial for smart city solutions, since cities run on data. Footnotes 1The NIST Definition of Cloud Computing, Peter Mell, Timothy Grance © Anders Lisdorf 2020 A. LisdorfDemystifying Smart Citieshttps://doi.org/10.1007/978-1-4842-5377-9_5 5. Intelligence Anders Lisdorf1 (1)Copenhagen, Denmark The “Smart” part of smart cities depends crucially on intelligence in the solutions. Cities have used technology for centuries, but what makes a smart city is the use of intelligent solutions.


pages: 272 words: 64,626

Eat People: And Other Unapologetic Rules for Game-Changing Entrepreneurs by Andy Kessler

23andMe, Andy Kessler, bank run, barriers to entry, Berlin Wall, Bob Noyce, British Empire, business cycle, business process, California gold rush, carbon footprint, Cass Sunstein, cloud computing, collateralized debt obligation, collective bargaining, commoditize, computer age, creative destruction, disintermediation, Douglas Engelbart, Eugene Fama: efficient market hypothesis, fiat currency, Firefox, Fractional reserve banking, George Gilder, Gordon Gekko, greed is good, income inequality, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, libertarian paternalism, low skilled workers, Mark Zuckerberg, McMansion, Netflix Prize, packet switching, personalized medicine, pets.com, prediction markets, pre–internet, profit motive, race to the bottom, Richard Thaler, risk tolerance, risk-adjusted returns, Silicon Valley, six sigma, Skype, social graph, Steve Jobs, The Wealth of Nations by Adam Smith, transcontinental railway, transfer pricing, wealth creators, Yogi Berra

For all the talk of giant data centers with zillions of servers, which sure as heck sounds like the center of the network, Google is the number one beneficiary of intelligence at the edge of the network. It’s the transport of data to and from those data centers that stays dumb. Our desktop computers and laptops and smartphones on the edge aren’t going away, but as bandwidth speeds increase, more and more computing can be done on the network of computers sitting in data centers—also known as the “cloud.” Cloud computing provides search results, processes company payrolls, coordinates video games played by thousands of people simultaneously; even the complex graphics for those games are starting to be drawn in the cloud. Anyone can do it, but it’s not cheap. These clouds are multibillion-dollar investments in data centers and fiber optics. The Internet is far from mature, and its growth is an ever more high-stakes game.

Once you build the cloud, it’s all about network operations. Whoever can deliver search results faster wins. Users usually only realize this subconsciously, but it’s true: Google’s dominant share is as much about speed as it is about relevant results. Compare it with Microsoft or Yahoo! search and you’ll see. Google often builds its data centers next to waterfalls so electricity can be cheap enough to help it win the speed war. New cloud computing applications appear every day—backing up files, managing your money, editing photos, running the back end of multiplayer games like World of Warcraft. Now corporate America is moving its accounting, scheduling, order management, and the like into the cloud, and speed is a top priority. Now wait one second. I invoke the IGA (the Inevitable Google Analogy—all you have to do is show that Google does something to prove it’s the greatest strategy ever!)

INDEX Abundance and scarcity abundance, recognizing cheap versus expensive and cost cutting economic principles main scarcities tech examples of waste, benefits of and wealth creation Activision Advertising Google sales and scarcity Airports, global comparisons Ajax Alinsky, Saul Amazon, recommendations to customers America Online (AOL), instant messaging virtual pipe Anderson, Chris Apache Apple and cloud computing Stores and vertical integration virtual pipe of See also specific products Application, versus features, businesses Applied Semantics Arkwright, Richard Artificial intelligence AT&T Bell Labs vertical integration Wireless Baby boomers Bach, David Back to basics Banking FDIC Federal Reserve, roles of fractional reserve banking money supply as Thieves Behavioral economics Bell Labs Bennett Mechanical Comprehension Test Bernanke, Ben Bessemer, Henry Bezos, Jeff Bionetworks Biotech industry, personalized medicine Birdseye, Clarence BitTorrent Blink (Gladwell) Books, digital Boulton, Matthew Brain and original thought Stroud number Brenner, Reuven Brin, Sergey Broadcasters, as Thieves Brotherhood Brown, Charles Buffett, Warren Burger, Warren Businesses FAB (Feature, Application, Business) with highest returns profitability.


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

Radical connectivity has played a key role in collapsing the advantages of scale, removing barriers to entry to the marketplace and—more importantly—allowing small companies to share resources that previously were only available to Big Companies. Inside the Cloud Cloud computing exemplifies such resource sharing. You hear people talking about the cloud all the time in the context of the Internet, but a lot of us still have a fairly cloudy notion of what exactly cloud computing means. From the perspective of companies, cloud computing makes it easier to share computing resources that otherwise would be too expensive. Let’s take a look at how this works. The Internet physically exists on computers called servers. Technically, any computer can be a server, even your smartphone. (For a while, I kept a server in my closet to host my personal Web site, http://nicco.org, using an old laptop I had retired from travel duty.)

Buy it from Amazon. Suddenly need 10,000 times more space because your tiny start-up has gone viral? Amazon can turn it on in seconds. Hundreds if not thousands of vendors now offer cloud computing. The complicated technical resources required to host and manage Web sites used to cost a lot of money and take a lot of time. They still do. But thousands or even millions of small businesses, groups, or individuals can now share that cost, allowing your tiny start-up access to the same kind of computing resources mobilized by a giant corporation like Google or Facebook. This kind of level playing field offered by cloud computing threatens Big Companies—and it ultimately even challenges the hegemony of the large, Even Bigger platforms described in chapter 4. Harvard Business School’s Wessel articulates, in effect, an argument for why loose coalitions of small businesses banding together can achieve comparable competitive power as large platform players like Amazon.

Finding a burgeoning culture of self-employed Americans like him, he excitedly wrote a landmark article for Fast Company magazine about the “free agent nation” he had stumbled upon.7 The article hit a nerve, and he went on to turn it into a best-selling book. Almost fifteen years later, Harvard Business Review revisited Pink’s argument and found that workplace trends did indeed show an astonishing movement toward individual employment.8 Why is that? I argue here that radical connectivity—in particular the efficiencies provided by cloud computing for sharing resources and collaboration—is dramatically reducing scale effects and will continue to reduce them in the coming decades. Add in technologies that enable on-demand fabrication, and you’ll see a significant erasure of the advantages of size in commerce, and economies that are far more local than at present. The Future That Is upon Us I admit, talk of scale economies is abstract.


pages: 283 words: 85,824

The People's Platform: Taking Back Power and Culture in the Digital Age by Astra Taylor

A Declaration of the Independence of Cyberspace, American Legislative Exchange Council, Andrew Keen, barriers to entry, Berlin Wall, big-box store, Brewster Kahle, citizen journalism, cloud computing, collateralized debt obligation, Community Supported Agriculture, conceptual framework, corporate social responsibility, creative destruction, cross-subsidies, crowdsourcing, David Brooks, digital Maoism, disintermediation, don't be evil, Donald Trump, Edward Snowden, Fall of the Berlin Wall, Filter Bubble, future of journalism, George Gilder, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, Internet Archive, Internet of things, invisible hand, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Julian Assange, Kevin Kelly, Kickstarter, knowledge worker, Mark Zuckerberg, means of production, Metcalfe’s law, Naomi Klein, Narrative Science, Network effects, new economy, New Journalism, New Urbanism, Nicholas Carr, oil rush, peer-to-peer, Peter Thiel, plutocrats, Plutocrats, post-work, pre–internet, profit motive, recommendation engine, Richard Florida, Richard Stallman, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, slashdot, Slavoj Žižek, Snapchat, social graph, Steve Jobs, Stewart Brand, technoutopianism, trade route, Whole Earth Catalog, WikiLeaks, winner-take-all economy, Works Progress Administration, young professional

On Google traffic figures, see Robert McMillan, “Google Serves 25 Percent of North American Internet Traffic,” Wired.com, July 22, 2013, http://www.wired.com/wiredenterprise/2013/07/google-internet-traffic/. Amazon’s centrality in the cloud computing space is hard to exaggerate: in 2012, an outage of one of Amazon’s data centers in Northern Virginia temporarily took down a wide array of dependent sites including Pinterest, Instagram, and Netflix. Related to this, the fact that WikiLeaks never recovered after being booted off of Amazon’s servers is another indication of the company’s dominance of the cloud computing space. Amazon also has a cloud computing contract with the CIA worth a reported $600 million. For the one-in-three-Internet-users figure, see Patrick Thibodeau, “Amazon Cloud Accessed Daily by a Third of All ’Net Users,” Computerworld.com, April 18, 2012, http://www.computerworld.com/s/article/9226349/Amazon_cloud_accessed_daily_by_a_third_of_all_Net_users.

Originality and depth eat away at profits online, where faster fortunes are made by aggregating work done by others, attracting eyeballs and ad revenue as a result. Indeed, the advertising industry is flourishing as never before. In a world where creative work holds diminishing value, where culture is “free,” and where fields like journalism are in crisis, advertising dollars provide the unacknowledged lifeblood of the digital economy. Moreover, the constant upgrading of devices, operating systems, and Web sites; the move toward “walled gardens” and cloud computing; the creep of algorithms and automation into every corner of our lives; the trend toward filtering and personalization; the lack of diversity; the privacy violations: all these developments are driven largely by commercial incentives. Corporate power and the quest for profit are as fundamental to new media as old. From a certain angle, the emerging order looks suspiciously like the old one.

A handful of Internet and technology companies have become as enormous and influential as the old leviathans: they now make up thirteen of the thirty largest publicly traded corporations in the United States.28 The omnipresent Google, which, on an average day, accounts for approximately 25 percent of all North American consumer Internet traffic, has gobbled up over one hundred smaller firms, partly as a method of thwarting potential rivals, averaging about one acquisition a week since 2010; Facebook now has well over one billion users, or more than one in seven people on the planet; Amazon controls one-tenth of all American online commerce and its swiftly expanding cloud computing services host the data and traffic of hundreds of thousands of companies located in almost two hundred countries, an estimated one-third of all Internet users accessing Amazon’s cloud at least once a day; and Apple, which sits on almost $140 billion in cash reserves, jockeys with Exxon Mobil for the title of the most valuable company on earth, with a valuation exceeding the GDP (gross domestic product) of most nations.29 Instead of leveling the field between small and large, the open Internet has dramatically tilted it in favor of the most massive players.


pages: 602 words: 177,874

Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations by Thomas L. Friedman

3D printing, additive manufacturing, affirmative action, Airbnb, AltaVista, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Bob Noyce, business cycle, business process, call centre, centre right, Chris Wanstrath, Clayton Christensen, clean water, cloud computing, corporate social responsibility, creative destruction, crowdsourcing, David Brooks, demand response, demographic dividend, demographic transition, Deng Xiaoping, Donald Trump, Erik Brynjolfsson, failed state, Fall of the Berlin Wall, Ferguson, Missouri, first square of the chessboard / second half of the chessboard, Flash crash, game design, gig economy, global pandemic, global supply chain, illegal immigration, immigration reform, income inequality, indoor plumbing, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the steam engine, inventory management, Irwin Jacobs: Qualcomm, Jeff Bezos, job automation, John Markoff, John von Neumann, Khan Academy, Kickstarter, knowledge economy, knowledge worker, land tenure, linear programming, Live Aid, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, mass immigration, Maui Hawaii, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Nelson Mandela, pattern recognition, planetary scale, pull request, Ralph Waldo Emerson, ransomware, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, South China Sea, Steve Jobs, supercomputer in your pocket, TaskRabbit, The Rise and Fall of American Growth, Thomas L Friedman, transaction costs, Transnistria, uber lyft, undersea cable, urban decay, urban planning, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y2K, Yogi Berra, zero-sum game

Google started as a search engine and is now also becoming a car company and a home energy management system. Apple is a computer manufacturer that is now the biggest music seller and is also going into the car business, but in the meantime, with Apple Pay, it’s also becoming a bank. Amazon, a retailer, came out of nowhere to steal a march on both IBM and HP in cloud computing. Ten years ago neither company would have listed Amazon as a competitor. But Amazon needed more cloud computing power to run its own business and then decided that cloud computing was a business! And now Amazon is also a Hollywood studio. On January 12, 2016, CNNMoney.com ran a story about the Golden Globes award ceremony that began: “I want to thank Amazon, Jeff Bezos…” Those words were spoken at a Hollywood awards show [by the director Jill Soloway] for the first time on Sunday as Amazon’s comedic television series Transparent picked up two Golden Globe awards, beating shows from HBO, Netflix, and the CW.

., high-speed Internet in Chattanoogan.com Chávez, Hugo Chesky, Brian chess Chicago Mercantile Exchange chickens, poverty and child marriage child mortality Children First Child Tax Credit China; as authoritarian state; “century of humiliation” in; debt of; Madagascar and; nationalism in; nuclear weapons of; 2015 economic slowdown in; U.S. relations with; workforce in China Daily “China Shock, The” (Autor) Chipman, John chlorofluorocarbons Chopra, Karan Chow, Alex Yong-Kang Citibank Citizens United decision Citrix CityLab.com civic idealism Civilian Conservation Corps (CCC) civil liberties civil rights, movements for Civil Rights Act (1964) Clapper, James clean energy Clear Channel Outdoor Inc. ClimateCentral.org climate change; acceleration of; in Africa; agriculture and; biodiversity loss and; black elephants in; developing countries and; in Earth history; extreme weather in; geopolitics and; Moore’s law and; population growth and; Republican denial of; 2016 Paris conference on; weak states and Clinton, Bill Clodd, Edward cloud computing, see supernova (cloud computing) CNET.com CNN.com CNNMoney.com Coast Guard Academy, U.S. Codecademy Code Division Multiple Access (CDMA) Coen, Joel and Ethan Cold War; U.S. economic growth in; U.S.-Soviet competition for allies in Coleman, David collaboration, software innovation and College Advising Corps College Board college degrees, skill sets and colleges: continuous innovation in; traditional role of Collins, Thomas J.

“That’s because technology stands on its own shoulders—each generation of invention stands on the inventions that have come before,” said Teller. “So by 1900, it was taking twenty to thirty years for technology to take one step big enough that the world became uncomfortably different. Think of the introduction of the car and the airplane.” Then the slope of the curve started to go almost straight up and off the graph with the convergence of mobile devices, broadband connectivity, and cloud computing (which we will discuss shortly). These developments diffused the tools of innovation to many more people on the planet, enabling them to drive change farther, faster, and more cheaply. “Now, in 2016,” he added, “that time window—having continued to shrink as each technology stood on the shoulders of past technologies—has become so short that it’s on the order of five to seven years from the time something is introduced to being ubiquitous and the world being uncomfortably changed.”


pages: 255 words: 78,207

Web Scraping With Python: Collecting Data From the Modern Web by Ryan Mitchell

AltaVista, Amazon Web Services, cloud computing, en.wikipedia.org, Firefox, Guido van Rossum, meta analysis, meta-analysis, natural language processing, optical character recognition, random walk, self-driving car, Turing test, web application

Thanks to the economies of scale, buying a small compute instance with a large com‐ pany is about the same as buying your own physical, dedicated, machine—except that now, you don’t need to hire an IT guy to keep it running. Of course, step-by-step instructions for setting up and running cloud computing instances are somewhat outside of the scope of this book, but you will likely find that step-by-step instructions are not needed. With both Amazon and Google (not to mention the countless smaller companies in the industry) vying for cloud computing dollars, they’ve made setting up new instances as easy as following a simple prompt, thinking of an app name, and providing a credit card number. As of this writing, both Amazon and Google also offer hundreds of dollars worth of free computing hours to further tempt new clients.

Remote Hosting | 205 Additional Resources Many years ago, running “in the cloud” was mostly the domain of those who felt like slogging through the documentation and already had some server administration experience. However, today, the tools have improved dramatically, due to increased popularity and competition among cloud computing providers. Still, for building large-scale or more-complex scrapers and crawlers, you might want a little more guidance on creating a platform for collecting and storing data. Google Compute Engine by Marc Cohen, Kathryn Hurley, and Paul Newson is a straightforward resource on using Google Cloud Computing with both Python and JavaScript. Not only does it cover Google’s user interface, but also the command-line and scripting tools that you can use to give your application greater flexibility. If you prefer to work with Amazon, Mitch Garnaat’s Python and AWS Cookbook is a brief but extremely useful guide that will get you started with Amazon Web Services and show you how to get a scalable application up and running.

There are two main reference pages for browsing Google APIs. The first is the Prod‐ ucts page, which serves as an organized repository of its APIs, software development kits, and other projects that might be of interest to software developers. The other is the APIs console, which provides a convenient interface to turn API services on and off, view rate limits and usage at a glance, and even spin up a Google-powered cloud computing instance if you feel like it. Most of Google’s APIs are free although some, such as its search API, require a paid license. Google is fairly liberal with its collection of free APIs allowing from 250 requests per day to 20,000,000 requests per day with a basic account. There is also the option to raise the rate limits on some of the APIs by verifying your identity with a credit card (the card is not charged).


pages: 269 words: 70,543

Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global by Rebecca Fannin

Airbnb, augmented reality, autonomous vehicles, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, Deng Xiaoping, digital map, disruptive innovation, Donald Trump, El Camino Real, Elon Musk, family office, fear of failure, glass ceiling, global supply chain, income inequality, industrial robot, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, megacity, Menlo Park, money market fund, Network effects, new economy, peer-to-peer lending, personalized medicine, Peter Thiel, QR code, RFID, ride hailing / ride sharing, Sand Hill Road, self-driving car, sharing economy, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart transportation, Snapchat, social graph, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, supply-chain management, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, winner-take-all economy, Y Combinator, young professional

•A state-led blueprint, “Made in China 2025,” to close the gap in technology leadership by building national firms into globally competitive tech champions and gaining technological leadership in emerging sectors including robotics, new-energy vehicles, biotech, power equipment, aerospace, and next-generation information technology—all to achieve supremacy.6 •The nation’s “Internet Plus” plan to build up China’s companies as world-class competitors in mobile internet, big data, cloud computing, and Internet of Things.7 The proposal’s focus on optimizing health care, manufacturing, and finance by leveraging internet connectivity and big data.8 •Chinese president Xi Jinping’s Belt and Road initiative to build a twenty-first-century Silk Road land and maritime trade corridor that could outdo America’s postwar reconstruction Marshall Plan to foster economic integration with neighboring countries, boost demand for Chinese products, and develop China’s poorer western provinces.

Then, Alibaba’s original e-commerce site for small businesses in China and its online auction–style copy of eBay, Taobao, were just beginning to take off. But in 2010, when Alibaba beat eBay’s then CEO Meg Whitman thanks to promotional stunts that got press, free customer listings, easy returns, and the dynamic leadership of Ma, suddenly the world spotlight began to shine on Alibaba. Taobao today counts around 700 million mobile users monthly. Its house of brands—Alipay payments, Alicloud cloud computing services, Alimama marketing platform, Aliwangwang instant messaging for customer negotiations, and its PR-ish news hub, Alizila—have taken it to the extreme in naming, like the Apple family of iPhone, iPad, and iTunes. Ma broadened his view of the world as an English translator for tourists in Hangzhou. He loves to talk up Alibaba, its mission to help small businesses selling globally and ambition to last more than 100 years.

Tencent CEO Ma and his handpicked president, Marvin Lau, a former Goldman Sachs banker with dual master’s degrees from Stanford and Northwestern, wants to make sure that doesn’t happen. Tencent recently launched a program to nurture young talent by committing to promote younger employees to one in five open positions. Now in its twentieth year, Tencent was restructured for the first time in six years to focus on business services such as cloud computing and payments. Tellingly, a technology committee was formed to strengthen research and development. Games and More Game Buys Acquiring and investing in games and more games have kept Tencent and its investment bankers and legal team very busy over the years. In 2018 alone, Tencent invested or became a majority owner in four of the five biggest gaming deals of 2018, among them $2 billion for Vivendi’s stake in French video game developer Ubisoft as well as several smaller investments in Chinese gaming players.13 In the United States, Tencent spent $400 million in 2015 to acquire Los Angeles–based Riot Games, operator of the highly popular PC game League of Legends.


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European Founders at Work by Pedro Gairifo Santos

business intelligence, cloud computing, crowdsourcing, fear of failure, full text search, information retrieval, inventory management, iterative process, Jeff Bezos, Joi Ito, Lean Startup, Mark Zuckerberg, natural language processing, pattern recognition, pre–internet, recommendation engine, Richard Stallman, Silicon Valley, Skype, slashdot, Steve Jobs, Steve Wozniak, subscription business, technology bubble, web application, Y Combinator

But I happen to be a big fish in a small pond and I'm happy there. Santos: Of all these experiences that you had, including EINSTEINet, what were the main learning points? Varsavsky: I think that Fon exists today thanks to EINSTEINet because in EINSTEINet, I sold the company for one euro to save the jobs of the employees thinking that I was the only crazy person to think that cloud computing had a future. And then two years later, cloud computing really took off. Now cloud computing is huge. So when Wi-Fi looked like it was failing, I remembered EINSTEINet and I said, “I'm going to put my money there because my ideas are not crazy. It's just sometimes ahead of their time and if I have enough money to wait, the market will help me.” Santos: What advice would you give to a new entrepreneur? Varsavsky: Well, I would be realistic and I would say, “Look, if you think you are the lucky sperm that's going to get the ovule, go ahead and start the business.”

In 2000 Jos White, again with his brother Ben, co-founded MessageLabs where he served as Chief Marketing Officer and later as President of the US region. MessageLabs became the market leader for messaging and web security services. In 2008 MessageLabs was acquired by Symantec for approximately $700M, marking the second largest transaction for a private company in the history of the IT security industry. In 2009 Jos White co-founded Notion Capital, again with his brother Ben and three other partners. Notion invests in next generation cloud computing companies. Pedro Santos: Tell me a bit about MessageLabs' history. Jos White: The background is that my brother, Ben White, and I have founded four businesses. We founded our first one with Rory Sweet in 1993, which was called RBR Networks. Originally we were buying and selling second-hand IBM equipment, which is a horrible business with very, very small margins— this business required a lot of wheeling and dealing and definitely attracted its fair share of cowboys!

I worked for a few months in Symantec and then I left, had some time off, and then Ben and I, and three other guys, founded a VC called Notion Capital. We only invest in B2B, cloud-based businesses—really the market we've come from. The best way to add value and to be informed investors is to invest in the market where you have direct experience. Notion was set up in 2009. We've made eight investments and we are pretty excited about the portfolio that we have. Just generally, we were huge believers in this megatrend of cloud-computing. It's a big sort of a tectonic shift in the tech landscape, and we want to try and take advantage of that by backing multiple businesses that are going to play a part in this transition. Santos: Going back to MessageLabs, where were you thinking of doing the IPO? In the UK? In the US? White: We had a big debate about this. I was in the US, so my preference was always a bit more to do it in the US.


pages: 482 words: 121,173

Tools and Weapons: The Promise and the Peril of the Digital Age by Brad Smith, Carol Ann Browne

Affordable Care Act / Obamacare, AI winter, airport security, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Boeing 737 MAX, business process, call centre, Celtic Tiger, chief data officer, cloud computing, computer vision, corporate social responsibility, Donald Trump, Edward Snowden, en.wikipedia.org, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, school vouchers, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, The Rise and Fall of American Growth, Tim Cook: Apple, WikiLeaks, women in the workforce

The legislative process kicked off in earnest in 2015 when a bipartisan group of three Senators and two Representatives introduced the LEADS Act, short for Law Enforcement Access to Data Stored Abroad. It was co-sponsored in the Senate by Orrin Hatch, Chris Coons, and Dean Heller, and in the House by Tom Marino and Suzan DelBene. Patrick Maines, “The LEADS Act and Cloud Computing,” The Hill, March 30, 2015, https://thehill.com/blogs/pundits-blog/technology/237328-the-leads-act-and-cloud-computing. Back to note reference 9. There was naturally a long and winding road between our initial loss before Judge Francis in 2014 and our arrival at the steps of the Supreme Court in 2018. We lost the next round of litigation at the District Court level before Chief Judge Loretta Preska, who ruled against us in July 2014.

When the caller from Microsoft asked to be transferred to the company’s CEO, the phone was simply handed to the only other employee, who happened to sit across the desk. Not surprisingly, the acquisition negotiation progressed quickly.5 I can’t help but think about Giant Company Software when I visit one of our data centers. You can still create a new software app the way Bill and Paul got started. Open-source developers do it all the time. But providing the platforms needed for cloud computing at a global scale? That’s a different story. As I walk among the thousands of blinking computers, racks of batteries, and enormous generators, it feels like more than a different era. It seems like a different planet. Data center campuses cost hundreds of millions of dollars to build. And once construction ends, the work to maintain and upgrade the facility begins. Sites are expanded, and servers, hard drives, and batteries are upgraded or swapped for newer and more efficient equipment.

Data collected about people—their political, religious, and social views—can fall into the wrong hands and cause all sorts of problems.” Back in Redmond when I talked with employees about privacy, Scheidler’s story helped illuminate what was at stake when we handled our customers’ data. Privacy wasn’t just a regulation that we had to abide by, but a fundamental human right that we had an obligation to protect. The story also helped people understand that when cloud computing went global, it involved more than laying fiber-optic cables under oceans and building data centers on other continents. It also meant adapting to other countries’ cultures while maintaining our commitments to core values by respecting and protecting other people’s privacy rights. A decade ago, some in the tech sector thought they could serve the customers of the world solely from data centers in the United States.


Martin Kleppmann-Designing Data-Intensive Applications. The Big Ideas Behind Reliable, Scalable and Maintainable Systems-O’Reilly (2017) by Unknown

active measures, Amazon Web Services, bitcoin, blockchain, business intelligence, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, database schema, DevOps, distributed ledger, Donald Knuth, Edward Snowden, Ethereum, ethereum blockchain, fault tolerance, finite state, Flash crash, full text search, general-purpose programming language, informal economy, information retrieval, Internet of things, iterative process, John von Neumann, Kubernetes, loose coupling, Marc Andreessen, microservices, natural language processing, Network effects, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, statistical model, undersea cable, web application, WebSocket, wikimedia commons

As we shall see, you may not even know whether something succeeded or not, as the time it takes for a message to travel across a network is also nondeterministic! This nondeterminism and possibility of partial failures is what makes distributed sys‐ tems hard to work with [5]. Cloud Computing and Supercomputing There is a spectrum of philosophies on how to build large-scale computing systems: • At one end of the scale is the field of high-performance computing (HPC). Super‐ computers with thousands of CPUs are typically used for computationally inten‐ sive scientific computing tasks, such as weather forecasting or molecular dynamics (simulating the movement of atoms and molecules). • At the other extreme is cloud computing, which is not very well defined [6] but is often associated with multi-tenant datacenters, commodity computers connected with an IP network (often Ethernet), elastic/on-demand resource allocation, and metered billing. • Traditional enterprise datacenters lie somewhere between these extremes.

Transactions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 The Slippery Concept of a Transaction 222 The Meaning of ACID Single-Object and Multi-Object Operations Weak Isolation Levels Read Committed Snapshot Isolation and Repeatable Read Preventing Lost Updates Write Skew and Phantoms Serializability Actual Serial Execution Two-Phase Locking (2PL) Serializable Snapshot Isolation (SSI) Summary 223 228 233 234 237 242 246 251 252 257 261 266 8. The Trouble with Distributed Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Faults and Partial Failures Cloud Computing and Supercomputing Unreliable Networks Network Faults in Practice Detecting Faults Timeouts and Unbounded Delays Synchronous Versus Asynchronous Networks Unreliable Clocks Monotonic Versus Time-of-Day Clocks Clock Synchronization and Accuracy Relying on Synchronized Clocks Process Pauses Knowledge, Truth, and Lies The Truth Is Defined by the Majority Byzantine Faults System Model and Reality Summary 274 275 277 279 280 281 284 287 288 289 291 295 300 300 304 306 310 9.

.: “Availability in Glob‐ ally Distributed Storage Systems,” at 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI), October 2010. [6] Brian Beach: “Hard Drive Reliability Update – Sep 2014,” backblaze.com, Septem‐ ber 23, 2014. [7] Laurie Voss: “AWS: The Good, the Bad and the Ugly,” blog.awe.sm, December 18, 2012. Summary | 23 [8] Haryadi S. Gunawi, Mingzhe Hao, Tanakorn Leesatapornwongsa, et al.: “What Bugs Live in the Cloud?,” at 5th ACM Symposium on Cloud Computing (SoCC), November 2014. doi:10.1145/2670979.2670986 [9] Nelson Minar: “Leap Second Crashes Half the Internet,” somebits.com, July 3, 2012. [10] Amazon Web Services: “Summary of the Amazon EC2 and Amazon RDS Ser‐ vice Disruption in the US East Region,” aws.amazon.com, April 29, 2011. [11] Richard I. Cook: “How Complex Systems Fail,” Cognitive Technologies Labora‐ tory, April 2000. [12] Jay Kreps: “Getting Real About Distributed System Reliability,” blog.empathy‐ box.com, March 19, 2012. [13] David Oppenheimer, Archana Ganapathi, and David A.


Ubuntu 15.04 Server with systemd: Administration and Reference by Richard Petersen

Amazon Web Services, bash_history, cloud computing, Debian, Firefox, Mark Shuttleworth, MITM: man-in-the-middle, RFC: Request For Comment, SpamAssassin, web application

umount –t cifs /mylinux You could also specify a username and password as options, if user-level access is required: mount -t cifs -o userhris passwd=mypass //lizard/windata /mylinux You can also use the cifs type in an /etc/fstab entry to have a Samba file system mounted automatically: //lizard/windata /mylinux cifs defaults 0 0 13. Cloud Computing Ubuntu features fully integrated support for cloud computing. Ubuntu provides private and public cloud support. The public cloud accesses the Amazon EC2 cloud system, and the private cloud sets up your own cloud computing service with the Ubuntu Enterprise Cloud software. Both use EC2 (Elastic Computing), which is the standard for cloud computing. Cloud support is still very much a work in progress. An overview of Ubuntu cloud computing with links is located at: http://www.ubuntu.com/cloud/ You will need to use a Web browser to set up access and manage your cloud. Use either a command line browser like elinks or lynx, or, if you have installed the ubuntu desktop or basic GNOME interface, you can use Firefox or Epiphany.

Topics covered include software management, systemd service management, AppArmor security, OpenSSH, and the Network Time Protocol. Key servers are examined, including Web, FTP, CUPS printing, NFS, and Samba Windows shares. Network support servers and applications covered include the Squid proxy server, the Domain Name System (BIND) server, DHCP, distributed network file systems, IPtables firewalls, and cloud computing. The book is organized into five parts: getting started, services, shared resources, network support, and shells. Part 1 focuses on basic tasks such as installing the Ubuntu Server CD, managing software from the Ubuntu repository, and basic usage for the desktop and the command line interfaces. Part 2 examines Internet servers as well as how services are managed by systemd using unit files.

Configuration and implementation of the Postfix mail server, the vsftpd FTP server, the Apache Web server, as well as news and database servers are covered in detail. Part 3 deals with servers that provide shared resources on a local network or the Internet. Services examined include the CUPS printing server, NFS Linux network file server, and Samba Windows file and printing server, the GFS distributed file system, and cloud computing services supported by Ubuntu. Part 4 covers servers that provide network support, like the Squid proxy server, the Bind Domain Name System (DNS) server, DHCP servers, and the IPtables and FirewallD firewalls. Key networking operations are also examined like IPv6 auto-configuration, TPC/IP networking, and network monitoring tools. Part 5 provides a review of shell commands, including those used for managing files, as well as shell scripts, variables, and configuration files.


pages: 274 words: 58,675

Puppet 3 Cookbook by John Arundel

Amazon Web Services, cloud computing, continuous integration, Debian, defense in depth, DevOps, don't repeat yourself, GnuPG, Larry Wall, place-making, Ruby on Rails, web application

Puppet 3 Cookbook Build reliable, scalable, secure, and high-performance systems to fully utilize the power of cloud computing John Arundel BIRMINGHAM - MUMBAI Puppet 3 Cookbook Copyright © 2013 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Table of Contents Preface Chapter 1: Puppet Infrastructure Introduction Installing Puppet Creating a manifest Managing your manifests with Git Creating a decentralized Puppet architecture Writing a papply script Running Puppet from cron Deploying changes with Rake Bootstrapping Puppet with Rake Automatic syntax checking with Git hooks Chapter 2: Puppet Language and Style Introduction Using community Puppet style Checking your manifests with puppet-lint Using modules Using standard naming conventions Using inline templates Iterating over multiple items Writing powerful conditional statements Using regular expressions in if statements Using selectors and case statements Using the in operator Using regular expression substitutions 1 7 7 8 10 11 14 16 18 22 26 29 33 34 34 36 38 42 44 45 47 49 50 53 54 Table of Contents Chapter 3: Writing Better Manifests Introduction Using arrays of resources Using definitions Using dependencies Using tags Using run stages Using node inheritance Passing parameters to classes Using class inheritance and overriding Writing reusable, cross-platform manifests Getting information about the environment Importing dynamic information Passing arguments to shell commands Chapter 4: Working with Files and Packages Introduction Making quick edits to config files Using Augeas to automatically edit config files Building config files using snippets Using ERB templates Using array iteration in templates Using GnuPG to encrypt secrets Installing packages from a third-party repository Building packages automatically from source Comparing package versions Chapter 5: Users and Virtual Resources Introduction Using virtual resources Managing users with virtual resources Managing users' SSH access Managing users' customization files Efficiently distributing cron jobs Using schedules to limit when resources can be applied Using host resources Using multiple file sources Distributing directory trees Cleaning up old files ii 57 58 58 59 61 65 68 71 73 75 79 81 83 84 87 87 88 89 91 94 96 98 103 106 108 111 112 112 115 118 121 126 129 132 133 135 137 Table of Contents Auditing resources Temporarily disabling resources Chapter 6: Applications Introduction Managing Apache servers Creating Apache virtual hosts Creating Nginx virtual hosts Managing MySQL Managing Ruby Chapter 7: Servers and Cloud Infrastructure Introduction Building high-availability services using Heartbeat Managing NFS servers and file shares Using HAProxy to load-balance multiple web servers Managing firewalls with iptables Managing EC2 instances Managing virtual machines with Vagrant Chapter 8: External Tools and the Puppet Ecosystem Introduction Creating custom facts Adding external facts Setting facts as environment variables Importing configuration data with Hiera Storing secret data with hiera-gpg Generating manifests with puppet resource Generating manifests with other tools Testing your manifests with rspec-puppet Using public modules Using an external node classifier Creating your own resource types Creating your own providers Creating your own functions Chapter 9: Monitoring, Reporting, and Troubleshooting Introduction Doing a dry run Logging command output Logging debug messages 139 140 143 143 144 145 150 153 158 165 165 166 171 174 178 188 193 199 200 200 202 205 206 210 213 214 218 221 223 226 228 231 235 235 236 237 239 iii Table of Contents Generating reports Producing automatic HTML documentation Drawing dependency graphs Understanding Puppet errors Inspecting configuration settings Index iv 240 242 245 248 251 253 Preface A revolution is underway in the field of IT operations. The new generation of configuration management tools can build servers in seconds and automate your entire network. Tools such as Puppet are essential to take full advantage of the power of cloud computing, and build reliable, scalable, secure, high-performance systems. This book takes you beyond the basics and explores the full power of Puppet, showing you in detail how to tackle a variety of real-world problems and applications. At every step, it shows you exactly what commands you need to type and includes complete code samples for every recipe. It takes the reader from rudimentary knowledge of Puppet to a more complete and expert understanding of Puppet's latest and most advanced features, community best practices, writing great manifests, scaling and performance, and how to extend Puppet by adding your own providers and resources.

