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Platform Scale: How an Emerging Business Model Helps Startups Build Large Empires With Minimum Investment by Sangeet Paul Choudary
3D printing, Airbnb, Amazon Web Services, barriers to entry, bitcoin, blockchain, business process, Chuck Templeton: OpenTable:, Clayton Christensen, collaborative economy, commoditize, crowdsourcing, cryptocurrency, data acquisition, frictionless, game design, hive mind, Internet of things, invisible hand, Kickstarter, Lean Startup, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, means of production, multi-sided market, Network effects, new economy, Paul Graham, recommendation engine, ride hailing / ride sharing, shareholder value, sharing economy, Silicon Valley, Skype, Snapchat, social graph, social software, software as a service, software is eating the world, Spread Networks laid a new fibre optics cable between New York and Chicago, TaskRabbit, the payments system, too big to fail, transport as a service, two-sided market, Uber and Lyft, Uber for X, uber lyft, Wave and Pay
In contrast, the journey to platform scale for a large pipe-based business starts with the data layer. 1. Build A Culture Of Data Acquisition The first step a traditional pipe-based business needs to take is cultural. It needs to create a culture of data acquisition. Most pipe-based businesses have been designed with a culture of dollar acquisition. Sales representatives who acquire revenue are incentivized accordingly. The key metrics measured are structured around the sole priority of dollar acquisition. To kickstart the journey towards platform scale, businesses will need to create a culture of data acquisition. Businesses like LinkedIn and Netflix demonstrate that higher data acquisition opens greater monetization opportunities. LinkedIn acquires significantly more data from its users than Monster.
Traditional agents also connect producers to consumers. However, they can never operate at platform scale because their ability to match the two sides doesn’t scale. A platform’s ability to scale matchmaking helps it to achieve platform scale (see Figure 16). Matchmaking is accomplished through data. As a result, data acquisition becomes an important priority for platforms. Designing the data model – specifications for what data are required for the value unit and the filter – is a critical step in platform design. This informs a platform’s data acquisition strategy. Data acquisition is subtle but critical. LinkedIn’s progress bar encourages users to provide more data to the platform by showing them the completeness of their profiles and suggesting simple actions to enrich them further. Distracted users are often taken through an initial sign-up process on multiple platforms using similar progress bars.
To be strategic, a free app should be a data acquisition interface that powers a larger business model. Every app by Facebook is structured as a user benefit in exchange for data. Facebook’s news feed itself is the best example of a user benefit in exchange for data. As Facebook and LinkedIn demonstrate, a digital strategy, particularly one that intends to leverage platform scale, should start with a cohesive data strategy. This needs to be executed using a culture of data acquisition. 2. Enable Data Porosity And Integration Platform business models are enabled by platform organizations. An organization that is not integrated at the data layer cannot enable an ecosystem that is orchestrated by data. With a clear platform strategy in mind and having set a culture of data acquisition, a pipe organization must institute infrastructural change.
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
In addition, as we mentioned in the previous chapter, the success of the Internet of Things largely depends on various industries embracing M2M technologies to solve their business problems. In this chapter, we present the parts of the technology ecosystem and its challenges, players, and future direction. Overall, the M2M technology ecosystem can be split into three major groups: data acquisition, data transport, and data analysis. Data acquisition is the device or hardware space — this is where data is being collected from various sensors and sent to the network. Examples are body sensors that measure pulse or calorie consumption, automotive OBD-II14 devices that measure car acceleration, and many others. RFID tags and readers belong to this category as well. To transmit data, devices are equipped with a radio transmitter, which can be cellular, Wi-Fi, or short range.
Through technology innovation, working with huge data sets has become extremely affordable, compared to the realities of a couple of years ago. This enables us to find correlations, spot business trends, detect and prevent potential criminal activity, or optimize workflows of all kinds. To ensure the smooth flow of data, there are platforms that enable communications between any two of the three major groups in the technology ecosystem. For example, between data acquisition and data transport there is a Connected Device Platform (CDP). The CDP, sometimes referred to as middleware, ensures that the devices and sensors can be easily connected, primarily to a cellular network, and that the devices can be remotely managed. Imagine trying to reset thousands or hundreds of thousands of devices manually in the field. This is just one example of a nightmare that a CDP is supposed to prevent.
I think that is why we have seen the mobile operators go away from fixed pricing for a little while. I think they want to instill the sense of efficiency in the developer community. When they had a fixed price for unlimited mobile data, nobody had to worry about being efficient; it did not matter if the app checked the status of a device every ten seconds. Let’s take a closer look at the M2M technology ecosystem and its parts. Device hardware (data acquisition) is one of the most challenging areas of the ecosystem, primarily because it comes in all sizes and colors. You may think of black box–type devices that are usually installed on industrial equipment, but there are also OBD-II devices that get installed in cars, elegant body-worn fitness devices, connectivity modules that get embedded in home appliances, moisture sensors that go in the soil, RFID readers, and so on.
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
More to Big Data Than Meets the Eye Dealing with the Nuances of Big Data An Open Source Brings Forth Tools Caution: Obstacles Ahead Chapter 2: Why Big Data Matters Big Data Reaches Deep Obstacles Remain Data Continue to Evolve Data and Data Analysis are Getting More Complex The Future is Now Chapter 3: Big Data and the Business Case Realizing Value The Case for Big Data The Rise of Big Data Options Beyond Hadoop With Choice Come Decisions Chapter 4: Building the Big Data Team The Data Scientist The Team Challenge Different Teams, Different Goals Don’t Forget the Data Challenges Remain Teams versus Culture Gauging Success Chapter 5: Big Data Sources Hunting for Data Setting the Goal Big Data Sources Growing Diving Deeper into Big Data Sources A Wealth of Public Information Getting Started with Big Data Acquisition Ongoing Growth, No End in Sight Chapter 6: The Nuts and Bolts of Big Data The Storage Dilemma Building a Platform Bringing Structure to Unstructured Data Processing Power Choosing among In-house, Outsourced, or Hybrid Approaches Chapter 7: Security, Compliance, Auditing, and Protection Pragmatic Steps to Securing Big Data Classifying Data Protecting Big Data Analytics Big Data and Compliance The Intellectual Property Challenge Chapter 8: The Evolution of Big Data Big Data: The Modern Era Today, Tomorrow, and the Next Day Changing Algorithms Chapter 9: Best Practices for Big Data Analytics Start Small with Big Data Thinking Big Avoiding Worst Practices Baby Steps The Value of Anomalies Expediency versus Accuracy In-Memory Processing Chapter 10: Bringing it All Together The Path to Big Data The Realities of Thinking Big Data Hands-on Big Data The Big Data Pipeline in Depth Big Data Visualization Big Data Privacy Appendix: Supporting Data “The MapR Distribution for Apache Hadoop” “High Availability: No Single Points of Failure” About the Author Index WILEY & SAS BUSINESS SERIES The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.
Many more data sets are available from Amazon S3, and it is definitely worth visiting http://aws.amazon.com/publicdatasets/ to track these down. Another site to visit for a listing of public data sets is http://www.quora.com/Data/Where-can-I-get-large-datasets-open-to-the-public, a treasure trove of links to data sets and information related to those data sets. GETTING STARTED WITH BIG DATA ACQUISITION Barriers to Big Data adoption are generally cultural rather than technological. In particular, many organizations fail to implement Big Data programs because they are unable to appreciate how data analytics can improve their core business. One the most common triggers for Big Data development is a data explosion that makes existing data sets very large and increasingly difficult to manage with conventional database management tools.
That alone is probably reason enough for the majority of businesses to start evaluating how Big Data analytics can affect the bottom line, and those businesses should probably start evaluating Big Data promises sooner rather than later. Delving into the value of Big Data analytics reveals that elements such as heterogeneity, scale, timeliness, complexity, and privacy problems can impede progress at all phases of the process that create value from data. The primary problem begins at the point of data acquisition, when the data tsunami requires us to make decisions, currently in an ad hoc manner, about what data to keep, what to discard, and how to reliably store what we keep with the right metadata. Adding to the confusion is that most data today are not natively stored in a structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display but not for semantic content and search.
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
It means the authority to raise rates and to direct spending. Did someone tell you security was cheap? Would you rather buy everyone an emergency generator and months of fuel? Second, launch a major program using the best private-sector threat hunter firms to find and remove foreign implants, backdoors, and remote access to the industrial control systems (ICS) and supervisory control and data acquisition systems (SCADA) on the grid. This will not be easy. Ask the U.S. Navy how easy it was to get the Iranians out of their network (and by the way, the Russians are better). Third, put in place that combination of state-of-the-art cybersecurity best practices that have achieved success in America’s most secure corporations. Private-sector expert panels can design the essential set of controls, but they are likely to include permanent threat hunting software and teams, continuous monitoring applications, privileged access management controls, microsegmentation, endpoint detection, remediation systems, limited remote access, and vendor/supply-chain controls.
The latter is the world of industrial machine controls that preceded the internet and generally use an entirely different kind of software. ICS and SCADA: These two types of software generally belong to the OT world. The former are industrial control systems, software from companies such as Siemens, Johnson Controls, and General Electric. ICS software runs factories and the machines in them. SCADA software can be thought of as a subset of programs that engage in supervisory controls and data acquisition. SCADA software often runs on little sensors that report data such as temperatures or voltages or pressure levels. Based on that data, some controls react automatically to avoid overloads and, uh, explosions. The acronym SCADA is often used to describe the software that runs the power grid. It is also used to describe the controlling software on other networks such as railroad lines, pipelines, and petrochemical facilities.
Identity and Access Management (IAM): A class of software used to authenticate network users in order to prevent unauthorized access to data or services. Modern identity and access management products often integrate with user directory databases to manage permissions, and utilize multifactor authentication for an extra layer of security. Industrial Control System (ICS): A blanket term used to describe a collection of programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, and various other control devices used to manage industrial processes. Industrial control systems interpret data from sensors with command functions and translate these inputs into actions that manipulate devices such as valves, regulators, actuators, relays, or switches. Information Sharing and Analysis Center (ISAC): A consortium of companies in a particular industry created for the purpose of sharing data about computer security threats and security best practices.
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
Thus, while cyberspace was once just a realm of communication and then e-commerce (reaching over $10 trillion a year in sales), it has expanded to include what we call “critical infrastructure.” These are the underlying sectors that run our modern-day civilization, ranging from agriculture and food distribution to banking, healthcare, transportation, water, and power. Each of these once stood apart but are now all bound together and linked into cyberspace via information technology, often through what are known as “supervisory control and data acquisition” or SCADA systems. These are the computer systems that monitor, adjust switching, and control other processes of critical infrastructure. Notably, the private sector controls roughly 90 percent of US critical infrastructure, and the firms behind it use cyberspace to, among other things, balance the levels of chlorination in your city’s water, control the flow of gas that heats your home, and execute the financial transactions that keep currency prices stable.
It was this combination that led him to play a role in the discovery of one of the most notable weapons in history; and not just cyber history, but history overall. Since 1988, Ralph and his team of security experts had been advising on the safety of large-scale installations. Their special focus was industrial control systems, the computer systems like SCADA (short for “supervisory control and data acquisition”) that monitor and run industrial processes. SCADA is used in everything from the management and operation of power plants to the manufacture of candy wrappers. In 2010, like many other industrial control experts, Ralph grew concerned about a cyber “worm” of unknown origin that was spreading across the world and embedding itself in these control systems. Thousands of computers in places like India and the United States had been infected.
Lord and Travis Shard (Washington, DC: Center for a New American Security, 2011), pp. 14–15. “the click of a switch” Ibid., p. 9. registered sites hit 550 million by 2012 Jon Russell, “Importance of Microblogs in China Shown as Weibos Pass 500 Million Users,” The Next Web, last modified November 11, 2011, http://thenextweb.com/asia/2011/11/11/importance-of-microblogs-in-china-shown-as-weibos-pass-550-million-users/. “supervisory control and data acquisition” Beidleman, “Defining and Deterring Cyber War,” p. 6. “the control system of our economy” Ibid., p. 1. “knowingly or not, it is life” Ben Hammersley, “Speech to the UK’s Information Assurance Advisory Council,” remarks at the Information Assurance Advisory Council, London, September 6, 2011, http://www.benhammersley.com/2011/09/my-speech-to-the-iaac/. WHERE DID THIS “CYBER STUFF” COME FROM ANYWAY?
Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Thomas H. Davenport
Automated Insights, autonomous vehicles, bioinformatics, business intelligence, business process, call centre, chief data officer, cloud computing, commoditize, data acquisition, disruptive innovation, Edward Snowden, Erik Brynjolfsson, intermodal, Internet of things, Jeff Bezos, knowledge worker, lifelogging, Mark Zuckerberg, move fast and break things, move fast and break things, Narrative Science, natural language processing, Netflix Prize, New Journalism, recommendation engine, RFID, self-driving car, sentiment analysis, Silicon Valley, smart grid, smart meter, social graph, sorting algorithm, statistical model, Tesla Model S, text mining, Thomas Davenport
Gardner, the CEO, explained the importance of controlling leakage: “Some large health systems report upwards of 50% leakage from hospital networks, while best-in-class organizations have leakage rates of under 20% . . . If we were to change leakage rates by just a few percentage points, systems that were operating at a loss could become profitable.”a Kyruus is structured into three major groups: data acquisition, integration, and processing; analytics; and applications and the user interface. The company’s data platform includes features to display and analyze data. a. Robert F. Higgins, Penrose O’Donnell, and Mehul Bhatt, “Kyruus: Big Data’s Search for the Killer App,” Case 813-060 (Boston: Harvard Business School, 2012), 13. Chapter_07.indd 162 12/11/13 1:47 PM What You Can Learn from Start-Ups and Online Firms 163 Take Advantage of Free and Low-Cost Stuff In the distant past—say, a decade ago—the costs of computing, data management, and data analysis were major impediments to big data (assuming you could find some in the first place).
These days, the company is using big data technologies to accelerate the integration of petabytes of customer, product, sales, and campaign data in order to understand how to increase marketing returns and bring customers back into its stores. The retailer uses Hadoop to not only store but also process data transformations and integrate heterogeneous data more quickly and efficiently than ever. “We’re investing in real-time data acquisition as it happens,” says Oliver Ratzesberger, (at the time of the interview) Vice President of Information Analytics and Innovation at Sears Holdings. “No more ETL. Big data technologies make it easy to eliminate sources of latency that have built up over a period of time.” The company is now leveraging open-source projects Apache Kafka and Storm to enable real-time processing. “Our goal is to be able to measure what’s just happened.”
The company’s CTO, Phil Shelley (who has since left to start his own big data company), has cited big data’s capability to decrease the release of a set of complex marketing campaigns from eight weeks to one week—and the improvements are still being realized. Faster and more targeted campaigns are just the tip of the iceberg for the retailer, which recently launched a subsidiary, MetaScale, to provide non-retailers with big data services in the cloud. “Sears is investing in real-time data acquisition and integration as it happens,” says Ratzesberger. “We’re bringing in open-source solutions and changing our applications architecture. We’re creating a framework that, over time, any application can leverage.” Chapter_08.indd 192 03/12/13 12:57 PM What You Can Learn from Large Companies 193 Moreover, it’s easier to measure new process improvements against traditional methods, so quantifying faster product time to market, higher return on marketing investment, or fewer patient readmissions makes quantifying return on investment that much easier.