Once you have verified that the changes are good, then you can merge them into the master branch and roll them out. 187 Servers and Cloud Infrastructure Managing EC2 instances It doesn't make sense for many of us to own and host our own servers, in the same way as it doesn't make sense for us to generate our own electricity. Just like electricity, computing is now a utility. Utility computing (often called cloud computing, for no known reason) allows you to buy as much compute power as you need, for as long as you need it. This makes it easy and cost-efficient to scale your service in response to fluctuating demand. Being able to create new cloud server instances, use them for a few minutes or hours, and then delete them also makes it a lot easier to test and experiment with new software or configurations. If you had to build the servers by hand every time, this process would be too lengthy to make it worthwhile, but with automated configuration management, it's a snap.


pages: 161 words: 44,488

The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology by William Mougayar

Airbnb, airport security, Albert Einstein, altcoin, Amazon Web Services, bitcoin, Black Swan, blockchain, business process, centralized clearinghouse, Clayton Christensen, cloud computing, cryptocurrency, disintermediation, distributed ledger, Edward Snowden, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, fixed income, global value chain, Innovator's Dilemma, Internet of things, Kevin Kelly, Kickstarter, market clearing, Network effects, new economy, peer-to-peer, peer-to-peer lending, prediction markets, pull request, QR code, ride hailing / ride sharing, Satoshi Nakamoto, sharing economy, smart contracts, social web, software as a service, too big to fail, Turing complete, web application

Blockchains are like a virtual computer somewhere in a distributed cloud that is virtual and does not require server setups. Whoever opens a blockchain node runs the server, but not users or developers. So, the blockchain is like a networked infrastructure of computing machinery. With that in mind, we could easily imagine how computer programs can run on this new infrastructure. But we should not take the cloud computing analogy literally. The blockchain infrastructure does not replace cloud computing. It unbundles it, and democratizes parts of it. More likely, the blockchain infrastructure resembles a layer of cloud computing infrastructure. Blockchain virtual machines may be too expensive if we are to literally compare their functionality to a typical cloud service such as Amazon Web Services or DigitalOcean, but they will be be certainly useful for smart contracts that execute their logic on the blockchain’s virtual machinery, or decentralized applications, also called Dapps.

The novelty with virtual machine costing is that you are paying to run the business logic on the blockchain, which is otherwise running on physical servers (on existing cloud infrastructure), but you do not have to worry about setting up these servers because they are managed by other users who are getting paid anyways for running that infrastructure via mining. Therefore, the blockchain cloud has a form of micro-value pricing model that parallels the traditional cloud computing stack, but via a new layer. It is not a physical unbundling of the cloud, rather it is a new layering of cryptography-based transaction validation and state transition recordings on a parallel, but thinner cloud. But here is the challenge to running applications on this new infrastructure: you need to do some work. That work comes in the form of adhering to a new paradigm of decentralized apps that follows a new tiered architecture coined as “web3” by Gavin Wood.6 Web3 is an architecture that runs specifically on the blockchain.

Running business logic that contains trust and verification components will be plug and play in the practical sense. Peer-to-peer decentralized base layers will be common in data storage, computing infrastructure, identity, and reputation. Decentralized trust will be relegated to the network and embedded inside the applications instead of controlled by intermediaries. University degrees in Cryptography and Game Theory will become popular. More decentralized forms of cloud computing will emerge. This all comes with one warning from a key lesson I learned during the Internet dot-com crash of the year 2000. Speed kills. Speed in hyping what the blockchain can do will end-up derailing it, putting us ahead of reality. This type of disconnect is guaranteed to disappoint those who expect benefits faster than what is possible. That said, keeping with Carlota Perez’s6 model of explaining how technological revolutions unfold, there may be no escaping the fact a crash will happen somewhere between the blockchain’s installation phase (2015–2018), and its resulting deployment phase (2018 and beyond).


pages: 603 words: 141,814

Python for Unix and Linux System Administration by Noah Gift, Jeremy M. Jones

Amazon Web Services, bash_history, Bram Moolenaar, cloud computing, create, read, update, delete, database schema, Debian, distributed revision control, Firefox, Guido van Rossum, industrial robot, inventory management, job automation, Mark Shuttleworth, MVC pattern, skunkworks, web application

Some of the products you may want to consider scripting are VMware Site Recovery Manager, VMware ESX Server, VMware Server, and VMware Fusion. We won’t have room to cover scripting these technologies, as they fall outside the scope of this book, but it would pay to closely monitor these products and examine what role Python will play. Cloud Computing Just when the buzz was settling from virtualization, suddenly cloud computing is raising the buzz yet again. Simply put, “cloud computing” is about using resources that respond on demand to workload requirements. The two big players in cloud computing are Amazon and Google. Google just literally dropped the “C” bomb just a few weeks before this book went to the publisher. Google offered an interesting twist in it that only currently supports Python. This being a book on Python programming, we are sure this doesn’t disappoint you too much.

Object-Relationship Mapping (ORM), Storm ORM, Storm ORM (see also SQLAlchemy ORM; Storm ORM) objects, pinfo, psearch, psearch, who, whos listing, functions for, who, whos obtaining information on, with pinfo, pinfo searching for, with psearch, psearch, psearch OIDs (object identifiers), SNMP Overview open() method, Creating files open() method (shelve), shelve OpenLDAP using with Python, Using LDAP with OpenLDAP, Active Directory, and More with Python, Importing an LDIF File operating systems, Introduction, Using Zenoss to Manage Windows Servers from Linux, Cross-Platform Unix Programming in Python, Creating a Cross-Platform Build Network, PyInotify, PyInotify, OS X, Managing Plist Files from Python, Red Hat Linux Systems Administration, Ubuntu Administration, Solaris Systems Administration, Virtualization, Cloud Computing, Building a sample Google App Engine application cloud computing, Cloud Computing, Building a sample Google App Engine application GNU/Linux, PyInotify with, PyInotify, PyInotify OS X, OS X, Managing Plist Files from Python Red Hat systems administration, Red Hat Linux Systems Administration Solaris systems administration, Solaris Systems Administration Ubuntu administration, Ubuntu Administration Unix programming, cross-platform, Cross-Platform Unix Programming in Python, Creating a Cross-Platform Build Network Virtualization, Virtualization OperatingSystem class (Django), Simple Database Application option with multiple arguments usage pattern (optparse), Option with Multiple Arguments Usage Pattern optparse, Introduction to Optparse, Option with Multiple Arguments Usage Pattern ORM (Object-Relationship Mapping), Storm ORM, Storm ORM (see also SQLAlchemy ORM; Storm ORM) os module, Using the OS Module to Interact with Data, Using the OS Module to Interact with Data, Copying, Moving, Renaming, and Deleting Data, Copying, Moving, Renaming, and Deleting Data, Working with Paths, Directories, and Files, Working with Paths, Directories, and Files, Using os.list copying, moving, renaming, and deleting data, Copying, Moving, Renaming, and Deleting Data, Copying, Moving, Renaming, and Deleting Data listdir() function, Using os.list paths, directories, and files, Working with Paths, Directories, and Files, Working with Paths, Directories, and Files OS X programming, OS X, Managing Plist Files from Python, OS X, Managing Plist Files from Python OSA (Open Scripting Architecture), OS X Scripting APIs Out built-in variable, Interacting with IPython output, Standard Input and Output, Standard Input and Output standard input and output, Standard Input and Output, Standard Input and Output output history, History results, History results output paging, with page function, page output prompts, Python versus IPython, Interacting with IPython P package management, Introduction, EPM Summary: It Really Is That Easy, Registering a Package with the Python Package Index, Registering a Package with the Python Package Index, Distutils, Distutils, Distutils, Buildout, Developing with Buildout, Developing with Buildout, virtualenv, Creating a Custom Bootstrapped Virtual Environment, EPM Package Manager, EPM Summary: It Really Is That Easy building pages with setuptools, Distutils (see setuptools) Buildout tool, Buildout, Developing with Buildout, Developing with Buildout developing with, Developing with Buildout creating packages with disutils, Distutils, Distutils EPM package manager, EPM Package Manager, EPM Summary: It Really Is That Easy registering packages with Python Package Index, Registering a Package with the Python Package Index, Registering a Package with the Python Package Index virtualenv tool, virtualenv, Creating a Custom Bootstrapped Virtual Environment package version, changing active, Change Active Version of Package packet manipulation program, Scapy (see Scapy program) page function, page paramkio library, SSH, SSH parse() method (ElementTree), ElementTree parsing logfiles (example), Log Parsing, Log Parsing parsing XML files with ElementTree, ElementTree, ElementTree partition re-imaging, Automatically Re-Imaging Machines password-protected sites, installing eggs on, Authenticating to a Password Protected Site paths, walking with os module, Working with Paths, Directories, and Files, Working with Paths, Directories, and Files pattern matching, re (see regular expressions) pattern matching with files and directories, Pattern Matching Files and Directories, Pattern Matching Files and Directories pdef function, pdef PDF files, saving data as, PDFs, PDFs pdoc function, pdoc Perez, Fernando, IPython Perl, Python versus, Why Python?

Bayer, Michael, SQLAlchemy ORM Bicking, Ian, virtualenv blocks of code, editing, Magic Edit bookmark command, bookmark bookmarks, cd navigating to bookmarked directories, cd bootstrapped virtual environment, custom, Creating a Custom Bootstrapped Virtual Environment Boto (Amazon Web services), Amazon Web Services with Boto Buildout tool, Buildout, Developing with Buildout, Developing with Buildout developing with, Developing with Buildout bzip2 compression, Using tarfile Module to Create TAR Archives C callbacks, Callbacks, Callbacks capitalization, Built-in methods for str data extraction (see case) case (capitalization), Built-in methods for str data extraction converting for entire string, Built-in methods for str data extraction cd command, cd, cd, cd, dhist -<TAB> option, dhist -b option, cd charts, creating, Graphical Images checksum comparisons, MD5 Checksum Comparisons, MD5 Checksum Comparisons choices usage pattern (optparse), Choices Usage Pattern close( ) function (socket module), socket close() method, Creating files close() method (shelve), shelve cloud computing, Cloud Computing, Building a sample Google App Engine application, Amazon Web Services with Boto, Google App Engine, Building a sample Google App Engine application Amazon Web services with Boto, Amazon Web Services with Boto cmp() function (filecmp module), Using the filecmp Module combining strings, Built-in methods for str data extraction command history, History command line, Introduction, Summary, Basic Standard Input Usage, Basic Standard Input Usage, Introduction to Optparse, Option with Multiple Arguments Usage Pattern, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes, Integrating Configuration Files, Integrating Configuration Files basic standard input usage, Basic Standard Input Usage, Basic Standard Input Usage integrating configuration files, Integrating Configuration Files, Integrating Configuration Files integrating shell commands, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes optparse, Introduction to Optparse, Option with Multiple Arguments Usage Pattern community of Python users, Why Python?


pages: 348 words: 97,277

The Truth Machine: The Blockchain and the Future of Everything by Paul Vigna, Michael J. Casey

3D printing, additive manufacturing, Airbnb, altcoin, Amazon Web Services, barriers to entry, basic income, Berlin Wall, Bernie Madoff, bitcoin, blockchain, blood diamonds, Blythe Masters, business process, buy and hold, carbon footprint, cashless society, cloud computing, computer age, computerized trading, conceptual framework, Credit Default Swap, crowdsourcing, cryptocurrency, cyber-physical system, dematerialisation, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, failed state, fault tolerance, fiat currency, financial innovation, financial intermediation, global supply chain, Hernando de Soto, hive mind, informal economy, intangible asset, Internet of things, Joi Ito, Kickstarter, linked data, litecoin, longitudinal study, Lyft, M-Pesa, Marc Andreessen, market clearing, mobile money, money: store of value / unit of account / medium of exchange, Network effects, off grid, pets.com, prediction markets, pre–internet, price mechanism, profit maximization, profit motive, ransomware, rent-seeking, RFID, ride hailing / ride sharing, Ross Ulbricht, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, smart contracts, smart meter, Snapchat, social web, software is eating the world, supply-chain management, Ted Nelson, the market place, too big to fail, trade route, transaction costs, Travis Kalanick, Turing complete, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, universal basic income, web of trust, zero-sum game

The service encouraged clients to work with their supply-chain partners to create private blockchains that were structured entirely around an integration with IBM’s pre-existing cloud service. Having IBM host your blockchain-relevant data—relying on a “trusted third party”—kind of goes against the whole, disruptive, self-help spirit of the blockchain. The cynicism in part stems from the misleading imagery fostered by the term “cloud computing.” When IBM, Amazon, Google, or any other cloud computing provider stores your files or runs out-sourced computing services for you, that operation runs on identifiable servers owned by those companies. They are the landlords of our rented server space. The “cloud” conjures images of an amorphous, decentralized system when it’s very much a centralized solution with total dependence on a trusted third party. The grand vision of blockchain technology lies in decentralization, in users not having to depend on any single entity to execute an operation on their behalf.

Here is sampling of possible use cases, and it is by no means an exhaustive list: •  Inviolable property registries, which people can use to prove that they own their houses, cars, or other assets; •  Real-time, direct, bank-to-bank settlement of securities exchanges, which could unlock trillions of dollars in an interbank market that currently passes such transactions through dozens of specialized institutions in a process that takes two to seven days; •  Self-sovereign identities, which don’t depend on a government or a company to assert a person’s ID; •  Decentralized computing, which supplants the corporate business of cloud computing and Web hosting with the hard drives and processing power of ordinary users’ computers; •  Decentralized Internet of Things transactions, where devices can securely talk and transact with each other without the friction of an intermediary, making possible big advances in transportation and decentralized energy grids; •  Blockchain-based supply chains, in which suppliers use a common data platform to share information about their business processes to greatly improve accountability, efficiency, and financing with the common purpose of producing a particular good; •  Decentralized media and content, which would empower musicians and artists—and, in theory, anyone who posts information of value to the Net—to take charge of their digital content, knowing they can track and manage the use of this “digital asset.”

Domain name registrations are managed by increasingly large, centralized, third-party providers while lightweight IoT devices are getting into the hands of an ill-prepared general public. That combination is a hacker’s dream. And what a pool of data we are gathering for those hackers to play with. In 2014, IBM estimated that human beings were creating 2.5 exabytes, otherwise expressed as 2.5 quintillion bytes of data, every day, most of them now stored permanently thanks to a cloud computing era in which storage has become so cheap that it no longer makes sense to destroy data. Let’s lay that number out numerically, with all seventeen zeroes: 2,500,000,000,000,000,000. (Another way of expressing it: the equivalent of 2.5 trillion PDF versions of The Age of Cryptocurrency.) According to the IBM team, this number meant that human beings had created 90 percent of all data accumulated throughout history in just two years—most of it stored on the servers of cloud service providers like the ones IBM runs.


pages: 292 words: 85,151

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

Just imagine what that will make possible. When Marc Andreessen proclaimed in a 2011 Wall Street Journal article that “software is eating the world,” he was addressing this very phenomenon. Andreessen, who helped invent the Internet browser and is now one of Silicon Valley’s most powerful venture capitalists, argued that in every industry, and at every level, software is automating and accelerating the world. Cloud computing and the app store ecosystems are clear testaments to this trend, with the Apple and Android platforms each hosting more than 1.2 million applications programs, most of them crowdsourced from customers. Nowhere is this staggering pace of change more apparent than with the consumer Internet. Many products are now launched early—unfinished and in perpetual beta—for the sole purpose of gathering data from users as early as possible to determine how to “finish” the product.

The era of traditional, hierarchical market domination by dinosaur companies is coming to an end. The world now belongs to smarter, smaller and faster-moving enterprises. This is certainly true now for information-based industries, and it will soon be true for more traditional industries as well. 7. Rent, Don’t Own An important mechanism empowering individuals and small teams everywhere is low-cost access to technology and tools. Emblematic of this new reality is cloud computing, which offers the ability to store and manage massive amounts of information with unlimited processing, all on a cost-per-use basis requiring no upfront costs or capital investments. In practice, this makes memory almost free. The cloud also puts small companies on the same footing as—or even gives them an advantage over—big companies, which are burdened by expensive internal IT operations.

CHAPTER SIX Starting an ExO From the dawn of the Internet, we’ve seen fundamental changes in how businesses are built and grown. In particular, the earliest playbook for building a hyper-growth company emerged during the dot-com boom of 1998 to 2000. That narrative gained a new chapter in 2005 with the rise of social media, and 2008 saw yet another chapter thanks to the widespread availability of low-cost cloud computing. Today, we are seeing the addition of the most important text yet with the rise of the Exponential Organization. Driven by accelerating technologies, ExOs allow us to organize ourselves in new ways to tap into this information-enabled world. Local Motors is a good example of an ExO startup. Founded by Jeff Jones and Jay Rogers in 2007, and based in Phoenix, Arizona, it is a global co-creation platform that empowers its community to design, build and sell custom-built vehicles.


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The Decline and Fall of IBM: End of an American Icon? by Robert X. Cringely

AltaVista, Bernie Madoff, business cycle, business process, cloud computing, commoditize, compound rate of return, corporate raider, full employment, if you build it, they will come, immigration reform, interchangeable parts, invention of the telephone, Khan Academy, knowledge worker, low skilled workers, Paul Graham, platform as a service, race to the bottom, remote working, Robert Metcalfe, Robert X Cringely, shareholder value, Silicon Valley, six sigma, software as a service, Steve Jobs, Toyota Production System, Watson beat the top human players on Jeopardy!, web application

Sales are flat to down, and earnings are too. More IBM customers are probably unhappy with Big Blue right now than are happy. After years of corporate downsizing, employee morale is at an all-time low. Bonuses and even annual raises are rare. But for all that, IBM is still an enormous multinational corporation with high profits, deep pockets, and grand ambitions for new technical initiatives in cloud computing, Big Data analytics, and artificial intelligence as embodied in the company’s Jeopardy game-show-winning Watson technology. Yet for all this, IBM seems to have lost some of its mojo, or at least that’s what Wall Street and the business analysts are starting to think. Just starting to think? The truth is that IBM is in deep trouble and has been since before the Great Recession of 2008. The company has probably been doomed since 2010.

One analyst estimated that IBM would report adjusted earnings of $17.80 at year’s end, missing its target of $18. “We remain concerned that while IBM is re-inventing parts of its product portfolio, it is not moving fast enough to keep up with industry changes and grow both revenue and gross margins,” ISI Group analyst Brian Marshall wrote in a note to his clients. Ben Reitzes, an analyst at Barclays Plc., suggested Rometty should drop her 2015 earnings target and focus on the transition to cloud computing and other new product sales. CEO Rometty, in a rare public appearance, reiterated her stance that the 2015 $20 per share goal was still on target. But back to why I think IBM will destroy itself. IBM’s biggest moneymaker is Global Services, which also employs the most people. But the company is now making a lot less on its contracts, and the turnover of business is brisk. It is in Global Services where you see the most jobs being shipped offshore.

The client experience would be greatly improved, with fewer problems, and things generally running better. If IBM’s Services customers were happy, business retention would be better, and more products and services could be sold. Most importantly, IBM’s new businesses would have a strong resource to help deploy and support their products and services. If IBM doesn’t invest in Services, if it doesn’t embrace quality, some other company will. THE CLOUD PROBLEM: Cloud computing is one of IBM’s gambles to find its next big thing. Cloud means different things to different people, but what is important for IBM is to understand the business reasons behind the Cloud. It is part of an evolutionary process to reduce the cost of computing. This means less expensive computing for customers and lower profit margins for IBM. It means reduced hardware sales. It implies there will be reduced support costs from Services, too.


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The Flat White Economy by Douglas McWilliams

"Robert Solow", access to a mobile phone, banking crisis, Big bang: deregulation of the City of London, bonus culture, Boris Johnson, Chuck Templeton: OpenTable:, cleantech, cloud computing, computer age, correlation coefficient, Edward Glaeser, en.wikipedia.org, Erik Brynjolfsson, eurozone crisis, George Gilder, hiring and firing, income inequality, informal economy, Kickstarter, knowledge economy, loadsamoney, low skilled workers, mass immigration, Metcalfe’s law, Network effects, new economy, offshore financial centre, Pareto efficiency, Peter Thiel, Productivity paradox, Robert Metcalfe, Silicon Valley, smart cities, special economic zone, Steve Jobs, working-age population, zero-sum game

In the UK, for example, roughly 50% of adults over 16 (and possibly a higher proportion of those below 16) used the internet every day, while the proportion who never used the internet fell down to only a quarter. But the intensity and usage of the internet for commercial purposes only started to grow massively towards the end of the decade. In part this was because of the coming together of a range of digital technologies. Cloud computing, for example, has taken off in the past five years, moving data storage away from the hard drives of personal computers onto mass databases, hosted and accessible online. The Cebr report4 on cloud computing showed that this technology alone had the potential to generate over €750 billion of cumulative economic benefits and an additional 2.4 million jobs. Big data, which enables online retailers to target customers, was forecast by Cebr5 to yield benefits of over £200 billion in the UK alone between 2012 and 2017.

Gordon Earle Moore was the co-founder of the Intel Corporation no less (and still its Chairman Emeritus), who in 1965 postulated that the number of transistors in a dense integrated circuit doubles roughly every two years.1 Amazingly the prediction seems to have been proved roughly accurate even half a century on, though my friends in the industry claim that this is partly because the ‘Law’ is used as the benchmark for driving research activity. Whatever the cause, this continued improvement in technological performance means that the scope for improved applications continues to grow. And the two current most highly publicised areas in IT – cloud computing and big data – are if anything even more dependent on improved technology and in turn particularly potent generators of new applications. There has been some discussion of whether the tendency towards monopoly for parts of the IT industry such as search engines (Google), social media (Twitter and Facebook), chips (Intel), operating software (Microsoft) and hardware (Apple) will constrain growth.

‘The Economics of Information’, George J Stigler, Chicago University Journal of Political Economy, June 1961, pp213–225. 2. Originally published as ‘Metcalfe’s Law and Legacy’, Forbes ASAP, September 1993, and developed in Telecosm, George Gilder, Simon & Schuster, 1996. 3. I first presented these arguments with some accompanying detail to the IBM Computer Users Association in May 1989 – they have changed remarkably little in the past 25 years! 4. ‘The Economic Impact of Cloud Computing: a Cebr report for EMC2’, Cebr, December 2010. 5. ‘The Value of Data Equity: A Cebr report for SAS’, Cebr, April 2012. 6. ‘Britons are the biggest online shoppers in the developed world’, James Hall, Daily Telegraph, 1 Feb 2012. 7. www.internetretailing.net/2014/03/uk-retailers-expected-to-make-online-sales-of-45bn-this-year-study/ 8. ‘Advertising Pays’, Deloitte, Jan 2013:www.adassoc.org.uk/pdfs/advass_advertising_pays_report.pdf 9.


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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

Together they threaten to destroy the fragile ecosystem we have come to take for granted. Social networking, cloud computing, and mobile forms of connectivity are convenient and fun, but they are also a dangerous brew. Data once stored on our actual desktops and in filing cabinets now evaporates into the “cloud,” entrusted to third parties beyond our control. Few of us realize that data stored by Google, even data located on machines in foreign jurisdictions, are subject to the U.S. Patriot Act because Google is headquartered in the United States and the Act compels it to turn over data when asked to do so, no matter where it is stored. (For this reason, some European countries are debating laws that will ban public officials from using Google and/or other cloud computing services that could put their citizens’ personal information at risk.)

These companies’ systems are used to parse enormous databases, scour all existing social networking platforms, integrate data from the vast troves in the hands of telecommunications companies and ISPs, and piece it all together to provide decision makers with actionable intelligence. As former CIA director David Petraeus explained at In-Q-Tel’s CEO Summit in March 2012, “New cloud computing technologies developed by In-Q-Tel partner companies are driving analytic transformation in the way organizations store, access, and process massive amounts of disparate data via massively parallel and distributed IT systems … among the analytic projects underway with In-Q-Tel startups is one that enables the collection and analysis of worldwide social media feeds, along with projects that use either cloud computing or other methods to explore and analyze big data. These are very welcome additions to the initiatives we have underway to enable us to be the strongest swimmers in the ocean of big data.”

It leveraged our readiness to extend trust with our eagerness to click on links in a world that has become intensely interactive. The age of mass Internet access is less than twenty years old, and social networking, cloud computing, and mobile connectivity are, for most people, innovations only of the last few years. We have embraced these new technologies at such a pace that regulatory agencies have been left in the dust, and we have overlooked extraordinary user vulnerabilities. Today, data is transferred from laptops to USB sticks, and over wireless networks at cafés, and stored across cloud computing systems whose servers are located in far-off jurisdictions. We produce massive amounts of personal data as we navigate this new ecosystem and click on website addresses and documents like lab mice clicking on pellet dispensers.


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Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Bruce Schneier

23andMe, Airbnb, airport security, AltaVista, Anne Wojcicki, augmented reality, Benjamin Mako Hill, Black Swan, Boris Johnson, Brewster Kahle, Brian Krebs, call centre, Cass Sunstein, Chelsea Manning, citizen journalism, cloud computing, congestion charging, disintermediation, drone strike, Edward Snowden, experimental subject, failed state, fault tolerance, Ferguson, Missouri, Filter Bubble, Firefox, friendly fire, Google Chrome, Google Glasses, hindsight bias, informal economy, Internet Archive, Internet of things, Jacob Appelbaum, Jaron Lanier, John Markoff, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, linked data, Lyft, Mark Zuckerberg, moral panic, Nash equilibrium, Nate Silver, national security letter, Network effects, Occupy movement, Panopticon Jeremy Bentham, payday loans, pre–internet, price discrimination, profit motive, race to the bottom, RAND corporation, recommendation engine, RFID, Ross Ulbricht, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, smart cities, smart grid, Snapchat, social graph, software as a service, South China Sea, stealth mode startup, Steven Levy, Stuxnet, TaskRabbit, telemarketer, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, undersea cable, urban planning, WikiLeaks, zero day

The relationship is more feudal: Bruce Schneier (26 Nov 2012), “When it comes to security, we’re back to feudalism,” Wired, http://www.wired.com/2012/11/feudal-security. We like having someone else: Rachel King (15 Oct 2012), “Consumers actually really like cloud storage, report says,” ZDNet, http://www.zdnet.com/consumers-actually-really-like-cloud-storage-report-says-7000005784. the rise of cloud computing: This is a good introduction to cloud computing. Michael Armbrust et al. (10 Feb 2009), “Above the clouds: A Berkeley view of cloud computing,” Technical Report No. UCB/EECS-2009-28, Electrical Engineering and Computer Sciences, University of California at Berkeley, http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf. they turn our data over: Both Google and Microsoft have turned child porn suspects over to the FBI on their own initiative. Robert Macpherson (4 Aug 2014), “Google defends child porn tip-offs to police,” Yahoo!

NSA revelations made executives: NTT Communications (28 Mar 2014), “NSA after-shocks: How Snowden has changed ICT decision-makers’ approach to the cloud,” http://nsaaftershocks.com/wp-content/themes/nsa/images/NTTC_Report_WEB.pdf. Estimates of how much business: Daniel Castro (5 Aug 2013), “How much will PRISM cost the U.S. cloud computing industry?” Information Technology and Innovation Foundation, http://www.itif.org/publications/how-much-will-prism-cost-us-cloud-computing-industry. Andrea Peterson (7 Aug 2013), “NSA snooping could cost U.S. tech companies $35 billion over three years,” Washington Post, http://www.washingtonpost.com/blogs/the-switch/wp/2013/08/07/nsa-snooping-could-cost-u-s-tech-companies-35-billion-over-three-years. Forrester Research believes: James Staten (14 Aug 2013), “The cost of PRISM will be larger than ITIF projects,” James Staten’s Blog, http://blogs.forrester.com/james_staten/13-08-14-the_cost_of_prism_will_be_larger_than_itif_projects.

We like automatic security updates and automatic backups; the companies do a better job of protecting our devices than we ever did. And we’re really happy when, after we lose a smartphone and buy a new one, all of our data reappears on it at the push of a button. In this new world of computing, we’re no longer expected to manage our computing environment. We trust the feudal lords to treat us well and protect us from harm. It’s all a result of two technological trends. The first is the rise of cloud computing. Basically, our data is no longer stored and processed on our computers. That all happens on servers owned by many different companies. The result is that we no longer control our data. These companies access our data—both content and metadata—for whatever profitable purpose they want. They have carefully crafted terms of service that dictate what sorts of data we can store on their systems, and can delete our entire accounts if they believe we violate them.


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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

According to Massoud Amin of the University of Minnesota, power outages and power quality disturbances cost the US economy between $80 billion and $188 billion a year.32 A back-of-the-envelope calculation published by International Working Group on Cloud Computing Resiliency tagged the economic cost of cloud outages between 2007 and mid-2012 at just $70 million (not including the July 2012 Amazon outage).33 But as more and more of the vital functions of smart cities migrate to a handful of big, vulnerable data centers, this number is sure to swell in coming years. Cloud-computing outages could turn smart cities into zombies. Biometric authentication, for instance, which senses our unique physical characteristics to identify individuals, will increasingly determine our rights and privileges as we move through the city—granting physical access to buildings and rooms, personalizing environments, and enabling digital services and content.

The grass roots may be a source of new ideas, but what they need is someone who can design and deliver a robust infrastructure that is centrally planned to be safe, efficient, and reliable at a reasonable cost. To an extent, they’re right. Scaling up things that work at the grass roots is a challenge few have overcome. Foursquare, even with all its resources, went through a wrenching series of outages before it was able to work out a scalable database scheme (although one of the worst problems was caused by an outage on Amazon’s cloud-computing services, the epitome of large-scale smart infrastructure). Even when they can manage the technical hurdles that come with growth, many civic hacks never get that far. They solve a problem for a small group of users, but fail to sustain the effort to refine their design into something that can connect to a larger audience. As DIYcity’s Geraci explained, “it’s dead simple to prototype version one of a smart city app.

But Moldova was also an opportunity to airlift the same ideas about openness that Zoellick was using to reinvent the bank and drop them onto an entire country. e-Transformation aimed to sweep aside Moldova’s entire Soviet-era paper-based bureaucracy and put all government services online. Even in 2010, basic transactions—such as obtaining an exit visa to work overseas—required a long and costly trip to the capital. With $23 million in loans from the World Bank, parceled out over five years, the new government would build a “g-cloud” (a cloud-computing infrastructure that would allow for the delivery of services to both fixed and mobile devices), create a new digital citizen-identity program, and rewrite legislation to encourage private investment in online services. In a country where most rural people still stored their savings under their mattress or in a hole in the backyard, new rules would allow mobile banking. Zoellick spent an hour and a half of his day in Moldova at our workshop (just one of several more conventional programs launched that day).


Virtual Competition by Ariel Ezrachi, Maurice E. Stucke

Airbnb, Albert Einstein, algorithmic trading, barriers to entry, cloud computing, collaborative economy, commoditize, corporate governance, crony capitalism, crowdsourcing, Daniel Kahneman / Amos Tversky, David Graeber, demand response, disintermediation, disruptive innovation, double helix, Downton Abbey, Erik Brynjolfsson, experimental economics, Firefox, framing effect, Google Chrome, index arbitrage, information asymmetry, interest rate derivative, Internet of things, invisible hand, Jean Tirole, John Markoff, Joseph Schumpeter, Kenneth Arrow, light touch regulation, linked data, loss aversion, Lyft, Mark Zuckerberg, market clearing, market friction, Milgram experiment, multi-sided market, natural language processing, Network effects, new economy, offshore financial centre, pattern recognition, prediction markets, price discrimination, price stability, profit maximization, profit motive, race to the bottom, rent-seeking, Richard Thaler, ride hailing / ride sharing, road to serfdom, Robert Bork, Ronald Reagan, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart meter, Snapchat, social graph, Steve Jobs, supply-chain management, telemarketer, The Chicago School, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, Travis Kalanick, turn-by-turn navigation, two-sided market, Uber and Lyft, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, women in the workforce, yield management

The algorithm, however, computed a strategy for two-player limit Texas Hold 18 Setting the Scene ’Em poker so that it cannot be beaten with statistical significance in a human’s lifetime.59 For poker aficionados, we’ll mention that the algorithm confirmed (for two-player limit Texas Hold ’Em) that the dealer has a substantial advantage, and the nondealer’s optimal strategy is more often to play than to fold.60 The significance of this advancement lies in the computer’s ability to address the “real-world” complexity of imperfect information, unleashing the possibility for complex “ human-like” interaction and decision making. Cloud Computing and the Internet of Things As the breadth and quality of data increase over the next decade, the positive feedback loop between machine learning and Big Data will accelerate. One contributing factor will be the developments in cloud computing. Amazon’s cloud division in 2015, for example, added an Amazon Machine Learning ser vice. Amazon’s algorithms help the client find patterns in its existing data.61 Then Amazon creates models, which process the client’s incoming data and generate predictions. The models could predict likely fraudulent purchases, products or ser vices that might appeal to the client’s customer, or consumer trends. As more data is processed, the predictive models are refined. Google and Microsoft likewise provide as part of their cloud computing ser vices machine-learning algorithms to analyze data and predict future outcomes.62 A positive feedback loop can ensue: Clients will have an even greater incentive to collect data and use the cloud computing ser vices if they can obtain a competitive advantage through these predictive models.

Google and Microsoft likewise provide as part of their cloud computing ser vices machine-learning algorithms to analyze data and predict future outcomes.62 A positive feedback loop can ensue: Clients will have an even greater incentive to collect data and use the cloud computing ser vices if they can obtain a competitive advantage through these predictive models. And access to the many different clients’ data will improve Amazon’s, Microsoft’s and Google’s algorithms. Another contributing factor will be the “Internet of Things,” that is, the integration of soft ware and sensors embedded in everyday objects. This technology enables machine-to-machine communication (M2M), as well as the collection and analysis of information gathered through sensors. For instance, Amazon in 2015 launched its “IoT platform,” which “lets connected devices easily and securely interact with cloud applications and other devices.”63 The platform is designed to process trillions of messages from billions of devices “and can process and route those messages to [Amazon Web Ser vice] endpoints and to other devices reliably and securely.”64 The research firm International Data Corp estimated the “global market for Internet of Things” to nearly triple to $1.7 trillion by 2020.65 The firm also New Economic Reality 19 notes how technology firms, like Google, Intel Corp, Cisco Systems, Samsung Electronics and the major telecoms such as Vodafone and Verizon, “are betting heavily on it to drive revenue and profit in the future.” 66 Whereas traditional data is harvested through our interaction with online sellers and our digitalized environment, the Internet of Things would widen the scope of data for the algorithms.

Two former Uber employees told reporters that “[t]racking customers is easy using an internal company tool called ‘God View.’ ”1 Uber’s “God View” apparently shows the location of all Uber vehicles and customers who have requested a car. Borrowing Uber’s terminology, we refer to God View as competitors using Big Data and Big Analytics for a clearer overview of the marketplace at any given moment. The wealth of data generated from the online environment, cloud computing, and smart sensors can provide a panoramic God-like view of our state of being. Firms can see on a giant screen, for any city, their own driverless trucks, their rivals’ driverless trucks, their customers’ trucks, what the trucks are carry ing, and where they are traveling. Each firm can track the movement of its own and its rivals’ products traveling through the supply chain. They can see when the item enters their customers’ factories or homes.


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Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble, Shel Israel

Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, Edward Snowden, Edward Thorp, Elon Musk, factory automation, Filter Bubble, G4S, Google Earth, Google Glasses, Internet of things, job automation, John Markoff, Kickstarter, lifelogging, Marc Andreessen, Mars Rover, Menlo Park, Metcalfe’s law, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Robert Metcalfe, Saturday Night Live, self-driving car, sensor fusion, Silicon Valley, Skype, smart grid, social graph, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Tesla Model S, Tim Cook: Apple, ubercab, urban planning, Zipcar

Similarly, significant innovations and accuracy improvements in voice recognition make systems like Apple’s Siri, Google Now and Google Voice Search possible. The foundation for the Age of Context—all of these technologies working together—is the cloud computing infrastructure, which continues to grow exponentially in capability and capacity. And it had better keep growing: A self-driving car, which we describe in Chapter 5, generates about 700 megabytes of data per second. We talked with GM, Ford, Toyota—and Google—about what would happen if every car had that technology. Well, for one thing, today’s cloud computing technology would melt down. Rackspace, a cloud hosting provider and Scoble’s employer, was the first and largest sponsor of this book. Since 2009, it has funded Scoble to travel the world interviewing hundreds of entrepreneurs and innovators.

No part of this book may be reproduced in any form by any means without the express permission of the authors. This includes reprints, excerpts, photocopying, recording, or any future means of reproducing text. Published in the United States by Patrick Brewster Press 1st Edition About the Authors Robert Scoble is among the world’s best-known tech journalists. In his day job as Startup Liaison for Rackspace, the Open Cloud Computing Company, Scoble travels the world looking for what’s happening on technology’s bleeding edge. He’s interviewed thousands of executives and technology innovators and reports for Rackspace TV and in social media. He can be found at scobleizer.com. You can email him at Scobleizer@gmail.com, and on social networks as Robert Scoble. Shel Israel helps businesses tell their stories in engaging ways as a writer, consultant and presentation coach.

They need to change and advance to meet the rising expectations of modern customers. Today, we are in the midst of a customer revolution where the world is being reshaped by the convergence of social and mobile cloud technologies. The combination of these technologies enables us to connect everything together in a new way and is dramatically transforming the way we live and work. Now, cloud computing over powerful LTE wireless networks is delivering on the promise of billions of computers interconnecting. Not just the mobile phones in our pockets, but different kinds of computers—our watches, our cameras, our cars, our refrigerators, our toothbrushes. Every aspect of our lives is somehow on the network, a wireless network, and in the cloud. This is the third wave of computing. Research firm IDC reports that there will be 3.5 billion networked products by 2015.


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The Web Application Hacker's Handbook: Finding and Exploiting Security Flaws by Dafydd Stuttard, Marcus Pinto

call centre, cloud computing, commoditize, database schema, defense in depth, easy for humans, difficult for computers, Firefox, information retrieval, lateral thinking, MITM: man-in-the-middle, MVC pattern, optical character recognition, Ruby on Rails, Turing test, web application

Failing to segregate different tiers properly often leaves an application vulnerable, enabling an attacker who has found a defect in one component to quickly compromise the entire application. A different range of threats arises in shared hosting environments, where defects or malicious code in one application can sometimes be exploited to compromise the environment itself and other applications running within it. This chapter also looks at the range of threats that arise in the kinds of shared hosting environments that have become known as "cloud computing." Chapter 18, "Attacking the Application Server," describes various ways in which you can target a web application by targeting the web server on which it is running. Vulnerabilities in web servers are broadly composed of defects in their configuration and security flaws within the web server software. This topic is on the boundary of the subjects covered in this book, because the web server is strictly a different component in the technology stack.

Popular consciousness about these trends exists by means of various rather misleading buzzwords, the most prominent of which are these: ■ Web 2.0 — This term refers to the greater use of functionality that enables user-generated content and information sharing, and also the adoption of various technologies that broadly support this functionality, including asynchronous HTTP requests and cross-domain integration. Chapter 1 i Web Application (ln)security 15 ■ Cloud computing — This term refers to greater use of external service providers for various parts of the technology stack, including application software, application platforms, web server software, databases, and hardware. It also refers to increased usage of virtualization technologies within hosting environments. As with most changes in technology, these trends have brought with them some new attacks and variations on existing attacks.

Chapter 11 Attacking Application Logic 407 Hence, insights gathered from studying a sample of logic flaws should help you uncover new flaws in entirely different situations. Example 1: Asking the Oracle The authors have found instances of the "encryption oracle" flaw within many different types of applications. They have used it in numerous attacks, from decrypting domain credentials in printing software to breaking cloud computing. The following is a classic example of the flaw found in a software sales site. The Functionality The application implemented a "remember me" function whereby a user could avoid logging in to the application on each visit by allowing the application to set a permanent cookie within the browser. This cookie was protected from tampering or disclosure by an encryption algorithm that was run over a string composed of the name, user ID, and volatile data to ensure that the resultant value was unique and could not be predicted.


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

Cloudy Weather Ahead Though massive amounts of data are leaking from our mobile devices, an increasing number of big-data risks come from the world of “cloud computing.” The cloud refers to the massive network of computing resources available online and the practice of using these remote servers to store, manage, and process the world’s information. The changing paradigm in computing means that less information is stored locally on our machines and is instead being hosted elsewhere on earth. We mostly do not buy software anymore; we just rent it or receive it for free using a new business model known as Software as a Service (SaaS). On the personal front, cloud computing means Google is storing our mail, Instagram our photographs, and Dropbox our documents—not to mention what mobile phones are automatically uploading to the cloud for us.

The availability of such cheap computing resources and a growing array of SaaS offerings will have untold positive impact on personal productivity, entrepreneurship, and innovation, which in turn will only hasten the inevitable transition to cloud computing. But with this move to store all available data in the cloud come additional risks. Think of the largest hacks to date—Target, Heartland Payment Systems, TJX, and Sony PlayStation Network. All of these thefts of hundreds of millions of accounts were made possible because the data were stored in the same virtual location. The cloud is equally convenient for individuals, businesses, and criminals. To deal with these risks, organizations such as the nonprofit Cloud Security Alliance have been formed to promote best practices and improve security in the age of cloud computing. The virtualization and storage of all of these data are highly complex and raise a wide array of security, public policy, and legal issues.

As a result, we’ve entered the age of weaponized computing, where literally anybody with a few dollars to spare can have access to previously unimaginable levels of computing power to use for good or ill. For example, the hackers who broke into the Sony PlayStation Network used the vast computing power of Amazon’s cloud-computing services to break several of Sony’s encryption keys, providing access to hundreds of thousands of user accounts and credit card details. This “cloud cracking” significantly reduces the time it takes to break even the strongest passwords and in the process leaves us all less secure. Today, using the distributed computing power of the cloud and tools such as CloudCracker, you can try 300 million variations of your potential password in about twenty minutes at a cost of about $17. This means that anyone could rent Amazon’s cloud-computing services to crack the average encryption key protecting most Wi-Fi networks in just under six minutes, all for the paltry sum of $1.68 in rental time (sure to drop in the future thanks to Moore’s law).


pages: 340 words: 97,723

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb

Ada Lovelace, AI winter, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, Bayesian statistics, Bernie Sanders, bioinformatics, blockchain, Bretton Woods, business intelligence, Cass Sunstein, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Deng Xiaoping, distributed ledger, don't be evil, Donald Trump, Elon Musk, Filter Bubble, Flynn Effect, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, natural language processing, New Urbanism, one-China policy, optical character recognition, packet switching, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Sand Hill Road, Second Machine Age, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, technological singularity, The Coming Technological Singularity, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

The program crunches huge amounts of local data, from smart city cameras and sensors to government records and individual social media accounts. Alibaba uses its AI framework for predictive modeling: to suss out in advance traffic management, urban development, public health needs, and whether there might be social unrest on the horizon. Under Ma’s direction, Alibaba has made inroads into delivery logistics, online video, data centers, and cloud computing, investing billions of dollars into various companies in an attempt to build a sprawling digital behemoth, connecting commerce, home, work, cities, and government. In fact, before the Amazon Go store launched in Seattle, Alibaba had opened Hema, an automated, cashless multifunctional retail operation combining groceries; a fast, casual food market; and delivery service. There’s one more odd similarity worth noting here.

One comes to mind that describes this particular moment in time: , which literally translates as “the grain sheds its husk and comes forth.”51 China is now fully showing the world its might and power, and in a very public way. Xi’s consolidation of power, coupled with China’s economic rise and might, has created the right conditions for AI’s tribes to flourish, especially given the country’s unified, top-down AI effort. There’s a $2 billion research park being built just outside of Beijing, which will focus on deep learning, cloud computing, and biometrics and will have a state-level R&D lab. Not only is the government investing in the BAT, it’s protecting them from the world’s most formidable competition. The Chinese government bans Google and Facebook, and it’s made it impossible for Amazon to break into the market. BAT companies are at the heart of the government’s 2030 plan, which rely heavily on their technologies: Baidu’s autonomous driving systems, Alibaba’s IoT and connected retail systems, and Tencent’s work in conversational interfaces and health care.

While Xi was consolidating domestic power and publicly launching his 2030 plan for global AI dominance, Trump’s deputy assistant for technology policy, Michael Kratsios, told a group of industry leaders convened at the White House that the best way forward for America was for Silicon Valley to chart its own course independently without government intervention.70 There is an imbalance of power because the US government hasn’t been able to create the networks, databases, and infrastructure it needs to operate. So it needs the G-MAFIA. For example, Amazon’s government cloud-computing business will likely hit $4.6 billion in 2019—while Jeff Bezos’s private space company, Blue Origin, is expected to start supporting NASA and the Pentagon on various missions. In America, the government relies on the G-MAFIA, and since we’re a market-driven economy with laws and regulations in place to protect businesses, the Valley has a significant amount of leverage. Let me be very clear: I do not begrudge the G-MAFIA’s role as successful, profitable companies.


pages: 476 words: 125,219

Digital Disconnect: How Capitalism Is Turning the Internet Against Democracy by Robert W. McChesney

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, access to a mobile phone, Albert Einstein, American Legislative Exchange Council, American Society of Civil Engineers: Report Card, Automated Insights, barriers to entry, Berlin Wall, business cycle, Cass Sunstein, citizen journalism, cloud computing, collaborative consumption, collective bargaining, creative destruction, crony capitalism, David Brooks, death of newspapers, declining real wages, Double Irish / Dutch Sandwich, Erik Brynjolfsson, failed state, Filter Bubble, full employment, future of journalism, George Gilder, Gini coefficient, Google Earth, income inequality, informal economy, intangible asset, invention of agriculture, invisible hand, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Julian Assange, Kickstarter, Mark Zuckerberg, Marshall McLuhan, means of production, Metcalfe’s law, mutually assured destruction, national security letter, Nelson Mandela, Network effects, new economy, New Journalism, Nicholas Carr, Occupy movement, offshore financial centre, patent troll, Peter Thiel, plutocrats, Plutocrats, post scarcity, price mechanism, profit maximization, profit motive, QWERTY keyboard, Ralph Nader, Richard Stallman, road to serfdom, Robert Metcalfe, Saturday Night Live, sentiment analysis, Silicon Valley, single-payer health, Skype, spectrum auction, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, Stewart Brand, Telecommunications Act of 1996, the medium is the message, The Spirit Level, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, transfer pricing, Upton Sinclair, WikiLeaks, winner-take-all economy, yellow journalism

Google has been described as “the antithesis of the open-source movement.”35 And when matters turn to the preeminent issue of privacy, Google locks arms with Facebook, abandoning any claim to the moral high ground. A key development that accompanies and enables proprietary systems is cloud computing, wherein each of the giants stores vast amounts of material on their battalions of servers. Users do not need to have massive computer memories to store their own material; they can—indeed, must—access everything they have from a small device just by gaining access to the cloud. There are still “little guys” who offer hosting services, and that is a constructive activity. At the other end of the spectrum, though, the digital monopolists, including Google, Facebook, Amazon, Apple, and Microsoft, have all invested to build enormous private clouds. Cloud computing is a brilliant way to make the Internet more efficient and less expensive to users and society, but whether having the preponderance of cloud capacity in the hands of a few giant firms is a wise policy is another matter altogether.

Cloud computing is a brilliant way to make the Internet more efficient and less expensive to users and society, but whether having the preponderance of cloud capacity in the hands of a few giant firms is a wise policy is another matter altogether. The clouds can be a treasure chest full of valuable data for the giants to exploit.36 Cloud computing requires serious capital outlays—old-fashioned barriers to entry—which lock in oligopoly or monopoly. Consider Google, which spends many billions annually on computers so it can provide nearly instantaneous response to queries.37 Any Google search query “fires up between 700 and 1,000 separate computers in several huge data centers around the United States.”38 Like the other giants, Google has enormous “server farms” all over the world to keep all this information in its cloud. The companies are secretive about their location and size, but evidence suggests a server farm is an enormous industrial undertaking that would have more than held its own in Akron, Ohio, or Gary, Indiana, in 1963 or in China today.

See corporate acquisitions Acxiom, 265n125 Adams, John, 57, 243n100 addiction, 10, 11, 45 Advance Publications, 177 advertising, 20, 41–46, 52–53, 58–59, 75, 123, 154–58, 185 Jerry Mander on, 240–41n65 on mobile apps, 148 tax break for, 78 Twitter stance on, 153 See also broadcast advertising; Internet advertising; newspaper advertising; personalized advertising advertising and marketing to children, 76–77, 216, 247n46, 266n151 advertising-free media, 211–14 advertising regulation, 93, 146, 216 Africa, 2, 8, 163 agriculture, 25, 70–71 Alabama, 177 Alden Global Capital, 185 Alinsky, Saul, 220 alphabet, 71 Alterman, Eric, 272n25 Amazon, 127–28, 131, 136–37, 138, 140, 142, 193 American Civil Liberties Union (ACLU), 165, 167, 270n238 American Legislative Exchange Council (ALEC), 198 America Online. See AOL ancient Greece, 71 Anderson, Chris, 101, 132, 141, 143 Andrews, Lori, 142, 149 Android, 133, 134, 150 Anti-Counterfeiting Trade Agreement (ACTA), 125–26 antiterrorism, 160–62, 170 antitrust regulation, 142, 143 AOL, 103, 111, 123–24, 137, 149, 189, 191 Apple, 28, 32, 101, 108, 127–28, 131, 137, 139, 140 cloud computing, 136 government relations, 142 monopolistic enterprises, 138 patents, 134, 260n25 proprietary platforms, 135 tax evasion, 145 Apple iPad, 131, 187 Apple iPhone, 135, 150 Apple iTunes, 127, 131 apps, mobile. See mobile apps Arab Spring, 2011, 8, 174, 234n36 Ariely, Dan, 35–36 Aristotle: Politics, 53–54 Armstrong, Tim, 189, 191 ARPAnet, 99, 103 arrest of journalists, 209 artificial scarcity, 114–15, 124, 127, 132, 187, 219, 223 artists, 10, 74 Artzt, Edwin, 146–47 Assange, Julian, 195 Associated Content, 188 AT&T, 93, 94–95, 103, 110, 112–13, 115, 119, 122, 131 cooperates with government wiretapping, 163 declines offer to control ARPAnet, 99–100 gets “the bill they wrote,” 253n60 law enforcement relations, 165 Athens (ancient Greece), 71 Automated Insights, 193 “Baby Bells,” 106, 110, 111 Bagdikian, Ben, 84, 93, 179 Baker, Dean, 211, 213, 214 Baker, Randy, 211 Baltimore, 179, 182 Baltimore Sun, 179, 274n52 Bamford, James, 161 Banks, Russell: Lost Memory of Skin, 11 banks and banking, 38–39, 41, 131, 164, 283n30 Baran, Paul, 99 Baran, Paul A., 42, 224, 227, 228 Barlow, John Perry, 81, 105 Barnes, Peter, 115 Barton, David Watts, 191 Battelle, John, 135 Bauerlein, Mark, 9 Beck, Glenn, 94 Becker, Gary S., 44 beer, marketing of, 43 Benkler, Yochai, 15, 108, 126, 173, 231 The Penguin and the Leviathan, 6–7 Berners-Lee, Tim, 103, 133–34, 134–35 Bezos, Jeff, 138 billionaires, 27–29, 30 Bliven, Bruce, 158 blogs, 196 Boeing, 163 Bogusky, Alex, 77 book publishing, 78–79, 120, 121, 122, 127–28, 138 bookselling, 131, 138 Botsman, Rachel, 15 bourgeois.


pages: 239 words: 70,206

Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else by Steve Lohr

"Robert Solow", 23andMe, Affordable Care Act / Obamacare, Albert Einstein, big data - Walmart - Pop Tarts, bioinformatics, business cycle, business intelligence, call centre, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, David Brooks, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, Frederick Winslow Taylor, Google Glasses, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, Johannes Kepler, John Markoff, John von Neumann, lifelogging, Mark Zuckerberg, market bubble, meta analysis, meta-analysis, money market fund, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, speech recognition, statistical model, Steve Jobs, Steven Levy, The Design of Experiments, the scientific method, Thomas Kuhn: the structure of scientific revolutions, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!

Its customers provide a window into the progress data techniques are making, as well as the challenges, across a spectrum of industries. IBM itself has lined up its research, its strategy, and its investment behind the big-data business. “We are betting the company on this,” Virginia Rometty, the chief executive, told me in an interview. But for IBM, big data is a threat as well as an opportunity. The new, low-cost hardware and software that power many big-data applications—cloud computing and open-source code—will supplant some of IBM’s traditional products. The company must expand in the new data markets faster than its old-line businesses wither. No company can match IBM’s history in the data field; the founding technology of the company that became IBM, punched cards, developed by Herman Hollerith, triumphed in counting and tabulating the 1890 census, when the American population grew to sixty-three million—the big data of its day.

The goal is to nurture a flourishing commercial ecosystem, in the way that Apple’s iOS and Google’s Android have done in smartphone software, and Microsoft’s Windows did in personal computer software. In early 2014, IBM also focused the activities of the 3,000 researchers in its labs to make data projects the priority. The realignment, according to Rometty, is the most significant shift at IBM research since the 1990s, when retooling for the Internet era became the imperative. A program of fundamental research, as in the materials science of computer hardware, will continue. Along with cloud computing, research will be concentrated on big-data projects in specific industries and the underlying machine-learning technologies used to find answers and insights in data, as Watson does. IBM refers to these machine-learning capabilities as “cognitive” computing. Pick your term, Rometty says—big data, analytics, or cognitive—but it’s all in the same data neighborhood, and it is the direction in which IBM is unequivocally headed.

And a sizable swath of IBM’s services business involves engineers writing applications, using traditional software, for corporate customers. Today’s big-data applications typically use cloud-style computing in which processing and software are delivered remotely, from distant data centers, over the Internet. Under Rometty, IBM is making huge investments in the future—big-data technology and cloud computing. But the dilemma facing the company is whether the new business will grow faster than the old business erodes. In early 2014, when I spoke to Rometty, she talked of the lessons she had learned about the imperative of constant corporate evolution. “Don’t fight cannibalization,” she says at one point. Trying to preserve the past is a formula for failure, she notes, a lesson IBM learned the hard way in the 1990s.


Mastering Ansible by Jesse Keating

cloud computing, Debian, DevOps, don't repeat yourself, microservices, remote working

His interests include SDN, NFV, Network Automation, DevOps, and Cloud technologies. He also likes to try out and follow open source projects in these areas. You can find him on his blog at https://sreeninet.wordpress.com/. Tim Rupp has been working in various fields of computing for the last 10 years. He has held positions in computer security, software engineering, and most recently, in the fields of Cloud computing and DevOps. He was first introduced to Ansible while at Rackspace. As part of the Cloud engineering team, he made extensive use of the tool to deploy new capacity for the Rackspace Public Cloud. Since then, he has contributed patches, provided support for, and presented on Ansible topics at local meetups. He is currently stationed at F5 Networks, where he is involved in Solution development as a senior software engineer.

We do not use the /usr/bin/ env mechanism as it requires the remote user's path to be set right and also assumes that the Python executable is named Python, where the executable might be named something like python26. ansible\_\*\_interpreter This works for anything such as Ruby or Perl and works just like ansible_python_ interpreter. This replaces the shebang of modules which run on that host Dynamic inventories A static inventory is great and enough for many situations. But there are times when a statically written set of hosts is just too unwieldy to manage. Consider situations where inventory data already exists in a different system, such as LDAP, a cloud computing provider, or an in-house CMDB (inventory, asset tracking, and data warehousing) system. It would be a waste of time and energy to duplicate that data, and in the modern world of on-demand infrastructure, that data would quickly grow stale or disastrously incorrect. Another example of when a dynamic inventory source might be desired is when your site grows beyond a single set of playbooks. Multiple playbook repositories can fall into the trap of holding multiple copies of the same inventory data, or complicated processes have to be created to reference a single copy of the data.

Once again, we'll delegate the task to foo-lb: - name: enable member in balancer haproxy: backend: foo-app host: "{{ inventory_hostname }}" state: enabled delegate_to: foo-lb Of course, we still need to define our reload nginx handler: handlers: - name: reload nginx service: name: nginx state: restarted This playbook, when run, will now perform a rolling in-place upgrade of our application. Expanding and contracting An alternative to the in-place upgrade strategy is the expand and contract strategy. This strategy has become popular of late thanks to the self-service nature of ondemand infrastructure, such as cloud computing or virtualization pools. The ability to create new servers on demand from a large pool of available resources means that every deployment of an application can happen on brand new systems. This strategy avoids a host of issues. These include a build up of cruft on long running systems, such as: • The configuration files no longer managed by Ansible are left behind [ 136 ] Chapter 6 • The run-away processes consume resources in the background • Things manually changed by humans with shell access to the server Starting afresh each time also removes differences between an initial deployment and an upgrade.


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

A frugal services revolution GAPPAA.ORG Companies are finding new, highly effective and affordable ways to deliver services or services bundled with products. Such business models include: software as a service (SaaS) in computing; power by the hour in aircraft engines; massive open online courses (MOOCs) in education; hub-and-spoke and yield management models in airlines; online retailing; and cloud computing. By flexing their assets, airlines such as Southwest Airlines, easyJet and Ryanair have created a new, low-cost market segment for flyers within the US and Europe, and have succeeded in challenging long-haul incumbents. First, the low-cost carriers rebased the existing airline business model by maximising the time that their most valuable assets – their aircraft – spend in the air, and reducing the time they spend on the ground.

In retailing, Amazon has from the outset sought ways to flex its assets. The company first used its book distribution platform to sell music and household goods as well. It then used its installed customer base for peer-to-peer sales, as on eBay. Amazon then made and sold consumer electronics such as the Kindle (on which to read its books) and market-research tools such as mTurk, to captive customers. Lastly, it used its server space for cloud computing, which it sells as a service. Given its interest in drones as a mode of product delivery, Amazon might one day expand into travel and transport. Meanwhile, a wave of creative destruction is crashing through the education industry and, by extension, the textbook publishing world. The arrival of MOOCs has threatened higher-education models. Startups such as Coursera, Udacity and EdEx in the US and FutureLearn in the UK are now offering courses on an ever-widening range of subjects to students worldwide.

SNCF has also tied up with Orange and Total to launch Ecomobilité Ventures, Europe’s first multi-corporate investment fund, which invests in a portfolio of promising start-ups that can collectively deliver end-to-end solutions in sustainable mobility. Non-competing brands can also work together, not only to respond to the current needs of their shared customers, but also to anticipate their future needs. Simon Mulcahy, senior vice-president and managing director of financial services industry at Salesforce. com, which offers a customer relationship management platform based on cloud computing, encourages companies to adopt a wide lens to perceive their customer needs through the diverse perspectives of other industries, and to address these needs by borrowing best practices from other sectors.9 For example, diverse industries such as construction, interior design and renovation, food, retail, entertainment, logistics, health, financial services, energy and communication all share a single customer in one particular location: their home.


pages: 903 words: 235,753

The Stack: On Software and Sovereignty by Benjamin H. Bratton

1960s counterculture, 3D printing, 4chan, Ada Lovelace, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, basic income, Benevolent Dictator For Life (BDFL), Berlin Wall, bioinformatics, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, carbon footprint, carbon-based life, Cass Sunstein, Celebration, Florida, charter city, clean water, cloud computing, connected car, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, Ethereum, ethereum blockchain, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, Hyperloop, illegal immigration, industrial robot, information retrieval, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Joan Didion, John Markoff, Joi Ito, Jony Ive, Julian Assange, Khan Academy, liberal capitalism, lifelogging, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, MITM: man-in-the-middle, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, peer-to-peer, performance metric, personalized medicine, Peter Eisenman, Peter Thiel, phenotype, Philip Mirowski, Pierre-Simon Laplace, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Robert Bork, Sand Hill Road, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, Slavoj Žižek, smart cities, smart grid, smart meter, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, TaskRabbit, the built environment, The Chicago School, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, undersea cable, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, Westphalian system, WikiLeaks, working poor, Y Combinator

The computational future of energy and the infrastructural program of computation form such a coil, one end feeding on the other like Ouroboros, the ancient symbol of a snake eating its own tail. Whether or not the risks associated with the energy costs of Stack infrastructure will outpace the efficiencies provided by calculative technologies as they become pervasive across industrial sectors is unknown, and probably unknowable at the moment. Prognostications vary from measured good news to very bad news. According to a Greenpeace report on cloud computing and climate change, the electricity consumed by cloud computing globally will increase from 632 billion kilowatt-hours (0.6 terawatts) in 2007 to 1,963 billion kilowatt-hours (1.9 terawatts) by 2020, and the associated carbon dioxide equivalent emissions would reach 1.034 megatons (currently the world economy's total energy appetite is roughly 15 terawatts). If imagined as an emergent nation-state, the Cloud would be today the fifth largest consumer of electricity, ahead of India, Germany, Canada, France, Brazil, and the United Kingdom.

Climate Group, “SMART 2020: Enabling the Low Carbon Economy in the Information Age,” 2008, http://www.smart2020.org/_assets/files/02_smart2020Report.pdf. 58.  For a taste of that ecstasy, http://www.ft.com/intl/cms/s/0/f5228aec-7b36-11e0-9b06-00144feabdc0.html. 59.  See Nick Land, “Lure of the Void, pt. 1,” August 2012, http://www.scribd.com/doc/242684419/Nick-Land-Lure-of-the-Void#scribd. 60.  See Pete Foster, “Cloud Computing—a Green Opportunity or Climate Change Risk?” Guardian, August 18, 2011. http://www.guardian.co.uk/sustainable-business/cloud-computing-climate-change. 61.  On smart grids and data ownership, see Jon Bruner, “Two Crucial Questions for the Smart Grid,” O’Reilly Radar, November 5, 2012, http://radar.oreilly.com/2012/11/two-crucial-questions-for-the-smart-grid.html. 62.  See Sally Daultrey, “Adaptation on the Roof of the World,” December 30, 2010, http://designgeopolitics.org/blog/2010/12/adapatation-on-the-roof-of-the-world/. 63. 

Unlike modern political geography, which divided up horizontal maps, Stack geography also vertically layers spaces on top of one another. Instead of surveying all the various forms of planetary-scaled computation—cloud computing, smart cities, ubiquitous computing, massive addressing systems, next-generation interfaces, nonhuman users, and so on—as different genres or species of computing, each off on its own, this model locates them on layers of a consolidated metaplatform, an accidental megastructure. We observe these bottom-up from the Earth layer up to the User layer. Energy drawn from planetary resources at the Earth layer drives Cloud computation, and its global platforms organize new political topologies. The City layer is animated by those Cloud platforms from within, organizing things, events, and relations at the Address layer into Interfacial regimes that provide a window into the whole system for Users.


pages: 409 words: 112,055

The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats by Richard A. Clarke, Robert K. Knake

A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, Amazon Web Services, autonomous vehicles, barriers to entry, bitcoin, Black Swan, blockchain, borderless world, business cycle, business intelligence, call centre, Cass Sunstein, cloud computing, cognitive bias, commoditize, computer vision, corporate governance, cryptocurrency, data acquisition, DevOps, don't be evil, Donald Trump, Edward Snowden, Exxon Valdez, global village, immigration reform, Infrastructure as a Service, Internet of things, Jeff Bezos, Julian Assange, Kubernetes, Mark Zuckerberg, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, move fast and break things, Network effects, open borders, platform as a service, Ponzi scheme, ransomware, Richard Thaler, Sand Hill Road, Schrödinger's Cat, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, software as a service, Steven Levy, Stuxnet, technoutopianism, Tim Cook: Apple, undersea cable, WikiLeaks, Y2K, zero day

Now, businesses can get an instant, real-time snapshot of the state of their security and can use cloud computing power to analyze the data provided. Cloud providers are also being clear where their responsibility for security ends and where their customers’ begins. All major cloud services will provide their customers raw computing power. “Elastic” computing, which can shrink or expand based on demand, is what makes many regard Amazon as the leader in the space. Instead of a company building its own data center or stuffing servers into its closet, Amazon builds and maintains the computing environment and leases it to the company on a metered rate. It has proven the perfect solution for start-ups that need infrastructure on which they can build their own applications. This type of cloud computing is known as infrastructure as a service (IAAS).

The only way to use Salesforce was (and is) to use a web browser to access it. The software sits safely on Salesforce’s servers. It can’t be downloaded by cyber criminals to look for vulnerabilities. When Salesforce discovers a problem, there is no delay between when the patch is ready and when it is installed. Malicious actors have no window through which to attack the weakened software. The New York Cyber Task Force has repeatedly called out cloud computing as one of the best ways for the defender to gain leverage over the attacker. Amoroso, a member of the task force, identified a series of advantages that cloud technologies have over traditional IT environments. First, they offer greater automation: tasks like securely configuring devices are done automatically for you in the cloud. Second, cloud technologies are “self-tailoring,” meaning that once services are selected, they automatically work together without needing to patch cables together or install software.

Amazon provides a solid baseline by monitoring its own infrastructure and providing data on the state of that infrastructure to users, but allows third parties to sell security as a bolt-on to its core offerings in its marketplace. Google is so confident in its security capabilities that, instead of arguing that it shouldn’t be expected to be able to stop government intelligence organizations, it is actively working to protect its customers from them and will notify individual Google account holders if they are being targeted by an APT actor. The danger with cloud computing is that it is concentrating risk in the hands of a few players that now have a near monopoly. Almost all SaaS providers start out building their services on top of Amazon Web Services or Microsoft Azure and many stay that way. Netflix, now in a heated rivalry with Amazon Prime for eyeballs in the streaming wars, uses Amazon, as do other giants of the internet age such as Airbnb. Dropbox, the online file storage company, until a few years ago was also an Amazon customer.


pages: 320 words: 87,853

The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale

Affordable Care Act / Obamacare, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, Chuck Templeton: OpenTable:, cloud computing, collateralized debt obligation, computerized markets, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, Debian, don't be evil, drone strike, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, financial innovation, financial thriller, fixed income, Flash crash, full employment, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, informal economy, information asymmetry, information retrieval, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, Marc Andreessen, Mark Zuckerberg, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, quantitative easing, race to the bottom, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, search engine result page, shareholder value, Silicon Valley, Snapchat, social intelligence, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, WikiLeaks, zero-sum game

Bob Brown, “What Is a ‘Certified EHR’?,” Journal of Health Care Compliance 12, No. 1 (2010): 31– 67. See generally Nicolas P. Terry, “Certification and Meaningful Use: Reframing Adoption of Electronic Records as a Quality Imperative,” Indiana Health Law Review 8 (2011): 45–70 (examining meaningful use as the condition for receiving EHR subsidy funds). 32. Cloud computing transfers “application software and server-based databases to centralized, large data centers.” Jared A. Harshbarger, “Cloud Computing Providers and Data Security Law,” Journal of Technology Law and Policy 16 (2011): 230–231. 33. David A. Moss, When All Else Fails: Government as the Ultimate Risk Manager (Cambridge, MA: Harvard University Press, 1999). 34. Richard A. Posner, A Failure of Capitalism: The Crisis of ’08 and the Descent into Depression (Cambridge, MA: Harvard University Press, 2009); Andrew Ross Sorkin, Too Big to Fail: The Inside Story of How Wall Street and Washington Fought to Save the Financial System— and Themselves (New York: Viking Penguin, 2009). 35.

Others will invest, as venture capitalist Marc Andreessen recommends. Though he strikes fear into publishers, Amazon’s Jeff Bezos has not yet reduced writers at his newspaper (the Washington Post) to the status of Mechanical Turkers or warehouse pickers.213 But we should not assume media independence as tech firms swallow more of the revenue that might have once gone to journalists. After Amazon inked a $600 million deal to provide the CIA with cloud computing services, 30,000 people petitioned the Post with the message “Washington Post: Readers Deserve Full Disclosure in Coverage of CIA.”214 Such inquiries will only become more common as Washington and Silicon Valley develop more partnerships for information dominance. Of course, we can see why large firms want to keep their industry (and government) alliances under wraps. People want to feel like there is someone looking out for them.

Google found itself needing more compelling content, and that content would only materialize for a price.216 These are trust issues. In a classic example of what philosopher Langdon Winner called “technological somnambulism,”217 we have given the search sector an almost unimaginable power to determine what we see, where we spend, how we perceive. Top legal scholars have already analogized the power relationships in virtual worlds and cloud computing to medieval feudalism.218 Technological advance goes hand-in-hand with politico-economic regression. Toward a Digital New Deal In the late 1990s, tech enthusiasts looked to search engines as an extraordinary democratization of the Internet. They permitted content creators from all over the world to reach far-flung audiences. Web 2.0 promised even more “democratization” by enabling selforganization of virtual communities.


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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

We’ve made a balance between the absolute don’t-use-other-people’scomputers argument and the maybe-it’s-OK-sometimes argument in the Franklin Street Statement. Time will tell whether we can cra a culture around Free Network Services that is respectful of users’ autonomy, such that we can use other computers with some measure of confidence.” —Evan Prodromou, “RMS on Cloud Computing: “Stupidity””, CC BY-SA, <autonomo.us/2008/09/rms-on-cloud-computing-stupidity/> 109 The Franklin Street Statement on Freedom and Network Services is an initial a empt to distill actions that users, service providers (the “other people” here), and developers should take to retain the benefits of free so ware in an era of so ware services: “The current generation of network services or So ware as a Service can provide advantages over traditional, locally installed so ware in ease of deployment, collaboration, and data aggregation.