Data Wrangling With Python: Tips and Tools to Make Your Life Easier by Jacqueline Kazil
Amazon Web Services, bash_history, cloud computing, correlation coefficient, crowdsourcing, data acquisition, database schema, Debian, en.wikipedia.org, Firefox, Google Chrome, job automation, Nate Silver, natural language processing, pull request, Ronald Reagan, Ruby on Rails, selection bias, social web, statistical model, web application, WikiLeaks
Ask them politely and directly how you might access the data. If the dataset is part of a government entity (federal, state, or local), then you may have legal standing under the Freedom of Information Act to obtain direct access to the data. We’ll cover data acquisition more fully in Chapter 6. Once you have identified the datasets you want and acquired them, you’ll need to get them into a usable format. In Chapters 3, 4, and 5, you will learn various techniques for programmatically acquiring data and transforming data from one form to another. Chapter 6 will look at some of the logistics behind human-to-human interac‐ tion with regard to data acquisition and lightly touch on legalities. In the same Chap‐ ters 3 through 5, we will present how to extract data from CSV, Excel, XML, JSON, and PDF files, and in Chapters 11, 12, and 13 you will learn how to extract data from websites and APIs.
Or imagine you are able to transform your data in such a way that you can execute tasks you never could before because you simply did not have the ability to process the information in its current form. But after working through Python exerci‐ ses with this book, you should be able to more effectively gather information from data you previously deemed inaccessible, too messy, or too vast. We will guide you through the process of data acquisition, cleaning, presentation, scaling, and automation. Our goal is to teach you how to easily wrangle your data, so you can spend more time focused on the content and analysis. We will overcome the limitations of your current tools and replace manual processing with clean, easy-toread Python code. By the time you finish working through this book, you will have automated your data processing, scheduled file editing and cleanup tasks, acquired and parsed data from locations you may not have been able to access before, and pro‐ cessed larger datasets.
zip zip is a built-in Python function that takes two iterable objects and outputs them into a list of tuples. Tuples A tuple is like a list, but immutable, meaning it cannot be updated. To update a tuple, it would have to be stored as a new object. Summary | 125 Concept Purpose dict dict is a built-in Python function that attempts to convert the input into a dictionary. To be used properly, conversion the data should look like key-value pairs. In the next chapter, we’ll talk about data acquisition and storage. This will provide more insight on how to acquire alternative data formats. In Chapters 7 and 8, we cover data cleaning, which will also help in the complexity of processing PDFs. 126 | Chapter 5: PDFs and Problem Solving in Python CHAPTER 6 Acquiring and Storing Data Finding your first dataset(s) to investigate might be the most important step toward acheiving your goal of answering your questions.
Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb
"Robert Solow", Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, artificial general intelligence, autonomous vehicles, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, On the Economy of Machinery and Manufactures, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steven Levy, strong AI, The Future of Employment, The Signal and the Noise by Nate Silver, Tim Cook: Apple, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game
Many commercial AI applications have this structure: use a combination of input data and outcome measures to create the prediction machine, and then use input data from a new situation to predict the outcome of that situation. If you can obtain data on outcomes, then your prediction machine can learn continually through feedback. Decisions about Data Data is often costly to acquire, but prediction machines cannot operate without it. They require data to create, operate, and improve. You therefore must make decisions around the scale and scope of data acquisition. How many different types of data do you need? How many different objects are required for training? How frequently do you need to collect data? More types, more objects, and more frequency mean higher cost but also potentially higher benefit. In thinking through this decision, you must carefully determine what you want to predict. The particular prediction problem will tell you what you need.
KEY POINTS * * * Prediction machines utilize three types of data: (1) training data for training the AI, (2) input data for predicting, and (3) feedback data for improving the prediction accuracy. Data collection is costly; it’s an investment. The cost of data collection depends on how much data you need and how intrusive the collection process is. It is critical to balance the cost of data acquisition with the benefit of enhanced prediction accuracy. Determining the best approach requires estimating the ROI of each type of data: how much will it cost to acquire, and how valuable will the associated increase in prediction accuracy be? Statistical and economic reasons shape whether having more data generates more value. From a statistical perspective, data has diminishing returns. Each additional unit of data improves your prediction less than the prior data; the tenth observation improves prediction by more than the one thousandth.
See also data; judgment value of, 165 complexity, 103–110 if-then logic and, 104–109 compromises, 107. See also trade-offs computers cheap arithmetic from, 12 programming of, effect of prediction on, 38, 40 conditional average, 33 consumer preferences data, 176–177 Consumer Reports, 169 Cook, Tim, 189–190 cookies, 175 Copenhagen metro, 104 corn, hybrid, 158–160, 181 correlations, unanticipated, 36–37 cost, 7–20 of data acquisition, 44 effects of reduced AI, 9–11 of foundational inputs, 11–13 internet, 10–11 of prediction, 13–15, 29 strategy and, 15–17, 169 counterfactual, 62–63 crashes, 200 creative destruction, 215 Creative Destruction Lab (CDL), 2, 134 credit card fraud detection, 24–25, 27, 91 judgment in, 84–88 creditworthiness, 27–28, 66–67 Croesus, King of Lydia, 23 crowd behavior, alterations in, 191 crystal balls, 24 customer churn, 32–36 Daimler, 164 Dartmouth College conference, 31–32, 39 data, 18, 43–51 acquiring, 46–47 business transformation and, 174–176 on customer churn, 35–36 decisions about, 47–49 economies of scale and, 49–50, 216 feedback, 43, 46, 204–205 homogeneity in, 201–202 how machines learn from, 45–47 input, 43 necessity of for prediction, 44–45 prediction with little, 98–102 privacy issues with, 189–190 quality of, 163, 200 real world, autonomous vehicles and, 186–187 retention practices, 216 roles of, 43 security risks with, 199–205 selling consumer, 176–177 signal vs. noise in, 48 strategic advantage from unique, 176–177 as strategic asset, 163–164 strategy and, 174–176 training, 43, 45–47, 202–204 types that humans have and machines don’t, 98 decision making, 18, 73–82.
The Transhumanist Reader by Max More, Natasha Vita-More
23andMe, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, Bill Joy: nanobots, bioinformatics, brain emulation, Buckminster Fuller, cellular automata, clean water, cloud computing, cognitive bias, cognitive dissonance, combinatorial explosion, conceptual framework, Conway's Game of Life, cosmological principle, data acquisition, discovery of DNA, Douglas Engelbart, Drosophila, en.wikipedia.org, endogenous growth, experimental subject, Extropian, fault tolerance, Flynn Effect, Francis Fukuyama: the end of history, Frank Gehry, friendly AI, game design, germ theory of disease, hypertext link, impulse control, index fund, John von Neumann, joint-stock company, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, Louis Pasteur, Menlo Park, meta analysis, meta-analysis, moral hazard, Network effects, Norbert Wiener, pattern recognition, Pepto Bismol, phenotype, positional goods, prediction markets, presumed consent, Ray Kurzweil, reversible computing, RFID, Ronald Reagan, scientific worldview, silicon-based life, Singularitarianism, social intelligence, stem cell, stochastic process, superintelligent machines, supply-chain management, supply-chain management software, technological singularity, Ted Nelson, telepresence, telepresence robot, telerobotics, the built environment, The Coming Technological Singularity, the scientific method, The Wisdom of Crowds, transaction costs, Turing machine, Turing test, Upton Sinclair, Vernor Vinge, Von Neumann architecture, Whole Earth Review, women in the workforce, zero-sum game
If the time during which measurements are taken is relatively small or does not involve a sufficiently thorough set of events observed, then it is possible to miss pairs of I/O that would indicate the presence of latent function. There are some ways to improve upon this by using patterns of stimulation in order to put each component through its paces, but then we run into the problem that the brain is plastic. Components may change their responses as a result of the exercises. Latent function may be better obtained from structural data acquisition. Even if purely structural or purely functional data acquisition could provide all the necessary information for a whole brain emulation, then such a constraint would still carry a burden of risk that is better avoided from the perspective of sensible engineering. It seems unwise to construct an enormously complex emulation by carrying out a single-shot transformation. It is far better to turn it into a problem of successive partial transformations.
Possible interactions are constrained by the existing functional connections between the components – the functional connectome, which is in turn reflected by physical neuroanatomy in the structural connectome. We consider a strategy of straightforward duplication of the activity, and look at the numbers of some of the components. The human brain has up to one hundred billion (1011) neurons and between one hundred trillion (1014) and one quadrillion (1015) synapses. But we have reached a point where for purposes of data acquisition these objects are now considered fairly large (e.g. 200 nm to 2,000 nm for synaptic spines and 4,000 nm to 100,000 nm for the neural soma), at least by the standards of the current nanotechnology industry (working with precision at 10s to 100s of nanometers). And in terms of their activity those components are mostly quiet. I coined the term whole brain emulation around February/March of 2000 during a discussion on the old “mind uploading research group” (MURG) mailing list, in an effort to remove confusion stemming from the use of the term “mind uploading”, which better refers to a process of transfer of a mind from a biological brain to another substrate.
An increasing number of projects are explicitly building the sort of tools that are needed to acquire data from a brain at the large scope and high resolution required. There are by now at least three different versions of the Automated Tape-Collecting Lathe Ultramicrotome that was developed at the Lichtman Lab at Harvard University (Hayworth et al. 2007). Ken Hayworth is presently working on its successor that employs focused ion beam scanning electron microscopy (FIBSEM) to improve accuracy, reliability, and speed of structural data acquisition from whole brains at a resolution of 5 nm (Hayworth 2011). Meanwhile, the Knife-Edge Scanning Microscope (KESM) developed by Bruce McCormick is presently able to acquire neuronal fiber and vasculature data from entire mouse brains at a slightly lower resolution (McCormick and Mayerich 2004). A number of labs, including the MIT Media Lab of Ed Boyden, are aiming at the development arrays of recording electrodes with tens of thousands of channels.
Smart Grid Standards by Takuro Sato
business cycle, business process, carbon footprint, clean water, cloud computing, data acquisition, decarbonisation, demand response, distributed generation, energy security, factory automation, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Iridium satellite, iterative process, knowledge economy, life extension, linear programming, low earth orbit, market design, MITM: man-in-the-middle, off grid, oil shale / tar sands, packet switching, performance metric, RFC: Request For Comment, RFID, smart cities, smart grid, smart meter, smart transportation, Thomas Davenport
Smart Grid Standards 80 Table 3.1 Standard list of advanced metering infrastructure Function field Standard name Short introduction Product Data exchange Service IEC/TR 62357 IEC 61850 IEC 61968 Product IEC 60870-5 Data exchange IEC 61334 Product IEC 60834 Service Data exchange IEC 61970 IEC 61400-25 Data exchange IEC 60255-24 Electrical transmission IEC 61954 Service-oriented architecture (SOA) Substation automation Common information model (CIM)/distribution management Telecontrol equipment and systems – Part 5 – transmission protocols Distribution automation using distribution line carrier systems Teleprotection equipment of power systems – performance and testing Common information model (CIM)/energy management Wind turbines – Part 25: communications for monitoring and control of wind power plants Electrical relays – Part 24: common format for transient data exchange (COMTRADE) for power systems Power electronics for electrical transmission and distribution systems 3.1 Power Grid Systems Figure 3.1 shows a basic model of smart power grid architecture. Staff in the power control center should analyze the data and make corresponding changes in operation to ensure the implementation of Supervisory Control and Data Acquisition (SCADA), which provides efficiency, reliability, and safety control and monitor the Smart Grid. There are mainly four types of staff in a control center: dispatcher, check engineer, planner, and relay protection engineer. These staff have different responsibilities, and are interested in different aspects of the Smart Grid data. The dispatcher is the commander of the power grid operation and fault handling.
The three potential services of battery storage and the interfaces to other Smart Grid systems are summarized as follows: • Battery storage can be directly implemented in the electricity-generating plant or transmission grid for electricity control and management. This kind of battery storage is usually controlled by Energy Management Systems (EMSs) and interfaced with a Wide Area Situational Awareness (WASA) systems. • Battery storage can be integrated into the distribution grid for distribution voltage control. This kind of battery storage is usually controlled by Supervisory Control and Data Acquisition (SCADA) and interfaced with a Distribution Management System (DMS). • Battery storage can also be integrated directly into a smart home or building automation system for demand response and load control, power source backup, and so on. This kind of battery storage is usually controlled by the Home Energy Management System (HEMS) or Building Energy Management System (BEMS) and interfaced with the Energy Service Interface (ESI) systems.
There are QoS classes defined as follows: Unsolicited Grant Service, real-time Polling Service, Extended real-time Polling Service (only in mobile WiMAX), nonreal-time Polling Service, and Best Effort for non-QoS traffic. Communications in the Smart Grid 291 6.4.5 Satellite Communication Satellite communication has been utilized to connect remote substations and offer Supervisory Control and Data Acquisition (SCADA) and other Smart Grid-related applications for years. However, the major limitation is very high cost, which forced satellite communication to be used only in the edge situations, where there is no better cost-efficient option. Current satellite technologies have evolved significantly by reducing costs and latency, and increasing reliability and data rates. These improvements bring this very long-range communication technology back into the Smart Grid market.
Cyber War: The Next Threat to National Security and What to Do About It by Richard A. Clarke, Robert Knake
barriers to entry, complexity theory, data acquisition, Just-in-time delivery, MITM: man-in-the-middle, nuclear winter, packet switching, RAND corporation, Robert Hanssen: Double agent, Ronald Reagan, Silicon Valley, smart grid, South China Sea, Steve Jobs, trade route, undersea cable, Y2K, zero day
The gas quickly extended well over a mile along the creek. Then it caught fire. Two ten-year-old boys playing along the stream were killed, as was an eighteen-year-old farther up the creek. The nearby municipal water-treatment plant was severely damaged by the fire. When the U.S. National Transportation Safety Board examined why the pipeline burst, it focused on “the performance and security of the supervisory control and data acquisition (SCADA) system.” In other words, the software failed. The report does not conclude that in this case the explosion was intentionally caused by a hacker, but it is obvious from the analysis that pipelines like the one in Bellingham can be manipulated destructively from cyberspace. The clearest example of the dependency and the vulnerability brought on by computer controls also happens to be the one system that everything else depends upon: the electric power grid.
They were also allowed to buy and sell power to each other anywhere within one of the three big power grids in North America. At the same time, they were, like every other company, inserting computer controls deep into their operations. Computer controls were also installed to manage the buying and selling, generation, and transmission. A SCADA system was already running each electric company’s substations, transformers, and generators. That Supervisory Control and Data Acquisition system got and sent signals out to all of the thousands of devices on the company’s grid. SCADAs are software programs, and most electric companies use one of a half dozen commercially available products. These control programs send signals to devices to regulate the electric load in various locations. The signals are most often sent via internal computer network and sometimes by radio. Unfortunately, many of the devices also have other connections, multiple connections.
SIPRNET: Secret Internet Protocol Router Network is the Defense Department’s global intranet for transmitting confidential and secret-level information. The Defense Department classifies information into five catergories: unclassified, confidential, secret, top secret, top secret/SCI (specially compartmented information). The SIPRNET is supposed to be air-gapped from, i.e., not physically touching, the unclassified NIPRNET and the Internet. Supervisory Control and Data Acquisition System (SCADA): Software for networks of devices that control the operation of a system of machines such as valves, pumps, generators, transformers, and robotic arms. SCADA software collects information about the condition of and activities on a system. SCADA software sends instructions to devices, often to do physical movements. Instructions sent to devices on SCADA networks are sometimes sent over the Internet or broadcast via radio waves.
Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost, Tom Fawcett
Albert Einstein, Amazon Mechanical Turk, big data - Walmart - Pop Tarts, bioinformatics, business process, call centre, chief data officer, Claude Shannon: information theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, data acquisition, David Brooks, en.wikipedia.org, Erik Brynjolfsson, Gini coefficient, information retrieval, intangible asset, iterative process, Johann Wolfgang von Goethe, Louis Pasteur, Menlo Park, Nate Silver, Netflix Prize, new economy, p-value, pattern recognition, placebo effect, price discrimination, recommendation engine, Ronald Coase, selection bias, Silicon Valley, Skype, speech recognition, Steve Jobs, supply-chain management, text mining, The Signal and the Noise by Nate Silver, Thomas Bayes, transaction costs, WikiLeaks
In Signet’s case, data could be generated on the profitability of customers given different credit terms by conducting experiments. Different terms were offered at random to different customers. This may seem foolish outside the context of data-analytic thinking: you’re likely to lose money! This is true. In this case, losses are the cost of data acquisition. The data-analytic thinker needs to consider whether she expects the data to have sufficient value to justify the investment. So what happened with Signet Bank? As you might expect, when Signet began randomly offering terms to customers for data acquisition, the number of bad accounts soared. Signet went from an industry-leading “charge-off” rate (2.9% of balances went unpaid) to almost 6% charge-offs. Losses continued for a few years while the data scientists worked to build predictive models from the data, evaluate them, and deploy them to improve profit.
., 2012). Note Deployment can also be much less “technical.” In a celebrated case, data mining discovered a set of rules that could help to quickly diagnose and fix a common error in industrial printing. The deployment succeeded simply by taping a sheet of paper containing the rules to the side of the printers (Evans & Fisher, 2002). Deployment can also be much more subtle, such as a change to data acquisition procedures, or a change to strategy, marketing, or operations resulting from insight gained from mining the data. Deploying a model into a production system typically requires that the model be re-coded for the production environment, usually for greater speed or compatibility with an existing system. This may incur substantial expense and investment. In many cases, the data science team is responsible for producing a working prototype, along with its evaluation.
This example also illustrates that the data products themselves can increase the cost to competitors of replicating the data asset. Consumers value the data-driven recommendations and product reviews/ratings that Amazon provides. This creates switching costs: competitors would have to provide extra value to Amazon’s customers to entice them to shop elsewhere—either with lower prices or with some other valuable product or service that Amazon does not provide. Thus, when the data acquisition is tied directly to the value provided by the data, the resulting virtuous cycle creates a catch-22 for competitors: competitors need customers in order to acquire the necessary data, but they need the data in order to provide equivalent service to attract the customers. Entrepreneurs and investors might turn this strategic consideration around: what historical circumstances now exist that may not continue indefinitely, and which may allow me to gain access to or to build a data asset more cheaply than will be possible in the future?
The Hacker and the State: Cyber Attacks and the New Normal of Geopolitics by Ben Buchanan
active measures, Bernie Sanders, bitcoin, blockchain, borderless world, Brian Krebs, British Empire, Cass Sunstein, citizen journalism, credit crunch, cryptocurrency, cuban missile crisis, data acquisition, Donald Trump, drone strike, Edward Snowden, family office, hive mind, Internet Archive, Jacob Appelbaum, John Markoff, John von Neumann, Julian Assange, Kickstarter, kremlinology, MITM: man-in-the-middle, Nate Silver, profit motive, RAND corporation, ransomware, risk tolerance, Robert Hanssen: Double agent, rolodex, Ronald Reagan, Silicon Valley, South China Sea, Steve Jobs, Stuxnet, technoutopianism, undersea cable, uranium enrichment, Vladimir Vetrov: Farewell Dossier, WikiLeaks, zero day
For the hackers, the employee-access mechanism offered a simple way in. Rather than defeat the defenses the Ukrainian companies had set up, the hackers just needed to impersonate the right people. Logging in as those employees gave the hackers access to the operational side of the network and all that it controlled. The operational side of the network contained machines used for technical work known as supervisory control and data acquisition (SCADA). SCADA systems directly manage components of critical infrastructure all over the world. A hacker with the opportunity and skill to manipulate such a system could do substantial damage. The United States and Israel proved this to the world with the Stuxnet attack, and now Russian hackers were preparing to try their hand at it in Ukraine. When the hackers made their way to the Ukrainian grid’s operational network, they gained the access they needed to control the SCADA systems responsible for managing power in the targeted areas.
See also election interference, Russian (2016); GRU; Kaspersky Lab; Soviet Union Saakashvili, Mikheil, 155 sabotage, 8, 9, 129–147; in Cold War, 6–7; prioritized over spying, 295; secrecy and, 146; Shadow Brokers as, 262–267. See also shaping; Stuxnet; Wiper San Bernardino attack, 40, 42, 44 sanctions, 13 Sanders, Bernie, 225. See also election interference, Russian (2016) Sands Casino attack, 160–164, 165, 310 Sandworm, 202 Sanger, David, 147 Saudi Arabia: Aramco attack (Shamoon) and, 148–153, 159, 164, 165; hacking operations of, 317; listening stations in, 33 SCADA (supervisory control and data acquisition) systems, 192. See also blackouts in Ukraine; Stuxnet Schelling, Thomas, 4, 145, 310 Schmidt, Eric, 59 Schneier, Bruce, 256 Schultz, Debbie Wasserman, 225 ScreenOS, 76. See also Juniper Networks SEA-ME-WE-4, 34 secrecy, 101; decryption and, 60; home-field advantage and, 16; importance of, 39, 240 (see also exposure); signaling and, 308–309; Stuxnet and, 139, 146, 147. See also exposure; operational security security: firewalls, 190–192; network segmentation, 190–191 security clearance files, 104–106 selector, 26 serial-to-Ethernet converters, 193–194, 195 SF-86, 104 Shadow Brokers, 1–2, 242–247, 294; desire for publicity, 245–246, 247; desire to stay hidden, 304; effects of, 265, 280–281, 290; focus on American media, 247; identity of, 255–256; interpreting, 311–312; investigation into, 266–267; materials acquired by, 248–250, 256–257; motivations of, 261–262; pattern of revelations by, 264; political commentary by, 246, 250–251, 252; publicity for, 243–244; revelations of, 253–254 (see also DOUBLEPULSAR; ETERNALBLUE); Russian ties of, 1–2, 261, 265; as sabotage, 262–267; as signaling, 261–262; sources of, 257–261; staging server theory, 257–258; subscription service, 254; threats by, 252; Trump administration and, 250–251 Shamoon (Aramco attack), 148–153, 159, 164, 165 shaping: in Cold War, 5–7; corporate access and, 39; cyber operations and, 7, 9, 306; described, 3; with home-field advantage, 16; importance of, 5; by North Korea, 287; passive collection and, 39; by Shadow Brokers, 262–267.
See also espionage staging server theory, 257–258 standards: International Organization for Standardization, 69; NIST, 66–70 statecraft: during Cold War, 4–5; competitive aspects of, 3; by signaling, 4 Stein, Jill, 233 Stone, Roger, 226–227, 230 stored communications, 26 Stuxnet, 92, 129–130, 131, 149, 197, 213, 289; discovery of, 137–142, 147; effects of, 136–137; geopolitical implications of, 141; lawyers and, 135; objective of, 144, 146, 164; operational security of, 139, 146, 147; payload of, 135–136; self-restraints of, 135; spread of, 134–142; versions of, 134–135 Su Bin, 99–102, 116 Sullivan, Margaret, 180 supernotes, 268–269, 270–271 supervisory control and data acquisition (SCADA) systems, 192. See also blackouts in Ukraine; Stuxnet surveillance, 21, 26; AT&T and, 21; checks and balances and, 55; internet companies and, 21, 26 (see also PRISM) SWIFT (Society for Worldwide Interbank Financial Telecommunication) system, 272–275, 276–277, 283, 284, 286, 287 Symantec, 140, 294 Tailored Access Operations (TAO), 112–115, 117, 258 Tait, Matt, 256 target adaptation, 39, 60 targeting, 110 targets, foreign, 56–57 Task Force Orange, 307 TeDi (Territorial Dispute) program, 117–118, 120 telecommunications companies, 15; AT&T, 14–15, 20–25; Five Eyes’ partnerships with, 37; networks and, 17; NSA’s partnership with, 20–24; Verizon, 24; Vodafone, 28, 29, 30.
Industrial Internet by Jon Bruner
autonomous vehicles, barriers to entry, commoditize, computer vision, data acquisition, demand response, en.wikipedia.org, factory automation, Google X / Alphabet X, industrial robot, Internet of things, job automation, loose coupling, natural language processing, performance metric, Silicon Valley, slashdot, smart grid, smart meter, statistical model, web application
In this manner, software running on an inexpensive processor, reading data from inexpensive sensors, can substitute for more expensive capital equipment and labor. Better understanding of maintenance needs means better allocation of equipment — since the timing of maintenance can be optimized if it’s proactive rather than reactive — and workers can similarly avoid being idled or having their time absorbed in detecting maintenance needs. Large manufacturers have invested billions of dollars in SCADA (supervisory control and data acquisition — the low-level industrial control networks that operate automated machines). Comprehensive stacks of specialized software link these systems all the way to management dashboards, but many of these systems have their roots in automation, not high-level intelligence and analysis. Factory managers are understandably conservative in managing these systems, and demand highly-robust, proven technologies in settings where the functioning of a big machine or assembly line is at stake.
The Story of Crossrail by Christian Wolmar
So one little change can have a ripple effect on station design, acoustic mitigation and the design of the fan itself. And that is just one example that when you do the development design, the assumptions may not be correct. The parameters put in by the engineers can change. Tucker had more to say about unforeseen side effects: Another example is communications. All the communications run from the control centre at Romford through a system called SCADA [Supervisory Control and Data Acquisition – a fancy term for a system that uses computers and radio communications to provide the overall control of the system]. So assumptions are made on the volume of data and the required number of points of interface that are going to be needed to link all the pieces of equipment in the stations and on the track. But if, he continues, ‘a designer picks a different pump or switch gear, it might then require a different number of input or output points.