., beholden to users and volunteers, not investors and employees. Maybe. Would I be less sanguine about the long term prospects of Wikipedia if it were forprofit? I don’t know of evidence for or against this feeling. —Mike Linksvayer, “Constitutionally open services”, CC0, <gondwanaland.com/mlog/2006/07/06/constitutionally-open-services/> So ware services are rapidly developing and subjected to much hype, o en referred to the buzzword Cloud Computing. However, some of the most potent means of encouraging autonomy may be relatively boring—for example, making it easier to maintain one’s own computer and deploy slightly customized so ware in a secure and foolproof fashion. Any such development helps traditional users of free so ware as well as makes doing computing on one’s own computer (which may be a “personal server” or virtual machine that one controls) more a ractive. 112 Perhaps one of the most hopeful trends is relatively widespread deployment by end users of free so ware web applications like WordPress and MediaWiki.

Previously he co-founded Bitzi, an early open data/open content/mass collaboration service, and worked as a web developer and so ware engineer. In 1993 he published one of the first interviews with Linus Torvalds, creator of Linux. He is a co-founder and currently active in Autonomo.us, which investigates and works to further the role of free so ware, culture, and data in an era of so ware-as-a-service and cloud computing. His chapter on “Free Culture in Relation to So ware Freedom” was published in FREE BEER, a book wri en by speakers at FSCONS 2008. Linksvayer holds a degree from the University of Illinois at UrbanaChampaign in economics, a field which continues to strongly inform his approach. He lives in Oakland, California. 146 Michael Mandiberg is known for selling all of his possessions online on Shop Mandiberg, making perfect copies of copies on A erSherrieLevine.com, and creating Firefox plugins that highlight the real environmental costs of a global economy on TheRealCosts.com.


pages: 255 words: 55,018

Architecting For Scale by Lee Atchison

Amazon Web Services, business process, cloud computing, continuous integration, DevOps, Internet of things, microservices, platform as a service, risk tolerance, software as a service, web application

The intent with this book is to provide content that stands the test of time. Navigating This Book Managing scale is not only about managing traffic volume—it also involves managing risk and availability. Often, all these things are different ways of describing the same problem, and they all go hand in hand. Thus, to properly discuss scale, we must also consider availability, risk management, and modern architecture paradigms such as microservices and cloud computing. As such, this book is organized as follows: Part I, “Availability” Availability and availability management are often the first areas that are affected when an application begins to scale. Chapter 1, What Is Availability? To begin, we’ll establish what high availability means and how it differs from reliability. Chapter 2, Five Focuses to Improve Application Availability In this chapter, I provide five core areas to focus on in building your application in order to improve its availability.

Chapter 19, Continuous Improvement This chapter provides techniques and guidelines for how to improve the overall scalability of your application. Part V, “Cloud Services” Cloud-based services are becoming increasingly important in building and managing large, critical applications with significant scaling requirements. Chapter 20, Change and the Cloud This chapter explores the ways cloud computing has changed how we think about building highly scaled web applications. Chapter 21, Distributing the Cloud This chapter outlines how to effectively use regions and availability zones to improve availability and scale. Chapter 22, Managed Infrastructure This chapter describes how you can use managed services such as RDS, SQS, SNS, and SES to scale your application and reduce management load.

By thinking about how your application will grow long before it grows to those painful levels, you can preempt many problems and build and improve your applications so that they can handle these growing pains safely and securely. 1 A more likely account-based partitioning mechanism would be to partition by an account identifier rather than the account name. However, using account name makes this example easier to follow. 2 Selecting and utilizing appropriate partition keys is an art in and of itself, and is the subject of many books and articles. Part V. Cloud Services Today’s forecast: “cloudy with a chance of scaling…” Chapter 20. Change and the Cloud Cloud computing has changed the way we think about building and running our applications. But, while how we build applications has changed around the cloud, the cloud itself has changed, and the way we think about the cloud has changed as well. What Has Changed in the Cloud? The cloud has matured over the past decade. Cloud providers have increased their product offerings. They no longer simply provide file storage and compute capacity.


pages: 761 words: 80,914

Ansible: Up and Running: Automating Configuration Management and Deployment the Easy Way by Lorin Hochstein

Amazon Web Services, cloud computing, continuous integration, Debian, DevOps, domain-specific language, don't repeat yourself, general-purpose programming language, Infrastructure as a Service, job automation, MITM: man-in-the-middle, pull request, side project, smart transportation, web application

Using Ansible with EC2 is a large enough topic that you could write a whole book about it. In fact, Yan Kurniawan is writing a book on Ansible and AWS. After digesting this chapter, you should have enough knowledge under your belt to pick up these additional modules without difficulty. 1 The National Institute of Standards and Technology (NIST) has a pretty good definition of cloud computing The NIST Definition of Cloud Computing. 2 You can add tags to entities other than instances, such as AMIs, volumes, and security groups. 3 Or maybe it’s ~/.bashrc? I’ve never figured out the difference between the various Bash dotfiles. 4 You might need to use sudo or activate a virtualenv to install this package, depending on how you installed Ansible. 5 And, to be honest, I have no idea where the package managers install this file. 6 Amazon’s internal network is divided up into subnets, but users do not have any control over how instances are allocated to subnets. 7 Go to Amazon for more details on VPC and whether you have access to EC2-Classic in a region. 8 It’s possible to retrieve the host key by querying EC2 for the instance console output, but I must admit that I never bothing doing this because I’ve never gotten around to writing a proper script that parses out the host key from the console output. 9 Visit Amazon for a list of the regions that it supports. 10 There’s also a handy (unofficial) website that provides a single table with all of the available EC2 instance types. 11 For more information on Jinja2 tests, see the Jinja2 documentation page on built-in tests. 12 This example happens to correspond to a special IP address range named TEST-NET-3, which is reserved for examples.

Even if you’re using dynamic inventory scripts, the add_host module is useful for scenarios where you start up new virtual machine instances and configure those instances in the same playbook. If a new host comes online while a playbook is executing, the dynamic inventory script will not pick up this new host. This is because the dynamic inventory script is executed at the beginning of the playbook, so if any new hosts are added while the playbook is executing, Ansible won’t see them. We’ll cover a cloud computing example that uses the add_host module in Chapter 12. Invoking the module looks like this: add_host name=hostname groups=web,staging myvar=myval Specifying the list of groups and additional variables is optional. Here’s the add_host command in action, bringing up a new vagrant machine and then configuring the machine: - name: Provision a vagrant machine hosts: localhost vars: box: trusty64 tasks: - name: create a Vagrantfile command: vagrant init {{ box }} creates=Vagrantfile - name: Bring up a vagrant server command: vagrant up - name: add the Vagrant hosts to the inventory add_host: > name=vagrant ansible_ssh_host=127.0.0.1 ansible_ssh_port=2222 ansible_ssh_user=vagrant ansible_ssh_private_key_file=/Users/lorinhochstein/.vagrant.d/ insecure_private_key - name: Do something to the vagrant machine hosts: vagrant sudo: yes tasks: # The list of tasks would go here - ...

IAM Identity and Access Management, a feature of Amazon’s Elastic Compute Cloud that allows you to manage user and group permissions. Idempotent An action is idempotent if executing the action multiple times has the same effect as executing it once. Instance A virtual machine. The term is commonly used to refer to a virtual machine running inside an infrastructure-as-a-service cloud, such as Amazon’s Elastic Cloud Compute (EC2). Inventory The list of hosts and groups Lookups Code that executes on the control machine to obtain some configuration data needed by Ansible while a playbook is running. Module Modules are Ansible scripts that perform one specific task. Examples include creating a user account, installing a package, or starting a service. Most Ansible modules are idempotent. Ohai A tool used by Chef to retrieve information about a host.


pages: 364 words: 99,897

The Industries of the Future by Alec Ross

23andMe, 3D printing, Airbnb, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, blockchain, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, David Brooks, disintermediation, Dissolution of the Soviet Union, distributed ledger, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, fiat currency, future of work, global supply chain, Google X / Alphabet X, industrial robot, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Joi Ito, Kickstarter, knowledge economy, knowledge worker, lifelogging, litecoin, M-Pesa, Marc Andreessen, Mark Zuckerberg, Mikhail Gorbachev, mobile money, money: store of value / unit of account / medium of exchange, Nelson Mandela, new economy, offshore financial centre, open economy, Parag Khanna, paypal mafia, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rubik’s Cube, Satoshi Nakamoto, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, The Future of Employment, Travis Kalanick, underbanked, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator, young professional

Sanger, “Obama Had Security Fears on JPMorgan Data Breach,” New York Times, October 8, 2014, http://dealbook.nytimes.com/2014/10/08/cyberattack-on-jpmorgan-raises-alarms-at-white-house-and-on-wall-street/?_r=0. Items of interest will be located: Spencer Ackerman, “CIA Chief: We’ll Spy on You through Your Dishwasher,” Wired, March 15, 2012, http://www.wired.com/2012/03/petraeus-tv-remote. One study pegged the lost business: Daniel Castro, “How Much Will PRISM Cost the US Cloud Computing Industry?” Information Technology and Innovation Foundation, August 2013, http://www2.itif.org/2013-cloud-computing-costs.pdf. In February 2015, President Obama: “Executive Order: Improving Critical Infrastructure Cybersecurity,” White House, February 12, 2013, https://www.whitehouse.gov/the-press-office/2013/02/12/executive-order-improving-critical-infrastructure-cybersecurity. Justifying the unusual step: Joyce Brayboy, “Army Cyber Defenders Open Source Code in new GitHub Project,” US Army, January 28, 2015, http://www.army.mil/article/141734/Army_cyber_defenders_open_source_code_in_new_GitHub_project/.

By taking North Korea offline, they were reminding the North Koreans who controls their networks, and they were doing a favor for the United States that served their own purposes. CYBERATTACKING EVERYTHING As the Internet grows, it is expanding not simply to new users but to entirely new devices, well beyond standard computers, tablets, and smartphones. Electronic communications and electronic sensors have been around for some time, but the costs of sensors and data storage have recently plummeted—in part due to cloud computing. As a result, the stage is now set for what has become known as the “Internet of Things,” where any object has the potential to transmit and receive data, from cars and farm equipment to watches and appliances, even clothing. The digitization of nearly everything is poised to be one of the most consequential economic developments of the next ten years. Cisco Systems chairman John Chambers has said, “We will look back one decade from today [2014] and you’ll look at the impact of the Internet of Everything, and I predict it will be five to ten times more impactful in one decade than the whole Internet to date has been.”

The Code War has no such simple organization, and traditional alliances have fractured. After the revelations of Edward Snowden, the governments and public of European countries condemned American cyberpractices. Billions of dollars of business were lost by American telecommunications and technology companies, which were no longer trusted. One study pegged the loss to American businesses in the cloud computing industry alone at between $22 billion and $25 billion over three years. Yet there is little to no prospect for any sort of short-term progress to be made developing international law, treaties, or other frameworks establishing norms and rules for cyberactivity. The United States won’t agree to anything that the Europeans would demand that limits intelligence-gathering activities. The Chinese won’t admit to, much less agree to, anything related to industrial espionage.


pages: 518 words: 107,836

How Not to Network a Nation: The Uneasy History of the Soviet Internet (Information Policy) by Benjamin Peters

Albert Einstein, American ideology, Andrei Shleifer, Benoit Mandelbrot, bitcoin, Brownian motion, Claude Shannon: information theory, cloud computing, cognitive dissonance, computer age, conceptual framework, continuation of politics by other means, crony capitalism, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Graeber, Dissolution of the Soviet Union, Donald Davies, double helix, Drosophila, Francis Fukuyama: the end of history, From Mathematics to the Technologies of Life and Death, hive mind, index card, informal economy, information asymmetry, invisible hand, Jacquard loom, John von Neumann, Kevin Kelly, knowledge economy, knowledge worker, linear programming, mandelbrot fractal, Marshall McLuhan, means of production, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Network effects, Norbert Wiener, packet switching, Pareto efficiency, pattern recognition, Paul Erdős, Peter Thiel, Philip Mirowski, RAND corporation, rent-seeking, road to serfdom, Ronald Coase, scientific mainstream, Steve Jobs, Stewart Brand, stochastic process, technoutopianism, The Structural Transformation of the Public Sphere, transaction costs, Turing machine

Vladimir Starovsky, the head of the Central Statistical Administration, “harshly objected to the whole project,” Glushkov recalled in the late 1960s—out of opposition not to the economic reform but to the prospect that the Central Statistical Administration would have to cede control over some element of the governance of his administrative turf (economic statistics) to future OGAS directors. Starovsky rejected the remote-access portion of Glushkov’s proposal (a precursor to “cloud computing”). If realized, the OGAS was going to provide access to information and processing power to any authenticated user anywhere on the network. Even though the permission hierarchy for authenticated users presumably could still reaffirm the strong hierarchical structure supporting his administration, Starovsky opposed what we now recognize as a cloud computing provision as being politically “unnecessary” because the Central Statistical Administration was “organized by the initiative of Lenin” and already does everything that Lenin asked of it. Reversing Lenin’s original question, “What is to be done?

This kernel vision of a network as an expression of the nervous system of a factory, writ large across a nation, magnified the image of the workplace until it incorporated the whole command economy—a sort of simultaneously metaphorical and mechanical collectivization of the industrial household (or what Hannah Arendt calls the oikos). The OGAS Project might be seen as preceding, although not precipitating, the current trends in so-called cloud computing. The national network was to provide “collective access,” “remote access,” and “distance access” on a massive scale to civilian users who could “access,” “input,” “receive,” and “process” data related to the command economy (such older terms appear to bear more descriptive heft than the modern computing metaphors such as upload, download, share, and stream). The decentralized network was designed so that information for economic planning could be transmitted, modified, and managed in relative real time up, down, and laterally across the networked administrative pyramid.

Figure 4.1 Map of the three tiers (I, II, III) of planned computing center sites behind the OGAS (All-State Automated System), 1964. Figure 4.2 Map of the EGSVTs (Unified State Network of Computing Centers) that were projected to be operational in 1990, possibly from 1964.4 As communication scholar Vincent Mosco has recently noted, the Soviets offer perhaps the first glimpse of the modern imagining of decentralized remote computing (what recently has been called cloud computing) on a massive scale.5 In Glushkov’s design, the network would afford interactive and collective remote access and communication vertically up and down the planning pyramid and horizontally among peer and associated computing centers. Glushkov writes: “the characteristic quality of the network was a distributed database with zero-address access from any point of the system to all the information after automatic verification of the qualified user.”


pages: 567 words: 122,311

Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll, Benjamin Yoskovitz

Airbnb, Amazon Mechanical Turk, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, barriers to entry, Bay Area Rapid Transit, Ben Horowitz, bounce rate, business intelligence, call centre, cloud computing, cognitive bias, commoditize, constrained optimization, en.wikipedia.org, Firefox, Frederick Winslow Taylor, frictionless, frictionless market, game design, Google X / Alphabet X, Infrastructure as a Service, Internet of things, inventory management, Kickstarter, lateral thinking, Lean Startup, lifelogging, longitudinal study, Marshall McLuhan, minimum viable product, Network effects, pattern recognition, Paul Graham, performance metric, place-making, platform as a service, recommendation engine, ride hailing / ride sharing, rolodex, sentiment analysis, skunkworks, Skype, social graph, social software, software as a service, Steve Jobs, subscription business, telemarketer, transaction costs, two-sided market, Uber for X, web application, Y Combinator

Salesforce, Gmail, Basecamp, and Asana are all examples of popular SaaS products. If you’re running a SaaS business, here’s what you need to know about metrics. Most SaaS providers generate revenue from a monthly (or yearly) subscription that users pay. Some charge on a consumption basis—for storage, for bandwidth, or for compute cycles—although this is largely confined to Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) cloud computing companies today. Many SaaS providers offer a tiered model of their service, where the monthly fee varies depending on some dimension of the application. This might be the number of projects in a project management tool, or the number of customers in a customer relationship management application. Finding the best mix of these tiers and prices is a constant challenge, and SaaS companies invest considerable effort in finding ways to upsell a user to higher, more lucrative tiers.

If you can find ways for customers to self-upsell, as companies like 37Signals, Wufoo, and FreshBooks have done, then you can hook your users on basic features and tempt them with an upgrade path that adds functionality as they need it. This means you’ll not only add revenue from new users, but from existing ones, too. You’re tied to a disruptive change. If you’re part of a growing trend—people sharing information, mobile devices, cloud computing—then you’ve got a better chance of growth. A rising tide floats all boats, and a rising tech sector floats all valuations and exits. Adopters automatically become advocates. Just look at the classic example of online marketing—Hotmail. A simple message appended to every email invited the recipient to switch to Hotmail. The result was an exponential growth rate and a huge exit for the founders.[60] An expense management system like Expensify makes it as easy as possible to add others to the approval workflow, because this is a vector for inherent virality.

This is a good time to fire up a spreadsheet and start playing with numbers: you now know you need 5.7 months’ burn to keep the company running. EBITDA Breakeven EBITDA—earnings before income tax, depreciation, and amortization—is an accounting term that fell out of favor when the dot-com bubble burst. Many companies used this model because it let them ignore their large capital investments and crushing debt. But in today’s startup world, where up-front capital expenses have been replaced by pay-as-you-go costs like cloud computing, EBITDA is an acceptable way to consider how well you’re doing. Hibernation Breakeven A particularly conservative breakeven metric is hibernation. If you reduced the company to its minimum—keeping the lights on, servicing existing customers, but doing little else—could you survive? This is often referred to as “ramen profitability.” There’s no new marketing spend. Your only growth would come from word of mouth or virality, and customers wouldn’t get new features.


pages: 261 words: 10,785

The Lights in the Tunnel by Martin Ford

"Robert Solow", Albert Einstein, Bill Joy: nanobots, Black-Scholes formula, business cycle, call centre, cloud computing, collateralized debt obligation, commoditize, creative destruction, credit crunch, double helix, en.wikipedia.org, factory automation, full employment, income inequality, index card, industrial robot, inventory management, invisible hand, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, knowledge worker, low skilled workers, mass immigration, Mitch Kapor, moral hazard, pattern recognition, prediction markets, Productivity paradox, Ray Kurzweil, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, strong AI, technological singularity, Thomas L Friedman, Turing test, Vernor Vinge, War on Poverty

.* *[ http://folding.stanford.edu and http://boinc.berkeley.edu ] In the future, we can anticipate that grid computing will become increasingly important. In addition, it is already evolving into what computer scientists refer to as “cloud computing.” Essentially this will amount to a new architecture for leveraging the power of huge numbers of computers on an as needed basis: computational capability, together with specific applications, will be delivered as though it were a utility much like electric power. The trend toward grid and cloud computing offers a fantastic opportunity to deploy our incredible new computational capacity in areas that will undoubtedly bring positive advances in fields such as science and medicine. Our next example, however, is far less benign. Meltdown As nearly everyone knows, the “subprime” meltdown of 2007 was triggered when borrowers who did not have the best credit ratings began to default on their mortgages.

Your support of the author’s rights is appreciated. This book is available for purchase in paper and electronic formats at: www.TheLightsintheTunnel.com CONTENTS A Note to Kindle Users Introduction Chapter 1: The Tunnel The Mass Market Visualizing the Mass Market Automation Comes to the Tunnel A Reality Check Summarizing Chapter 2: Acceleration The Rich Get Richer World Computational Capability Grid and Cloud Computing Meltdown Diminishing Returns Offshoring and Drive-Through Banking Short Lived Jobs Traditional Jobs: The “Average” Lights in the Tunnel A Tale of Two Jobs “Software” Jobs and Artificial Intelligence Automation, Offshoring and Small Business “Hardware” Jobs and Robotics “Interface” Jobs The Next “Killer App” Military Robotics Robotics and Offshoring Nanotechnology and its Impact on Employment The Future of College Education Econometrics: Looking Backward The Luddite Fallacy A More Ambitious View of Future Technological Progress: The Singularity A War on Technology Chapter 3: Danger The Predictive Nature of Markets The 2008-2009 Recession Offshoring and Factory Migration Reconsidering Conventional Views about the Future The China Fallacy The Future of Manufacturing India and Offshoring Economic and National Security Implications for the United States Solutions Labor and Capital Intensive Industries: The Tipping Point The Average Worker and the Average Machine Capital Intensive Industries are “Free Riders” The Problem with Payroll Taxes The “Workerless” Payroll Tax “Progressive” Wage Deductions Defeating the Lobbyists A More Conventional View of the Future The Risk of Inaction Chapter 4: Transition The Basis of the Free Market Economy: Incentives Preserving the Market Recapturing Wages Positive Aspects of Jobs The Power of Inequality Where the Free Market Fails: Externalities Creating a Virtual Job Smoothing the Business Cycle and Reducing Economic Risk The Market Economy of the Future An International View Transitioning to the New Model Keynesian Grandchildren Transition in the Tunnel Chapter 5: The Green Light Attacking Poverty Fundamental Economic Constraints Removing the Constraints The Evolution toward Consumption The Green Light Appendix / Final Thoughts Are the ideas presented in this book WRONG?

.* *[ Even much of biotechnology and genetics could be considered a type of information science because it is focused on cataloging and understanding the information in our DNA. ] I have the feeling that this staggering increase in our computational capability represents a pent up resource that is poised to burst out in new and unexpected ways. In the future, we can expect that many more traditional technologies, and in fact nearly every aspect of our lives, will change—perhaps very rapidly—in ways that we cannot foresee. As examples of what we might expect, let’s look at two things that have already occurred: one that, at least so far, has been generally positive, and one that has been decidedly negative. Grid and Cloud Computing Grid computing is a rapidly growing field that focuses on leveraging not just the power of an individual computer, but also the large number of such computers now available. The idea is to tie many computers together using special software. A big computational problem can then be broken down into pieces and distributed across hundreds or even thousands of computers so that they can work on it simultaneously.


pages: 319 words: 89,477

The Power of Pull: How Small Moves, Smartly Made, Can Set Big Things in Motion by John Hagel Iii, John Seely Brown

Albert Einstein, Andrew Keen, barriers to entry, Black Swan, business process, call centre, Clayton Christensen, cleantech, cloud computing, commoditize, corporate governance, creative destruction, disruptive innovation, Elon Musk, en.wikipedia.org, future of work, game design, George Gilder, intangible asset, Isaac Newton, job satisfaction, Joi Ito, knowledge economy, knowledge worker, loose coupling, Louis Pasteur, Malcom McLean invented shipping containers, Maui Hawaii, medical residency, Network effects, old-boy network, packet switching, pattern recognition, peer-to-peer, pre–internet, profit motive, recommendation engine, Ronald Coase, shareholder value, Silicon Valley, Skype, smart transportation, software as a service, supply-chain management, The Nature of the Firm, the new new thing, too big to fail, trade liberalization, transaction costs

Of course, exponential rates like these cannot be sustained forever, but for the foreseeable future the technologists driving innovation in these domains see scant evidence of a flattening of performance-improvement curves for key digital technology components—even if other advances in the labs today, such as quantum computing, might eventually leapfrog today’s technologies altogether. The absence of stabilization in the core technology components suggests that we are not likely to see stabilization in the digital infrastructure either. More than thirty years into this technology revolution, we are just now beginning to explore the contours of cloud computing. The cloud-computing approach suggests that the most efficient way to deliver digital technology is through big centralized data centers that can flexibly deliver computing, storage, and transport services to users. Given all the recent hype surrounding this new development, it is sometimes difficult to keep in mind that only a tiny fraction of the total digital technology resources is today delivered through this kind of infrastructure.

But we’re calling for something that would extend beyond institutional boundaries to connect talent wherever it resides, even outside the firm. These outside-in architectures would get their start working across enterprises, but ultimately they would penetrate back into the enterprise, where their advantages would quickly become apparent. Already we may be seeing the beginnings of this transition in the movement to cloud computing. As companies access more and more of their IT resources from cloud service-providers, these providers will learn to facilitate coordination of activities across large numbers of business partners, each of whom may have different ways of doing business as well as different terminology, policies, and procedures. This will set the stage for further evolution of IT architectures to support more complex, long-lived interactions across networks of diverse participants.12 Pull-based IT platforms represent a very tangible way to amplify the efforts of institutional leaders to move their firms from scalable push to scalable pull.

At minimal cost to SAP—relative to push models—SAP harnessed the collective power of hundreds of thousands of talented individuals to help achieve the company’s strategic goals. 10 For more about this crucial question, see John Hagel III and Marc Singer, “Un-bundling the Corporation,” Harvard Business Review, March 1, 1999, which asserts that most companies are an unnatural bundle of three very different types of businesses: They are customer-relationship businesses, infrastructure-management businesses, and product-development and innovation businesses. 11 See Thomas H. Davenport, Thinking for a Living: How to Get Better Performance and Results from Knowledge Workers (Cambridge: Harvard Business School Press, 2005). 12 Thomas B. Winans and John Seely Brown, “Cloud Computing: A Collection of Working Papers,” July 31, 2009, Deloitte Development. Chapter 7 1 Zoe Baird and James Barksdale et al., “Creating a Trusted Network for Homeland Security,” Markle Foundation, December 2, 2003, http://www.markle.org/down-loadable_assets/nstf_report2_overview.pdf. 2 See Saxby Chambliss, “Counterterrorism Intelligence Capabilities and Performance Prior to 9-11,” Subcommittee on Terrorism and Homeland Security, A Report to the Speaker of the House of Representatives and the Minority Leader, July 2002, http://www.fas.org/irp/congress/2002_rpt/hpsci_ths0702.html. 3 John Franke, “SAP CEO Heir-Apparent Resigns,” March 28, 2007, TechTarget.com, http://searchsap.techtarget.com/news/article/0,289142,sid21_gci1249379,00.html#. 4 This and other details are drawn from Daniel Roth, “Driven: Shai Agassi’s Audacious Plan to Put Electric Cars on the Road,” Wired, August 18, 2008, http://www.wired.com/cars/futuretransport/magazine/16-09/ff_agassi?


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

For example, Hilbert and López (2009) estimated that only 25 per cent of data was stored in digital form in 2000, with the remainder being held in analogue forms such as books, magazines, photos and magnetic tapes. By 2007, digital data constituted 94 per cent of stored data. Since then, the relative share of digital data has continued to grow, especially with the development of distributed storage and services through cloud computing and data centres. Cloud computing takes two forms that often work cooperatively: utility clouds and data clouds (Farber et al. 2011). Utility clouds provide IT capabilities as locationindependent, on-demand services accessible via the Internet, including ‘infrastructure as a service’ (IaaS) such as storage, servers and networks, ‘platform as a service’ (PaaS) comprising an execution environment for the development of custom applications and databases, and ‘software as a service’ (SaaS) that enables users to access their applications and to process data remotely (Farber et al. 2011; Hancke et al. 2012).

Rather than being scarce and limited in access, the production of data is increasingly becoming a deluge; a wide, deep torrent of timely, varied, resolute and relational data that are relatively low in cost and, outside of business, increasingly open and accessible. A data revolution is underway, one that is already reshaping how knowledge is produced, business conducted, and governance enacted. This revolution is founded on the latest wave of information and communication technologies (ICTs), such as the plethora of digital devices encountered in homes, workplaces and public spaces; mobile, distributed and cloud computing; social media; and the internet of things (internetworked sensors and devices). These new technical media and platforms are leading to ever more aspects of everyday life – work, consumption, travel, communication, leisure – and the worlds we inhabit to be captured as data and mediated through data-driven technologies. Moreover, they are materially and discursively reconfiguring the production, circulation and interpretation of data, producing what has been termed ‘big data’ – vast quantities of dynamic, varied digital data that are easily conjoined, shared and distributed across ICT networks, and analysed by a new generation of data analytics designed to cope with data abundance as opposed to data scarcity.

These include the production of mainframe computers in the 1950s and 60s; the nascent Internet in the 1970s and 80s that linked such computers together; the wide-scale roll-out of personal computers in the 1980s and 90s; the massive growth of the Internet in the 1990s and the development of Web-based industries, alongside a huge growth in mobile phones and digital devices such as games consoles and digital cameras; the development of mobile, distributed and cloud computing and Web 2.0 in the 2000s; the roll-out of ubiquitous and pervasive computing in the 2010s. Throughout this period a number of transformative effects took place: computational power grew exponentially; devices were networked together; more and more aspects and processes of everyday life became mediated by digital systems; data became ever more indexical and machine-readable; and data storage expanded and became distributed.


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

Adoption of M2M has been distributed across many markets, and fourteen or fifteen years later, it feels like it has snuck up on us. Why has it taken so long for the world to catch on to what some of us have known for over a decade? It is because in the majority of cases, new technologies that can disrupt business models take much more time to be accepted than anyone in that market wants or expects. In the U.S. market, it took cell phones about twenty years to get to major mass-market penetration. Cloud computing (think Software as a Service, timesharing) can trace its origins back many, many years. With a handful of exceptions, tablets being possibly the best-known outlier, technology adoption takes years. I think we were fooled, like many early market entrants, into thinking we would move right from the visionary customers and early adopters directly into mainstream adoption. The reality, well explained in Geoffrey Moore’s book Crossing the Chasm, is that in order to get across that chasm between the early adopters and the early majority, you need to package that technology into something that is easy for companies to consume.

Since investment in the Internet of Things has until now been more of a futuristic topic, and the understanding and definition of the market varies notably, forecasts are all over the place. For example, IDC, a market research firm, estimates the value of intelligent systems at $1.7 trillion already, growing to $2.4 trillion by 2017.30 It’s interesting if you look at the high-growth markets that are currently developing around cloud computing, big data, and business intelligence. These markets are in the double-digit billions, and are often not counted toward the M2M market. This shows how blurry the borders are, and that we can expect a number of additional growth segments that we do not see or envision today. The question many investors raise is: How does this growth come about? In addition, what are the core growth segments?

As we mentioned in chapter 1, the key macroeconomic trends that will enable this new ecosystem to grow exponentially are described here: First, we see reduction in size and increases in processing power, driven by Moore’s Law, but also by improvements in electric power management. The second important factor is affordability — we see a strong reduction of production costs in areas like fixed and mobile networks, hardware, software, cloud computing, mobile technologies, and robotics. In most areas, technology production costs have decreased by more than 90 percent in the past several years and will continue to fall, also just as predicted by Gordon Moore and his Law. The third crucial trend is de-wireization. As more things are becoming wireless, it means their location can be almost anywhere. The growing ubiquity of cellular and Wi-Fi networks have enabled this trend.


pages: 133 words: 42,254

Big Data Analytics: Turning Big Data Into Big Money by Frank J. Ohlhorst

algorithmic trading, bioinformatics, business intelligence, business process, call centre, cloud computing, create, read, update, delete, data acquisition, DevOps, fault tolerance, linked data, natural language processing, Network effects, pattern recognition, performance metric, personalized medicine, RFID, sentiment analysis, six sigma, smart meter, statistical model, supply-chain management, Watson beat the top human players on Jeopardy!, web application

Currently, Hadoop, an open source software framework derived from Google’s MapReduce and Google File System papers, is being used by several technology vendors to do just that. Hadoop maps tasks across a cluster of machines, splitting them into smaller subtasks, before reducing the results into one master calculation. It’s really an old grid-computing technique given new life in the age of cloud computing. Many of the challenges of yesterday remain today, and technology is just now catching up with the demands of Big Data analytics. However, Big Data remains a moving target. As the future brings more challenges, it will also deliver more solutions, and Big Data has a bright future, with tomorrow delivering the technologies that ease leveraging the data. For example, Hadoop is converging with other technology advances such as high-speed data analysis, made possible by parallel computing, in-memory processing, and lower-cost flash memory in the form of solid-state drives.

See Business intelligence (BI) Big Data and Big Data analytics analysis categories application platforms best practices business case development challenges classifications components defined evolution of examples of 4Vs of goal setting introduction investment in path to phases of potential of privacy issues processing role of security (See Security) sources of storage team development technologies (See Technologies) value of visualizations Big Science BigSheets Bigtable Bioinformatics Biomedical industry Blekko Business analytics (BA) Business case best practices data collection and storage options elements of introduction Business intelligence (BI) as Big Data analytics foundation Big Data analytics team incorporation Big Data impact defined extract, transform, and load (ETL) information technology and in-memory processing limitations of marketing campaigns risk analysis storage capacity issues unstructured data visualizations Business leads Business logic Business objectives Business rules C Capacity of storage systems Cassandra Census data CERN Citi Classification of data Cleaning Click-stream data Cloud computing Cloudera Combs, Nick Commodity hardware Common Crawl Corpus Communication Competition Compliance Computer security officers (CSOs) Consulting firms Core capabilities, data analytics team Costs Counterintelligence mind-set CRUD (create, retrieve, update, delete) applications Cryptographic keys Culture, corporate Customer needs Cutting, Doug D Data defined growth in volume of value of See also Big Data and Big Data analytics Data analysis categories challenges complexity of as critical skill for team members data accuracy evolution of importance of process technologies Database design Data classification Data discovery Data extraction Data integration technologies value creation Data interpretation Data manipulation Data migration Data mining components as critical skill for team members defined examples methods technologies Data modeling Data protection.

See Technologies Sources of data. See Data sources Space program Specificity of information Speed-accuracy tradeoff Spring Data SQL limitations NoSQL Integration scaling Stale data Statistical applications Storage Storm Structured data Success, measurement of Supplementary information Supply chain T Tableau Public Taxonomies Team members Technologies application platforms Cassandra cloud computing commodity hardware decision making processing power security storage Web-based tools worst practices See also Hadoop Telecommunications Text analytics Thin provisioning T-Mobile Training Transportation Trends Trusted applications Turk Twitter U United Parcel Service (UPS) Unstructured data complexity of defined forms growth of project goal setting social media’s collection technologies varieties of U.S. census User analysis Utilities sector V Value, extraction of Variety Velocity Vendor lock-in Veracity Videos Video surveillance Villanustre, Flavio Visualization Volume W Walt Disney Company Watson Web-based technologies Web sites click-stream data logs traffic distribution White-box systems Worst practices Wyle Laboratories X XML Y Yahoo


pages: 719 words: 181,090

Site Reliability Engineering: How Google Runs Production Systems by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy

Air France Flight 447, anti-pattern, barriers to entry, business intelligence, business process, Checklist Manifesto, cloud computing, combinatorial explosion, continuous integration, correlation does not imply causation, crowdsourcing, database schema, defense in depth, DevOps, en.wikipedia.org, fault tolerance, Flash crash, George Santayana, Google Chrome, Google Earth, information asymmetry, job automation, job satisfaction, Kubernetes, linear programming, load shedding, loose coupling, meta analysis, meta-analysis, microservices, minimum viable product, MVC pattern, performance metric, platform as a service, revision control, risk tolerance, side project, six sigma, the scientific method, Toyota Production System, trickle-down economics, web application, zero day

., an offline MapReduce or Hadoop pipeline). It’s advantageous to design your interfaces to hinder developers unfamiliar with your code from circumventing soft deletion features with new code. One effective way of achieving this is to implement cloud computing offerings that include built-in soft deletion and undeletion APIs, making sure to enable said feature.7 Even the best armor is useless if you don’t put it on. Soft deletion strategies cover data deletion features in consumer products like Gmail or Google Drive, but what if you support a cloud computing offering instead? Assuming your cloud computing offering already supports a programmatic soft deletion and undeletion feature with reasonable defaults, the remaining accidental data deletion scenarios will originate in mistakes made by your own internal developers or your developer customers.