Pensions Ltd 137 geology of London, and the Crossrail works 175, 177–8, 179, 224 Germany megaprojects 165 S-Bahn railways 36–7, 41, 107–8 GLA (Greater London Authority) 66, 94, 147, 151 GLC (Greater London Council) 28, 29, 34, 49–50 abolition of 66, 82, 90 and Thameslink 49 Goldman Sachs 221 Gordon, Douglas 221 Grayling, Chris 278 Great Eastern Railway 20 Great Northern & City 21 Great Northern Railway 5 Great Western Allotment Association 137 Great Western Railway 5, 42, 97, 106, 123, 126, 247 Greater Anglia line 107 Greater London Authority (GLA) 66, 94, 147, 151 Greater London Plan (Abercrombie Report) 25–6 Greathead Shield 10–11, 13, 171, 184 Green Belt policy 30, 41 Green, Leslie 227–8 Greening, Justine 189 Gunnell, Barbara 269–70 Hackney cabs 4 Halcrow Fox Associates 64 Hamburg 37 Hammersmith & City Line 119, 217 Hammond, Philip 154, 155 Harmsworth, Alfred 17 Harris, Nigel 272 Havering, London Borough of 137–8 Heath, Don 37–8, 71–2, 78–9, 89, 122 Heathrow Airport 5, 30, 72, 92–3, 96, 97, 99, 100, 121, 131, 146 BAA and Crossrail funding 149–50 control of station 250 Crossrail route to 124–5, 127 Crossrail services 236 Express service 92–3, 101, 126, 209–10 and HS2 268 Junction 106 signalling system 236–7, 245–6, 249 terminals 127, 210, 268, 275 trains for 242 Hendy, Peter 158 Henry VIII, King 201 Herrenknecht AG 191 Heseltine, Michael 28, 56 historical finds, during Crossrail construction 199–201 Hitier-Abadie, Claire 231 Holden, Rob 164, 165, 208–9 Hollobone, Philip 134 Holt, Brian 231 Hong Kong 116 Hopkins, Kelvin 134 horse-drawn vehicles omnibuses 2, 3–4, 8, 9 trams 8–9 House of Commons, Opposed Bill Committees 74–5 housing, and the Crossrail 2 project 278 HS2 project 99–100, 268, 275, 279, 280 HS1 see Channel Tunnel Rail Link (now HS1) Hughes, Simon (now Sir Simon) 64 Iacobescu, George 221 Independent London 83, 87 industrial action 104 Innovate18 275–6 Inter-City trains 33 Isle of Dogs 57, 58, 59, 62, 97–8, 99, 128, 158, 196 Iver Parish Council 142 James of Blackheath, Lord 176, 201–2 James, Siân C. 134 Joffe, Chantal 221 Johnson, Boris 153, 158, 161, 189, 204 Jubilee Line 20, 29, 41–2, 67, 119 construction 31 and the Crossrail route 123 and Crossrail stations 119 Extension 41–2, 55, 64–6, 69, 70, 78, 89–90, 100–1, 118, 274, 277 cost-benefit analysis of 47, 50 signalling system 247 stations 213, 220, 222–3 Karsa, Maria 231 Khan, Sadiq 278 King’s Cross St Pancras 79, 227 King’s Cross station 7 fire 38–40, 89 Kingston branch 104–6 Kirkpatrick, Scott Wilson 76 Labour governments commitment to Crossrail 145–7 revival of Crossrail 90–1 transport policies 28–9 Labour Party 78, 84–5, 155 Lambeth Group stratum 179, 196 Laurence, George 82 Liddell-Grainger, Ian 134 lifts Central Railway 15 City & South London 15 Limehouse Link 58 Limmo Peninsula 189, 190–1 Liverpool and Manchester Railway 4 Liverpool Street 42, 71, 99, 100, 122, 123, 124 Crossrail control centre 250 Crossrail service from 236 Crossrail station 128, 224–6, 271 artwork 221 construction 211 design 216 and the London Rail Study 29, 30 ticket gates 136 ticket halls 140–1 tunnel construction 187, 194–5 archaeological finds during 200 Livingstone, Ken 90, 109, 145–6, 153 Location of Offices Bureau 32 Londinium (Roman settlement) 177, 200 London & North Western Railway 5, 6 London Assembly Transport Committee 160 London City Airport 197–8 London Docklands Development Corporation 56–9 London East-West Study 91–2, 93–4, 104 London and Greenwich Railway 4 London Overground 214, 222–3 night use 265 London Planning Charge 149 London Rail Study (1974) 29–32 London Transport 25, 47, 59, 61, 63, 66, 71, 85 and the Central London Rail Study 69–70 and Cross London Rail Links 69 and Crossrail routes 122 ‘Docklands Public Transport Scheme’ 62 and the first Crossrail bill 74, 82, 83–4 and Oxford Circus 120 safeguarding Crossrail 87, 88, 90 London Underground 66, 74, 89–90, 271 apprentices 276 and Crossrail routes 122 and Crossrail stations 250 interface savings 148–9, 161 and the Jubilee Line Extension 64–5, 213 King’s Cross fire 38–40, 89 night use 265 overcrowding 35–6, 40, 41, 95, 96 passenger numbers 32, 40–1, 74, 159–60 PPP funding for 102, 114–15, 146, 164 signalling systems 248–9 small-bore Tube lines 21 stations 141, 227–8 Long, Jeremy 235 Lovelace, Ada 188 Luton Airport 121 McDonald, Neil 70 MacGregor, John 78 McNulty, Tony 117–19, 143 Maidenhead 95, 97, 106, 126, 243 Main, Peter 193–4, 195 Major, John 77, 78, 85 Major Project Association 51 Malins, Richard 73 Manchester 37–8 Marble Arch 2 Marek, John 85 Marlow, Tony 75, 78, 84 Maryland station 226, 242 Mayfair residents, objections to Crossrail 78, 79–80 mayor of London, office of 84, 90–1, 146, 153, 161 Meads, Richard 34, 73 Meale, Alan 134 megaprojects benefit–cost ratios of 45–54, 59 cost escalation of 165 Metropolitan Line 7, 25, 42, 69, 71, 72, 92, 121, 122, 123–4, 215, 249 Metropolitan Railway 2, 3, 6–8, 21, 24, 134 Middleton, James 142 Millennium Dome 63 Miller, Linda 197 Montague, Adrian 102–3, 105–6, 110 Montague Review 102–4, 105–6, 110, 111, 112–13, 115, 116, 117 Morgan, Terry 164–5, 166, 168, 169, 198, 207, 208, 220, 221–2, 264, 272, 275 and apprenticeships 276–7 Morton, Sir Alastair 91 motorway boxes 27–8 motorway building 26, 33 Mott, Basil 13 Mott MacDonald 172–3 MTR (Mass Transit Railway) 234–5, 250 Munich, S-Bahn railway 37, 107–8 Museum of London Docklands, Archaeology of Crossrail 200–1 National Audit Office (NAO), report on the Crossrail project 162, 272–3 National Council of the Cycling Touring Club 138 National Rail Network 93 Network Rail 249 and Crossrail stations 250 and Crossrail trains 235, 240 funding Crossrail 148, 151, 160–1, 274 interfaces with Crossrail 250 Newcastle 38 newts, great crested 131–2 nineteenth century canals 23–5 London transport developments 2–6 Norris, Steve 66, 84, 85, 146 North London line 31–2, 57, 105 North London Railway 5–6, 13 North Metropolitan Railway and Canal Company 24–5 Northern Line 19–20, 120, 122 northern regions, and the Crossrail 2 project 278–9 Oakervee, Douglas 158, 164 Old Oak Common 123–4, 132, 141, 242, 268, 280 Olympia & York 54, 57, 58, 59–60, 61–5, 66–7 Olympic Games (2012) 129, 190, 275 omnibuses 2, 3–4, 8, 9 One Canada Square 57 Open Spaces Society 142 optimum bias 49, 50, 104 Osborne, George 161, 162 Oxford Circus 120, 125 Oxford Street 141, 280 Oxford Street and City Railway proposal 10 Paddington 7, 42, 106, 109, 161 and the Crossrail route 122, 124, 125, 267, 268 Crossrail services from 236 Crossrail station 128, 250 artwork 221–2 construction 210–11, 216–21 contract for 207 design 216 platforms 219–20, 242 Crossrail tunnels 71, 119 construction 176, 179 and the London Rail Study 29, 30 Paddington–Liverpool Street proposed rail route 91, 92 Paris, RER (Réseau Express Régional) 34–6, 41, 81–2, 107 Parker, Peter 32–3 Parkinson, Cecil 70–1, 77–8 Parliamentary bills first Crossrail bill (1991) 73–85 objections to 78–81 hybrid bill process 93–4, 110, 134, 167 Opposed Bill Committees 74–5 second Crossrail bill (2005) 117–19, 124 becomes law 143 House of Commons select committee 134–41 House of Lords select committee 141–3, 176 petitioning process 135–9, 141–3 political opposition to 145 Pearman, Hugh 181, 215, 222 Pearsall, Phyllis 188 Pearson, Charles 6, 121–2 Pelton, John 168 Periton, Simon 221 Pettit, Gordon 108 PFI (Private Finance Initiative) 66, 103, 114–15, 146, 275 and Crossrail trains 237–40 Piccadilly Line 19–20, 97 Pidgeon, Caroline 160 Pidgley, Tony 154 plague bacteria 177, 202 Plumstead 256, 257, 258 politicians 145–7 Post Office Railway 194, 217 PPP (public–private partnerships) London Underground 102, 114–15, 146, 164 Tube Lines 276 Prescott, John 90, 91, 93 property development, financing Crossrail through 112–14 Pudding Mill Lane 126, 128, 136–7, 190, 193, 256 Pugh, John 134 Purchase, Ken 79, 85 Race Equality Impact Statement 132–3 Rail magazine 152, 272 Railtrack 77, 166 Railway Canal Company 24 Railways Act (1993) 77 Ramblers’ Association 142 Raynsford, Nick 139 Reading 95, 97, 106, 126, 236, 243, 250, 251 Regent’s Canal 23–5 Reichmann brothers 54, 57, 58, 62 Richmond–Kingston branch 104–6 Rifkind, Malcolm 78 Riordan, Linda 134 road schemes 27–8 benefit–cost ratios of 52, 53 Docklands 58 robotic theodolites 181 Romford 137–8, 141 control centre for Crossrail 249–50, 262–3 Royal Commission on Metropolitan Railway Termini 9–10 Royal Docks 126, 128, 132, 196–7 Royal Oak 119, 126, 199, 218, 256 SCADA (Supervisory Control and Data Acquisition) 263 Schabas, Michael 43, 47, 63, 69, 70, 71, 120, 122, 123, 125 and the DLR 58, 60, 61 on funding Crossrail 149, 150 and Superlink 106–7, 109 Scottish Parliament building 157 Second World War 17, 20, 37, 55 Crossrail construction and risks from unexploded bombs 176, 197, 202–3 the Shard 57 Shenfield 85, 95, 99, 107, 122–3, 124, 127, 236, 244, 250 Shillibeer, George 3 Siemens 237, 239 signalling systems 72, 139, 155, 243, 245–9, 265 ATP (Automatic Train Protection) 245–6, 247 CBTC (Communications-Based Train Control) 139, 236, 247–8, 249 equipment 259 ETCS (European Train Control System) 236–7, 245–7, 248, 249 Simmonds, Ellie 190 small-bore Tube lines 21 Smith, David 124 Smith, Howard 94–5, 107–8, 233, 234, 235, 238, 241, 243, 252 Smithfield Market 121 Smithfield Market Traders 142 Snow Hill Tunnel 25, 29, 49–50 social media 117 Soulsby, Sir Peter 134 Southend Arterial Road Action Group 138 SRA (Strategic Rail Authority) 90, 91, 92, 94, 95, 101, 104, 139, 143 Stansted Airport 100, 107, 121 stations Jubilee Line Extension 213 mainline stations and commuter trains 41, 96 Victoria Line 35–6 stations (Crossrail) 3, 72, 73, 81, 128 artwork 220–2 construction 140, 207–28 box method of 210–11, 217–18 Canary Wharf 150, 158–9 contacts for 207–8 improving spaces around the station 223, 227 lorries carrying material 229–32 removal of spoil 218 sprayed concrete lining technique 212, 215 and TBMs 182, 207, 211–12, 219 control of 250 design 73, 210, 212–16, 225–6 accessibility 141, 214, 226, 250–1 entrances 119–20, 121, 122 and the Environment Statement 130–2 fitting out 258–9, 261 House of Commons committee proposals 139–41 platform-edge doors 219–20 platforms 128, 130, 219–20, 241–2, 244 property development at 112–14 size of 271 step-free access to 141 surface stations 214, 225–6 test station in Bedfordshire 215 testing 264 toilet facilities 251–2 ventilation shafts 128 Steer Davies Gleave 80 Steer, Jim 42–3, 80–3, 87–8, 104–5 Stepney Green 126, 190, 193, 196 Stockley Junction 132 strikes 47 Submarine Delivery Agency 209 Superlink scheme 106–9 sustainable development 129 tax increment financing 111–12 United States 115–16 TBMs see tunnel boring machines (TBMs) technological innovation 53 TfL see Transport for London (TfL) Thames Gateway 129 Thames Valley 124 Thameslink 29, 79, 89, 107, 108, 121, 129, 249 and the Crossrail 2 project 278 extension plan 49–50 Programme 94–5 signalling system 247 timetable changes 252, 265 trains 237, 239, 239–40, 251 Thamesmead 154, 280 Thanet Sand Formation 179 Thatcher, Margaret 32, 34, 57, 60, 66, 70–1, 78, 274 Tindall, Gillian 6, 200 Tkáčik, René 228–9, 263 tollgates 2, 7 Tottenham Court Road 120, 128, 158 control of station 250 station construction 211, 221, 229–30 tunnel construction 182–3, 187, 194, 195, 201 Tower Hamlets residents 78, 80–1, 97, 132–3 Town and Country Planning Act (1990) 114 Train, George 8 trains 233–49 articulated 240 bi-mode 126 commuter trains 41, 98 Crossrail Aventra model 243–4 average journey times 251 CCTV cameras on 244 design 138 design faults 244–5 doors 220, 238, 242 driver-operated 243–4 electrification 125–6 facilities 33, 108–9 lack of toilets on 251–2 length of 121, 128, 236, 241–2 main depot 141 management through tunnels 252 procurement 237–49 size of 73 and the tunnel envelope 262 fitting out Crossrail tunnels 254–8 freight trains 139–40 housing 242–3 operating 233–7 rolling stock contract 128 Thameslink 237, 239, 239–40 see also signalling systems trams 8–9, 18–19, 24 Transcend 167–8 Transport 2010 91 Transport for London (TfL) 32, 94, 95, 96 and benefits of the Crossrail scheme 48 construction of Crossrail 143 and the Crossrail 2 project 277–8 and Crossrail stations 141, 209, 214, 220, 227 accessibility 250–1 and Crossrail trains 237–8, 239, 240, 241, 243, 251 Elizabeth Line as part of 249–50 funding Crossrail 111–12, 147, 148–9, 160, 161, 274 legal status of Crossrail 157–8 project delivery agreement for Crossrail 163, 164 and revenue from Crossrail fares 234 transport megaprojects, cost–benefit analysis of 45–54 Travelstead, G.
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
The vulnerability investigators sought to explore is endemic in North America’s electrical grid—the habit of attaching the controlling hardware of critical machinery to the Internet so it can be remotely operated, and “protecting” it with passwords, firewalls, encryption, and other safeguards that crooks routinely cut through like hot knives through butter. The device that controlled DHS’ tortured generator is present throughout our national energy network. It is known as a supervisory control and data acquisition, or SCADA, system. SCADA systems don’t just control devices in the electrical grid, but all manner of modern hardware, including traffic lights, nuclear power plants, oil and gas pipelines, water treatment facilities, and factory assembly lines. SCADA has become almost a household acronym because of the phenomenon called Stuxnet. Stuxnet, and its cousins Duqu and Flame, have convinced even the most hardened skeptics that the energy grid can be attacked.
., III Machine Intelligence Research Institute (MIRI) Singularity Summit machine learning Madoff, Bernie malware Mazzafro, Joe McCarthy, John McGurk, Sean military battlefield robots and drones DARPA, see DARPA energy infrastructure and nuclear weapons, see nuclear weapons Mind Children (Moravec) Minsky, Marvin Mitchell, Tom mobile phones see also iPhone Monster Cat Moore, Gordon Moore’s Law morality see also Friendly AI Moravec, Hans Moravec’s Paradox mortality, see immortality mortgage crisis Mutually Assured Destruction (MAD) nano assemblers nanotechnology “gray goo” problem and natural language processing (NLP) natural selection Nekomata (Monster Cat) NELL (Never-Ending-Language-Learning system) neural networks neurons New Scientist New York Times Newman, Max Newton, Isaac Ng, Andrew 9/11 attacks Normal Accidents: Living with High-Risk Technologies (Perrow) normalcy bias North Korea Norvig, Peter Novamente nuclear fission nuclear power plant disasters nuclear weapons of Iran Numenta Ohana, Steve Olympic Games (cyberwar campaign) Omohundro, Stephen OpenCog Otellini, Paul Page, Larry paper clip maximizer scenario parallel processing pattern recognition Pendleton, Leslie Perceptron Perrow, Charles Piaget, Jean power grid Precautionary Principle programming bad evolutionary genetic ordinary self-improving, see self-improvement Rackspace rational agent theory of economics recombinant DNA Reflections on Artificial Intelligence (Whitby) resource acquisition risks of artificial intelligence apoptotic systems and Asilomar Guidelines and Busy Child scenario and, see Busy Child scenario defenses against lack of dialogue about malicious AI Precautionary Principle and runaway AI Safe-AI Scaffolding Approach and Stuxnet and unintended consequences robots, robotics Asimov’s Three Laws of in dangerous and service jobs in sportswriting Rosenblatt, Frank Rowling, J. K. Rubin, Andrew “Runaround” (Asimov) Safe-AI Scaffolding Approach Sagan, Carl SCADA (supervisory control and data acquisition) systems Schmidt, Eric Schwartz, Evan Scientist Speculates, The (Good, ed.) Searle, John self-awareness Self-Aware Systems self-improvement self-preservation September 11 attacks serial processing SETI (Search for Extraterrestrial Intelligence) Shostak, Seth Silicon Valley Singularitarians Singularity definitions of Kurzweil and technological Singularity Is Near, The (Kurzweil) Singularity Summit Singularity University Sir Groovy Siri 60 Minutes Skilling, Jeffrey Smart Action smart phones see also iPhone software complexity of malware see also programming solar energy space exploration “Speculations Concerning the First Ultraintelligent Machine” (Good) speech recognition SRI International stealth companies Sterrit, Roy Stibel, Jeff Stuxnet subprime mortgage crisis Symantec SyNAPSE Technological Risk (Lewis) technology journalism Terminator movies terrorism 9/11 attacks Thiel, Peter Thinking Machines, Inc.
REST API Design Rulebook by Mark Masse
Architectural Styles and the Design of Network-based Software Architectures, Doctoral dissertation, University of California, Irvine, 2000 (http://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm).  http://www.crummy.com/writing/speaking/2008-QCon/act3.html  Leonard Richardson also co-authored the milestone book, RESTful Web Services (O’Reilly) which really helped move REST forward.  http://www.methods.co.nz/asciidoc Chapter 1. Introduction Hello World Wide Web The Web started in the “data acquisition and control” group at the European Organization for Nuclear Research (CERN), in Geneva, Switzerland. It began with a computer programmer who had a clever idea for a new software project. In December of 1990, to facilitate the sharing of knowledge, Tim Berners-Lee started a non-profit software project that he called “WorldWideWeb.” After working diligently on his project for about a year, Berners-Lee had invented and implemented: The Uniform Resource Identifier (URI), a syntax that assigns each web document a unique address The HyperText Transfer Protocol (HTTP), a message-based language that computers could use to communicate over the Internet.