We might say data integrity is a measure of the accessibility and accuracy of the datastores needed to provide users with an adequate level of service. But this definition is insufficient. For instance, if a user interface bug in Gmail displays an empty mailbox for too long, users might believe data has been lost. Thus, even if no data was actually lost, the world would question Google’s ability to act as a responsible steward of data, and the viability of cloud computing would be threatened. Were Gmail to display an error or maintenance message for too long while “only a bit of metadata” is repaired, the trust of Google’s users would similarly erode. How long is “too long” for data to be unavailable? As demonstrated by an actual Gmail incident in 2011 [Hic11], four days is a long time—perhaps “too long.” Subsequently, we believe 24 hours is a good starting point for establishing the threshold of “too long” for Google Apps.

Choosing a Strategy for Superior Data Integrity There are many possible strategies for rapid detection, repair, and recovery of lost data. All of these strategies trade uptime against data integrity with respect to affected users. Some strategies work better than others, and some strategies require more complex engineering investment than others. With so many options available, which strategies should you utilize? The answer depends on your computing paradigm. Most cloud computing applications seek to optimize for some combination of uptime, latency, scale, velocity, and privacy. To provide a working definition for each of these terms: Uptime Also referred to as availability, the proportion of time a service is usable by its users. Latency How responsive a service appears to its users. Scale A service’s volume of users and the mixture of workloads the service can handle before latency suffers or the service falls apart.


pages: 281 words: 95,852

The Googlization of Everything: by Siva Vaidhyanathan

1960s counterculture, activist fund / activist shareholder / activist investor, AltaVista, barriers to entry, Berlin Wall, borderless world, Burning Man, Cass Sunstein, choice architecture, cloud computing, computer age, corporate social responsibility, correlation does not imply causation, creative destruction, data acquisition, death of newspapers, don't be evil, Firefox, Francis Fukuyama: the end of history, full text search, global pandemic, global village, Google Earth, Howard Rheingold, informal economy, information retrieval, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge worker, libertarian paternalism, market fundamentalism, Marshall McLuhan, means of production, Mikhail Gorbachev, moral panic, Naomi Klein, Network effects, new economy, Nicholas Carr, PageRank, Panopticon Jeremy Bentham, pirate software, Ray Kurzweil, Richard Thaler, Ronald Reagan, side project, Silicon Valley, Silicon Valley ideology, single-payer health, Skype, Social Responsibility of Business Is to Increase Its Profits, social web, Steven Levy, Stewart Brand, technoutopianism, The Nature of the Firm, The Structural Transformation of the Public Sphere, Thorstein Veblen, urban decay, web application, zero-sum game

Because of the mistakes Google made in the Books program, federal regulators and many important segments of the reading public grew concerned with the scope of Google’s ambitions.15 In the public mind, Google’s informal motto, “Don’t be evil,” resonates more than its formal mission statement. But the mission statement is far more interesting. It is a stunning statement. What other institution would define changing the world as its unifying task? The Web-using public has adopted Google services at an astounding rate, and Google has expanded to master widely used Internet functions such as Web search, e-mail, personal “cloud computing,” and online advertising. Chapter 6 and the conclusion consider how Google is changing and challenging both the technologies and the companies that govern human I NT ROD UCT I ON 11 communication. The book concludes with a call for more explicitly public governance of the Internet. Such governance might take the form of greater privacy guarantees for Web users or strong antitrust scrutiny of companies like Google.

It now offers online software such as a word processor, spreadsheets, presentation software, and a REN D E R UNTO CA ESA R 17 calendar service—all operating “in the cloud” and thus freeing users from managing multiple versions of their files and applications on different computers, and easing collaboration with others. In 2008 Google released its own Web browser called Chrome, despite many years of collaborating with the Mozilla foundation in supporting the opensource Firefox browser. And in 2009 it previewed its Chrome operating system for cloud computing, a direct assault on Microsoft’s core product, Windows. It hosts health records online. On top of all that, since its beginning in 2004, its Google Books project has scanned millions and millions of volumes and has made many of them available online at no cost, simultaneously appropriating the functions of libraries on the one hand and the rights of publishers on the other. In 2007 Google announced plans for a mobile-phone operating system and attempted, but failed, to change the ways that the United States government allocates radio bandwidth to mobile companies in an attempt to open up competition and improve service.11 And since 2005 the company has been Googlizing the real world through Google Maps, Street View, and Google Earth, a service that allows users to manipulate satellite images to explore the Earth from above.

However, university officials who negotiate contracts with Google often must sign nondisclosure agreements to ensure that Google’s competitors do not have too clear a picture of what the company is doing with its academic partners. 196 TH E G OOGL IZATION OF MEMORY Computing in the cloud is both radically empowering and potentially worrying. One downside involves the tangle of rights claims that a widespread collaboration among individual researchers, university technology-transfer offices, and major computer companies might generate.39 Such a confusing, complicated set of claims not only risks years of litigation among the parties but could attract significant antitrust scrutiny as well. Cloud computing and massive, distributed computation have already been declared the next great intellectual revolution by Wired magazine, which prides itself on predicting such trends. Its editor, Chris Anderson, wrote in June 2008 that the ability to collect and analyze almost unimaginable collections of data renders the standard scientific process of hypothesis, data collection, testing, revision, publication, and further revision almost obsolete.


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Using Open Source Platforms for Business Intelligence: Avoid Pitfalls and Maximize Roi by Lyndsay Wise

barriers to entry, business intelligence, business process, call centre, cloud computing, commoditize, different worldview, en.wikipedia.org, Just-in-time delivery, knowledge worker, Richard Stallman, software as a service, statistical model, supply-chain management, the market place

For instance, it may pay for a company to pay more for software if that software provides outof-the-box capabilities that limit the amount of customization required as opposed to using free software and spending months and multiple resources to get the solution up and running. So Adopting OSBI 35 even though other solutions may be less expensive in the beginning, the overall solution costs may actually be more in the long-term despite the lower initial price point. Broader deployment methods. Outside of the addition of cloud computing, the effects of diversity in deployment have had more of an indirect effect when looking at the OSBI market. Organizations can now select how they want to deploy BI. Choices include desktop, Web, hosted or as a service, and in the cloud. This relates to increased flexibility and autonomy, which transfers the power into the hands of business and technical users. In the past, BI offerings were controlled by solution providers selling their products without focusing on the intrinsic value provided to the customer.

However, it is important to note the fact that for community offerings, IT developers still remain the main access point to OSBI adoption. Obviously, with additional types of deployment available, businesses are able to pick and choose what serves them best. The same can be said within OSBI. IT developers have always been able to create customized solutions without the limitations of traditional software offerings. Add to this cloud computing options and commercial availability and OSBI expands to broader flexibility that compares with broader deployment overall. Proprietary no longer. When solution availability was limited and the market was less mature, many organizations flocked to proprietary offerings due to lack of selection and perceived viability. With few large scale BI vendors and viable industry solutions to choose from, enterprise organizations were stuck implementing large scale BI infrastructures based on integration with other proprietary offerings.

., and find solutions that meet these varying requirements. In addition to market expansion, over the past several years, BI has gone from a multi-dimensional analysis and reporting solution for the select few to an organization-wide solution used to drive business success. Its overall importance within companies keeps growing in relation to C-level executive priority and broad adoption.2 Add to this the increase in cloud computing, big data, virtualization, and expanding OS options, and businesses are now in the position to apply BI 1 For instance, those discussed in Chapter 1, including SaaS or cloud-based offerings. In Gartner’s study, Amplify the Enterprise: the 2012 CIO Agenda, Analytics and BI rate number one on what is important to CIOs minds. This study interviewed over 2300 CIOs. http://bi-software.blogspot.ca/2012/02/gartner-study-showsbi-importance.html, with the actual report being found at: http://www.gartner.com/id=1901814. 2 Using Open Source Platforms for Business Intelligence © 2012 Elsevier Inc.


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

Anyone with a credit card could rent hardware and storage space, dynamically, for minutes, hours, months, or years. Practically speaking, AWS, and the cloud market it created, removed the final cost constraint on developer creativity. As Flip Kromer, CTO of data startup Infochimps put it, “EC2 means anyone with a $10 bill can rent a 10-machine cluster with 1TB of distributed storage for 8 hours.” For all of the focus on the technology of cloud computing, its real import has been the elimination of up-front capital expense costs and making any class of hardware instantly accessible. Hardware had certainly been available via a network before, but never this cheaply, and never in such an on-demand fashion. With the creation of the cloud market, developers had, for the first time in history, access to both no-cost software and infrastructure affordable for even an individual.

Developers are attracted to its platform because of the size of the market…those developers create thousands of new applications…the new applications give consumers thousands of additional reasons to buy Apple devices rather than the competition…and those new Apple customers give even more developers reason to favor Apple. Apple not only profits from this virtuous cycle, it benefits from ever-increasing economies of scale, realizing lower component costs than competitors. None of which would be possible without the developers Apple has recruited and, generally, retained. Amazon Web Services The company that started the cloud computing craze was founded in 1994 as a bookstore. The quintessential Internet company, Amazon.com competed with the traditional brick-and-mortar model via an ever-expanding array of technical innovations: some brilliant, others mundane. The most-important of Amazon’s retail innovations co-opted its customers into contributors. From affiliate marketing programs to online reviews, Amazon used technology to enable its customers’ latent desire to more fully participate in the buying process.


pages: 274 words: 72,657

The Power of Moments: Why Certain Experiences Have Extraordinary Impact by Chip Heath, Dan Heath

Cal Newport, call centre, clean water, cloud computing, crowdsourcing, desegregation, fear of failure, Mahatma Gandhi, mental accounting, meta analysis, meta-analysis, school choice, six sigma, Steve Ballmer

Because of the facilitators’ questions, people in the villages are made to “see” what had been in front of their eyes the whole time. And that’s not a serendipitous “aha!” moment, it’s an engineered moment. How do we engineer powerful insights in more ordinary organizational situations? Consider the way Scott Guthrie handled a situation at Microsoft in 2011. He’d been tapped by Steve Ballmer to lead the company’s fast-growing cloud computing service, called Azure. Guthrie visited Azure customers, and their feedback about their experience with the service was clear: Azure’s underlying technology was good, but it was hard to use. Guthrie knew Azure would never meet its growth expectations until it was much more customer-friendly. But how could he get his colleagues to understand, viscerally, how far off track they were? He called an off-site meeting with his senior managers and software architects, and he gave them a challenge: Build an app using Azure, just as one of their customers might.

Handbook on Community-Led Total Sanitation. http://www.communityledtotalsanitation.org/sites/communityledtotalsanitation.org/files/cltshandbook.pdf. Declined from 34% to 1%. The decline in open defecation is from CLTS annual report, 2014–15, and CLTS report “Igniting Action/Asia.” “The naked truth is out.” Quote from Dan’s interview with Kar. Scott Guthrie, Microsoft Azure. Story from http://fortune.com/microsoft-fortune-500-cloud-computing/. Course Design Institute. Dan interviewed Michael Palmer in June 2015 and attended the CDI in July 2015. The teacher quotes are from that workshop. Dan also interviewed Christ (January 2016) and Lawrence (August 2015). The course evaluation data and the “exponentially improved” quote are from http://cte.virginia.edu/programs/course-design-institute/testimonials/. Chapter 6: Stretch for Insight Lea Chadwell opens a bakery.

See also specific occasion Chadwell, Lea, 113–16, 117, 120, 131, 132 Chadwell, Sam, 114 change career, 258–62 See also breaking the script; surprise; “trip over the truth” Children’s Hospital of Pittsburgh, 32–33 Chiles, Cynthia, 191 Chiluzi, Umelu, 99 Chinese restaurant: clinic about, 134–36 Christ, George, 109 civil rights movement, 177–81, 185, 193. See also Nashville, Tennessee Clark, Josh, 159–61 clinics and boss has flash of insight, 196–99 and improving a restaurant, 134–36 and missed moments of retail banking, 37–39 purpose of, 37, 89 and refreshing meetings, 89–92 and “silo” mentality, 249–52 clothes: at peak events, 62 cloud computing, 104, 105, 106 CLTS (Community-Led Total Sanitation), 99–102, 103–4, 105, 106, 132, 255 college admission to, 253–55 remembering, 9 See also Signing Day (YES Prep School) color slides study, 191–92 coming of age rituals, 19. See also specific ritual common sense, 28, 29 Community-Led Total Sanitation. See CLTS connecting to meaning, 207–11, 219–20, 247 connections and building peaks, 61 characteristics of, 14, 16, 246, 247 and “Couch to 5K” program, 162 and courage, 211 and deepening ties, 136, 198, 223–46 as element of defining moments, 14, 15, 16, 43, 91–92, 135–36, 157, 198, 252 emotions and, 157 group, 203–4, 205–13, 214–16, 247 intimacy and, 241–46 and making moments matter, 255, 257, 263, 266 to meaning, 207–11, 219–20, 247 overview about, 203–4 and pain/struggle, 214–16, 247 and pride, 211 and purpose, 219, 221, 247 purpose of, 203, 247 role-playing and, 91 and shared meaning, 92, 198, 204, 205–22, 247, 251, 252 “silo” mentality and, 251–52 as social moments, 203 and thinking in moments, 39 and time, 247 and timing of defining moments, 34 whirlwind review of, 247–48 See also celebrations; relationships contributions, and shared meaning, 220–21 Cool Runnings, 161 cooperation: fostering, 249–52 Corporate Executive Board (CEB), 239–41 “Couch to 5K” program, 13–14, 159–62 courage and connections, 211 as contagious, 193, 195 creating moments of, 182–88 definition of, 181 and ethics education, 186–88, 195, 198 exposure therapy and, 183–85 and “implementation intentions,” 185–87 and making moments matter, 257 and managing fear, 183–85 and military, 182–83 practicing, 140, 177–93, 194, 195, 198, 251, 257 and pride, 139, 140, 177–93, 194, 195, 197, 198, 251 and protest movements, 177–81, 189 and resolve of others, 190–93 and role playing, 180–81, 185, 188–89 “silo” mentality and, 251 Course Design Institute (CDI) (University of Virginia), 106–11 crystallization of discontent, 103–5, 116, 259–60 Cuijpers, Pim, 190 Cullowhee Experience, 142 culture, and defining moments, 18–19 Cummins Northeast.


pages: 515 words: 126,820

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

With widespread adoption of the Internet, specifically the World Wide Web, individuals and companies could use their computers to share information—initially as text documents and later as images, videos, other multimedia content, and eventually software apps.3 Sharing began to democratize the information landscape. But it was short-lived. In the 1990s, a new variant of time-sharing appeared, initially called virtual private networks (VPNs) and then cloud computing. Cloud computing enabled users and companies to store and process their software and data in third-party data centers. New technology companies like Salesforce.com built fortunes by harnessing the cloud model to save customers the big costs of developing and running their own software. Cloud service providers like Amazon and IBM built ginormous multibillion-dollar businesses. During the 2000s, social media companies like Facebook and Google created services that ran on their own vast data centers.

Summoned by an unknown person or persons with unclear motives, at an uncertain time in history, the genie is now at our service for another kick at the can—to transform the economic power grid and the old order of human affairs for the better. If we will it. Let us explain. The first four decades of the Internet brought us e-mail, the World Wide Web, dot-coms, social media, the mobile Web, big data, cloud computing, and the early days of the Internet of Things. It has been great for reducing the costs of searching, collaborating, and exchanging information. It has lowered the barriers to entry for new media and entertainment, new forms of retailing and organizing work, and unprecedented digital ventures. Through sensor technology, it has infused intelligence into our wallets, our clothing, our automobiles, our buildings, our cities, and even our biology.

He posited that a firm would expand until the cost of performing a transaction inside the firm exceeded the cost of performing the transaction outside the firm.5 Don argued that the Internet would reduce a firm’s internal transaction costs somewhat; but we thought, because of its global accessibility, it would reduce costs in the overall economy even more, in turn lowering barriers to entry for more people. Yes, it did drop search costs, through browsers and the World Wide Web. It also dropped coordination costs through e-mail, data processing applications like ERP, social networks, and cloud computing. Many companies benefited from outsourcing such units as customer service and accounting. Marketers engaged customers directly, even turning consumers into producers (prosumers). Product planners crowdsourced innovations. Manufacturers leveraged vast supply networks. However, the surprising reality is that the Internet has had peripheral impact on corporate architecture. The industrial-age hierarchy is pretty much intact as the recognizable foundation of capitalism.


pages: 502 words: 107,510

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

Because so many different online platforms come with ways for users to tag or annotate their data in other ways, SSL techniques don’t require that a researcher create and annotate two separate models over the data, since one of the models is already provided by the users (though there is, of course, always some messiness in user-generated content). NLP Online and in the Cloud The idea of cloud computing has been invading nearly every aspect of people’s lives: from using cloud storage to store music and back up data to the return of the idea of distributed computers. Additionally, there has been a lot of movement to create central repositories, and more efforts to create interoperability standards as well as software that adheres to those standards. In this section we will examine some of the ways that cloud computing can affect NLP and annotation, and ways that the Internet is being used to organize NLP resources. Distributed Computing A common problem with NLP systems is that they are often very processing-intensive.

And Finally... In this chapter our goal was to show you the role that annotation is playing in cutting-edge developments in computational linguistics and machine learning. We pointed out how the different components of the MATTER development cycle are being experimented with and improved, including new ways to collect annotations, better methods for training algorithms, ways of leveraging cloud computing and distributed data, and stronger communities for resource sharing and collaboration. Because of a lack of understanding of the role that annotation plays in the development of computational linguistics systems, there is always some discussion that the role of annotation is outdated; that with enough data, accurate clusters can be found without the need for human-vetted categories or other labels.

., Clustering and Unsupervised Learning–Semi-Supervised Learning K-nearest neighbor, Other Classifiers to Know About Maximum Entropy classifiers (MaxEnt), Maximum Entropy Classifiers Naïve Bayes learning, Naïve Bayes Learning–Sentiment classification Support Vector Machine (SVM), Other Classifiers to Know About classifier algorithms, Classifier Algorithms–Other Classifiers to Know About, Decision Tree Learning–Decision Tree Learning, Other Classifiers to Know About, Other Classifiers to Know About decision tree learning, Decision Tree Learning–Decision Tree Learning macro-averaging, Other Classifiers to Know About micro-averaging, Other Classifiers to Know About closure rules, Refining Your Goal: Informativity Versus Correctness Cloud computing and NLP, NLP Online and in the Cloud–Shared Language Applications, Distributed Computing, Shared Language Resources, Shared Language Applications distributed computing, Distributed Computing shared language resources, Shared Language Resources, Shared Language Applications ClueWeb09 corpus, Corpora Today clustering, Clustering and Unsupervised Learning, Clustering and Unsupervised Learning–Semi-Supervised Learning, Clustering and Unsupervised Learning, Clustering and Unsupervised Learning, Clustering and Unsupervised Learning, Clustering and Unsupervised Learning classification vs., Clustering and Unsupervised Learning–Semi-Supervised Learning exclusive clustering, Clustering and Unsupervised Learning hierarchical clustering, Clustering and Unsupervised Learning overlapping clustering, Clustering and Unsupervised Learning probabilistic clustering, Clustering and Unsupervised Learning clustering algorithms, Clustering Cohen’s Kappa (κ), Cohen’s Kappa (κ)–Cohen’s Kappa (κ), Fleiss’s Kappa (κ), Interpreting Kappa Coefficients–Calculating κ in Other Contexts, Calculating κ in Other Contexts, Confusion Matrices and confusion matrices, Confusion Matrices Fleiss’s Kappa (κ), Fleiss’s Kappa (κ) interpreting, Interpreting Kappa Coefficients–Calculating κ in Other Contexts skewed data, potential for, Calculating κ in Other Contexts collocations, N-grams concordances, Early Use of Corpora, Early Use of Corpora, Early Use of Corpora Corpus Pattern Analysis, Early Use of Corpora Key Word in Context index (KWIC), Early Use of Corpora condition-action pair, Defining Our Learning Task conditional probability, Joint Probability Distributions Conditional Random Field models (CRF), Structured Pattern Induction, Sequence Induction Algorithms Conference on Computational Linguistics (COLING), Organizations and Conferences Conference on Natural Language Learning (CoNLL) Shared Task (Special Interest Group on Natural Language Learning of the Association for Computational Linguistics), NLP Challenges confusion matrix, Cohen’s Kappa (κ), Confusion Matrices consuming tags, Annotate with the Specification corpus analytics, Corpus Analytics–Language Models, Basic Probability for Corpus Analytics–Bayes Rule, Joint Probability Distributions–Joint Probability Distributions, Counting Occurrences–N-grams, Language Models joint probability distributions, Joint Probability Distributions–Joint Probability Distributions language models, Language Models lexical statistics for, Counting Occurrences–N-grams probability principles for, Basic Probability for Corpus Analytics–Bayes Rule (see also probability) corpus linguistics, A Brief History of Corpus Linguistics–A Brief History of Corpus Linguistics, A Brief History of Corpus Linguistics–Corpora Today history of, A Brief History of Corpus Linguistics–A Brief History of Corpus Linguistics Corpus of Contemporary American English (COCA), A Brief History of Corpus Linguistics Corpus Pattern Analysis, Early Use of Corpora corpus, corpora, The Importance of Language Annotation, What Is a Corpus?


pages: 186 words: 49,251

The Automatic Customer: Creating a Subscription Business in Any Industry by John Warrillow

Airbnb, airport security, Amazon Web Services, asset allocation, barriers to entry, call centre, cloud computing, commoditize, David Heinemeier Hansson, discounted cash flows, high net worth, Jeff Bezos, Network effects, passive income, rolodex, sharing economy, side project, Silicon Valley, Silicon Valley startup, software as a service, statistical model, Steve Jobs, Stewart Brand, subscription business, telemarketer, time value of money, zero-sum game, Zipcar

If Time Warner Cable has to send a technician to a SignatureHome subscriber’s house, he or she will wear specially designed booties to keep the customer’s floors tidy—a small touch designed to make SignatureHome subscribers feel valued. Similarly, Microsoft has also made plays in the area of subscription services with Microsoft Office, the most successful and pervasive software program in history. The folks in Redmond don’t want you buying Office at Staples anymore; today, they want to sell you a subscription to Office 365. Microsoft’s aggressive push into cloud computing has been accelerated by Google Apps, another office productivity suite that is available to businesses exclusively through subscription. Big companies like Apple, Time Warner Cable, Amazon, Target, Microsoft, and Google are not necessarily walking away from their traditional business models entirely. In many cases, they are adding a subscription business to build recurring revenue, expand their relationships with existing customers, and understand what customers want.

Let’s say you make 70% gross profit after paying the expenses of onboarding and any hard costs associated with adding each new subscriber. Using the example above, BVP would express your CAC payback period as approximately 7 months: 500 divided by ($100 × .70). An acceptable CAC payback period depends on how sticky your customers are and how much they spend with you. BVP elaborates on this concept in its white paper “Bessemer’s Top 10 Laws of Cloud Computing”: For SMB (Small & Medium Business) customers with higher churn rates and thus shorter monetization windows, CAC Payback Periods of 6–18 months are typically needed, whereas enterprise businesses with high up sells and long retention periods may be able to subsidize payback periods of 24–36 months. A CAC Payback Period of 36+ months is typically a cause for concern and suggests you may want to slam on the brakes until you can improve sales efficiency, whereas a Payback Period of under 6 months means you should invest more money immediately and step on the gas.1 The concept of a CAC payback period can be described visually.

Reports Q2 2012 Financial Results,” Ancestry.com, press release, July 25, 2012. ir.ancestry.com/releasedetail.cfm?ReleaseID=695393. 3. Nunogawa, Matt, “Notes and Summary of Gail Goodman’s ‘The Long Slow SaaS Ramp of Death,’” @amattn, September 11, 2013. amattn.com/p/notes_summary_long_slow_saas_ramp_of_death.html. CHAPTER 13: THE CASH SUCK VS. THE CASH SPIGOT 1. Botteri, Philippe, et al., “Bessemer’s Top 10 Laws of Cloud Computing and SaaS,” Bessemer Venture Partners, Winter 2010. bvp.com/sites/default/files/bvps_10_laws_of_cloud_saas_winter_2010_release.pdf. 2. McDerment, Mike, “An Open Letter from FreshBooks Founder Mike McDerment,” FreshBooks.com, August 21, 2012. freshbooks.com/cloud-accounting-Letter. 3. Davidoff, Steven M., “In Venture Capital Deals, Not Every Founder Will Be a Zuckerberg,” New York Times, April 30, 2013. dealbook.nytimes.com/2013/04/30/in-venture-capital-deals-not-every-Founder-will-be-a-Zuckerberg. 4.


pages: 301 words: 85,126

AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Air France Flight 447, Albert Einstein, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, Flash crash, Grace Hopper, Gödel, Escher, Bach, Harvard Computers: women astronomers, index fund, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

The third AI enabler is cloud computing. This trend is nearly invisible to consumers, but it’s had an enormous democratizing effect on AI. To illustrate this, we’ll draw an analogy here between data and oil. Imagine if all companies of the early twentieth century had owned some oil, but they had to build the infrastructure to extract, transport, and refine that oil on their own. Any company with a new idea for making good use of its oil would have faced enormous fixed costs just to get started; as a result, most of the oil would have sat in the ground. Well, the same logic holds for data, the oil of the twenty-first century. Most hobbyists or small companies would face prohibitive costs if they had to buy all the gear and expertise needed to build an AI system from their data. But the cloud-computing resources provided by outfits like Microsoft Azure, IBM, and Amazon Web Services have turned that fixed cost into a variable cost, radically changing the economic calculus for large-scale data storage and analysis.

But the cloud-computing resources provided by outfits like Microsoft Azure, IBM, and Amazon Web Services have turned that fixed cost into a variable cost, radically changing the economic calculus for large-scale data storage and analysis. Today, anyone who wants to make use of their “oil” can now do so cheaply, by renting someone else’s infrastructure. When you put those four trends together—faster chips, massive data sets, cloud computing, and above all good ideas—you get a supernova-like explosion in both the demand and capacity for using AI to solve real problems. AI Anxieties We’ve told you how excited our students are about AI, and how the world’s largest firms are rushing to embrace it. But we’d be lying if we said that everyone was so bullish about these new technologies. In fact, many people are anxious, whether about jobs, data privacy, wealth concentration, or Russians with fake-news Twitter-bots.

See car accidents; Formula 1 racing; robot cars CDC. See Centers for Disease Control and Prevention (CDC) Centers for Disease Control and Prevention (CDC) chatbots China chatbots robotic automation tech companies toilet paper theft (Temple of Heaven Park) Churchill, Winston Cinematch (Netflix recommender system) civil rights activists and organizations Clinton, Bill Clinton, Hillary cloud computing as AI enabler coin clipping coin toss Bayes’s rule and New England Patriots and Cold War Columbia University: Statistical Research Group (SRG) computers BINAC compilers interpreters and compilers speed of subroutines UNIVAC See also Hopper, Grace conditional probability asymmetry of health care and personalization and weather and See also Bayes’s rule contraception and birth control assumptions and history of Natural Cycles (phone app) rhythm method Cook, E.


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

Compare the financial results experienced by two major retailers: traditional giant Walmart and online platform Amazon. Amazon has some thirty-three open APIs as well as over 300 API “mashups” (i.e., combination tools that span two or more APIs), enabling e-commerce, cloud computing, messaging, search engine optimization, and payments. By contrast, Walmart has just one API, an e-commerce tool.14 Partly as a result of this difference, Amazon’s stock market capitalization exceeded that of Walmart for the first time in June 2015, reflecting Wall Street’s bullish view of Amazon’s future growth prospects.15 Other platform businesses have reaped similar benefits from their APIs. Cloud computing and computer services platform Salesforce generates 50 percent of its revenues through APIs, while for travel platform Expedia, the figure is 90 percent.16 The third category of developers who add value to the interactions on a platform are data aggregators.

Consider SAP, the German-based multinational giant that produces software for large enterprises to use in managing their internal operations, customer relationships, and other processes. SAP, which operates a large business processes platform, has partnered with the U.S.-based firm ADP to provide payroll processing services to its users, partly in order to take advantage of ADP’s superior access to cloud computing capabilities. However, ADP has substantial customer relationships of its own and can serve as the platform host linking customers to a number of data/computing/storage partners. Thus, the partnership creates an opportunity for ADP to displace SAP as the primary manager of the customer relationship. This is an instance in which the platform manager (SAP) is in danger of losing control of the customer connection to an extension developer (ADP).

Nalebuff and Adam M. Brandenburger, Co-opetition (London: HarperCollins Business, 1996). 17. Steve Jobs, “Thoughts on Flash,” April 2010, http://www.apple.com/hotnews/thoughts-on-flash/. 18. Vardit Landsman and Stefan Stremersch, “Multihoming in Two-Sided Markets: An Empirical Inquiry in the Video Game Console Industry,” Journal of Marketing 75, no. 6 (2011): 39–54. 19. Ming Zeng, “How Will Big Data and Cloud Computing Change Platform Thinking?”, keynote address, MIT Platform Strategy Summit, July 25, 2014, http://platforms.mit.edu/2014. 20. “Top 20 Apps with MAU Over 10 Million,” Facebook Apps Leaderboard, AppData, appdata.com/leaderboard/apps?show_na=1. Accessed October 14, 2015. 21. Carl Shapiro and Hal R. Varian, “The Art of Standards Wars,” California Management Review 41, no. 2 (1999): 8–32. 22. Bill Gurley, “All Revenue Is Not Created Equal: Keys to the 10X Revenue Club,” Above the Crowd, May 24, 2011, http://abovethecrowd.com/2011/05/24/all-revenue-is-not-created-equal-the-keys-to-the-10x-revenue-club/. 23.


pages: 220

Startupland: How Three Guys Risked Everything to Turn an Idea Into a Global Business by Mikkel Svane, Carlye Adler

Airbnb, Ben Horowitz, Burning Man, business process, call centre, Chuck Templeton: OpenTable:, cloud computing, credit crunch, David Heinemeier Hansson, Elon Musk, housing crisis, Jeff Bezos, Kickstarter, Menlo Park, remote working, Ruby on Rails, Sand Hill Road, Silicon Valley, Silicon Valley startup, Skype, software as a service, South of Market, San Francisco, Steve Jobs, subscription business, Tesla Model S, web application

The Honeymoon industry with clean design—and in that he found motivation and meaning. “I’ll help keep the software beautifully simple,” he said. “We can take something terrible and have a field day and do something different and create something someone wants to use.” Exactly. Alex started exploring the idea with us and brought an innovative concept and new approach to building a modern-day customer service desk. He understood that it rested on leveraging the Internet and cloud computing as a delivery model. But he also challenged every single assumption or paradigm that existed in the customer service desk world. He wanted every feature and capability translated into something that made sense outside of the core industry lingo. He pushed us to build not just a beautifully simple system, but ultimately a system that people enjoyed using, something they felt helped them with their customer relationships rather than obstructing them.

The Salad Days like downloading an app from the App Store (although that didn’t arrive until 2008). It was so straightforward and provided instant gratification. By now you have heard of this model; it is a routine practice today. But when we started it was not common for enterprise software. I didn’t know it was a real business model. In fact, I didn’t think we had a business model! Today these ideas of cloud computing and Software-as-aService have emerged as proven business models—preferred business models—but at the time I never knew how we’d keep customers from one day to the next. The metrics that SaaS companies use to track retention and expansion didn’t exist yet. With nothing to reference, it felt like we were charting new territory, which was fascinating and terrifying at the same time. Finding Customers When You Have No Idea Where to Find Them Our customer acquisition “plan” went something like this: “Just make customers happy.”

See 37signals Benchmark, 106, 109 Benioff, Marc, 27 Bezos, Jeff, 24–25 Black, Alan, 159, 169 boring ideas, 23–25 Box, 24, 168 Buddha Machine, 51 C Calacanis, Jason, 43, 44, 45 Caput, 13–15 CBA. See Community Benefits Agreement (CBA) CEO role building the right management team, 162–164 growing into, 160–162 Charles River Ventures (CRV), 81–86, 87–89 chief product officer, 26. See also Aghassipour, Alexander chief technology officer, 26. See also Primdahl, Morten cloud computing, 47 Cohler, Matt, 106–107, 182 Columbus, Christopher, 3 197 Svane samind.tex V1 - 10/28/2014 12:18 A.M. Page 198 INDEX Community Benefits Agreement (CBA), 157–158 competition, 151–152 complacency, 165 Connery, Nancy, 136 conversation, 105 Costolo, Dick, 171 Cramer, Jim, 7 Cubic Telecom, 48 customer advocates, 73 customer support, 19 from the inside, 103 secrets of, 104–105 women in, 104 customer support software, 28 customers, 185–188 acquiring, 47–53 loyalty, 130, 140 responding to price increases, 143–150 treating customers with love and respect, 140–143 D The Deck, 49–50 dot-com crash of 2001, 14 Dropbox, 24, 124 E editing the authentic self, 160–162 employee options, 102 entrepreneurship, and paranoia, 94–95 Eventbrite, 129 F failure, 15–16 fear of flying, 74 conquering for business travel, 78–79 Fenton, Peter, 111–112, 135, 178, 182–183 finding investors, 57–62 among friends and family, 62–65 angel investors, 65–67 Forum.dk, 11–12 G GigaOM, 68 Glengarry Glen Ross, 46 going live, 45 going public.


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The Mesh: Why the Future of Business Is Sharing by Lisa Gansky

Airbnb, Amazon Mechanical Turk, Amazon Web Services, banking crisis, barriers to entry, carbon footprint, Chuck Templeton: OpenTable:, cloud computing, credit crunch, crowdsourcing, diversification, Firefox, fixed income, Google Earth, industrial cluster, Internet of things, Joi Ito, Kickstarter, late fees, Network effects, new economy, peer-to-peer lending, recommendation engine, RFID, Richard Florida, Richard Thaler, ride hailing / ride sharing, sharing economy, Silicon Valley, smart grid, social web, software as a service, TaskRabbit, the built environment, walkable city, yield management, young professional, Zipcar

Other available assets include cloud computing services, social networks, and national postal services, UPS, and FedEx package services. Leveraging existing, well-established, scaled, and trusted assets significantly lowers the cost and risk of starting a new enterprise. This is a big reason that Mesh businesses are starting to thrive. The enhanced ability to leverage existing platforms, and lower incremental costs, is a big reason that Mesh businesses are starting to thrive. If we were to start Ofoto today, offering the same products and services (reliable network storage, customer order systems, backend systems, printing and shipping facilities), I estimate that it would take 10 percent of the nearly $60 million we raised at the time. Why? The cloud computing networks, tools, talent pool, and software as a service (SaaS) vendors in place today would allow us to go to market faster with far less capital.