Dark Territory: The Secret History of Cyber War by Fred Kaplan
Cass Sunstein, computer age, data acquisition, drone strike, dumpster diving, Edward Snowden, game design, hiring and firing, index card, Internet of things, Jacob Appelbaum, John Markoff, John von Neumann, kremlinology, Mikhail Gorbachev, millennium bug, national security letter, packet switching, pre–internet, RAND corporation, Ronald Reagan, Silicon Valley, Skype, Stuxnet, uranium enrichment, Y2K, zero day
Take out those two addresses—whether with a bomb or an information warfare attack—and New York City would lose almost all of its phone service, at least for a while. The loss of phone service would affect other infrastructures, and on the cascading would go. Capping Greene’s briefing, the CIA—where Bill Studeman was briefly acting director—circulated a classified report on the vulnerability of SCADA systems. The acronym stood for Supervisory Control and Data Acquisition. Throughout the country, again for economic reasons, utility companies, waterworks, railway lines—vast sectors of critical infrastructure—were linking one local stretch of the sector to another, through computer networks, and controlling all of them remotely, sometimes with human monitors, often with automated sensors. Before the CIA report, few on the working group had ever heard of SCADA.
Cyber Operations Policy”), 217–20, 228, 314n–15n President Reagan: The Role of a Lifetime (Cannon), 287n–88n President’s Commission on Critical Infrastructure Protection, 49–55, 74 Marsh as chairman of, 50 members of, 49–50 Minihan’s Eligible Receiver briefing to, 72 report of, see Marsh Report (Critical Foundations) President’s Review Group on Intelligence and Communication Technologies (Review Group), 235, 238–40, 242–60, 264 cyber security prioritized by, 257–58 deadline of, 242 FBI’s briefings of, 254–55 K Street SCIF of, 243, 252 NSA metadata collecting examined by, 245–47, 252–54, 262 Obama’s meetings with, 242, 259 and potential for abuse by intelligence agencies, 251–52, 259, 260 PRISM and, 247–48 public trust as priority of, 237–38, 258 report of, see Liberty and Security in a Changing World staff of, 243, 258 PRISM, 228, 247–52 FISA Court and, 248, 249–50 programmable logic controllers (PLCs), 204–5 Protect America Act (2007), 193–95 civil liberties and, 194–95 Section 702 of, 248–49 Putin, Vladimir, 162 RageMaster, 136 RAND Corporation, 8, 10, 51, 278, 316n RATs (Remote Access Trojans), 225–26 Rattray, Gregory, 225 RCA, 19 Reagan, Ronald, 7, 19, 27, 67, 72, 183, 287n counter-C2 warfare and, 15–16 Executive Order 12333 of, 288n NSDD-145 of, 2–3, 7, 19–20, 27, 34, 54, 67, 72, 100, 188, 195, 241 “Star Wars” program and, 2 WarGames and, 1–3, 6, 10, 19, 175 Reagan administration, 54 cyber warfare and, 1–3, 6–7 Redford, Robert, 31 regulation, corporate fear of, 98–99, 101, 176, 200, 274–75 Remote Access Trojans (RATs), 225–26 Reno, Janet, 39–40 resilience, as goal of cyber security, 277 Review Group, see President’s Review Group on Intelligence and Communication Technologies Rhoads, Walter “Dusty,” 107–8, 120, 121 Rice, Condoleezza, 140–41, 150, 174 Rice, Susan, 238, 239 Riedel, Bruce, 199 Rogen, Seth, 269, 270 Rogers, Michael, 282, 285 Ronfeldt, David, 291n RTRG (Real Time Regional Gateway), 158–60, 195 Rumsfeld, Donald, 150–51, 155, 173 Iraq insurgency downplayed by, 148, 150 Russian Federation: CentCom hacking and, 182 and cyber attack on Georgia, 164–66 cyber attacks by, 4, 42, 164–66, 224 Estonian cyber attack and, 163–64, 165 Georgia invaded by, 164–66 Moonlight Maze and, 86–88, 213, 223 Sandia Laboratories, 111 Sare, Michael, 71 Saudi Aramco, Iranian cyber attack on, 213, 216 SCADA (Supervisory Control and Data Acquisition) systems, 45 Schaeffer, Richard, 181–82, 276 Schell, Roger, 293n Schmidt, Howard, 188 Schoomaker, Peter, 150–51 Schwarzkopf, Norman, 23, 25, 151 Science Applications International Corporation (SAIC), 132 Scowcroft, Brent, 44 2nd Circuit Court of Appeals, U.S., Section 215 ruling of, 262–63 Secret Service, North Korean cyber attack on, 213 “Security and Privacy in Computer Systems” (Ware), 8–9 Senate, U.S.: Armed Services Committee of, 46, 71, 283 Church Committee of, 37, 230, 252 Foreign Relations Committee of, 197 Governmental Affairs Committee of, 48, 94 Intelligence Committee of, 35–36 Select Committee on Intelligence of, 126, 127, 231–33, 256 sensitive compartmented information facilities (SCIFs), 243 September 11, 2001, terrorist attacks, 3, 140–41, 155, 171, 174, 192, 195, 241, 244, 261 Serbia, U.S. hacking of phone systems in, 113, 132 Shady RAT, Operation, 226 Shalikashvili, John, 67, 68, 146 Shamoon computer virus, 213–14 Shaw Air Force Base, 7, 108–9 Shiite Muslims, 147, 160 Shinseki, Eric, 111, 112 Siemens, logic controllers of, 204–5, 206, 211 Signal Security Agency, 11 609th Information Warfare Squadron, 7, 108–10, 120 60 Minutes (TV program), 240 Skype, PRISM and, 247 Slocombe, Walter, 44 Sneakers (film), 31–32, 33 Snowden, Edward, 194 NSA programs leaked by, 63–64, 228–30, 231, 234, 242, 244, 245, 251, 257–59, 262, 282, 285, 298n Social Security, 99 Social Security numbers, hacking of, 265, 268 Solar Sunrise cyber attack, 74–78, 80, 81, 98, 101, 119, 120, 123, 183, 187, 241 Sonic.net, 77 Sony Online Entertainment, hacking of, 268 Sony Pictures Entertainment, North Korean cyber attack on, 268–71, 272n South China Morning Post, 229 South Korea, North Korean cyber attacks on, 213, 269 South Ossetia, 164–65, 241 Soviet Union, 12, 13 collapse of, 162 Space Command, U.S., 122, 146 Spiegel, Der 228, 229, 298n Sputnik II, 119 Stabilization Force (SFOR), 110–12 “Star Wars” program, 2 Stasi, 235 Stellar Wind, 155n Stimpy (pseudonym), 77–78 Stimson, Henry, 11 Stoll, Cliff, 61–62, 82–83 Stone, Geoffrey: civil liberties expertise of, 239, 244, 251, 259, 264 in Review Group, 239, 244, 246, 250–52, 253, 254, 264 Strategic Command, U.S., 183 Studeman, William, 21–22, 26, 27, 28, 30, 42, 84, 128 as acting CIA director, 45 as CIA deputy director, 41 information warfare as focus of, 41 as NSA director, 126–27, 275–76 Stuxnet, 201, 213, 216, 217, 218–19, 228, 242, 304n–5n Alexander and, 204–5, 206 Bush and, 203, 205, 206, 208, 209, 212, 215 centrifuges speed manipulated by, 209 exposure of, 210–11 false data sent to monitors in, 208, 209 Gates and, 206 Iranian confidence as target of, 208 Israel and, 207 Natanz centrifuges targeted by, 203 Obama and, 203, 208–9, 210, 212 Siemens logic controllers infected by, 204–5, 211 successes of, 209–10 TAO and, 205–7 valve controls overridden by, 207–20 Summers, Lawrence, 200 Sunni Muslims, 147, 160 Sunstein, Cass, 239, 253 Suter, 161 Swire, Peter, 239–40, 243–44, 251, 253, 255 Sylvania Labs, 14–15 Symantec, 210, 211 Syria: cyber attacks by, 4 Israeli bombing of reactor in, 160–61, 198, 301n Taiwan, 224 Taliban, 149, 229 Tallinn, Estonia, 165 cyber attack on, 162–64 Tango, Operation, 111 TAO (Office of Tailored Access Operations), 135–37, 156, 158, 182, 195, 273n hacking software of, 136 Hayden and, 135 Minihan and, 134–35 Snowden leaks and, 229–30 Stuxnet and, 205–7 tools and techniques of, 298n Technical Advisory Group, 126 telecom companies: metadata collection and, 194, 247, 248, 253, 263 Snowden leaks and, 234 telecommunication networks, switches in, 44–45 Tenenbaum, Ehud (The Analyzer), 77, 78 Tenet, George, 113, 140 terrorism, terrorists: Bush (G.W.) administration complacency about, 140–41 CNE and, 139 cyber attacks by, 98 FISA and, 192 infrastructure as targets of, 39, 41, 42, 53 Internet and, 35 Obama’s focus on, 197–98 post-9/11 fear of, 195 Thompson, Fred, 95 thumb drives, malware on, 182, 207, 304n Thurman, Max, 145 Titan Rain, 224 Toyota Prius, hacking of, 273n Trailblazer, 132, 156–57, 158 Transportation Department, U.S., North Korean cyber attack on, 213 Treasury Department, U.S.: cyber security as low priority of, 172–73 North Korean cyber attack on, 213 “Trilateral Memorandum Agreement,” 216–17 Truman, Harry, 12 Turbulence, 157–58, 195 Unit 8200 (Israel), 161 United Arab Emirates, 75, 76 United States: Chinese relations with, 221–28 as digital communications hub, 191–92, 193, 248 see also five eyes university computers, as entry points for hackers, 61, 73, 82 UNIX operating system, Sun Solaris vulnerability in, 73–74 U.N.
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
They range from relatively static information such as identity, gender, and nationality to dynamic information such as location, relationship status, age, and point-in-time interest (as reflected in a search query). Sophisticated data models like the Facebook news feed may build a filter that considers all these factors as well as all of the participant’s previous activities on the platform. As part of the design process, platform companies need to develop an explicit data acquisition strategy. Users vary greatly in their willingness to share data and their readiness to respond to data-driven activity recommendations. Some platforms use incentives to encourage participants to provide data about themselves; others leverage game elements to gather data from users. LinkedIn famously used a progress bar to encourage users to progressively submit more information about themselves, thereby completing their personal data profiles.
Some mobile apps, such as the music streaming app Spotify, ask users to sign in using their Facebook identities, which helps the app pull in initial data to use in facilitating accurate matches. However, resistance from some users has led many app makers, including Spotify, to provide alternative ways to sign in that don’t require a Facebook link. Successful platforms create mutually rewarding matches on a consistent basis. As such, continual improvement of data acquisition and analysis methods is an important challenge for any organization seeking to build and maintain a platform. Balancing the three functions. All three key functions—pull, facilitate, and match—are essential to a successful platform. But not all platforms are equally good at all three. It’s possible for a platform to survive, at least for a time, thanks mainly to its strength at a particular function.
The Design and Engineering of Curiosity: How the Mars Rover Performs Its Job by Emily Lakdawalla
This was a huge and exciting instrument package. Some of the instruments looked familiar. Mastcam, MAHLI, and APXS all had direct parallels on the Mars Exploration Rovers (Pancam, Microscopic Imager, and APXS), but in each case the proposed MSL instrument had major improvements. Mastcam promised the possibility of color, stereo, high-definition video of rover traverses across Mars. APXS would have higher spatial resolution and speedier data acquisition than ever before. The novel instruments were just as exciting. ChemCam would provide remote elemental analysis capability unlike anything seen on a Mars mission before, and would do it with a high-powered laser zapping rocks. RAD would make measurements that would pave the way for human exploration of Mars. DAN would bring to the surface the neutron-detection capability that had led to the Odyssey discovery of ground ice.
The cooler also improves the Curiosity APXS resolution over that of the Spirit and Opportunity APXS.23 There is also no alpha channel on Curiosity’s APXS (that is, unlike Spirit and Opportunity, Curiosity does not detect backscattered alpha particles). Throwing out the alpha channel allowed the instrument to be designed with the X-ray detector much closer to the surface; the closest range of 19 millimeters compares to 30 for the Spirit and Opportunity APXS. That, in turn, increases the sensitivity of the Curiosity APXS by a factor of 3; reduces the spot size of an APXS measurement; and speeds up data acquisition by a factor of 5. Curiosity’s APXS can get a “quick look” measurement of the major elements in only 20 minutes, and high-quality results in only 2 hours.24 APXS and ChemCam both measure elemental compositions. Initially, APXS and ChemCam measurements of target compositions did not match very well, but the match has improved over time, especially as the ChemCam team has improved its calibration (see section 126.96.36.199).
Warnings by Richard A. Clarke
active measures, Albert Einstein, algorithmic trading, anti-communist, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, Bernie Madoff, cognitive bias, collateralized debt obligation, complexity theory, corporate governance, cuban missile crisis, data acquisition, discovery of penicillin, double helix, Elon Musk, failed state, financial thriller, fixed income, Flash crash, forensic accounting, friendly AI, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Watt: steam engine, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge worker, Maui Hawaii, megacity, Mikhail Gorbachev, money market fund, mouse model, Nate Silver, new economy, Nicholas Carr, nuclear winter, pattern recognition, personalized medicine, phenotype, Ponzi scheme, Ray Kurzweil, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Ronald Reagan, Sam Altman, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, smart grid, statistical model, Stephen Hawking, Stuxnet, technological singularity, The Future of Employment, the scientific method, The Signal and the Noise by Nate Silver, Tunguska event, uranium enrichment, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y2K
Why Natanz is important, says Weiss, is that it showed how corrupting digital control system software allows a hacker to send the wrong signals to a programmable logic controller (PLC), the computer inside machines that controls what that machine does and how it does it. Digital control system software packages are running millions of PLCs throughout the U.S. infrastructure, not just in the power grid, but also in pipelines, refineries, and manufacturing facilities. Also known as industrial control systems or supervisory control and data acquisition (SCADA) systems, these software packages are ubiquitous. Versions of them now run our automobiles and control the medical devices in hospitals. And these SCADA systems are networked, sending diagnostic data to and receiving software updates from the manufacturer. “This is about a lot more than just hacking the power grid,” Weiss told us. The same SCADA software used by the Iranians is used in thousands of U.S. manufacturing and operating plants and facilities and is susceptible to the same exploits that the U.S. used to destroy those Iranian centrifuges.