These businesses are relatively easy to start and are spreading like wildfire: bike sharing, home exchanges, fashion swap parties, energy cooperatives, shared offices, cohousing, music studios, tool libraries, food and wine cooperatives, and many more. They leverage hundreds of billions of dollars in available information infrastructure—telecommunications, mobile technology, enhanced data collection, large and growing social networks, mobile SMS aggregators, and of course the Web itself. They efficiently employ horizontal business to business services, such as FedEx, UPS, Amazon Web Services, PayPal, and an ever-increasing number of cloud computing services. All the Mesh businesses rely on a basic premise: when information about goods is shared, the value of those goods increases, for the business, for individuals, and for the community. Mesh businesses are legally organized as for-profit corporations, cooperatives, and nonprofit organizations. Once I started looking, I quickly uncovered over 1,500 relevant companies and organizations.


pages: 284 words: 92,688

Disrupted: My Misadventure in the Start-Up Bubble by Dan Lyons

activist fund / activist shareholder / activist investor, Airbnb, Ben Horowitz, Bernie Madoff, bitcoin, call centre, cleantech, cloud computing, corporate governance, disruptive innovation, dumpster diving, fear of failure, Filter Bubble, Golden Gate Park, Google Glasses, Googley, Gordon Gekko, hiring and firing, Jeff Bezos, Lean Startup, Lyft, Marc Andreessen, Mark Zuckerberg, Menlo Park, minimum viable product, new economy, Paul Graham, pre–internet, quantitative easing, ride hailing / ride sharing, Rosa Parks, Sand Hill Road, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, Snapchat, software as a service, South of Market, San Francisco, Stanford prison experiment, Steve Ballmer, Steve Jobs, Steve Wozniak, telemarketer, tulip mania, uber lyft, Y Combinator, éminence grise

Halligan used to work as a venture capitalist, so he thinks like an investor. Shah, before going to grad school at MIT, built a different software company and sold it. Also, from the perspective of Wall Street, HubSpot ticks all the right boxes. It sells to businesses, rather than to consumers. It’s a cloud computing company and uses a business model called software as a service, or SaaS, which means customers don’t install the software on their own computers but instead connect to it over the Internet and pay a monthly subscription fee. Cloud computing is hot right now. The whole tech industry is moving to this model. Investors love it. Over the years Halligan and Shah have come up with a creation myth about the company, which is that while they were in grad school they had a vision for how companies could transform their marketing departments.

The problem is that HubSpot files its paperwork right after another tech start-up, Box, announces its own plans to go public. Box is a high-profile company in Silicon Valley. It’s seen as a bellwether for other cloud computing companies, including HubSpot. Box has a charming, charismatic, twenty-something CEO, Aaron Levie, and everyone has been under the impression that the company is doing a booming business. But now it has published its financial results and the numbers are underwhelming. Sales are growing, but Box is spending way too much on sales and marketing, and losing huge amounts of money. To be sure, that’s the case for most of the other cloud software companies. But even by the relaxed standards of the second tech bubble, Box’s results are disappointing. Meanwhile, for some reason, shares in cloud computing and “software as a service” companies are starting to swoon. One index of thirty-seven publicly traded cloud-related companies loses $58 billion in market value over the course of two months.


pages: 290 words: 87,549

The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions...and Created Plenty of Controversy by Leigh Gallagher

Airbnb, Amazon Web Services, barriers to entry, Ben Horowitz, Bernie Sanders, cloud computing, crowdsourcing, don't be evil, Donald Trump, East Village, Elon Musk, housing crisis, iterative process, Jeff Bezos, Jony Ive, Justin.tv, Lyft, Marc Andreessen, Mark Zuckerberg, medical residency, Menlo Park, Network effects, Paul Buchheit, Paul Graham, performance metric, Peter Thiel, RFID, Sam Altman, Sand Hill Road, Saturday Night Live, sharing economy, side project, Silicon Valley, Silicon Valley startup, South of Market, San Francisco, Startup school, Steve Jobs, TaskRabbit, the payments system, Tony Hsieh, Travis Kalanick, uber lyft, Y Combinator, yield management

After the company saw that professionally photographed listings generated two to three times as many bookings as the market average, in late 2011 it expanded the photography program from one thousand shoots per month to five thousand, which fed a surge in bookings. Airbnb’s ability to scale all of this quickly was greatly helped by the fact that it was born in the newfound era of cloud computing. Instead of having to own and build out expensive, resource-intensive servers, warehouses, and data centers, it could store all of its online infrastructure in the cloud; it could rent features and tools from providers that were also in the cloud; and it could essentially outsource all of its computing power. It migrated all these functions to Amazon Web Services, the subsidiary of the online retail giant that has since grown to dominate the market for third-party cloud computing for businesses. Without having to spend any time or energy figuring out how to maintain and run a complicated infrastructure, the Airbnb engineering team could work only on building a robust site and solving the problems unique to its core business.

It brought the owner and customer together in a new, more intimate way, showcasing home renters’ personalities and displaying their homes with magazine-worthy photography. It was a self-contained system that handled everything: payments, messaging, and customer service. It had a sophisticated technological back end that benefited from all the novel breakthroughs coming from Silicon Valley’s new golden age—cheap and powerful cloud computing, fast horsepower, sophisticated searching and matching. And, perhaps most significantly, instead of focusing on vacation destinations in resort areas, it focused on cities. Despite the attention paid to the treehouses and tepees, Airbnb’s actual invention was that it was an almost entirely urban phenomenon from the very beginning, taking root with millennial travelers who were city-focused and millennial hosts who wanted to monetize their small urban apartments.

., 60 business travel, 145–47 C Campbell, Michael and Debbie, 68–69 Cap’n McCain’s, 21–23 Case, Steve, 174 castles, xii, 59–60, 61 Chafkin, Max, 172–73 challenges, 80–104 deaths and, 96–97 EJ incident, 50–55, 80, 93 fines and violations, 108–9 key exchange, 75–76 Paris Airbnb Open, 77–78 parties, 81–90 sexual assault incident, 90–93 Chan, Robert “Toshi,” 111 Chesky, Allison, 169 Chesky, Brian on accidents, 97 at Airbnb Open, 76, 77 background of, 3–4, 11, 42–43, 169 on corporate rentals, 115, 116–17 on culture, 182–83 on EJ safety crisis, 53–55 on future directions, 193–94, 197–98 on future regulations, 136 on home sharing, 130 hospitality and, 70–72 on hosts and brand, 117 on hotels, 140, 159 on law enforcement, 91–92 Los Angeles move, 4–5 on mission of Airbnb, 172 on NYC and politics, 105, 113, 133 praise for, 161–62 on public companies, 201 on racism, 101, 102, 103 on rebranding, 64–65, 78–79 at Rhode Island School of Design, 1–4, 169 on safety, 48 San Francisco move, 6–7 strengths, 167–69 on Wimdu competition, 49–50 Y Combinator and, 23–29 Chesky, Deb and Bob, 3–4, 23, 32, 166, 168–69, 174, 208–9 Chicago, short-term rentals, 125 Choice Hotels, 153 Cianci, Buddy, 2 City Hosts, 191 Civil Rights Act, 101, 103 Clampet, Jason, 93, 141, 148–49 Clinton, Bill, 124 Clooney, George and Amal, 209 cloud computing, 45 Clouse, Dave and Lynn, 149–50 Collins, Jim, 181 commercial listings, 110–13, 114, 115 Common, 156 “community compact,” 114 competitors, xi–xii, xvii Couchsurfing.com, 13, 14, 41, 46 Craigslist (see Craigslist) HomeAway (see HomeAway) tourism, 112–13, 191–96 VRBO.com, xi, xvii, 41, 87, 106, 149–50 Wimdu (Samwer brothers), 48–50 See also hotels compression pricing, 144 Conair internship, 1–3 Concur, 145 conferences Airbnb and corporate travel, 145–46 Berkshire Hathaway Annual Meeting, 166–67 Fortune’s Brainstorm Tech, 103, 131, 187 International Council of Societies of Industrial Design/Industrial Designers Society of America, ix, 1, 7–10 South by Southwest, 12–14, 39 Conley, Chip on business travelers, 146 on Chesky, 171 on company goals, 117, 172 on home-sharing history, 149 on hospitality industry, 73, 76–77, 139–40, 147 in joining Airbnb, 70–72 Corden, James, 191 core values, 36, 186–87, 219 Cornell Hospitality Quarterly, 70, 166 Couchsurfing.com, xi, 13, 14, 41, 46 Craigslist, 38–39, 41, 51, 82, 100, 108, 149, 179 crisis management, 48–50, 51–54, 77–78, 90–93 CritBuns, 5–6, 11, 209 Crittenden, Quirtina, 100–101 Crossing the Chasm (Moore), 181 Cuba, 161–62, 185–86 Culting of Brands, The (Atkin), 64 culture, of company, 35–38, 165, 174–75, 182–88 Cuomo, Andrew, 106–7, 108, 121, 126 Curtis, Mike, 77, 181, 184, 185 customer-service platform, 44, 52–54, 56, 86–90, 94 D Dandapani, Vijay, 115, 122 de Blasio, Bill, 113, 119 Democratic National Convention (Denver), 15, 18–20 Diller, Barry, 142 Dimon, Jamie, x discrimination controversy, xv, 99–104, 171 Disney, Walt, 166, 167, 197 diversity, 187–88 DogVacay, 56 Donahoe, John, 71, 165, 168 Dorsey, Jack, 165 Drybar, 152 Dubost, Lisa, 171 dukana, 56 E Ecolect.net, 11 Edition, 148, 152 Eisenhower, Dwight, 139 EJ incident, 50–55, 80, 93 emergency reaction policy, 91 “entrepreneur,” as term, 11 European market, 48–50 Everbooked, 75 Expedia, 142, 148, 154, 198 Experiences, 192 F Federal Highway Act, 139 fee structure, 39–40 Ferriss, Tim, 93 fines and violations, 108–9, 117, 129, 134 Firestarter, 127 Five Dysfunctions of a Team, The (Lencioni), 181 Flatbook, 156 FlipKey, 146 Friedman, Tom, 173 future directions, 130–31, 145–47, 177–79, 188–210 G Gandhi, 160, 227 Gates, Bill and Melinda, 209 Gatto, Chris, 132 Gebbia, Joe background of, 42–43 culture, of company, 185 hometown, 12 leadership of, xviii, 174–79 prototyping/design studio, 177–79 refugee crisis, 209 at Rhode Island School of Design, 1–3 San Francisco, ix, 5 TED talk, 172 Y Combinator and, 23–29 Gilbert, Elizabeth, 191 Giving Pledge, 209 Glassdoor survey, 185, 186 GLō, 152 Golden, Jonathan, 184 golf party incident, 82–90 Gonzales, Emily, 89, 90 Good to Great (Collins), 181 Google, 145, 188, 195, 197 Google AdWords, 38, 179 Gore, Al, 60, 124 Gothamist, 111 Graham, Paul on Chesky, 171–72 interview with, 23–24 mentoring of Airbnb founders, 26–27, 28–29, 164, 170–71 on Wimdu competition, 49–50 Y Combinator and, 15, 25–26, 59 Grandy, Nick, 36 Grazer, Brian, 191 Grove, Andy, 166 growth, xii–xiii, 38–41, 46–47, 56, 144, 162, 198–99 guest arrivals August 2009, 35 average age of, 66 fee structure, 39–40 growth of, xii, 41, 58–59, 180 number of, 26–27, 199 as term, ix–x Guesty, 75 Gupta, Prerna, 67–68 H Hantman, David, 109 Hartz, Kevin, 31 Hempel, Jessi, 201, 203–4 Hewlett, Bill, 1 High Output Management (Grove), 166 Hilton, Conrad, 139 Hilton hotels, 141–42, 152, 167 hiring, 25, 35–38, 49–50, 55, 56–57 Hoffman, Reid as adviser, 49–50, 164, 197 “Blitzscaling” course, 188 on Chesky, 167–68 on growth, 56, 199 as investor, 46–47 NYC politics, 121 on uniqueness, 62 Holder, Eric, 102, 171 Holiday Inn, origin, 138–39 home sharing, xvi–xvii, 125–26, 149 HomeAway, xvii, 41, 82, 106, 108, 133, 146, 150, 154, 198 HonorTab, 75 Hoplamazian, Mark, 152 Horowitz, Ben, 47, 52, 164, 171 hospitality, 70–73, 115, 117, 129–31, 139–45, 151–53, 165, 166 Host Assist platform, 76 Host Guarantee, 82, 86, 87, 88, 89, 94 hosts as asset and lobbyists, 111–12, 126–29 average age of, xii–xiii, 65 as career choice, 73–75 from Cuba, 185–86 data and behavior, 114–15 defined, x discrimination, 99–102 experiences offered by, 178 fee structure and earnings, 39–40, 73, 110, 112–13 growth challenges, 40–41, 180 hospitality and, 70–73, 117 initial public offering, 199–200 liability and legal issues, 97, 106, 109–10, 122, 128–29 matching with guests, 44–45 Verified ID, 95 See also Airbnb Open hotels vs.


iPad: The Missing Manual, Fifth Edition by J.D. Biersdorfer

clockwatching, cloud computing, Downton Abbey, Firefox, Google Chrome, Internet Archive, Skype, stealth mode startup

It’s in the App Store, along with Apple’s Movie Trailers app for streaming video film previews. Work with Online Apps WITH THE RISE OF mobile Internet-connected devices came the increased popularity of cloud computing—using programs that reside and store files online, up in the clouds, where you can get to them from any Web-enabled machine. That means you don’t have to drag around a seven-pound laptop stuffed with business software just to update a spreadsheet, because you can edit it online with an iPad—and it doesn’t have to be linked to an iCloud account (Chapter 17). Not every cloud-computing site works with the iPad—Adobe’s Flash-based Photoshop.com site, which lets you edit pictures online, is one example. However, in Safari for iOS 6, you can now upload photos and other files stored on your iPad to sites like Flickr.

If you use the Web-based email services Outlook.com or Hotmail, you can also view Office email attachments through Safari. Another cloud-computing company, Zoho (www.zoho.com), has a whole slew of business and productivity apps that work through your computer’s browser. Many of them are free for personal use; you just need to sign up for an account. Zoho Writer, Sheet, and Show roughly correspond to Microsoft Word, Excel, and PowerPoint, and they can open and edit files in those formats. The company built a website especially for mobile devices, too, at mobile.zoho.com; it has a less-cluttered interface than Zoho’s standard web page. If you’re a big fan of cloud computing and already use services like Dropbox for file-sharing (www.dropbox.com) or Basecamp for project management (http://basecamphq.com), take a run through the Productivity section of the App Store for iPad-friendly programs that work specifically with those sites.

., Find Newspaper and Magazine Apps Belkin, Protect Your iPad bit rate and audio quality, Change Import Settings for Better Audio Quality Block Pop-ups, Surf Securely Blu-ray discs, Video Formats That Work on the iPad Bluetooth, Extend Battery Life, Add Earbuds and Earphones, Add an External Keyboard, Make Music with GarageBand–Make Music with GarageBand, Make Music with GarageBand, Bluetooth, General GarageBand, Make Music with GarageBand–Make Music with GarageBand, Make Music with GarageBand headphones, Add Earbuds and Earphones keyboard, Add an External Keyboard Boingo, Use Public WiFi Hotspots Bookmarks, Read an iBook iBook, Read an iBook Brightness & Wallpaper, Change the iPad’s Wallpaper, Brightness & Wallpaper browsers, alternative, Use Other Web Browsers–Use Other Web Browsers, Use Other Web Browsers Buy (iBooks), Read an iBook Buy More Storage (iCloud), Set Up iCloud on Your iPad C cables, Play iPad Videos on Your TV–Play iPad Videos on Your TV, Play iPad Videos on Your TV, Play iPad Videos on Your TV CalDAV Account, Subscribe to an Online Calendar calendars, Syncing With iTunes, Set Up Your Calendars, Set Up Your Calendars, Set Up Your Calendars–Set Up Your Calendars, Set Up Your Calendars, Set Up Your Calendars, Set Up Your Calendars, Use the iPad Calendar, Use the iPad Calendar–Subscribe to an Online Calendar, Set Up an iPad Alert, Subscribe to an Online Calendar, Subscribe to an Online Calendar, Subscribe to an Online Calendar, Maintain Contacts, iCloud alerts, Set Up an iPad Alert Entourage 2004, Set Up Your Calendars events, Use the iPad Calendar iCal, Set Up Your Calendars iCloud, Set Up Your Calendars Outlook Express, Maintain Contacts setting up, Set Up Your Calendars–Set Up Your Calendars, Set Up Your Calendars settings, iCloud subscribing to online, Subscribe to an Online Calendar syncing, Syncing With iTunes, Set Up Your Calendars using iPad calendar, Use the iPad Calendar–Subscribe to an Online Calendar, Subscribe to an Online Calendar, Subscribe to an Online Calendar Camera Roll, Add Picture and Video Attachments to Mail Messages, Send Messages, Share Your Video Clips, Take Photos With the iPad’s Camera, Take Portraits with Photo Booth, Share and Print Photos, Set Up iCloud on Your iPad backups, Set Up iCloud on Your iPad deleting photos, Share and Print Photos cameras, Find the Home Button and Cameras, Your Home Screen Apps, Use Twitter, Shoot Your Own Videos, Take Photos With the iPad’s Camera, Take Photos With the iPad’s Camera, Take Photos With the iPad’s Camera exposure adjustment, Take Photos With the iPad’s Camera grid, Take Photos With the iPad’s Camera shooting videos, Shoot Your Own Videos taking still photos, Take Photos With the iPad’s Camera Twitter and, Use Twitter caps lock, iPad Keyboard Shortcuts cellular data service, Sign Up for Cellular Data Service, Check, Change, or Cancel Data Plans, Cellular Data (Wi-Fi + 4G/3G iPads Only) 4G/3G iPads, Cellular Data (Wi-Fi + 4G/3G iPads Only) account settings, Check, Change, or Cancel Data Plans cellular network, Airplane Mode, Cellular Data (Wi-Fi + 4G/3G iPads Only) characters, accented, iPad Keyboard Shortcuts Check for Updates, Update Apps Check Spelling, iPad Keyboard Shortcuts Chrome, Use Other Web Browsers cleaning screen, Keep the iPad Screen Clean–Protecting the iPad’s Screen, Protecting the iPad’s Screen Clear mobile broadband hotspot, Use a Mobile Broadband Hotspot cloud computing, Work with Online Apps–Work with Online Apps, Work with Online Apps, Work with Online Apps, Use iTunes in the Cloud iTunes in the Cloud service, Use iTunes in the Cloud ComiXology’s Comics app, Subscribe to ePublications Composers, Explore the Music Menu computers, Authorize Computers for iTunes and Home Sharing, Deauthorize Your Computer authorizing iTunes and Home Sharing, Authorize Computers for iTunes and Home Sharing deauthorizing iTunes, Deauthorize Your Computer contacts, Your Home Screen Apps, Use Information in Mail Messages, Use Information in Mail Messages, Syncing With iTunes, Maintain Contacts, Maintain Contacts, Maintain Contacts, Maintain Contacts, Maintain Contacts, Maintain Contacts, iCloud adding photo, Maintain Contacts changing information, Maintain Contacts Copy, Use Information in Mail Messages Create New Contact, Use Information in Mail Messages FaceTime video call, Maintain Contacts mapping address, Maintain Contacts passing along information, Maintain Contacts sending messages, Maintain Contacts settings, iCloud syncing with iTunes, Syncing With iTunes Contents (iBooks), Read an iBook Contrast (Photos), Find Third-Party Photo-Editing Apps Convert to AAC (iTunes), Change a Song’s File Format converting, Tour iTunes, Change a Song’s File Format large song files to smaller ones, Tour iTunes song’s file format, Change a Song’s File Format Copy, Use the Safari Action Menu Cover Flow (iTunes), Four Ways to Browse Your Collection Cover Lock/Unlock, General Create MP3 Version, Change a Song’s File Format Crop (Photos), Edit Photos on the iPad Cut, Copy, Paste, and Replace, Cut, Copy, Paste, and Replace Text–Cut, Copy, Paste, and Replace Text, Cut, Copy, Paste, and Replace Text, Cut, Copy, Paste, and Replace Text, Cellular: 4G LTE, 4G, and 3G Networks, Pick an AT&T Service Plan, Pick an AT&T Service Plan, Pick a Sprint Service Plan, Pick a Sprint Service Plan, Pick a Verizon Service Plan, Pick a Verizon Service Plan, Sign Up for Cellular Data Service, Find Newspaper and Magazine Apps AT&T DataConnect plans, Pick an AT&T Service Plan Data Calculator, Sign Up for Cellular Data Service data plans, Cellular: 4G LTE, 4G, and 3G Networks, Pick an AT&T Service Plan, Pick a Sprint Service Plan, Pick a Verizon Service Plan Sprint service plans, Pick a Sprint Service Plan The Daily, Find Newspaper and Magazine Apps Verizon Wireless service plans, Pick a Verizon Service Plan D DataViz Documents to Go Premium, Find Alternatives to iWork Date & Time, General Deauthorize Computer (iTunes), Deauthorize Your Computer dictation, Enter Text By Voice–Using Dictation on the iPad, Using Dictation on the iPad, Dictation Options for Older iPads Dictionary (iBooks), Use the Dictionary dictionary, global, Use the iPad’s Global Dictionary Digital AV Adapter, Play Slideshows on Your TV Do Not Disturb, Adjust Your FaceTime Settings, Hang Out the “Do Not Disturb” Sign, Hang Out the “Do Not Disturb” Sign, Do Not Disturb (iOS 6 only) Dock Connector, What’s in the Box, Connect Through iPad Jacks and Ports, Keep the iPad Screen Clean, Transfer Photos with iPad Camera Adapters, Play Slideshows on Your TV to VGA Adapter, Play Slideshows on Your TV Documents to Go Premium app, Find Alternatives to iWork Dolphin Browser for iPad, Use Other Web Browsers double-tap (finger move), Finger Moves for the iPad Downloads (iTunes), The iTunes Window drag (finger move), Finger Moves for the iPad Dropbox, Work with Online Apps, Find Alternatives to iWork DVDs, Video Formats That Work on the iPad E EDGE network, Use a Cellular Data Network email, Activate and Set Up Your iPad Over WiFi, Your Home Screen Apps, Save and Mail Images from the Web, Save and Mail Images from the Web, Use the Safari Action Menu, Set Up an Email Account (or Two), Copy your Desktop Mail Settings Using iTunes, Copy your Desktop Mail Settings Using iTunes, Tour the Mail Program, Tour the Mail Program, Tour the Mail Program, Tour the Mail Program, Tour the Mail Program, Tour the Mail Program, File Attachments, File Attachments–Use Information in Mail Messages, Use Information in Mail Messages, Use Information in Mail Messages, Use Information in Mail Messages, Use Information in Mail Messages, Use Information in Mail Messages, Write and Send Email, Add Picture and Video Attachments to Mail Messages, Format Your Messages, Set Up a VIP Mailbox, Manage Your Email, Manage Your Email–Manage Your Email, Manage Your Email, Manage Your Email, Manage Your Email, Manage Your Email, Manage Your Email, Manage Your Email, Manage Your Email, Adjust Mail Settings, Adjust Mail Settings, Adjust Mail Settings, Adjust Mail Settings, Adjust Mail Settings, Adjust Mail Settings, Adjust Mail Settings, Adjust Mail Settings, Webmail On the iPad, POP3 and IMAP Accounts on the iPad–POP3 and IMAP Accounts on the iPad, POP3 and IMAP Accounts on the iPad, POP3 and IMAP Accounts on the iPad, POP3 and IMAP Accounts on the iPad, Send Messages–Send Messages, Send Messages, iWork by iTunes Sync, Share Your Video Clips, Share and Print Photos, iCloud, Mail, Contacts, Calendars adjusting mail settings, Adjust Mail Settings attachments, File Attachments changing minimum font size, Adjust Mail Settings Check Mail, Tour the Mail Program Compose New Message, Tour the Mail Program contacts, Use Information in Mail Messages Copy, Use Information in Mail Messages custom signature, Adjust Mail Settings default mail account, Adjust Mail Settings deleting unwanted accounts, Adjust Mail Settings file attachments, File Attachments–Use Information in Mail Messages, Use Information in Mail Messages, Use Information in Mail Messages Forward, Reply, Print, Tour the Mail Program images from Web, Save and Mail Images from the Web IMAP accounts, POP3 and IMAP Accounts on the iPad–POP3 and IMAP Accounts on the iPad, POP3 and IMAP Accounts on the iPad, POP3 and IMAP Accounts on the iPad iMessage, Send Messages–Send Messages, Send Messages loading remote images, Adjust Mail Settings Mail settings, Mail, Contacts, Calendars managing messages, Tour the Mail Program, Format Your Messages, Manage Your Email, Manage Your Email–Manage Your Email, Manage Your Email, Manage Your Email, Manage Your Email, Manage Your Email, Manage Your Email, Manage Your Email, Adjust Mail Settings deleting all the junk at once, Manage Your Email, Manage Your Email filing in different folders, Manage Your Email formatting, Format Your Messages making new mailbox folders on the iPad, Manage Your Email moving messages to folder, Tour the Mail Program organizing by thread, Adjust Mail Settings searching mailboxes, Manage Your Email Move to Folder, Tour the Mail Program photos, Share and Print Photos picture and video, Add Picture and Video Attachments to Mail Messages preview in message list, Adjust Mail Settings Previous/Next Message, Tour the Mail Program reading, Use Information in Mail Messages Reply, Reply All, or Forward, Write and Send Email scanning for spam, Manage Your Email setting up, Activate and Set Up Your iPad Over WiFi, Set Up an Email Account (or Two), Copy your Desktop Mail Settings Using iTunes settings, iCloud syncing with iTunes, Copy your Desktop Mail Settings Using iTunes, POP3 and IMAP Accounts on the iPad videos, Share Your Video Clips VIP Mailbox, Set Up a VIP Mailbox Web-based email accounts, Webmail On the iPad emoticons, Send Messages eMusic, Get Music from Other Online Stores Enable Caps Lock, iPad Keyboard Shortcuts Enhance (Photos), Edit Photos on the iPad Entourage, Set Up Your Calendars, Subscribe to an Online Calendar meeting invitations, Subscribe to an Online Calendar Equalizer, Improve Your Tunes with the Graphic Equalizer–Improve Your Tunes with the Graphic Equalizer, Improve Your Tunes with the Graphic Equalizer, Improve Your Tunes with the Graphic Equalizer Erase Data, General Events, viewing photos by, Find Pictures on Your iPad Exchange, Subscribe to an Online Calendar Exposure (Photos), Find Third-Party Photo-Editing Apps F Facebook, Surf the Web, Take a Safari Tour, Zoom and Scroll Through Web Pages, Use Safari Reader, Use the Safari Action Menu, Social Networking on Your iPad, Social Networking on Your iPad, Use Facebook on the iPad, More Ways to Get Your Game On, iTunes and Social Media–Get iTunes News on Twitter, Get iTunes News on Twitter, View Pictures on Your iPad, Share and Print Photos, Facebook (iOS 6 only) Game Center and, More Ways to Get Your Game On iTunes, iTunes and Social Media–Get iTunes News on Twitter, Get iTunes News on Twitter posting photo to, View Pictures on Your iPad, Share and Print Photos Faces, Find Pictures on Your iPad FaceTime, The iPad With Retina Display vs. the iPad 2, Meet the iPad Mini, Find the Home Button and Cameras, Your Home Screen Apps, Make Video Calls with FaceTime, Adjust Your FaceTime Settings, Use Skype to Make Internet Calls, Tour the Mail Program, Maintain Contacts, Reminders (iOS 6 only) settings, Reminders (iOS 6 only) Find My iPad, Activate and Set Up Your iPad Over WiFi, Set Up iCloud on Your iPad, Find a Lost iPad Find My iPhone, Find a Lost iPad finger moves, Finger Moves for the iPad, Use Multitasking Gestures on the iPad, Use Multitasking Gestures on the iPad, View Pictures on Your iPad, General multitasking gestures, Use Multitasking Gestures on the iPad, Use Multitasking Gestures on the iPad, General fingerprint-resistant oleophobic coating, Protecting the iPad’s Screen flick (finger move), Finger Moves for the iPad Flickr, Social Networking on Your iPad folders (Home screen apps), Make Home Screen App Folders frames, Zoom and Scroll Through Web Pages Fraud Warning, Surf Securely, Safari free courses, Go to School at iTunes U frozen apps, Troubleshooting Basics Full Screen videos, Find and Play Videos on Your iPad G Game Center, Your Home Screen Apps, Play Games, Sign Up for Game Center, Sign Up for Game Center–Sign Up for Game Center, Sign Up for Game Center, Sign Up for Game Center, Sign Up for Game Center, Get Social with Game Center–Get Social with Game Center, Get Social with Game Center, Get Social with Game Center, Get Social with Game Center, Get Social with Game Center, Add Facebook Friends, Use Game Center with OS X 10.8 achievement points, Get Social with Game Center Facebook and, Add Facebook Friends Friends list, Sign Up for Game Center getting social with, Get Social with Game Center–Get Social with Game Center, Get Social with Game Center leaderboard scores, Sign Up for Game Center OS X 10.8 (Mountain Lion), Use Game Center with OS X 10.8 recommendations, Get Social with Game Center signing up, Sign Up for Game Center–Sign Up for Game Center, Sign Up for Game Center, Sign Up for Game Center turn-based games, Get Social with Game Center games, Play Games–An iPad Games Gallery, Find iPad Games, Find iPad Games, Find iPad Games, Find iPad Games, Find iPad Games, Find iPad Games, Play Games, Play Games, Play Games, Play Games, Play Games, Play Games, Sign Up for Game Center, Sign Up for Game Center, Sign Up for Game Center, Get Social with Game Center, Get Social with Game Center, More Ways to Get Your Game On, Use Game Center with OS X 10.8, Beam Games to an Apple TV, Beam Games to an Apple TV, Beam Games to an Apple TV, Play Multiplayer Games in Person, Play Multiplayer Games in Person, Play Multiplayer Games in Person, Play Multiplayer Games in Person, Play Multiplayer Games in Person, Play Multiplayer Games in Person, Play Multiplayer Games in Person, Troubleshoot Games, Troubleshoot Games, An iPad Games Gallery, An iPad Games Gallery, An iPad Games Gallery, An iPad Games Gallery, Stream and Mirror Files with AirPlay Angry Birds, An iPad Games Gallery Apple TV, Beam Games to an Apple TV Bluetooth symbol, Play Multiplayer Games in Person Call of Duty: World at War: Zombies for iPad, Play Multiplayer Games in Person cheat codes, Play Games Cut the Rope HD, Play Games Flight Control HD, Play Games Infinity Blade, An iPad Games Gallery iStunt 2 HD, Find iPad Games Monster Ball HD, Play Multiplayer Games in Person multiplayer, Play Multiplayer Games in Person New & Noteworthy titles, Find iPad Games OpenFeint, Sign Up for Game Center Pac-Man, Play Games Pac-Man for iPad, Find iPad Games Real Racing 2 HD, Play Games, Beam Games to an Apple TV Scrabble, Play Multiplayer Games in Person Search box, Find iPad Games Top Charts button, Find iPad Games troubleshooting, Troubleshoot Games video mirroring for, Stream and Mirror Files with AirPlay W.E.L.D.E.R., Use Game Center with OS X 10.8 Wi-Fi logo, Play Multiplayer Games in Person GarageBand, Manage and Play Music and Other Audio, Make Music with GarageBand–Make Music with GarageBand, Make Music with GarageBand, Make Music with GarageBand Jam Session, Make Music with GarageBand Get CD Track Names (iTunes), Edit Song Information Get Info, Edit Song Information getting online, Get Online–Travel Internationally with the iPad, Get Your WiFi Connection, Use a Cellular Data Network, Pick a Sprint Service Plan, Sign Up for Cellular Data Service, Turn Cellular Data Service Off or On, Use a Mobile Broadband Hotspot, Use a Mobile Broadband Hotspot, Use the iPad as a Personal Hotspot, Make Video Calls with FaceTime, Use Skype to Make Internet Calls, Travel Internationally with the iPad hotspots, Use a Mobile Broadband Hotspot, Use the iPad as a Personal Hotspot network options, Use a Cellular Data Network Ghostbird Software’s PhotoForge 2, Find Third-Party Photo-Editing Apps global dictionary, Use the iPad’s Global Dictionary Gmail, Activate and Set Up Your iPad Over WiFi, 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importing, iWork by iTunes Sync–Troubleshooting iWork Files, Troubleshooting iWork Files, Troubleshooting iWork Files, Change Import Settings for Better Audio Quality files in iWork, iWork by iTunes Sync–Troubleshooting iWork Files, Troubleshooting iWork Files, Troubleshooting iWork Files iTunes import settings, Change Import Settings for Better Audio Quality Info, syncing with iTunes, Sync Your Personal Info to the iPad installing apps, Buy, Download, and Install Apps international, Use an International or Emoji Keyboard, Delete a Keyboard, Travel Internationally with the iPad, General iOS 5 and later, Use Multitasking Gestures on the iPad, Enter Text By Voice–Using Dictation on the iPad, Using Dictation on the iPad, Watch, Create, and Edit Videos, Back Up and Sync Your Gadgets with iCloud–Set Up iCloud on Your iPad, Set Up iCloud on Your iPad, Set Up iCloud on Your iPad, Privacy (iOS 6) / Location Services (iOS 5), Use iPad Backup Files backing up iPad, Use iPad Backup Files 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and Set Up Your iPad Over WiFi microfiber cleaning cloth, Keep the iPad Screen Clean microphone, Connect Through iPad Jacks and Ports Microsoft Exchange, Set Up Mail Accounts on the iPad, POP3 and IMAP Accounts on the iPad Microsoft Office files, File Attachments Milliamp, Find an iPad Repair Shop Mini, Set Up Your iPad–Find the Home Button and Cameras, Meet the iPad, What’s in the Box, What’s in the Box, Meet the iPad Mini, Turn the iPad On and Off, Find the Home Button and Cameras, Sync Your iPad with iTunes syncing iTunes, Sync Your iPad with iTunes mini-iTunes window, Change the Size of the iTunes Window mirroring files, Stream and Mirror Files with AirPlay–Video Mirroring, Video Mirroring, Video Mirroring mobile broadband hotspot, Use a Mobile Broadband Hotspot MOV files, Video Formats That Work on the iPad MP3 files, Stream Web Audio and Video, Change Import Settings for Better Audio Quality, Change a Song’s File Format MP4 files, Video Formats That Work on the iPad music, Tour 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new features, Track Time With the iPad’s Clock–Timer, Timer, Hang Out the “Do Not Disturb” Sign, Set App Privacy Settings–Privacy and Location Services, Privacy and Location Services Clock app, Track Time With the iPad’s Clock–Timer, Timer Do Not Disturb, Hang Out the “Do Not Disturb” Sign Privacy settings page, Set App Privacy Settings–Privacy and Location Services, Privacy and Location Services New Playlist From Selection (iTunes), Playlist-Making Method #3 newspaper apps, Find Newspaper and Magazine Apps–Find Newspaper and Magazine Apps, Find Newspaper and Magazine Apps Newsstand, Your Home Screen Apps, Use Newsstand for Your ePeriodicals Nexvio’s ReelDirector, Edit Videos with iMovie Nook app for iPad, Read Other Ebooks on the iPad Notepad, Find Alternatives to iWork Notes, Your Home Screen Apps, Cut, Copy, Paste, and Replace Text, Take Notes–Take Notes, Take Notes, Take Notes, Take Notes, Take Notes, Reminders (iOS 6 only) new note, Take Notes settings, Reminders (iOS 6 only) stashing text, Cut, Copy, Paste, and Replace Text syncing, Take Notes with iCloud, Take Notes Notifications, Organize Your Life With the iPad’s Apps, Use Notifications, Customizing Notifications, Do Not Disturb (iOS 6 only) Novatel’s MiFi, Use a Mobile Broadband Hotspot Now Playing, You’re the Critic: Rate Your Music, Control the Now Playing Screen, Control the Now Playing Screen Numbers (iWork), Get Productive with iWork, Meet iWork, Create Spreadsheets in Numbers–Create Spreadsheets in Numbers, Create Spreadsheets in Numbers spreadsheets, Create Spreadsheets in Numbers–Create Spreadsheets in Numbers, Create Spreadsheets in Numbers O Office, Find Alternatives to iWork Office 365, Work with Online Apps oleophobic coating, Protecting the iPad’s Screen On/Off button, Turn the iPad On and Off online apps, Work with Online Apps–Work with Online Apps, Work with Online Apps, Work with Online Apps OnLive Desktop, Find Alternatives to iWork OnLive iPad app, Find Alternatives to iWork Opera Mini, Use Other Web Browsers Organize Library (iTunes), Where iTunes Stores Your Files OS X 10.8 (Mountain Lion), Use iCloud Tabs, More Ways to Get Your Game On Game Center, More Ways to Get Your Game On iCloud Tabs, Use iCloud Tabs Outlook Express, Maintain Contacts Outlook meeting invitations, Subscribe to an Online Calendar Outpost 2, Work with Online Apps Overdrive from Sierra Wireless, Use a Mobile Broadband Hotspot P Page Navigator (iBooks), Read an iBook Pages (iWork), Get Productive with iWork–Tips for Working with Text and Photos, Meet iWork, Meet iWork, Get Started with iWork, Get Started with iWork–Tips for Working with Text and Photos, Get Started with iWork, Get Started with iWork, Create Documents in Pages, Create Documents in Pages, Create Documents in Pages, Create Documents in Pages, Create Documents in Pages, Create Documents in Pages, Tips for Working with Text and Photos, Tips for Working with Text and Photos documents, Get Started with iWork–Tips for Working with 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Music photos, Your Home Screen Apps, Add Picture and Video Attachments to Mail Messages, Photo Sharing with iTunes, Automatically Download Photos with Photo Stream, Take Photos With the iPad’s Camera, Find Pictures on Your iPad, Find Pictures on Your iPad–Working with Albums, Find Pictures on Your iPad, Find Pictures on Your iPad, Find Pictures on Your iPad, Find Pictures on Your iPad, Find Pictures on Your iPad, Working with Albums, Edit Photos on the iPad, Edit Photos on the iPad, Edit Photos on the iPad, Find Third-Party Photo-Editing Apps, Find Third-Party Photo-Editing Apps, Find Third-Party Photo-Editing Apps, Find Third-Party Photo-Editing Apps, Find Third-Party Photo-Editing Apps, Videos albums, Find Pictures on Your iPad Contrast, Find Third-Party Photo-Editing Apps Crop, Edit Photos on the iPad downloading with Photo Stream, Automatically Download Photos with Photo Stream editing, Find Third-Party Photo-Editing Apps exposure, Take Photos With the iPad’s Camera, Find Third-Party Photo-Editing Apps Faces, Find Pictures on Your iPad file attachments, Add Picture and Video Attachments to Mail Messages finding on iPad, Find Pictures on Your iPad–Working with Albums, Find Pictures on Your iPad, Working with Albums Home Sharing, Photo Sharing with iTunes Places, Find Pictures on Your iPad Red Eye, Edit Photos on the iPad Rotate, Edit Photos on the iPad Saturation, Find Third-Party Photo-Editing Apps settings, Videos third-party apps, Find Third-Party Photo-Editing Apps viewing, Find Pictures on Your iPad by Events, Find Pictures on Your iPad Picture Frame, Turn the iPad into a Picture Frame, General, Picture Frame pinch (finger move), Finger Moves for the iPad Pinterest, Social Networking on Your iPad Places, Find Pictures on Your iPad Play Each Slide For… (Photos), Play Slideshows on Your iPad podcasts, The iTunes Window, Play Audiobooks, Use the Podcasts App iOS 6, Use the Podcasts App library’s contents, The iTunes Window POP accounts, POP3 and IMAP Accounts on the iPad Print, Use the Safari Action Menu printing, Print with Your iPad, Share and Print Photos Privacy, Set App Privacy Settings–Privacy and Location Services, Privacy and Location Services, Privacy (iOS 6) / Location Services (iOS 5) settings page, Set App Privacy Settings–Privacy and Location Services, Privacy and Location Services Private Browsing, Erase the History List, Surf Securely, Safari Project Gutenberg, Read Other Ebooks on the iPad protecting iPad, Protect Your iPad punctuation (keyboard shortcut), iPad Keyboard Shortcuts Purchase History (iTunes), See Your iTunes Purchase History and Get iTunes Store Help Purchased (iBooks), Browse and Search for Books Purchased on iPad list, The iTunes Window Q QuickOffice Pro HD, Find Alternatives to iWork QuickTime, Stream Web Audio and Video QuickTime Pro, Troubleshoot Syncing Problems, Video Formats That Work on the iPad quitting frozen app, Troubleshooting Basics R ratings (iTunes), You’re the Critic: Rate Your Music Red Eye (Photos), Edit Photos on the iPad Reminders, Your Home Screen Apps, Organize Your Life With the iPad’s Apps, Use Reminders, Use Reminders, Reminders (iOS 6 only) repair shops, Find an iPad Repair Shop Repeat (Photos), Play Slideshows on Your iPad Replace, Cut, Copy, Paste, and Replace Text Reset, General resetting iPad, Troubleshooting Basics–Reset Your iPad, Reset Your iPad, Reset Your iPad restarting, Troubleshooting Basics restoring software, Start Over: Restore Your iPad’s Software–Start Over: Restore Your iPad’s Software, Start Over: Restore Your iPad’s Software, Start Over: Restore Your iPad’s Software Restrictions, General Retina display, Meet the iPad, The iPad With Retina Display vs. the iPad 2, Meet the iPad Mini returning to Home screen, Use Multitasking Gestures on the iPad Reuters News Pro, Find Newspaper and Magazine Apps Rotate (Photos), View Pictures on Your iPad, Edit Photos on the iPad RSS Feed, Edit and Organize Bookmarks and Folders RTF files, File Attachments S Safari, Your Home Screen Apps, Take a Safari Tour–Take a Safari Tour, Take a Safari Tour, Take a Safari Tour, Take a Safari Tour, Take a Safari Tour, Take a Safari Tour, Take a Safari Tour, Take a Safari Tour, Use Browser Tabs in Safari, Use Browser Tabs in Safari, Use Safari’s Reading List, Jump to Other Web Pages, Use Autofill to Save Time, Create and Use Bookmarks, Add New Bookmarks on the iPad, Add New Bookmarks on the iPad, Make Home Screen Bookmarks, Call Up Your History List, Call Up Your History List, Call Up Your History List, Call Up Your History List, Edit and Organize Bookmarks and Folders, Edit and Organize Bookmarks and Folders, Edit and Organize Bookmarks and Folders, Edit and Organize Bookmarks and Folders, Sync Bookmarks–Save and Mail Images from the Web, Save and Mail Images from the Web, Use the Safari Action Menu, Surf Securely, Surf Securely, Surf Securely, Surf Securely, Surf Securely, Surf Securely, Surf Securely, General, Safari, Safari Accept Cookies, Surf Securely Action menu, Take a Safari Tour, Use the Safari Action Menu address bar, Take a Safari Tour Advanced, Surf Securely Always Show Bookmarks Bar, Safari Autofill, Use Autofill to Save Time Back, Forward, Take a Safari Tour Bookmarks, Take a Safari Tour, Create and Use Bookmarks, Add New Bookmarks on the iPad, Add New Bookmarks on the iPad, Make Home Screen Bookmarks, Call Up Your History List, Call Up Your History List, Edit and Organize Bookmarks and Folders, Edit and Organize Bookmarks and Folders, Edit and Organize Bookmarks and Folders, Sync Bookmarks–Save and Mail Images from the Web, Save and Mail Images from the Web adding new, Add New Bookmarks on the iPad editing and organizing, Edit and Organize Bookmarks and Folders History button, Call Up Your History List Clear Cache, Surf Securely Clear History, Surf Securely cookies, Surf Securely History, Call Up Your History List JavaScript, Surf Securely Reading List, Use Safari’s Reading List, Jump to Other Web Pages, Call Up Your History List RSS Feed, Edit and Organize Bookmarks and Folders screen elements, Take a Safari Tour–Take a Safari Tour, Take a Safari Tour Search box, Take a Safari Tour security, Surf Securely settings, Safari Stop, Reload, Take a Safari Tour tabs, Use Browser Tabs in Safari, Use Browser Tabs in Safari Safari Reader, Use Safari Reader, Use the Safari Action Menu Satchel, Work with Online Apps Satellite (Map view), See Maps in Different Views Saturation (Photos), Find Third-Party Photo-Editing Apps Saved Photos album, Delete Photos schools (online courses), Go to School at iTunes U screen, Keep the iPad Screen Clean–Protecting the iPad’s Screen, Protecting the iPad’s Screen Screen Brightness (iBooks), Read an iBook Screen Care Kit for iPad, Protect Your iPad Screen Orientation Lock, Use the Mute/Lock and Volume Buttons, Read an iBook searching, Search the iPad, Take Notes, Search for Apps, Search for Songs in iTunes, Find Pictures on Your iPad–Working with Albums, Find Pictures on Your iPad, Working with Albums, General for apps, Search for Apps for photos on iPad, Find Pictures on Your iPad–Working with Albums, Find Pictures on Your iPad, Working with Albums iTunes, Search for Songs in iTunes Notes, Take Notes Spotlight, Search the iPad, General Select a Wireless Network, Get Your WiFi Connection Set Up Personal Hotspot, Use the iPad as a Personal Hotspot settings, iPad Settings–App Preferences, Tour the iPad’s Settings, Airplane Mode, Airplane Mode, WiFi, WiFi, Bluetooth, Do Not Disturb (iOS 6 only), Notifications, General, General, General, General, General, General, General, General, General, General, General, General, Sounds, Picture Frame, Privacy (iOS 6) / Location Services (iOS 5), Privacy (iOS 6) / Location Services (iOS 5), iCloud, iCloud, iCloud, iCloud, Mail, Contacts, Calendars, Notes, Reminders (iOS 6 only), Reminders (iOS 6 only), Messages, FaceTime, Maps (iOS 6 only), Safari, Safari, iTunes & App Stores (iOS 6) / Store (iOS 5), Music, Music, Music, Photos & Camera (iOS 6) / Photos (iOS 5), Photos & Camera (iOS 6) / Photos (iOS 5), iBooks, App Preferences Accessibility, General Airplane Mode, Airplane Mode Bluetooth, Bluetooth calendars, iCloud Camera, Photos & Camera (iOS 6) / Photos (iOS 5) Contacts, iCloud FaceTime, FaceTime general, General, General iPad Cover Lock/Unlock, General Usage, General iBooks, iBooks iCloud, iCloud International, General keyboard, General Location Services, Privacy (iOS 6) / Location Services (iOS 5) Mail, Contacts, Calendars, Mail, Contacts, Calendars Maps, Maps (iOS 6 only) Messages, Messages Music, Music Notes, Notes Notifications, Notifications Passcode Lock, General Photos, Photos & Camera (iOS 6) / Photos (iOS 5) Picture Frame, Picture Frame Privacy, Privacy (iOS 6) / Location Services (iOS 5) Reminders, Reminders (iOS 6 only) Restrictions, General Safari, Safari Side Switch, General Store, iTunes & App Stores (iOS 6) / Store (iOS 5) video, Music VoiceOver, General Wi-Fi, Airplane Mode, WiFi Shared (iTunes), The iTunes Window Shared Photo Streams, Photos & Camera (iOS 6) / Photos (iOS 5) sharing, Share and Print Photos, Stream Photos with iCloud–Photo Stream on the Apple TV, Photo Stream on the Apple TV, Share Your Photo Stream–Deleting Photos and Shared Streams, Deleting Photos and Shared Streams, Photos & Camera (iOS 6) / Photos (iOS 5) Photo Stream, Share Your Photo Stream–Deleting Photos and Shared Streams, Deleting Photos and Shared Streams Photo Streams, Photos & Camera (iOS 6) / Photos (iOS 5) photos, Share and Print Photos with iCloud, Stream Photos with iCloud–Photo Stream on the Apple TV, Photo Stream on the Apple TV Show in Playlist (iTunes), Change or Delete an Existing Playlist Shuffle (Photos), Play Slideshows on Your iPad Side Switch, Use the Mute/Lock and Volume Buttons, General Sierra Wireless, Use a Mobile Broadband Hotspot Siri, Meet the iPad Mini, Activate and Set Up Your iPad Over WiFi, Command Your iPad with Siri, Using Siri, Enter Text By Voice, Dictation Options for Older iPads, Search the iPad, General dictation, Enter Text By Voice, Dictation Options for Older iPads searching, Search the iPad Skype, Use Skype to Make Internet Calls Sleep, Turn the iPad On and Off Sleep/Wake switch, Find the Home Button and Cameras slide (finger move), Finger Moves for the iPad slideshows, Play Slideshows on Your iPad–Play Slideshows on Your TV, Play Slideshows on Your TV, Play Slideshows on Your TV Smart Covers, Protect Your iPad Smart Playlists (iTunes), Smart Playlists: Another Way for iTunes to Assemble Song Sets–Smart Playlists: Another Way for iTunes to Assemble Song Sets, Smart Playlists: Another Way for iTunes to Assemble Song Sets, Smart Playlists: Another Way for iTunes to Assemble Song Sets social networking, Social Networking on Your iPad–Social Networking on Your iPad, Social Networking on Your iPad, Social Networking on Your iPad software, Start Over: Restore Your iPad’s Software–Start Over: Restore Your iPad’s Software, Start Over: Restore Your iPad’s Software, Start Over: Restore Your iPad’s Software restoring, Start Over: Restore Your iPad’s Software–Start Over: Restore Your iPad’s Software, Start Over: Restore Your iPad’s Software, Start Over: Restore Your iPad’s Software sorting by, Explore the Music Menu, Explore the Music Menu, Explore the Music Menu, Make Playlists albums, Explore the Music Menu, Make Playlists playlists, Explore the Music Menu song title, Explore the Music Menu Sound Check, Music SoundCloud, Make Music with GarageBand Sounds, Sounds Speak Auto-Text, iPad Keyboard Shortcuts, General speed control in audiobooks, Play Audiobooks spelling, iPad Keyboard Shortcuts Spotlight Search, General spread (finger move), Finger Moves for the iPad Sprint, Cellular: 4G LTE, 4G, and 3G Networks, Sign Up for Cellular Data Service, Use a Mobile Broadband Hotspot data calculator, Sign Up for Cellular Data Service mobile hotspots, Use a Mobile Broadband Hotspot Standard (Map view), See Maps in Different Views Stopwatch, Stopwatch Straighten (Photos), Find Third-Party Photo-Editing Apps streaming, Use the Home Button to Switch Apps, Stream Web Audio and Video–Stream Web Audio and Video, Stream Web Audio and Video, Stream Web Audio and Video, Stream and Mirror Files with AirPlay–Video Mirroring, Stream and Mirror Files with AirPlay, Video Mirroring, Video Mirroring, Get Video Onto Your iPad apps and websites, Get Video Onto Your iPad audio and video, Stream Web Audio and Video–Stream Web Audio and Video, Stream Web Audio and Video, Stream Web Audio and Video mirroring files and, Stream and Mirror Files with AirPlay subscriptions, Subscribe to ePublications switching between open apps, Use Multitasking Gestures on the iPad syncing, Sync Your iPad with iTunes, Sync Your iPad with iTunes–USB or Wi-Fi Sync?


pages: 525 words: 116,295

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

Our highly documented pasts will have an impact on our prospects, and our ability to influence and control how we are perceived by others will decrease dramatically. The potential for someone else to access, share or manipulate parts of our online identities will increase, particularly due to our reliance on cloud-based data storage. (In nontechnical language, cloud computing refers to software hosted on the Internet that the user does not need to closely manage. Storing documents or content “in the cloud” means that data is stored on remote servers rather than on local ones or on a person’s own computer, and it can be accessed by multiple networks and users. With cloud computing, online activities are faster, quicker to spread and better equipped to handle traffic loads.) This vulnerability—both perceived and real—will mandate that technology companies work even harder to earn the trust of their users. If they do not exceed expectations in terms of both privacy and security, the result will be either a backlash or abandonment of their product.

A team at Carnegie Mellon demonstrated in a 2011 study that the combination of “off-the-shelf” facial-recognition software and publicly available online data can match a large number of faces very quickly, thanks to technical advancements like cloud computing. In one experiment, unidentified pictures from dating sites (where people often use pseudonyms) were compared with profile shots from social-networking sites, which can be publicly accessed on search engines (i.e., no log-in required), yielding a statistically significant result. It was noted in the study that it would be unfeasible for a human to do this search manually, but with cloud computing, it takes just seconds to compare millions of faces. The accuracy improves regarding people with many pictures of themselves available online—which, in the age of Facebook, is practically everyone.

Data is rarely erased on computers; operating systems tend to remove only a file’s listing from the internal directory, keeping the file’s contents in place until the space is needed for other things. (And even after a file has been overwritten, it’s still occasionally possible to recover parts of the original content due to the magnetic properties of disc storage. This problem is known as “data remanence” by computer experts.) Cloud computing only reinforces the permanence of information, adding another layer of remote protection for users and their information. Such mechanisms of retention were designed to save us from our own carelessness when operating computers. In the future, people will increasingly trust cloud storage—like ATMs in banks—over physical machinery, placing their faith in companies to store some of their most sensitive information, avoiding the risks of hard-drive crashes, computer theft or document loss.


pages: 472 words: 117,093

Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"Robert Solow", 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, longitudinal study, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, Plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, ubercab, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

Many insiders believe that the single most important factor has been Moore’s law. Neural networks become much more powerful and capable as their size increases, and it’s only recently that sufficiently large ones have become cheap enough that they are available to many researchers. Cloud computing has helped open up AI research to these smaller budgets. Technology entrepreneur Elliot Turner estimates that the computing power required to execute a cutting-edge machine learning project could be rented from a cloud computing provider like Amazon Web Services for approximately $13,000 by the fall of 2016. Oddly enough, the popularity of modern video games has also been a great boost to machine learning. The specialized graphics processing units (GPUs) that drive popular gaming consoles turn out to be extremely well suited to the kinds of calculations required for neural networks, so they’ve been drafted in large numbers for this task.

Such speed improvements mean better and faster data accumulation, and they also mean that robots and flying drones can be in constant communication and thus coordinate their work and react together on the fly to quickly-changing circumstances. The cloud. An unprecedented amount of computing power is now available to organizations and individuals. Applications, blank or preconfigured servers, and storage space can all be leased for a long time or rented for a few minutes over the Internet. This cloud computing infrastructure, largely less than a decade old, accelerates the robotic Cambrian Explosion in three ways. First, it greatly lowers barriers to entry, since the kinds of computing resources that were formerly found only in great research universities and multinationals’ R&D labs are now available to startups and lone inventors. Second, it allows robot and drone designers to explore the important trade-off of local versus central computation: which information-processing tasks should be done in each robot’s local brain, and which should be done by the great global brain in the cloud?

Many of the businesses described in this chapter rely on powerful mobile computing devices, and as we’ve seen the smartphone era only started in 2007 with the iPhone (and apps from outside developers took another year to arrive). Smartphones were not only the first truly mobile computers; they were also the first location-aware ones, thanks to their GPS sensors. These are indispensable complements to almost every successful O2O system. Cloud computing was also critical to the success of many platform businesses, because it freed them from having to correctly predict just how successful they would be. With cloud providers, essentially unlimited amounts of additional computing capacity are available very quickly, instead of having to be planned for and purchased well in advance. As Charlie Songhurst, the former head of strategy at Microsoft and an early investor in ClassPass and Flexe, told us, it’s easier for startups and other online experiments to scale quickly because they don’t have to forecast their own success.


pages: 1,380 words: 190,710

Building Secure and Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems by Heather Adkins, Betsy Beyer, Paul Blankinship, Ana Oprea, Piotr Lewandowski, Adam Stubblefield

anti-pattern, barriers to entry, bash_history, business continuity plan, business process, Cass Sunstein, cloud computing, continuous integration, correlation does not imply causation, create, read, update, delete, cryptocurrency, cyber-physical system, database schema, Debian, defense in depth, DevOps, Edward Snowden, fault tolerance, fear of failure, general-purpose programming language, Google Chrome, Internet of things, Kubernetes, load shedding, margin call, microservices, MITM: man-in-the-middle, performance metric, pull request, ransomware, revision control, Richard Thaler, risk tolerance, self-driving car, Skype, slashdot, software as a service, source of truth, Stuxnet, Turing test, undersea cable, uranium enrichment, Valgrind, web application, Y2K, zero day

Since arriving at Google, I’ve learned more about how the SRE model was established here, how SRE implements DevOps philosophies, and how SRE and DevOps have evolved. Meanwhile, I’ve been translating my IT security experience in the financial services industry to the technical and programmatic security capabilities at Google. These two sectors are not unrelated, but each has its own history worth understanding. At the same time, enterprises are at a critical point where cloud computing, various forms of machine learning, and a complicated cybersecurity landscape are together determining where an increasingly digital world is going, how quickly it will get there, and what risks are involved. As my understanding of the intersection between security and SRE has deepened, I’ve become even more certain that it’s important to more thoroughly integrate security practices into the full lifecycle of software and data services.

Cloud logs Increasingly, organizations are moving parts of their business or IT processes to cloud-based services, ranging from data in Software-as-a-Service (SaaS) applications to virtual machines running critical customer-facing workloads. All of these services present unique attack surfaces and generate unique logs. For example, an attacker can compromise the account credentials for a cloud project, deploy new containers to the project’s Kubernetes cluster, and use those containers to steal data from the cluster’s accessible storage buckets. Cloud computing models commonly launch new instances daily, which makes detecting threats in the cloud dynamic and complex. When it comes to detecting suspicious activity, cloud services present advantages and disadvantages. Using services like Google’s BigQuery, it’s easy and relatively cheap to collect and store large amounts of log data, and even to run detection rules, directly in the cloud. Google Cloud services also offer built-in logging solutions like Cloud Audit Logs and Stackdriver Logging.

Additionally, regulations like the EU’s General Data Protection Regulation (GDPR) and service contracts with security-conscious customers continually push the boundaries of how quickly investigations must begin, progress, and complete. Today, it’s not unusual for a customer to ask for a notification of a potential security problem within 24 hours (or less) of initial detection. Incident notification has become a core feature of the security domain, alongside technological advances such as easy and ubiquitous use of cloud computing, widespread adoption of “bring your own device” (BYOD) policies in the workplace, and the Internet of Things (IoT). Such advances have created new challenges for IT and security staff—for example, limited control over and visibility into all of an organization’s assets. Is It a Crisis or Not? Not every incident is a crisis. In fact, if your organization is in good shape, relatively few incidents should turn into crises.


pages: 234 words: 63,522

Puppet Essentials by Felix Frank

cloud computing, Debian, DevOps, domain-specific language, Infrastructure as a Service, platform as a service, web application

The toolchain of types and providers has been explained, and you can even extend Puppet through your own custom plugins. Designing and structuring manifests through classes, defined types, and modules is becoming natural to you, and you have some more advanced language tools at your disposal as well. It is now time to look from a more practical angle. Let's take a look at designs that are useful in common real-world scenarios. With the general trend of cloud computing, we will focus on some techniques that cater especially to the use of Puppet in cloud environments. This will not be limited to the manifest and module design; you will also learn some generally useful configuration and deployment techniques. These are the topics that we'll cover in this final chapter: • Typical scopes of Puppet • Taking Puppet to the cloud • Building manifests for the cloud • Preparing for autoscaling • Ensuring successful provisioning Configuring Your Cloud Application with Puppet Typical scopes of Puppet Puppet was originally conceived for the automation and centralized maintenance of server configurations.

For example, the purging of authorized SSH keys must be configured through the owning user type instead, because the resources type cannot enumerate them: user { 'rcmd': ensure => present, uid => '2082', purge_ssh_keys => true, } [ 197 ] Configuring Your Cloud Application with Puppet Also, keep in mind that purging will only work for native resources and not instances of defined types. To clean these up, you will have to target their wrapped resources for purging. You did this already in the rcmd example—the file resources with the purge => true parameter took care of purging unmanaged rcmd::command resources by removing the files that the defined type had created. Preparing for autoscaling One advantage of cloud computing over classic data center operations is its ability to minimize the cost for infrastructure. You usually don't need to overprovision your cloud server resources, because you can add instances on short notice. If your workload is fluctuating, predictably or not, you can potentially further minimize the infrastructure through autoscaling features. Let the cloud provider add and remove instances as the load increases and decreases again.

Extending Puppet ISBN: 978-1-78398-144-1 Paperback: 328 pages Design, manage, and deploy your Puppet architecture with the help of real-world scenarios 1. Plan, test, and execute your Puppet deployments. 2. Write reusable and maintainable Puppet code. 3. Handle challenges that might arise in upcoming versions of Puppet. Please check www.PacktPub.com for information on our titles Puppet 3 Cookbook ISBN: 978-1-78216-976-5 Paperback: 274 pages Build reliable, scalable, secure, and high-performance systems to fully utilize the power of cloud computing 1. Use Puppet 3 to take control of your servers and desktops, with detailed step-by-step instructions. 2. Covers all the popular tools and frameworks used with Puppet: Dashboard, Foreman, and more. 3. Teaches you how to extend Puppet with custom functions, types, and providers. Mastering Puppet ISBN: 978-1-78398-218-9 Paperback: 280 pages Pull the strings of Puppet to configure enterprise-grade environments for performance optimization 1.


pages: 538 words: 141,822

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

Elude the Cat, Empower the Masses What Barbara Streisand Could Teach Nicolae Ceauşescu Russia’s First Pornographer Meets Russia’s Sarah Palin Fifty Cents Gets You a Long Way on the Spinternet Small Doses of Propaganda Are Still Bad for You Darning Mao’s Socks, One SMS at a Time chapter six - Why the KGB Wants You to Join Facebook Never Trust Anyone with a Website Why Databases Are Better Than Stasi Officers Say Hi. You’re on Camera! How to Lose Face on Facebook Think, Search, Cough The Myth of an Overprotected Activist Rainy Days of Cloud Computing On Mobile Phones That Limit Your Mobility chapter seven - Why Kierkegaard Hates Slacktivism Digital Natives of the World, Unite! Poking Kierkegaard Kandinsky and Vonnegut Are Now Friends! Killing the Slacktivist in You On the Increased Productivity of Lonely Warriors, or Why Some Crowds Are Wise ... Everybody Can’t Be Che Guevara Dissidents Without Dissent No Such Thing as Virtual Politics chapter eight - Open Networks, Narrow Minds: Cultural Contradictions of ...

For many antigovernment activists, cybercafés have become the new (and often the only) offices, as authorities keep a close eye on their home and office Internet connections. However, few Internet cafés allow their patrons to install new software or even use browsers other than Internet Explorer, which puts most innovative tools for secure communication out of easy reach. Rainy Days of Cloud Computing Some observers see many security-enhancing benefits to the Internet. For example, dissidents and NGOs can now use multifunctional online working environments to execute all their work remotely—“in the cloud”—without having to install any software or even store any data on poorly protected computers. All one needs is a secure browser and an Internet connection; there’s no need to download any files or carry a portable copy of your favorite word processor on a USB thumb drive.

The fact that many activists and NGOs now conduct all their business activities out of a single online system, most commonly Google—with calendar, email, documents, and budgets all easily available from just one account—means that should their password be compromised, they would lose control over all of their online activities. Running all those operations on their own laptops was not much safer, but at least a laptop could be locked in a safe. The centralization of information under one roof—as often happens in the case of Google—can do wonders from the perspective of productivity, but from the perspective of security it often only increases the risks. On Mobile Phones That Limit Your Mobility Much like cloud computing, the mobile phone is another activist tool that has not been subjected to thorough security analysis. While it has been rightly heralded as the key tool for organizing, especially in countries where access to the Internet and computers is prohibitively expensive, little has been said about the risks inherent to most “mobile activism.” The advantages of such activism are undeniable. Unlike blogging and tweeting, which require an Internet connection, text messaging is cheap and ubiquitous, and it doesn’t require much training.


pages: 460 words: 131,579

Masters of Management: How the Business Gurus and Their Ideas Have Changed the World—for Better and for Worse by Adrian Wooldridge

affirmative action, barriers to entry, Black Swan, blood diamonds, borderless world, business climate, business cycle, business intelligence, business process, carbon footprint, Cass Sunstein, Clayton Christensen, cloud computing, collaborative consumption, collapse of Lehman Brothers, collateralized debt obligation, commoditize, corporate governance, corporate social responsibility, creative destruction, credit crunch, crowdsourcing, David Brooks, David Ricardo: comparative advantage, disintermediation, disruptive innovation, don't be evil, Donald Trump, Edward Glaeser, Exxon Valdez, financial deregulation, Frederick Winslow Taylor, future of work, George Gilder, global supply chain, industrial cluster, intangible asset, job satisfaction, job-hopping, joint-stock company, Joseph Schumpeter, Just-in-time delivery, Kickstarter, knowledge economy, knowledge worker, lake wobegon effect, Long Term Capital Management, low skilled workers, Mark Zuckerberg, McMansion, means of production, Menlo Park, mobile money, Naomi Klein, Netflix Prize, Network effects, new economy, Nick Leeson, Norman Macrae, patent troll, Ponzi scheme, popular capitalism, post-industrial society, profit motive, purchasing power parity, Ralph Nader, recommendation engine, Richard Florida, Richard Thaler, risk tolerance, Ronald Reagan, science of happiness, shareholder value, Silicon Valley, Silicon Valley startup, Skype, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, Steven Levy, supply-chain management, technoutopianism, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Hsieh, too big to fail, wealth creators, women in the workforce, young professional, Zipcar

Tom Peters argues that “smaller firms are gaining in almost every market.”9 The Economist has frequently praised small startups and cautioned governments against protecting dying giants. The demise-of-size crowd certainly has some arguments on its side. “Lean manufacturing” and “just-in-time production” have shifted the emphasis from size to timeliness. And a host of technological advances—most recently the development of “cloud computing”—have given small companies command over resources that used to be reserved for their big brothers. As the advantages brought by economies of scale have shrunk, the disadvantages have grown. Giant companies generate bureaucratic bloat; giant factories create shop-floor alienation; and many giant corporations fail to attract creative workers, or to make good use of them if they do get hold of them.

The government has intervened to rescue too-big-to-fail companies such as General Motors and Citibank (the financial services sector is now more concentrated than ever). And the rise of the emerging world has projected a new set of giant conglomerates onto the global stage. In many ways the argument about size depends on a false antithesis: big and small companies are potentially allies rather than alternatives. Cloud computing could not provide startups with access to huge amounts of computer power if big companies had not created giant servers. Biotech startups could not survive if they were not given work by biotech giants. The most successful economic ecosystems contain a variety of big and small companies: Silicon Valley boasts long-established giants such as Hewlett-Packard as well as an ever-changing array of startups.

The Internet is a perfect technology for entrepreneurs. It can provide people with the wherewithal to truck, barter, and exchange without ever meeting each other. It can allow upstarts to take on long-established and well-capitalized businesses. News aggregators such as RealClearPolitics and Memeorandum have become part of every news junkie’s toolkit despite having almost no money (Memeorandum is little more than an algorithm). “Cloud” computing is shifting the playing field still further in favor of challengers: entrepreneurs can use their personal computers or laptops, whether they are in the office or a hotel halfway around the world, to gain access to sophisticated business services, such as tools for managing their relations with their customers. The mobile phone has been almost as revolutionary. The mobile has allowed entrepreneurs to break into what was once one of the world’s most regulated markets, telecoms.


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

“We’re seeing very early indicators that this market is coming into fruition immediately,” says Jon Callaghan, a founding partner in the early-stage tech venture capital firm True Ventures, in that same Entrepreneur article. “It’s super early, but it will hit very, very quickly, and we’ll look back on 2013 . . . as a year for robotics coming into its own.” Genomics and Synthetic Biology Throughout the past few chapters, we’ve been examining exponentials poised to explode over the next three to five years and seeing how these technologies reinforce and empower one another—the rise of cloud computing enables more capable and ubiquitous AI, which in turn allows the average entrepreneur to program robots. To close this chapter, we’re going to examine synthetic biology, a technology that’s a little further out—say, five to ten years—but is still transitioning from deception to disruption. And it’s going to be a sizable disruption. Synthetic biology56 is built around the idea that DNA is essentially software—nothing more than a four-letter code arranged in a specific order.

Put simpler, HLI’s goal is to make one hundred years old the new sixty. We launched HLI with $85 million in seed capital, raised at record speed. Part of the reason for this velocity is that the company sits at the intersection of many of the exponential technologies discussed in this chapter: robotics, which enables lightning-fast sequencing; AI and machine learning, which can make sense of petabytes of raw genomic data; cloud computing and networks for transmitting, handling, and storing that data; and synthetic biology for correcting and rewriting the corrupted genome of our aging stem cells. Couple that with the incredible value proposition of abundant, longer, and healthier lives—there is over $50 trillion locked up in the bank accounts of people over the age of sixty-five—and you understand the potential. And understanding this potential is critical if you’re going to succeed as an exponential entrepreneur.