., 213 Roper, William, 214 Ross, Bill, 136 Ross, Lee, 184 Royal Academy, 345 Royal Air Force, 10 Royal Navy, 9 Royal Netherlands Meteorological Institute, 253 Rubenstein, Ariel, 380n Ruby, Jack, 99 Rumsfeld, Donald, 28–29 Russo, Rene, 219 Rutgers University, 261 Sagan, Carl, 273–77 Sago Mine disaster, 129–30 Salling, John Peter, 122 Samuel, Arthur, 381n San Bruno pipeline explosion of 2010, 293–94 Sandler O’Neill & Partners, 154 Sandworm, 285 Sanriku earthquake of 869, 77–81, 91, 97–98 Sarbanes-Oxley Act (SOX), 157 Sarin, 23, 230 Satisficing, 116, 117, 180–81, 319, 322, 359 Savage, Stefan, 297–98 Scacco, Gus, 149 Scanning for problems, 354–56 Scarface (movie), 99 Scenario modeling, 360, 363–64 Schapiro, Mary, 118–19 Schlesinger, Michael, 240–41 Schneider, Stephen, 241 Science (journal), 242 Science Story (show), 226 Scientific American, 278–79 Scientific method, 248–49 Scientific reticence, 79–80, 186–87, 234, 248–49, 259, 335 “Scope neglect,” 174 Sea-level rise, 238, 244–60, 360 Search for extraterrestrial intelligence (SETI), 304 Seawalls, and Fukushima nuclear disaster, 77, 85, 89–90, 92–93 Securities and Exchange Commission (SEC), 100, 105–12, 114–20, 189–90 Security by obscurity, 270 Seismologist Warns, A (Ishibashi), 91–92 Selection effect, 380n Self-confidence, 184, 240, 365 Self-interest, of critics, 187–88 Sendai, Japan, 80, 81, 82 Sentinel intelligence, 3, 16, 356 “Separation of parts” policy, 270 September 11 attacks, 7–9, 230, 361–62 Seven Pillars of Wisdom: A Triumph (Lawrence), 57 Sextus Empiricus, 185 Shearson Lehman, 162 Shia Muslims, 63 Shoemaker, Gene, 306–7 Shultz, George, 280 Siberian Unified Dispatch Control Center (SUDCC), 290 Siegel, Jeremy, 157–58 Siegfried Line, 10 Sieur de Bienville, Jean-Baptiste Le Moyne, 41 Signal and the Noise, The (Silver), 15 Signal from noise, separating, 356–58 Silver, Nate, 13, 15 Silver mining, 128–29 Simon, Herbert, 180–81, 322 Simons, Daniel, 175 Singularity, the, 209 60 Minutes (TV show), 119, 162, 244 Skepticism, 151–53, 168, 185, 240, 248–49 Skynet, 205 Smith & Wesson, 99, 109 Snowden, Edward, 211 Solid rocket boosters, and Challenger disaster, 11–13 Somalia, 65 Soothsayers, 1–2 “Sophistication effect,” 187 South Africa, 42–43 Soviet Union, 25–26, 266, 267–68, 271, 273–74, 277–78 Spaceguard goal, 312–17, 319 Space Shuttle Challenger disaster, 11–13 SpaceX, 202 Spanish flu pandemic of 1918, 195, 198, 217, 221–24 Spielberg, Steven, 101 Split-strike conversion, 103–5 SSH (Sayano-Shushenskaya Hydro), 289–2917 Stalin, Joseph, 174, 213 Standard project hurricane (SPH), 52–53 “Standing start,” 266 Stanford University, 89, 184, 192, 226, 337, 338 Steam engine, 174–75 Stock trading. See also Financial crisis of 2008 weak AI and, 211–12 Storm, The (van Heerden), 51 Stuxnet, 291–92 Subprime mortgage crisis, 147–48, 153–54, 157, 162 Suh, Simona, 117–18 Sunni Muslims, 63 Sunshine Mine disaster of 1972, 128–29 Sun Yat-sen University, 340 SUNY Downstate Medical Center, 186 Super Aegis II, 214 Superintelligence, 201, 203–16 Supervisory control and data acquisition (SCADA), 292, 293 Surveillance, 359–60 “Swarm boats,” 214 Swine flu, 195–98, 218 Symposium Greek Restaurant (New York City), 237, 252–53 Syria, 57–74 Ford scenario, 65–66, 67–69 slippery slope of intervention, 70–74 Syrian Civil War, 60–61, 62–64, 72–73 Szostak, Jack, 327 Tactical nuclear weapons, 267–69 “Take It Easy” (song), 305 Tamiflu, 225, 233 Taubenberger, Jeffery, 222 Team Louisiana Report, 55 Technical expertise, 182–83 Technological evolution, 212–13 Technological singularity, 209 Tectonic plates, 80, 81 “Tells,” 25–27, 29–30, 36–37 Tenet, George, 8 Terminator, The (movie), 205 Tesla, 202 Tetlock, Philip, 13–15 Thierry de la Villehuchet, René, 102–3, 109, 113 “Tickling the dragon’s tail,” 83 Titan III rockets, 11–12 Tōhoku earthquake and tsunami of 2011, 81–82, 84–85 Tohoku Electric Power Co., 91 Tokyo Electric Power Company (TEPCO), 76–78, 86–98, 92–98 Toon, Owen, 273, 278–79 Trenberth, Kevin, 253 Troy, 1–2 Truman, Harry, 127 TTAPS, 273–77 Tunguska event, 301–3, 316 Tunisia, 57, 58 Turco, Richard P., 273, 276–77 Turkey, 62–63 Tyrosinemia, 332, 334 UBS, 149 Ukraine power grid cyber attack of 2015, 283–85, 287–88, 289, 291 Umea University, 329 Unemployment, 212–13 United Arab Emirates (UAE), 28 United Nations Climate Change Conference (2015), 247–50 United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), 88 Universal hackability, 296–300 University of California, Berkeley, 13–14, 226, 327, 329 University of California, San Diego, 297 University of Colorado, 254, 328 University of Hawaii, 256, 315, 326 University of Iowa, 238, 243 University of Massachusetts, 296 University of Texas Southwestern Medical Center, 332 University of Tokyo School of Engineering, 92 Upper Big Branch Mine disaster, 121–22, 130–37 accident report, 133 Cassandra system, 137–38, 140–41 ventilation system, 133–37 Van Allen, James, 238 Van Heerden, Ivor, 41–55 background of, 41, 42–43 coastal restoration program, 43–44, 53 government failures and, 50–55 New Orleans Scenario, 45, 46–50, 52 resignation of, 44 Veracode, 295 Vinge, Vernor, 202 Vulnerabilities, and complexity, 366–67 Wall Street Journal, 115, 119, 154, 158, 163 Ward, Grant, 106 Warfare and AI, 199, 200, 213–14 Warning, the, 168, 170, 170–76 Warsaw Pact, 278 Washington Post, 243, 340 Waterman Award, 328–29 Watson (computer), 202, 209 Watson, James, 328 Watt, James, 174–75 Weak AI, 201, 210–13 Weapons of mass destruction (WMDs), 30–31, 358 Webster, Robert G., 223–25, 231–32, 235–36 Weidner, David, 158, 163 Weiss, Joe, 283–84, 286–89, 291–96, 298–300 West Antarctic Ice Sheet, 239, 246, 360 West Berlin, 25 Wharton School, 157–58 White, Ryan, 227, 384n White House National Warning Office, 355–56 Principals Committee, 29 Situation Room, 26–27, 181 Whitney, Meredith, 143–46, 148–54, 160–65 background of, 151, 153–54 Citigroup downgrade, 143–46, 154, 156–60, 164–65 Wide-field Infrared Survey Explorer (WISE), 315–16 Wiesel, Elie, 113 Wilson, E.
Solr in Action by Trey Grainger, Timothy Potter
business intelligence, cloud computing, commoditize, conceptual framework, crowdsourcing, data acquisition, en.wikipedia.org, failed state, fault tolerance, finite state, full text search, glass ceiling, information retrieval, natural language processing, openstreetmap, performance metric, premature optimization, recommendation engine, web application
Efficient field collapsing with the Collapsing query parser 11.8. Summary Chapter 12. Taking Solr to production 12.1. Developing a Solr distribution 12.2. Deploying Solr 12.2.1. Building your Solr distribution 12.2.2. Embedded Solr 12.3. Hardware and server configuration 12.3.1. RAM and SSDs 12.3.2. JVM settings 12.3.3. The index shuffle 12.3.4. Useful system tricks 12.4. Data acquisition strategies Update Formats, Indexing Time, and Batching Data Import Handler Extracting text from files with Solr Cell 12.5. Sharding and replication 12.5.1. Choosing to shard 12.5.2. Choosing to replicate 12.6. Solr core management Defining cores Creating cores through the Core Admin API Reloading cores Renaming and swapping cores Unloading and deleting cores Splitting and merging indexes Getting the status of cores 12.7.
ulimit –n 100000 Many systems have a default file descriptor limit of 1024, but since each Solr index can consist of hundreds of files (or even thousands depending upon your MergePolicy settings), it may be necessary to increase this limit to 100,000 (from our example) or something even higher, especially if you expect to have many Solr cores on your server. You will probably want to set this limit permanently (running the command only applies to the current bash session) by setting it in a system-wide configuration such as /etc/security/limits.conf. You should ensure that your new file descriptor limit is sufficiently large that you will never run the chance of hitting it. 12.4. Data acquisition strategies So far, you have seen one way to post documents to Solr: through sending a document over HTTP to the Solr /update handler. We have utilized an included post.jar file as a convenience library for posting files containing Solr documents primarily in XML format, but under the covers it posts the contents of a file to Solr’s /update handler for you. It’s also possible to have Solr ingest documents in other ways, either through pushing documents to Solr in other formats or through having Solr import documents itself from any number of external data sources.
Content-Type header contributing patches coord factor (coord) <copyField> element, 2nd Core Admin API administration console creating cores CoreAdminHandler class core.properties file cos function cosh function CREATEALIAS command createNodeSet parameter cross-core joins cross-document joins CSS (cascading style sheets) CSV (comma-separated values) respose formats update handler support, 2nd CSVResponseWriter class custom hashing composite document ID limitations on overview targeting specific shard Czech language CzechStemFilterFactory D Damerau-Levenshtein distances Danish language data acquisition strategies batching documents DIH extracting text from files with Solr Cell Data Import Handler. See DIH. data model data redundancy data transformation functions data-config.xml file dataDir parameter <dataDir> element debug component, 2nd debug parameter def function DefaultSimilarity class, 2nd, 3rd defType parameter deg function Delete by id Delete by query Delete request, update handler deleteDataDir parameter deleteInstanceDir parameter deleting cores deletions, and segment merging DelimitedPayloadFilterFactory denormalization denormalized documents dependencies, in solrconfig.xml file dereferencing parameters df parameter DFRSimilarity class DFRSimilarityFactory class diacritcal marks, removing dictionary-based stemming DIH (Data Import Handler), 4th importing documents using indexing Stack Exchange indexing Wikipedia, 2nd direct routing Directory component DirectSolrSpellChecker dismax parameter dist function distanceMeasure parameter distrib parameter, 2nd distributed result grouping distributed searching distribution, creating own div function docfreq function [docid] field DocSet DocTransformer class document cache document router document transformers DocumentAnalysisRequestHandler class document-oriented Down state downloading Solr downtime DumpRequestHandler class duplicate documents skipping duplicate parameters reducing with parameter dereferencing durable writes Dutch language dynamic values, returning <dynamicField> element E e function Eclipse importing Lucene/Solr into running Solr from inside EdgeNGramFilter edismax parameter edit distance elasticity, SolrCloud ElisionFilterFactory, 2nd embedded Solr deployment within SolrJ application EmbeddedSolrServer class <enableLazyFieldLoading> element encoding HTML entities English language English Porter Stemmer EnglishMinimalStemFilter EnglishMinimalStemFilterFactory EnglishPossessiveFilter EnglishPossessiveFilterFactory entities, HTML escaping special characters eventual consistency eviction count ex local param excluded terms excludes, multiselect faceting execution order of speed of exists function exp function experimenting with relevancy [explain] field extensibility of Solr Extensible Markup Language.
Bad Pharma: How Medicine Is Broken, and How We Can Fix It by Ben Goldacre
data acquisition, framing effect, if you build it, they will come, illegal immigration, income per capita, meta analysis, meta-analysis, placebo effect, publication bias, randomized controlled trial, Ronald Reagan, selective serotonin reuptake inhibitor (SSRI), Simon Singh, WikiLeaks
These are widely celebrated, and everyone now speaks of ghostwriting as if it has been fixed by the ICMJE. But in reality, as we have seen so many times before, this is a fake fix: the guidelines are hopelessly vague, and are exploited in ways that are so obvious and predictable that it takes only a paragraph to describe. The ICMJE criteria require that someone is listed as an author if they fulfil three criteria: they contributed to the conception and design of the study (or data acquisition, or analysis and interpretation); they contributed to drafting or revising the manuscript; and they had final approval on the contents of the paper. This sounds great, but because you have to fulfil all three criteria to be listed as an author, it is very easy for a drug company’s commercial medical writer to do almost all the work, but still avoid being listed as an author. For example, a paper could legitimately have the name of an independent academic on it, even if they only contributed 10 per cent of the design, 10 per cent of the analysis, a brief revision of the draft, and agreed the final contents.
For example, a paper could legitimately have the name of an independent academic on it, even if they only contributed 10 per cent of the design, 10 per cent of the analysis, a brief revision of the draft, and agreed the final contents. Meanwhile, a team of commercial medical writers employed by a drug company on the same paper would not appear in the author list, anywhere at all, even though they conceived the study in its entirety, did 90 per cent of the design, 90 per cent of the analysis, 90 per cent of the data acquisition, and wrote the entire draft.91 In fact, often the industry authors’ names do not appear at all, and there is just an acknowledgement of editorial assistance to a company. And often, of course, even this doesn’t happen. A junior academic making the same contribution as many commercial medical writers – structuring the write-up, reviewing the literature, making the first draft, deciding how best to present the data, writing the words – would get their name on the paper, sometimes as first author.
Moon Rush: The New Space Race by Leonard David
agricultural Revolution, Colonization of Mars, cuban missile crisis, data acquisition, Donald Trump, Elon Musk, Google X / Alphabet X, gravity well, Jeff Bezos, life extension, low earth orbit, multiplanetary species, out of africa, self-driving car, Silicon Valley, telepresence, telerobotics
International participation should be encouraged, especially relative to science and engineering payloads and components and subsystems of low technical risk. No international hardware, software, or approvals, however, should be in the critical path to success. Commercial and international entities could take on a new role, shouldering the load of space activities that are not directly related to a geopolitical commitment to deep-space exploration. These new activities would include not only satellite communications but also data acquisition for space and terrestrial science, environmental monitoring, and space facility supply. Commercial and private entities also should take the lead in planetary pioneering, lunar and planetary base supply, space resource production, and settlement. So now what needs to happen to guarantee implementation of the president’s Space Policy Directive? I have come to the conclusion that the president must either transfer all unrelated NASA programs and projects to other relevant agencies and re-create in NASA the management efficiency of the Apollo program or create a new National Space Exploration Administration modeled after the NASA that existed in 1969.
Beautiful Data: The Stories Behind Elegant Data Solutions by Toby Segaran, Jeff Hammerbacher
23andMe, airport security, Amazon Mechanical Turk, bioinformatics, Black Swan, business intelligence, card file, cloud computing, computer vision, correlation coefficient, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, data acquisition, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, longitudinal study, Mars Rover, natural language processing, openstreetmap, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social graph, SPARQL, speech recognition, statistical model, supply-chain management, text mining, Vernor Vinge, web application
Unobtrusive Collaboration We also followed a common design guideline from the field of computer-supported cooperative work: collaborative features should not impede individual usage. As a result, we do not litter views with annotations by default. Rather, comments for a visualization are displayed unobtrusively on the right side of the screen, and graphical annotations are displayed “on demand” by the user. Voyagers and Voyeurs After these steps of data acquisition, design, and system implementation, we now had a running website and were ready to do “field tests” with users. We deployed the system in a set of user studies to observe how people would react to our system, what insights they might produce, and how we might improve the site. We invited 30 people into our lab to observe how they explored data with sense.us. Each person could view what the previous participants had contributed to the site.
Matt is currently vice-chair of the Computer-Human Interaction Forum of Oregon (CHIFOO, the Oregon chapter of the Association of Computing Machinery’s Special Interest Group on Computer-Human Interaction). In addition to his work in the online world, Matt is also a professional children’s book author and illustrator; more than one million copies of his award-winning, critically acclaimed Babymouse graphic novels (published by Random House) are currently in print. J. M. Hughes is an embedded systems and software engineer who is particularly fond of real-time control, data acquisition, and image processing. From 2003 to 2007 he was responsible for the design, implementation, and testing of the surface imaging software on the Phoenix Mars Lander. He is currently working on the electronics and control software for a multiwavelength laser interferometer system that will be used to verify the alignment of telescope mirror segments for a NASA project. He lives in Tucson, Arizona, with his wife and daughter.
Dark Mirror: Edward Snowden and the Surveillance State by Barton Gellman
4chan, A Declaration of the Independence of Cyberspace, active measures, Anton Chekhov, bitcoin, Cass Sunstein, cloud computing, corporate governance, crowdsourcing, data acquisition, Debian, desegregation, Donald Trump, Edward Snowden, financial independence, Firefox, GnuPG, Google Hangouts, informal economy, Jacob Appelbaum, job automation, Julian Assange, MITM: man-in-the-middle, national security letter, planetary scale, private military company, ransomware, Robert Gordon, Robert Hanssen: Double agent, rolodex, Ronald Reagan, Saturday Night Live, Silicon Valley, Skype, social graph, standardized shipping container, Steven Levy, telepresence, undersea cable, web of trust, WikiLeaks, zero day, Zimmermann PGP
They might not take the maxim to heart, but they knew in some abstract way that secret documents sometimes leaked. An American eagle as predator, the whole world its prey, was the sigil of an agency that could not even conceive of a public readership. I gave Baron the overview I wished I’d had when I first read these slides. Take a look farther down the cover page, I said, where “S35333” appears in smaller type. S stands for the Signals Intelligence Directorate, S3 for Data Acquisition, and each digit after that identifies a subordinate function. S353, the eagle people at Special Source Operations, pulled in monumental flows of information from the main trunk lines and switches that carry voice and data around the world. The owners of that infrastructure, mostly big corporations, were the “special sources.” The NSA paid them off, rerouted their traffic surreptitiously, hacked into their equipment, or relied on foreign allies with methods of their own.
The manager, whose name was Rick, led a project called PRISM, one of the agency’s most prolific operations. As a start-up back in 2007, PRISM had produced a grand total of three intelligence reports in its first month. Now, five and a half years later, it had become a principal engine of the U.S. surveillance machine. Rick was its collection manager and chief evangelist. The wire diagram of the NSA that year placed Rick’s operation within the unassumingly named subdirectorate of Data Acquisition, an arm of the Signals Intelligence Directorate. That is to say, Rick ran a spy shop, which is not a redundant thing to say in the context of the larger enterprise. The NSA did a whole lot of spying and a whole lot of other things, too. Great swaths of it, any one of which could swallow a lesser federal agency, took little or no part in the business of espionage. A chart of all those islands would divide the Fort Meade archipelago roughly in half.
Graph Databases by Ian Robinson, Jim Webber, Emil Eifrem
Amazon Web Services, anti-pattern, bioinformatics, commoditize, corporate governance, create, read, update, delete, data acquisition, en.wikipedia.org, fault tolerance, linked data, loose coupling, Network effects, recommendation engine, semantic web, sentiment analysis, social graph, software as a service, SPARQL, web application
There is another aspect to velocity, which is the rate at which the structure of the data changes. In other words, as well as the value of specific properties changing, the overall structure of the elements hosting those properties can change as well. This commonly occurs for two reasons. The first is fast-moving business dynamics: as the business changes, so do its data needs. The second is that data acquisition is often an experimental affair: some properties are captured “just in case”, others are introduced at a later point based on changed needs; the ones that prove valuable to the business stay around, others fall by the wayside. Both these forms of velocity are problematic in the relational world, where high write loads translate into a high processing cost, and high schema volatility has a high operational cost.
Competing on Analytics: The New Science of Winning by Thomas H. Davenport, Jeanne G. Harris
always be closing, big data - Walmart - Pop Tarts, business intelligence, business process, call centre, commoditize, data acquisition, digital map, en.wikipedia.org, global supply chain, high net worth, if you build it, they will come, intangible asset, inventory management, iterative process, Jeff Bezos, job satisfaction, knapsack problem, late fees, linear programming, Moneyball by Michael Lewis explains big data, Netflix Prize, new economy, performance metric, personalized medicine, quantitative hedge fund, quantitative trading / quantitative ﬁnance, recommendation engine, RFID, search inside the book, shareholder value, six sigma, statistical model, supply-chain management, text mining, the scientific method, traveling salesman, yield management
In order to comply with business, legal, and regulatory requirements for safety, security, privacy, and “auditability,” it must be strictly overseen. What Rules and Processes Are Needed to Manage the Data from Its Acquisition Through Its Retirement? Each stage of the data management life cycle presents distinctive technical and management challenges that can have a significant impact on an organization’s ability to compete on analytics.9 Data acquisition. Creating or acquiring data is the first step. For internal information, IT managers should work closely with business process leaders. The goals include determining what data is needed and how to best integrate IT systems with business processes to capture good data at the source. Data cleansing. Detecting and removing data that is out of date, incorrect, incomplete, or redundant is one of the most important, costly, and time-consuming activities in any business intelligence technology initiative.
What's Yours Is Mine: Against the Sharing Economy by Tom Slee
4chan, Airbnb, Amazon Mechanical Turk, asset-backed security, barriers to entry, Berlin Wall, big-box store, bitcoin, blockchain, citizen journalism, collaborative consumption, congestion charging, Credit Default Swap, crowdsourcing, data acquisition, David Brooks, don't be evil, gig economy, Hacker Ethic, income inequality, informal economy, invisible hand, Jacob Appelbaum, Jane Jacobs, Jeff Bezos, Khan Academy, Kibera, Kickstarter, license plate recognition, Lyft, Marc Andreessen, Mark Zuckerberg, move fast and break things, move fast and break things, natural language processing, Netflix Prize, Network effects, new economy, Occupy movement, openstreetmap, Paul Graham, peer-to-peer, peer-to-peer lending, Peter Thiel, pre–internet, principal–agent problem, profit motive, race to the bottom, Ray Kurzweil, recommendation engine, rent control, ride hailing / ride sharing, sharing economy, Silicon Valley, Snapchat, software is eating the world, South of Market, San Francisco, TaskRabbit, The Nature of the Firm, Thomas L Friedman, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ultimatum game, urban planning, WikiLeaks, winner-take-all economy, Y Combinator, Zipcar
Yet we should be careful about drawing general conclusions about the overall effect of any new technology: as criminologist Clive Norris has shown, license plate recognition has now become a way of tracking known individuals as they move around, and it is no surprise who is tracked more and who is tracked less.73 The underlying problem remains: there is still racism in the system, but it is now manifested in different ways. Data acquisition shifts the place where racism happens from the street to the database query. There is no evidence of intentional discrimination by the companies, and the patterns may change as the systems evolve, but we should be cautious about ascribing too much blame or credit to the companies involved. Money is one of the main points of contention for many jobs, but Uber is not just another employer.
The Filter Bubble: What the Internet Is Hiding From You by Eli Pariser
A Declaration of the Independence of Cyberspace, A Pattern Language, Amazon Web Services, augmented reality, back-to-the-land, Black Swan, borderless world, Build a better mousetrap, Cass Sunstein, citizen journalism, cloud computing, cognitive dissonance, crowdsourcing, Danny Hillis, data acquisition, disintermediation, don't be evil, Filter Bubble, Flash crash, fundamental attribution error, global village, Haight Ashbury, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Netflix Prize, new economy, PageRank, paypal mafia, Peter Thiel, recommendation engine, RFID, Robert Metcalfe, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, social graph, social software, social web, speech recognition, Startup school, statistical model, stem cell, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, the scientific method, urban planning, Whole Earth Catalog, WikiLeaks, Y Combinator
When a clothing company determines that knowing your favorite color produces a $5 increase in sales, it has an economic basis for pricing that data point—and for other Web sites to find reasons to ask you. (While OkCupid is mum about its business model, it likely rests on offering advertisers the ability to target its users based on the hundreds of personal questions they answer.) While many of these data acquisitions will be legitimate, some won’t be. Data are uniquely suited to gray-market activities, because they need not carry any trace of where they have come from or where they have been along the way. Wright calls this data laundering, and it’s already well under way: Spyware and spam companies sell questionably derived data to middlemen, who then add it to the databases powering the marketing campaigns of major corporations.
Whiplash: How to Survive Our Faster Future by Joi Ito, Jeff Howe
3D printing, Albert Michelson, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, bitcoin, Black Swan, blockchain, Burning Man, buy low sell high, Claude Shannon: information theory, cloud computing, Computer Numeric Control, conceptual framework, crowdsourcing, cryptocurrency, data acquisition, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, frictionless, game design, Gerolamo Cardano, informal economy, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, Nate Silver, Network effects, neurotypical, Oculus Rift, pattern recognition, peer-to-peer, pirate software, pre–internet, prisoner's dilemma, Productivity paradox, race to the bottom, RAND corporation, random walk, Ray Kurzweil, Ronald Coase, Ross Ulbricht, Satoshi Nakamoto, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, Simon Singh, Singularitarianism, Skype, slashdot, smart contracts, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, supply-chain management, technological singularity, technoutopianism, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, universal basic income, unpaid internship, uranium enrichment, urban planning, WikiLeaks
For those active in the field, some new piece of malicious code is hardly big news; by some estimates the security industry sees around 225,000 malware strains every day. But Stuxnet, as this particular sample was called, was a different animal. It was the first time anyone had seen malware that targeted the customized software that is used to control industrial machineries such as turbines and presses. After months of relentless analysis it became apparent that the code targeting these supervisory control and data acquisition (SCADA) systems had a very specific purpose: to disrupt the process of uranium enrichment in nuclear facilities. When the centrifuges connected to the system met certain conditions, the malware would forcibly alter the rotation speed of the motors, ultimately causing the centrifuges to break years before their normal life span. More importantly, the centrifuges would fail to properly enrich the uranium samples.
Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzić
Albert Einstein, bioinformatics, business cycle, business intelligence, business process, butter production in bangladesh, combinatorial explosion, computer vision, conceptual framework, correlation coefficient, correlation does not imply causation, data acquisition, discrete time, El Camino Real, fault tolerance, finite state, Gini coefficient, information retrieval, Internet Archive, inventory management, iterative process, knowledge worker, linked data, loose coupling, Menlo Park, natural language processing, Netflix Prize, NP-complete, PageRank, pattern recognition, peer-to-peer, phenotype, random walk, RFID, semantic web, speech recognition, statistical model, Telecommunications Act of 1996, telemarketer, text mining, traveling salesman, web application
The data-mining process. 1.4 LARGE DATA SETS As we enter the age of digital information, the problem of data overload looms ominously ahead. Our ability to analyze and understand massive data sets, as we call large data, is far behind our ability to gather and store the data. Recent advances in computing, communications, and digital storage technologies, together with the development of high-throughput data-acquisition technologies, have made it possible to gather and store incredible volumes of data. Large databases of digital information are ubiquitous. Data from the neighborhood store’s checkout register, your bank’s credit card authorization device, records in your doctor’s office, patterns in your telephone calls, and many more applications generate streams of digital records archived in huge business databases.
Time series or, more generally, temporal sequences, appear naturally in a variety of different domains, from engineering to scientific research, finance, and medicine. In engineering matters, they usually arise with either sensor-based monitoring, such as telecommunication control, or log-based systems monitoring. In scientific research they appear, for example, in spatial missions or in the genetics domain. In health care, temporal sequences have been a reality for decades, with data originated by complex data-acquisition systems like electrocardiograms (ECGs), or even simple ones like measuring a patient’s temperature or treatment effectiveness. For example, a supermarket transaction database records the items purchased by customers at some time points. In this database, every transaction has a time stamp in which the transaction is conducted. In a telecommunication database, every signal is also associated with a time.
Where Good Ideas Come from: The Natural History of Innovation by Steven Johnson
Ada Lovelace, Albert Einstein, Alfred Russel Wallace, carbon-based life, Cass Sunstein, cleantech, complexity theory, conceptual framework, cosmic microwave background, creative destruction, crowdsourcing, data acquisition, digital Maoism, digital map, discovery of DNA, Dmitri Mendeleev, double entry bookkeeping, double helix, Douglas Engelbart, Douglas Engelbart, Drosophila, Edmond Halley, Edward Lloyd's coffeehouse, Ernest Rutherford, Geoffrey West, Santa Fe Institute, greed is good, Hans Lippershey, Henri Poincaré, hive mind, Howard Rheingold, hypertext link, invention of air conditioning, invention of movable type, invention of the printing press, invention of the telephone, Isaac Newton, Islamic Golden Age, James Hargreaves, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Johannes Kepler, John Snow's cholera map, Joseph Schumpeter, Joseph-Marie Jacquard, Kevin Kelly, lone genius, Louis Daguerre, Louis Pasteur, Mason jar, mass immigration, Mercator projection, On the Revolutions of the Heavenly Spheres, online collectivism, packet switching, PageRank, patent troll, pattern recognition, price mechanism, profit motive, Ray Oldenburg, Richard Florida, Richard Thaler, Ronald Reagan, side project, Silicon Valley, silicon-based life, six sigma, Solar eclipse in 1919, spinning jenny, Steve Jobs, Steve Wozniak, Stewart Brand, The Death and Life of Great American Cities, The Great Good Place, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, transaction costs, urban planning
In this respect, Berners-Lee was supremely lucky in the work environment he had settled into, the Swiss particle physics lab CERN. It took him ten years to nurture his slow hunch about a hypertext information platform. He spent most of those years working at CERN, but it wasn’t until 1990—a decade after he had first begun working on Enquire—that CERN officially authorized him to work on the hypertext project. His day job was “data acquisition and control”; building a global communications platform was his hobby. Because the two shared some attributes, his superiors at CERN allowed Berners-Lee to tinker with his side project over the years. Thanks to a handful of newsgroups on the Internet, Berners-Lee was able to supplement and refine his ideas by conversing with other early hypertext innovators. That combination of flexibility and connection gave Berners-Lee critical support for his idea.