Moreover, liberation from proximity and prejudice increases access to new ideas. Since creativity is recombinatory—i.e., breakthroughs result from new ideas bumping into old thoughts to produce novel insights—this increased access to ideas amplifies the rate of innovation in communities. In fact, if you combine this amplified rate of innovation with our newfound ability to tap any expert anywhere in the world, the potency of technologies like 3-D printing and cloud computing, and the power of crowdfunding to capitalize such ventures, you find the second key difference in today’s communities: the scale of projects they can now undertake has grown exponentially. Communities are now empowered to tackle jobs far larger in scope and size than anything previously possible. For one example, the online hobbyist community DIY Drones has been able to build military-grade autonomous aircraft; for another, Local Motors is constructing fully customizable automobiles.4 Ten years ago, challenges of this size were the sole province of large corporations and governments.


pages: 340 words: 100,151

Secrets of Sand Hill Road: Venture Capital and How to Get It by Scott Kupor

activist fund / activist shareholder / activist investor, Airbnb, Amazon Web Services, asset allocation, barriers to entry, Ben Horowitz, carried interest, cloud computing, corporate governance, cryptocurrency, discounted cash flows, diversification, diversified portfolio, estate planning, family office, fixed income, high net worth, index fund, information asymmetry, Lean Startup, low cost airline, Lyft, Marc Andreessen, Myron Scholes, Network effects, Paul Graham, pets.com, price stability, ride hailing / ride sharing, rolodex, Sand Hill Road, shareholder value, Silicon Valley, software as a service, sovereign wealth fund, Startup school, Travis Kalanick, uber lyft, VA Linux, Y Combinator, zero-sum game

Beginning in the early 2000s, though, there were a few significant transformations in the startup ecosystem that would change things in the entrepreneurs’ favor. First, the amount of capital required to start a company began to decline; this continues in earnest even today. Not only did the absolute cost of servers, networking, storage, data center space, and applications begin to fall, but the procurement method evolved from up-front purchasing to much cheaper “renting” with the advent of what is known as cloud computing. As a startup, these changes are very significant, as they mean that the amount of money you need to raise from VCs to get started is much less than in the past. Y Combinator Cracks Open the “Black Box” The second material transformation in the startup ecosystem was the advent of an incubator known as Y Combinator (or YC for short). Started in 2005 by Paul Graham and Jessica Livingston, YC basically created startup school.

We know that Oracle is a huge company in the database market, so we can fairly easily posit that a startup going after that market opportunity is playing in a big market—easy enough. But what we don’t know is how the overall database market will play out over time. Will there be other new technologies that might supplant the functions of the database and thus hollow out the market? Or maybe the number of applications that require databases will grow exponentially as cloud computing dominates workflows, and thus the database market will become even bigger than it is today? Those are all good questions, but most VCs would probably be fine assuming that a startup going after the database market, if successful, has a big enough market to build a big company and thus become an investment home run. The more challenging aspects of market size estimation come from startups going after markets that do not exist currently or that are smaller markets today because they are constrained by the current state of technology.

There are of course other ways to achieve that differentiation in the marketplace, and no doubt new models will continue to emerge. How did we get to this point where capital ceased to be a scarce resource? We talked about this briefly in the introduction to this book, but a few things happened along the way. First, starting in the early 2000s, the costs required to start a new company began to fall precipitously. As cloud computing began to take off, the unit costs of all these hardware and software products began to fall. A variant of Moore’s law was sweeping through every segment of the technology stack. At the same time, software development systems also progressed, and engineering efficiency increased correspondingly. Today, developers can go to Amazon Web Services or competing providers and rent compute utility on demand, providing incremental pricing coupled with dramatically lowered input costs.


RDF Database Systems: Triples Storage and SPARQL Query Processing by Olivier Cure, Guillaume Blin

Amazon Web Services, bioinformatics, business intelligence, cloud computing, database schema, fault tolerance, full text search, information retrieval, Internet Archive, Internet of things, linked data, NP-complete, peer-to-peer, performance metric, random walk, recommendation engine, RFID, semantic web, Silicon Valley, social intelligence, software as a service, SPARQL, web application

Such an architecture induces a coordination of accesses to shared data that is implemented with a complex distributed lock manager. Oracle’s Real Application Clusters (RAC) is one implementation of the shared-disk approach. Among the three possible architectures, the shared-nothing approach is, by far, the most adapted to parallel databases, and we will soon see that it’s widely used in the NoSQL ecosystem and in cloud computing architectures. The main advantages of the shared-nothing approach are that it minimizes interference between each machine—that is, no exchanges of information stored in main memories and disks are needed—and it can be scaled out to thousands of machines, by adding new commodity machines to the cluster, without impacting the global infrastructure’s 21 22 RDF Database Systems Figure 2.4 Taxonomy of parallel architectures: (a) shared nothing, (b) shared memory, and (c) shared disk.

A main advantage over the other categories is its ability to support data distribution over a cluster of commodity hardware as well as its integration with MapReduce frameworks. We will come back to the distribution aspects in Chapter 7. We start our investigation with approaches proposed in Cudré-Mauroux et al. (2013) and then present complete systems that have been implemented in different contexts, such as cloud computing. Two popular and open-source column-family systems are Apache’s HBase and Cassandra. They both can be used to store RDF triples or quads using an index structure that is reminiscent of the YARS system (Harth et al., 2005). We consider that each triple Storage and Indexing of RDF Data element is encoded using a standard dictionary. Then, in the case of triples, the following three indexes are sufficient to ensure efficient index scans: SPO, POS, and OSP.

Some highly compressed, main memory single indexing approaches are emerging but are quite recent and cannot be considered as mature. Non-native systems are using an existing database management system that has not been originally developed for the RDF data model. These systems mainly concern 143 144 RDF Database Systems • • RDBMS, and more recently stores based on NoSQL systems are emerging and are tackling the cloud computing market. Several commercial RDF engines belong to these two categories. The fact that Oracle, IBM, and Microsoft are releasing components in their environment (i.e., RDBMS and/or cloud solutions) is an interesting feature toward the future of RDF data management. The fact that such companies are entering this market means that some commercial activities are emerging. A survey of the RDF store market clearly emphasizes the following group of production-ready systems (in alphabetic order): Sesame, 4Store, Allegrograph, BigData, Oracle, OWLIM, Stardog, and Virtuoso.


pages: 224 words: 64,156

You Are Not a Gadget by Jaron Lanier

1960s counterculture, accounting loophole / creative accounting, additive manufacturing, Albert Einstein, call centre, cloud computing, commoditize, crowdsourcing, death of newspapers, different worldview, digital Maoism, Douglas Hofstadter, Extropian, follow your passion, hive mind, Internet Archive, Jaron Lanier, jimmy wales, John Conway, John von Neumann, Kevin Kelly, Long Term Capital Management, Network effects, new economy, packet switching, PageRank, pattern recognition, Ponzi scheme, Ray Kurzweil, Richard Stallman, Silicon Valley, Silicon Valley startup, slashdot, social graph, stem cell, Steve Jobs, Stewart Brand, Ted Nelson, telemarketer, telepresence, The Wisdom of Crowds, trickle-down economics, Turing test, Vernor Vinge, Whole Earth Catalog

Chapter 4 Digital Peasant Chic Chapter 5 The City Is Built to Music Chapter 6 The Lords of the Clouds Renounce Free Will in Order to Become Infinitely Lucky Chapter 7 The Prospects for Humanistic Cloud Economics Chapter 8 Three Possible Future Directions PART THREE The Unbearable Thinness of Flatness Chapter 9 Retropolis Chapter 10 Digital Creativity Eludes Flat Places Chapter 11 All Hail the Membrane PART FOUR Making The Best of Bits Chapter 12 I Am a Contrarian Loop Chapter 13 One Story of How Semantics Might Have Evolved PART FIVE Future Humors Chapter 14 Home at Last (My Love Affair with Bachelardian Neoteny) Acknowledgments Preface IT’S EARLY in the twenty-first century, and that means that these words will mostly be read by nonpersons—automatons or numb mobs composed of people who are no longer acting as individuals. The words will be minced into atomized search-engine keywords within industrial cloud computing facilities located in remote, often secret locations around the world. They will be copied millions of times by algorithms designed to send an advertisement to some person somewhere who happens to resonate with some fragment of what I say. They will be scanned, rehashed, and misrepresented by crowds of quick and sloppy readers into wikis and automatically aggregated wireless text message streams.

Some of the better-known figures in this tradition include the late Joseph Weizenbaum, Ted Nelson, Terry Winograd, Alan Kay, Bill Buxton, Doug Englebart, Brian Cantwell Smith, Henry Fuchs, Ken Perlin, Ben Schneiderman (who invented the idea of clicking on a link), and Andy Van Dam, who is a master teacher and has influenced generations of protégés, including Randy Pausch. Another important humanistic computing figure is David Gelernter, who conceived of a huge portion of the technical underpinnings of what has come to be called cloud computing, as well as many of the potential practical applications of clouds. And yet, it should be pointed out that humanism in computer science doesn’t seem to correlate with any particular cultural style. For instance, Ted Nelson is a creature of the 1960s, the author of what might have been the first rock musical (Anything & Everything), something of a vagabond, and a counterculture figure if ever there was one.

CHAPTER 4 Digital Peasant Chic ANOTHER PROBLEM WITH the philosophy I am criticizing is that it leads to economic ideas that disfavor the loftiest human avocations. In this and the following sections I will address an orthodoxy that has recently arisen in the world of digital culture and entrepreneurship. Problems associated with overly abstract, complex, and dangerous financial schemes are connected with the ideals of “open” or “free” culture. Ruining an Appointment with Destiny The ideology that has overtaken much of the cloud-computing scene—exemplified by causes like free or open culture—has the potential to ruin a moment that has been anticipated since at least as far back as the nineteenth century. Once technological advances are sufficient to potentially offer all people lives filled with health and ease, what will happen? Will only a tiny minority benefit? While the relative number of desperately poor people is decreasing, income differences between the rich and the poor are increasing at an accelerating rate.


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

A good example of how I learned to be accountable to my team at Red Hat came in the wake of our acquisition of Qumranet. The decision was a bold step forward for the company into a white-hot sector called “virtualization,” a technical term for describing how you can get a computer to run multiple operating systems at the same time. Buying the company gave us a leadership position in a key strategic area that underlies cloud computing. But while much of its technology was open source, key components had been written in another proprietary language and were anything but open. Our internal tech estimated that it would take about six months to a year to rewrite those pieces of code. That left me in a dilemma. We had just spent more than $100 million to buy a company in a sector that was moving fast, and we would have to wait almost a year before deploying it.

If a post generates more than one response, it will typically move onto memo-list. Memo-list—An e-mail list where we post informal things that will likely have a companywide impact and where we expect significant debate to occur. It’s not atypical to have dozens or hundreds of messages on particularly important topics. A topic-specific list, such as Cloud-Strategy-List—Where associates most interested in cloud computing can discuss and debate topics in this area. Blogs—When someone has something really substantial and thoughtful to share, they blog about it. They know not everyone will read it, but it allows for much deeper exploration of narrower subjects. Wikis—When someone wants a more structured dialogue and more thoughtful feedback, the final weapon of choice is to use a wiki. Conversations in these different channels can sometimes seem chaotic or even out of control.

Rather, I use these opportunities to poke and prod on issues that I believe are important to Red Hat. The subsequent conversations and activities around the company will ultimately coalesce into the appropriate initiatives. My job as catalyst is to stir the debate and ignite the conversations. From there, our associates, through their own conversations and debate, will drive the ultimate actions. For instance, Red Hat has emerged as a leader in the field of cloud computing. We are the leading contributors to and sellers of the infrastructure software used to run public and private clouds. The specific initiatives to establish that position didn’t emerge from a top-down strategy process. They came from the senior team at Red Hat describing how important it was for Red Hat and open source to emerge as the default choice for next-generation IT infrastructure. That’s as directive as we needed to be.


Work in the Future The Automation Revolution-Palgrave MacMillan (2019) by Robert Skidelsky Nan Craig

3D printing, Airbnb, algorithmic trading, Amazon Web Services, anti-work, artificial general intelligence, autonomous vehicles, basic income, business cycle, cloud computing, collective bargaining, correlation does not imply causation, creative destruction, data is the new oil, David Graeber, David Ricardo: comparative advantage, deindustrialization, deskilling, disintermediation, Donald Trump, Erik Brynjolfsson, feminist movement, Frederick Winslow Taylor, future of work, gig economy, global supply chain, income inequality, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, off grid, pattern recognition, post-work, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Steve Jobs, strong AI, technoutopianism, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

Yet to maintain their lead—particularly against each other—these companies face a structural imperative to extract more and more data. This is not only a quantitative increase in a particular type of data (say, geolocation or financial data), but also a qualitative increase in the kinds of data that are being collected. Partly because of this, what we see is all of these companies starting to expand out from their core business to other places. Amazon is no longer just an ecommerce company; it is getting involved in cloud computing, media content, logistics and the consumer internet of things, to name just a few endeavours. Likewise, with Google and, to a lesser degree, with Facebook. Both are investing and buying up companies all across the tech space in areas that offer new data extraction possibilities. The old monopolies were based on vertical or horizontal integration—but today there is a more rhizomatic integration based upon data as a resource.

And at m that level, they start to become competitors. We are already witnessing emerging competition between these companies over the collection of data. For instance, Google Home versus Amazon Alexa is a key proxy battle in this war, with each of them making efforts to harm the other.9 There is obviously competition over smartphones; there is competition over personal assistants; and the major new front is now competition over cloud computing. The once peaceful harmony between these platform monopolies is now becoming a significantly more contentious space as they encroach on each other’s territory. This leads me to a core point, which is how AI affects monopolisation tendencies of the platform economy. AI has its own virtuous cycles: more data means better AI, better AI means better services and products, better services and products means more users, and more users means more data.

Strong, 99 Artisans, 12, 29, 38, 74, 93, 94 Attitudes to work, 1, 4, 53–62, 73, 75 Aubrey, 184 Austria, 68, 196 Authenticity, 116 Authority, 120, 165 Automation restrictions on, 95 speed of, 21, 137 task automation vs job automation, 92, 93, 110, 141 Autonomous cars, 114, 115, 118 Autor, David, 59, 126 Autor Levy Murnane (ALM) hypothesis, 126–128, 131 B Bailey, Olivia, 180 Bairoch, Paul, 44, 46 Banking (automation of ), 87, 147 Bargaining, 68, 70, 177, 181, 182, 184, 185 Bastani, Aaron, 179n2 Beckert, Sven, 44 Berger, Thor, 95 Bessen, James, 4 Blumenbach, Wenzel, 41 Bosch, Gerhard, 179 Bostrom, Nick, 112, 113 Bourgeois household, 39 Brain and AI, 113 analagous to computer, 100, 103, 104, 115 Brown, William, 185 Bullshit jobs psychological effects, 162 Bureaucracy, 169 C Capitalism, 12, 17, 28, 53, 57, 58, 61, 75, 135, 159 Capper, Phillip, 127, 128 Care work, 3, 48, 75, 117, 178 Carlyle, Thomas, 28 Catholic, 74 Central Europe, 38, 40 Centralisation, 69, 175, 176 Chalmers, David, 103 Chatbots, 91 Chen, Chinchih, 95 Chess (and AI), 112 China, 95, 135 Christian (view of work), 74, 75, 161, 166 Clark, A, 60 Class, 13–15, 17, 30, 39, 43, 46, 47, 118, 159, 160, 162, 165, 172 Classical economics, 54, 55 Climate change, 30, 198 Cloud computing, 139, 140 Coase, Ronald, 70 Coats, David, 184, 185 Collective bargaining, 68, 181, 182, 185 Communism, 13, 57, 58, 61 Competition, 12, 16–18, 39, 91, 94, 112, 115, 119, 139, 140, 152, 199 Index Computational Creativity, 109, 115, 120, 121 Computer aided design (CAD), 34, 35 Computer programming, 100, 116 Computer revolution, 90, 94, 95, 99 Computers, 20, 34, 84, 86, 90, 92–94, 99–107, 110, 111, 115, 116, 120, 131, 134, 146, 147, 151, 197 Consciousness of AI, 110–111 the hard problem, 103 of humans, 105 objective vs. subjective, 102, 103 Consumerism/consumer society, 30, 74, 161, 194 Consumption, 3, 5, 12, 13, 16, 19, 38, 41, 56, 59, 61, 62, 66, 85, 88, 166, 176, 192, 194, 197, 199 Contested concepts, 120 Cooperatives, 40, 61, 69 Craftsmanship, 3, 11, 35, 36, 39, 194 Craig, Nan, 4, 179 Creative work, 3, 48, 74 Creativity, 3, 5, 57, 91, 105–107, 110, 120, 121, 193–195 D D’Arcy, Conor, 177 Data, 2, 84, 92, 107, 129, 130, 137–140, 146, 149, 150, 153, 178, 191, 197, 198 Davies, W.H., 31 De Spiegelaere, Stan, 181, 183 205 Deep Blue, 91, 112, 129, 130 Dekker, Fabian, 180 Deliveroo, 136 Demand effects on automation, 4, 21, 86 elasticity, 86 of work, 4, 13, 15, 16, 76, 158, 164, 180, 199 Democracy, 28 Denmark, 68, 177, 180 Dennett, Daniel, 100, 102, 103 Developing countries, 145 Digital economy, 5, 19, 125–132, 140 Digital revolution, 70 Division of labour, 11, 35, 38, 43, 44, 55 Donkin, Richard, 3 Dosi, Giovanni, 192, 195 Do what you love, 73, 74, 76 Dreyfus, Herbert, 100 E Economics, 1, 4, 5, 7, 10, 12, 14, 15, 18, 29, 30, 53–62 Economic view of work, 53–62 Education, 41, 42, 48, 67–69, 126, 131, 169, 171, 196, 197 Efficiency, 5, 16, 75, 159, 168, 184 Empathy, 106, 107 Employment law, 68 rates, 67, 68, 70 English East India Company, 44 Entrepreneurs, 29, 70, 77, 190, 192, 197, 199 Environment, 25, 31, 56, 70, 87, 91, 109, 111, 113, 120, 178, 198 206 Index Equality of opportunity, 69 of outcome, 69 social, 163 Ethics of AI, 6, 110, 119, 145–153, 197 stagnation of, 151–152 of work, 28 Exit, 69 Experience, 36, 61, 85, 90, 94, 99–105, 116, 119, 189, 190 F Facebook, 136–141, 161 Factory system, 29–30 Families, 3, 26, 29, 37–48, 75, 76, 138, 159, 162, 178, 196 Feminist (arguments about work), 79 Finance, 48, 87, 170, 197 Fire, harnessing/discovery of, 29 Firestone, Shulamith, 159 Firms, 16, 17, 68, 70, 85, 87, 133, 148, 149, 151, 152, 168, 169, 172, 190 Flexicurity, 68 Ford, Henry, 30 Ford, Martin, 2, 59, 106 France, 4, 6, 66–70, 177, 181, 182 Franklin, Benjamin, 28 Freeman, Chris, 192 French Revolution, 43 Frey, Carl Benedikt, 4, 180 Friedman, Milton, 171 Fuzzy matching, 148, 149 G Galbraith, JK, 66 GDP, 19, 178 Gender, 38, 43, 44, 48, 151, 178 Gendered division of labour, 38, 43, 44 Germany, 6, 177, 180–182, 196 Gig economy, 27, 184 Globalisation, 20, 30, 90, 95 Google Google Cloud, 140 Google Home, 140 Google Maps, 35 Google Translate, 106 Google DeepMind, 112, 119 Gorz, A., 59 Graeber, David, 6, 76, 157, 161, 168 Greek ideas of work, 74 Growth, 2, 6, 7, 12, 25, 27, 30, 31, 55, 69, 75, 85, 86, 88, 110, 126, 128, 130, 135, 169, 176, 180, 183, 185, 190, 192, 198, 200 H Happiness, 5, 62, 195 Harrop, Andrew, 180 Hassabis, Demis, 119 Hayden, Anders, 182, 183 Healthcare, 3, 87, 94, 117, 165, 197 Heterodox economics, 54, 56, 62 Hierarchy, 46, 48, 55, 69, 170 High-skilled jobs, 128, 134 Homejoy, 135 Homo economicus, 56, 57 Homo laborans, 3 Homo ludens, 3 Household economy, 4, 38–40, 45, 47 Housewives, 42, 43, 46, 47 Housework, 39, 40, 42, 44, 47 Hunter-gatherers, 11, 26, 27, 30 Index I Idleness, 54 India, 44–47 Industrial Revolution, 2, 4, 14, 29, 37, 75, 93, 94, 175, 177, 190, 191 Inequality, 67–69, 86, 87, 192, 193, 199, 200 Informal economy, 47 Information technology, 86, 161 Infrastructure digital, 140 physical, 103 Innovation, 6, 10, 14, 16, 18, 34, 67, 69, 189–199 process innovation vs. product innovation, 16, 18, 190–191, 195 International Labour Organisation (ILO), 193 Internet of Things, 139, 191 Investment in capital, 114 in skills, 70 J Japan, 117 Jensen, C, 55 Job guarantee, 172 Jobs, Steve, 73 Journalism automation of, 118 clickbait, 118 Juries, algorithmic selection of, 150, 153 K Karstgen, Jack, 196 Kasparov, Garry, 91, 112, 129, 130 207 Katz, Lawrence, 198 Kennedy, John F., 160 Keune, Maarten, 180 Keynes, John Maynard, 6, 9, 11, 27, 60, 61, 160, 161, 176 King, Martin Luther, 171 Knowledge (tacit vs. explicit), 127 Komlosy, Andrea, 4, 75 Kubrick, Stanley, 26 Kurzweil, Raymond, 101, 103, 104 Kuznets, Simon, 190 L Labour, 3, 10, 11, 13–16, 18–21, 29, 34–36, 38, 43–46, 55, 59, 65–70, 73–76, 85–87, 89, 90, 93, 94, 96, 114, 125, 126, 128, 130, 131, 141, 158, 165, 176–180, 183–184, 189, 190, 192–196, 199–200 Labour market polarisation, 67, 70, 126 Labour markets, 67, 68, 70, 87, 90, 96, 125, 126, 128, 130, 131, 141, 178, 183–184, 189, 192, 193, 195, 196, 199–200 Labour-saving effect, 86 Lall, Sanjaya, 193 Language translation, 105, 106 Latent Damage Act 1986, 127 Law automation of, 145, 152, 153 ethics, 145–153 Lawrence, Mathew, 177 Layton, E., 58 Le Bon, Gustave, 101 Lee, Richard, 26 Legal search/legal discovery, 148–150 208 Index Leisure, 3, 10, 11, 19, 27, 48, 55, 56, 59–62, 65, 77, 79, 117, 118, 159, 161, 178, 180, 182, 184, 191, 195 Levy, Frank, 126 List, Friedrich, 193 Love, 55, 74, 76, 99, 103, 106, 112, 118 Low-income jobs, 96 Loyalty, 69 Luddites, 2, 14, 18, 35, 59, 94, 96 Lyft, 136 M Machine learning, 59, 84, 90, 91, 96, 138, 139 Machines, 2, 5, 10, 12–15, 17, 19, 20, 35, 36, 38, 59, 84–87, 90–96, 99–103, 105–107, 109–121, 127–131, 138, 139, 145, 147, 148, 160, 168, 191 Machine vision, 120 Malthusian, 19 Man, Henrik de, 79 Management, 27, 30, 41, 69, 70 management theory/ organisational theory (see also Scientific management) Mann, Michael, 46 Manual work, 1 Manufacturing, 86, 87, 90, 94, 95, 176, 184, 198 Markets/market forces, 5, 6, 21, 38, 44–46, 67, 68, 70, 79, 85–88, 90, 96, 120, 125, 126, 128, 130, 131, 140, 141, 150, 152, 159, 164, 165, 171, 178, 183, 189–193, 195, 196, 198–200 Marx, Karl, 17, 18, 27, 56–59, 61, 62, 78 Matrimonial relationships, 37 McCormack, Win, 159 Meaning, 4, 9, 10, 19, 25, 54, 57, 58, 66, 73, 76, 78, 79, 84, 106, 116, 176, 180 Mechanisation, 15, 17, 19, 20, 192 Meckling, W., 55 Méda, Dominique, 183 Medical diagnosis (automation of ), 128, 129 Menger, Pierre-Michel, 4 Mental labour, 3 Meritocracy, 28 Middle-income jobs, 90, 93, 94 Migration, 40, 47 Minimum wage, 67, 69 Mining, 26, 38, 197 Mokyr, J., 59 Monopolies, 6, 136, 138–140 Morals/morality, 48, 77, 159, 160, 162, 164, 166, 167 Moravec’s paradox, 131 Murnane, Richard, 126 N Nagel, Thomas, 100, 102 National Living wage, 184 Needs vs.


Smart Cities, Digital Nations by Caspar Herzberg

Asian financial crisis, barriers to entry, business climate, business cycle, business process, carbon footprint, clean water, cloud computing, corporate social responsibility, Dean Kamen, demographic dividend, Edward Glaeser, Edward Snowden, hive mind, Internet of things, knowledge economy, Masdar, megacity, New Urbanism, packet switching, QR code, remote working, RFID, rising living standards, risk tolerance, Ronald Reagan, shareholder value, Silicon Valley, Silicon Valley startup, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart meter, social software, special economic zone, Stephen Hawking, telepresence, too big to fail, trade route, transcontinental railway, upwardly mobile, urban planning, urban sprawl, women in the workforce, working poor, X Prize

Smart city work teams have provided a great deal of analysis, much of it necessarily quite technical. For this discussion, the reader need not be immersed in the details of IT engineering, but it will be of great use to understand the primary components of the optimal smart city construct and the means by which services intersect and reinforce each other. THE NETWORK Every city gains intelligence through a variety of data gateways. Cloud computing, mobile operations, social media, Ethernet cables in local area networks, and fiber-optic cables (some ruggedized for use in harsh environmental conditions, such as hot factories, oceans, or remote utilities) are all utilized, although over time we can expect that the variety of gateways will change as the technology does. Each city has operational centers where the data is collected and analyzed, and where communications throughout the various networks are monitored.

It was the beginning of a strange tale that soon involved a murder accusation against Bo’s wife and speculation about Bo’s own declining fortunes, the latter of which proved well-founded when Bo lost his party chairmanship and disappeared.18 The tabloid-ready details were not, to the thinking of many China experts, sufficient explanation for Bo’s demise. Soon enough, reports surfaced that Bo’s government had taken cyber surveillance to extremes; there was evidence he had used cloud computing and other sophisticated methods to create “a comprehensive package bugging system covering telecommunications to the Internet.”19 Wang Lijun had enabled the surveillance of internal party communications, including those of leaders who outranked Bo and whose favor he courted. Officially charged with embezzlement and bribery, by 2013, Bo’s fortunes were irreversible and he received a life imprisonment sentence.

This model required only that Cisco persuade the government to employ their consulting strategies and hardware. To ensure Cisco had a reliable sales channel for hardware in Hangzhou, the Cisco China team and the global S+CC specialists built a joint company, Connected Cloud International (CCI), with an Insigma subsidiary, Qware Technology. CCI was a vehicle for research, development, and sales of S+CC platforms and cloud computing services in the national and international markets. Cisco was obligated to fund initial rounds on the condition that affiliated companies would purchase agreed amounts of Cisco hardware during the funding years. Cisco was impressed by Insigma’s ability to discuss the concept of smart and connected city services with their end customer. Since that end customer most often was a municipal government, this often required business cases that demonstrated the values that could be added to a city’s holdings.


pages: 392 words: 108,745

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think by James Vlahos

Albert Einstein, AltaVista, Amazon Mechanical Turk, Amazon Web Services, augmented reality, Automated Insights, autonomous vehicles, Chuck Templeton: OpenTable:, cloud computing, computer age, Donald Trump, Elon Musk, information retrieval, Internet of things, Jacques de Vaucanson, Jeff Bezos, lateral thinking, Loebner Prize, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Mark Zuckerberg, Menlo Park, natural language processing, PageRank, pattern recognition, Ponzi scheme, randomized controlled trial, Ray Kurzweil, Ronald Reagan, Rubik’s Cube, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Levy, Turing test, Watson beat the top human players on Jeopardy!

The list of advances starts with the exponentially improved computing power as predicted by Moore’s Law. The rise of mobile—the fact that we all carry around potent pocket-size computers—has also been a significant enabler of voice. Machine learning—in which computers gain capabilities by analyzing data rather than being explicitly taught—has also been critical, allowing developers to blast through problems that have lingered for decades. And cloud computing is a final and often overlooked factor. Conversational AI requires immense power. Attempting to embed all of it on a phone is difficult; putting it into something like a dog collar would be nearly impossible and absurdly expensive. But thanks to the cloud, any device can become a voice-enabled one with the simple addition of a microphone and a Wi-Fi chip. Everything from showerheads to children’s dolls can leverage the might of thousands of globally distributed computers.

The team was perfect: Kittlaus knew the mobile-phone industry. And Cheyer had serious AI bona fides, particularly when it came to orchestrating multiple back-end computer services into a single system—he’d been working on that notion for his whole career. What’s more, the time was right. “What you’ve got here is the clouds parting because mobile is going to bring broadband to everybody,” Gruber remembers saying at the meeting. “This gives cloud computing to everybody, which means we actually have big AI in everybody’s hands with a microphone that you’re wearing all day long. It’s time to do an assistant.” If there was a weakness in what Gruber saw, it was the design of the prototype user interface. You communicated with the system as if it were some early 1980s PC, typing commands in a graceless font. Gruber, who had been invited simply to give his critique, found himself pitching Cheyer and Kittlaus.

That’s when people began to invent the world’s first robots—all-mechanical, life-emulating contraptions known as automata. One such creation was impressively demonstrated in the court of King Charles II by an Englishman named Thomas Irson. His invention took the form of a wooden doll. If you whispered a question into its ear, the doll would then speak the answer. The doll was, in fact, powered by a clever if primitive version of cloud computing. A long concealed tube connected the doll to a room where a learned priest, having overheard the question, would reply with the answer. In the eighteenth century, synthetic speech took its first step toward concrete reality with the help of Wolfgang von Kempelen, a serial inventor from Hungary. Kempelen is best known for one of his nonspeaking creations—the Mechanical Turk, an automaton styled as a turban-wearing mystic who sat behind a table and could beat human players at chess.


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

This perfect storm of cheap parallel computation, bigger data, and deeper algorithms generated the 60-years-in-the-making overnight success of AI. And this convergence suggests that as long as these technological trends continue—and there’s no reason to think they won’t—AI will keep improving. As it does, this cloud-based AI will become an increasingly ingrained part of our everyday life. But it will come at a price. Cloud computing empowers the law of increasing returns, sometimes called the network effect, which holds that the value of a network increases much faster as it grows bigger. The bigger the network, the more attractive it is to new users, which makes it even bigger and thus more attractive, and so on. A cloud that serves AI will obey the same law. The more people who use an AI, the smarter it gets. The smarter it gets, the more people who use it.

Dematerialization and decentralization and massive communication all lead to more platforms. Platforms are factories for services; services favor access over ownership. Clouds The movies, music, books, and games that you access all live on clouds. A cloud is a colony of millions of computers that are braided together seamlessly to act as a single large computer. The bulk of what you do on the web and phone today is done on cloud computing. Though invisible, clouds run our digital lives. A cloud is more powerful than a traditional supercomputer because its core is dynamically distributed. That means that its memory and work is spread across many chips in a massively redundant way. Let’s say you were streaming a long movie and suddenly an asteroid smashed one tenth of the machines that made up the cloud. You might not notice any interruption in the movie because the movie file did not reside in any particular machine but was distributed in a redundant pattern across many processors in such a way that the cloud can reconfigure itself if any of those units fail.

Anyone who owns a computer recognizes those burdens: They take up space, need constant expert attention, and go obsolete instantly. Who would want to own their computer? The answer increasingly is no one. No more than you want to own an electric station, rather than buy electricity from the grid. Clouds enable organizations to access the benefits of computers without the hassle of possession. Expandable cloud computing at discount prices has made it a hundred times easier for a young technology company to start up. Instead of building their own complex computing infrastructure, they subscribe to a cloud’s infrastructure. In industry terms, this is infrastructure as service. Computers as service instead of computers as product: access instead of ownership. Gaining cheap access to the best infrastructure by operating on the cloud is a chief reason so many young companies have exploded out of Silicon Valley in the last decade.


pages: 523 words: 148,929

Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100 by Michio Kaku

agricultural Revolution, AI winter, Albert Einstein, Asilomar, augmented reality, Bill Joy: nanobots, bioinformatics, blue-collar work, British Empire, Brownian motion, cloud computing, Colonization of Mars, DARPA: Urban Challenge, delayed gratification, double helix, Douglas Hofstadter, en.wikipedia.org, friendly AI, Gödel, Escher, Bach, hydrogen economy, I think there is a world market for maybe five computers, industrial robot, Intergovernmental Panel on Climate Change (IPCC), invention of movable type, invention of the telescope, Isaac Newton, John Markoff, John von Neumann, life extension, Louis Pasteur, Mahatma Gandhi, Mars Rover, mass immigration, megacity, Mitch Kapor, Murray Gell-Mann, new economy, oil shale / tar sands, optical character recognition, pattern recognition, planetary scale, postindustrial economy, Ray Kurzweil, refrigerator car, Richard Feynman, Rodney Brooks, Ronald Reagan, Search for Extraterrestrial Intelligence, Silicon Valley, Simon Singh, social intelligence, speech recognition, stem cell, Stephen Hawking, Steve Jobs, telepresence, The Wealth of Nations by Adam Smith, Thomas L Friedman, Thomas Malthus, trade route, Turing machine, uranium enrichment, Vernor Vinge, Wall-E, Walter Mischel, Whole Earth Review, X Prize

• Mass technology as a utility When technologies become widely dispersed, such as electricity and running water, they eventually become utilities. With capitalism driving down prices and increasing competition, these technologies will be sold like utilities, that is, we don’t care where they come from and we pay for them only when we want them. The same applies for computation. “Cloud computing,” which relies heavily on the Internet for most computing functions, will gradually gain in popularity. Cloud computing reduces computation to a utility, something that we pay for only when we need it, and something that we don’t think about when we don’t need it. This is different from the situation today, when most of us do our typing, word processing, or drawing on a desktop or laptop computer and then connect to the Internet when we want to search for information.

See also Intellectual capitalism Carbon nanotubes, 4.­1, 6.­1 Carbon sequestration Cars driverless electric maglev, 5.­1, 9.­1 Cascio, Jamais Catoms Cave Man Principle biotechnology and computer animations and predicting the future and replicators and, 4.­1, 4.­2 robotics/AI and, 2.­1, 2.­2 sports and Cerf, Vint, 4.­1, 6.­1 Chalmers, David Charles, Prince of Wales Chemotherapy Chernobyl nuclear accident Chevy Volt Chinese Empire, 7.­1, 7.­2 Church, George Churchill, Winston, itr.­1, 8.­1 Cipriani, Christian Civilizations alien civilizations characteristics of various Types entropy and information processing and resistance to Type I civilization rise and fall of great empires rise of civilization on Earth science and wisdom, importance of transition from Type 0 to Type I, itr.­1, 8.­1, 8.­2 Type II civilizations, 8.­1, 8.­2, 8.­3 Type III civilizations, 8.­1, 8.­2 waste heat and Clarke, Arthur C.­ Clausewitz, Carl von Cloning, 3.­1, 3.­2 Cloud computing, 1.­1, 7.­1 Cochlear implants Code breaking Collins, Francis Comets Common sense, 2.­1, 2.­2, 2.­3, 7.­1, 7.­2 Computers animations created by augmented reality bioinformatics brain simulations carbon nanotubes and cloud computing, 1.­1, 7.­1 digital divide DNA computers