Testing Extreme Programming by Lisa Crispin, Tip House
Flushing out the hidden assumptions in the stories helps programmers implement more accurately the first time around, which makes for happy customers. XP may be self-correcting in terms of bringing these things out in the open eventually, but clearing them up earlier allows for more velocity later on. Specifying acceptance tests up front with the stories is really a type of test-first development. It helps avoid some types of defects altogether and gives the team a head start on test automation and test data acquisition. This allows functional testing to keep up with the programmers at crunch time, when the end of the iteration approaches. Including acceptance test tasks in the stories and enabling accurate story estimates makes release and iteration planning more accurate and provides time to automate the tests, which pays off many times over in later iterations. In the next chapters, we'll expand on each of these goals, discuss effective ways to accomplish them, and provide examples.
Strategy Strikes Back: How Star Wars Explains Modern Military Conflict by Max Brooks, John Amble, M. L. Cavanaugh, Jaym Gates
The weapon, the Stuxnet computer virus, was designed not only to damage the centrifuges Iran used in its enrichment efforts but also to hide from the centrifuge operators that there was anything amiss.5 The worm virus was introduced into the closed networks through the laptops and personal electronic devices of civilian scientists working on the program. Once it was embedded in the supervisory-control and data-acquisition programs, it began to do its damage, while reporting to the system administrators that the system was performing without any issues. The employment of this new weapon was, based on the assessed aim, successful; however, the unintended consequence was that this new weapon was now also in an adversary’s hands (and due to a bug in the virus’s code that let it spread beyond the enrichment facility, in the hands of the wider public as well).
The Secret Lives of Bats by Merlin Tuttle
The bats appear to be going directly to intercept concentrations of what we presume to be crop pests. We make nightly recordings and would be happy to show them to you.” We met the next morning. I had been pointing out for years that these bats were likely having a major impact in keeping insect populations in check, but I hadn’t even dreamed that we might someday have proof that they were preferentially feeding on crop pests. Jim introduced me to his data acquisitions manager, Bill Runyon, and they jointly offered to collaborate. I was thrilled and immediately called free-tailed bat researcher Gary McCracken. I said, “You’re not going to believe the neat research opportunity I’ve just discovered!” When I described the radar images and Jim and Bill’s offer of help, he joined in my enthusiasm, promptly flying down to meet them. Though the radar images were quite convincing, we needed proof that the bats were actually eating crop pests, not just happening to go to the same locations.
@War: The Rise of the Military-Internet Complex by Shane Harris
Amazon Web Services, barriers to entry, Berlin Wall, Brian Krebs, centralized clearinghouse, clean water, computer age, crowdsourcing, data acquisition, don't be evil, Edward Snowden, failed state, Firefox, John Markoff, Julian Assange, mutually assured destruction, peer-to-peer, Silicon Valley, Silicon Valley startup, Skype, Stuxnet, undersea cable, uranium enrichment, WikiLeaks, zero day
“We knew what luring words and phrases the e-mails used before they were sent,” the former official says. “We told companies what to be on the lookout for. What e-mails not to open. We could tell them ‘You’re next on the list.’” Among the most worrisome people on those lists were employees of American oil and natural gas companies. These businesses own and operate major refineries and pipelines that are run by SCADA (supervisory control and data acquisition) systems, the same kinds of devices that the NSA attacked in the Iranian nuclear facility to make centrifuges break down. Chinese attempts to penetrate oil and natural gas companies “were never-ending,” the former official says. The campaign reached a fever pitch in the spring of 2012, when hackers penetrated the computer networks of twenty companies that own and operate natural gas pipelines.
The Rise of Superman: Decoding the Science of Ultimate Human Performance by Steven Kotler
Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Clayton Christensen, data acquisition, delayed gratification, deliberate practice, fear of failure, Google Earth, haute couture, impulse control, Isaac Newton, Jeff Bezos, jimmy wales, Kevin Kelly, Lao Tzu, lateral thinking, life extension, lifelogging, low earth orbit, Maui Hawaii, pattern recognition, Ray Kurzweil, risk tolerance, rolodex, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Stanford marshmallow experiment, Steve Jobs, Walter Mischel, X Prize
“A lot of people associate serotonin directly with flow,” says high performance psychologist Michael Gervais, “but that’s backward. By the time the serotonin has arrived the state has already happened. It’s a signal things are coming to an end, not just beginning.” These five chemicals are flow’s mighty cocktail. Alone, each packs a punch, together a wallop. Consider the chain of events that takes us from pattern recognition through future prediction. Norepinephrine tightens focus (data acquisition); dopamine jacks pattern recognition (data processing); anandamide accelerates lateral thinking (widens the database searched by the pattern recognition system). The results, as basketball legend Bill Russell explains in his biography Second Wind, really do feel psychic: Every so often a Celtic game would heat up so that it would become more than a physical or even mental game, and would be magical.
Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage by Douglas B. Laney
3D printing, Affordable Care Act / Obamacare, banking crisis, blockchain, business climate, business intelligence, business process, call centre, chief data officer, Claude Shannon: information theory, commoditize, conceptual framework, crowdsourcing, dark matter, data acquisition, digital twin, discounted cash flows, disintermediation, diversification, en.wikipedia.org, endowment effect, Erik Brynjolfsson, full employment, informal economy, intangible asset, Internet of things, linked data, Lyft, Nash equilibrium, Network effects, new economy, obamacare, performance metric, profit motive, recommendation engine, RFID, semantic web, smart meter, Snapchat, software as a service, source of truth, supply-chain management, text mining, uber lyft, Y2K, yield curve
SCOR agility metrics include flexibility and adaptability. • Utility of information for a range of purposes • Linked data, metadata, and master data measures • Ease of integrating new types of data or changing dimensions Costs The cost of operating the supply chain processes. This includes labor costs, material costs, management, and transportation costs. A typical cost metric is cost of goods sold. • Data acquisition cost • Data management costs • Data delivery costs (Each include labor and technology related costs) Asset Management Efficiency (Assets) The ability to efficiently utilize assets. Asset management strategies in a supply chain include inventory reduction and in-sourcing versus outsourcing. Metrics include: inventory days of supply and capacity utilization. • Information timeliness • Amount of available history • Actual usage (e.g., percent of data touched by users/apps) A New Supply Chain Model for Information Assets Certainly the classic product/service supply chain model is useful at a high level, especially to express information management needs to those more familiar with the physical supply chain concept.
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
The article’s author, former United Press International journalist Richard Sale, stated that the double agent was probably a member of the Iranian dissident group, the Mujahedeen-e Khalq (MEK), a shadowy organization with Israeli government connections that is believed to be behind the assassinations of key Iranian nuclear scientists. Stuxnet was specifically designed to infect only certain types of supervisory control and data acquisition (SCADA) systems used for real-time data collection, and to control and monitor critical infrastructure – hydro-electrical facilities, power plants, nuclear enrichment systems, and so on. The programs used to control the physical components of SCADA systems are called programmable logic controllers (PLCS), and Stuxnet was developed in such a way as to target only two types of PLC models controlled by the Siemens Step 7 software –S7–315 and S7–417 – both of which are used in the Iranian nuclear centrifuges.
The Future of the Brain: Essays by the World's Leading Neuroscientists by Gary Marcus, Jeremy Freeman
23andMe, Albert Einstein, bioinformatics, bitcoin, brain emulation, cloud computing, complexity theory, computer age, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, Drosophila, epigenetics, global pandemic, Google Glasses, iterative process, linked data, mouse model, optical character recognition, pattern recognition, personalized medicine, phenotype, race to the bottom, Richard Feynman, Ronald Reagan, semantic web, speech recognition, stem cell, Steven Pinker, supply-chain management, Turing machine, twin studies, web application
A presumption emerging in our field is that the strategy for success is to collect masses of data, and then, only afterward, to distill from that data an understanding of how the brain works. In some domains, this rather static strategy—collect data first, analyze later—may be both reasonable and profitable. Take, for example, the problem of segmenting neurons from anatomical images to identify connectivity. Achieving that goal will demand powerful algorithms, but the goal itself is clear, so the analysis can proceed somewhat independently of data acquisition and experiment. But the more we stray from such well-defined problems, the less realistic that sort of static strategy may be. In most cases, we do not quite yet know which data we want to collect. Even if it is clear which kinds of measurements we want to make (for example, whole-brain calcium imaging of the larval zebrafish, two-photon imaging of multiple areas of mouse cortex), it is not clear which behaviors the organism should be performing while we collect those data, or which environment it should be experiencing.
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
So the broader Google’s reach becomes—the more it Googlizes us—the more likely it is that even informed and critical Internet users will stay in the Google universe and allow Google to use their personal information. For Google, quantity yields quality. For us, resigning ourselves to the Google defaults enhances convenience, utility, and status. But at what cost? T H E P ROBL EM WI T H P RI VACY Google is far from the most egregious offender in the world of personal data acquisition. Google promises (for now) not to sell your data to third parties, and it promises not to give it to agents of the state unless the agents of the state ask for it in a legal capacity. (The criteria for such requests are lax, however, and getting more lax around the world.) But Google is the master at using information in the service of revenue generation, and many of its actions and policies are illustrative of a much larger and deeper set of social and cultural problems.
Beautiful Visualization by Julie Steele
barriers to entry, correlation does not imply causation, data acquisition, database schema, Drosophila, en.wikipedia.org, epigenetics, global pandemic, Hans Rosling, index card, information retrieval, iterative process, linked data, Mercator projection, meta analysis, meta-analysis, natural language processing, Netflix Prize, pattern recognition, peer-to-peer, performance metric, QR code, recommendation engine, semantic web, social graph, sorting algorithm, Steve Jobs, web application, wikimedia commons
There is a turf battle between the CSI guys and the police regarding keeping the body in the cold storage room. The police are keen on having the autopsy done as soon as possible. The CSI guys try to close the crime scene investigation before the autopsy takes place. Postmortem imaging solves this problem. A preliminary report from the postmortem CT examination makes it possible to preserve the body in the cold storage room. Data Acquisition The traditional physical autopsy at CMIV is extended by adding the CT and MRI as VA activities. In most cases, the forensic pathologist comes to the crime scene and oversees the handling of the human cadaver, which is placed in a sealed body bag before being transported to the forensic department and put in cold storage. The following morning, a full-body dual source CT (DSCT) scan is performed at CMIV with a state-of-the-art SOMATOM Definition Flash scanner (from Siemens Medical Solutions in Germany).
The New New Thing: A Silicon Valley Story by Michael Lewis
Albert Einstein, Andy Kessler, business climate, creative destruction, data acquisition, family office, high net worth, invention of the steam engine, invisible hand, Jeff Bezos, Marc Andreessen, Menlo Park, pre–internet, risk tolerance, Sand Hill Road, Silicon Valley, Silicon Valley startup, the new new thing, Thorstein Veblen, wealth creators, Y2K
If it wasn't the engine and it wasn't the sensor, then the problem lay somewhere between the sensor and the computers. The closer Robert came to the computers, the less sure of himself he became. "I've got to work out how the system is getting its informationhow the PLCs sends it to SCADA," said Robert. SCADA was an acronym for the hopeful title they'd given to their software: Superior Control and Data Acquisition. SCADA was what Steve and Lance and Tim and Clark had spent most of their time writing. It picked up the digitized information from all over the boat and manipulated it in any way it needed to be manipulated. Steve and Robert left the engine room and made for the neighboring computer room, to find out what SCADA had to say. The twenty-five slender black machines were arranged lengthwise along a wall, which resembled a sales rack in a discount outlet for VCRs.
Every Patient Tells a Story by Lisa Sanders
data acquisition, discovery of penicillin, high batting average, index card, medical residency, meta analysis, meta-analysis, natural language processing, pattern recognition, Pepto Bismol, randomized controlled trial, Ronald Reagan
Poking, prodding, and thumping in places where it just won’t tell them anything.” And he found residents almost universally grateful when he showed them a better way of doing it. “The physical exam just becomes a much more useful tool when you use it correctly.” In a paper first promulgating the use of direct observation as a tool in evaluating residents, Eric wrote: “Direct observation of trainees is necessary to evaluate the process of data acquisition and care. A trainee’s ability to take a complete history; perform an accurate, thorough physical examination; communicate effectively; and demonstrate appropriate interpersonal and professional behavior can best be measured through the direct sampling of these clinical skills.” It seems obvious and yet it’s been a remarkably hard sell—not just to residents but to training programs as well.
A Man on the Moon by Andrew Chaikin
It is this envelope of dimly glowing gas, the corona, that frames the moon’s silhouette during a total solar eclipse on earth. For a glimpse of its cold, eerie light astronomers will travel halfway around the world, but Mattingly now saw the corona as only the space traveler could, in the last minutes of orbital night, while the sun still hid below the unseen horizon. It was Mattingly’s task to capture the corona on film using the Data Acquisition Camera (DAC). And the tape—that was a matter of efficiency. Mattingly knew the inside of the command module so well that even in pitch darkness he could find his way around. And he knew that if he flicked on a flashlight to glance at a checklist, even for a moment, he’d ruin his night vision. So he’d spent an hour during the trip out to the moon reading his checklist into the portable tape recorder.
., 518, 521, 523, 536 astronauts’ wives and, 416-17 backup crew for, 284, 397, 400-401, 407-8 debriefings for, 447, 448 Endeavour in, 412, 417, 433, 434-436, 448 Falcon in, 402, 406, 412-18, 421, 423, 426, 432, 433, 440, 442, 444, 449 and first-day covers stamp deal, 445, 496-98, 549,551,640, 642, 646 geological information from, 414-15, 453, 474 geological training for, 399-400, 414, 424-25,434-35, 448, 639 Griffin in, 408, 428, 440 Hadley Rille as objective of, 402-3, 406, 408,412-15,419, 420, 423 Irwin in, 400, 403-4, 405, 408, 409-51,497, 549 Irwin-Scott relationship in, 404, 638 liftoff of, 417 lunar observations in, 434-35, 506, 507 lunar orbit of, 412, 433, 434 McDivitt in, 406, 407, 440, 442 orbiting lunar science platform for, 434, 453 parachutes of, 444 Powered Descent in, 406-7, 412-14 preflight training for, 416-17 quarantine for, 444 satellite released in, 434 Saturn V booster for, 417 Scott in, 398, 399-400, 403, 404-51,496-98, 549 Silver in, 398-99, 400, 404-8, 410, 414, 415, 419-21, 424-25, 428, 432, 448 simulation for, 407, 412, 413, 416, 424 Slayton in, 416, 428, 443, 497 sleeping in, 414, 415-16 space program and, 448-51 splashdown of, 444-48 success of, 448-49 trajectory for, 406-7, 412-13 Worden in, 400, 409, 416-17, 433, 434-36, 444-48, 497, 506, 507, 549 Apollo 15 moonwalks, 417-44 Apollo Lunar Surface Experiment Package (ALSEP) in, 422-23, 432,436, 438, 453, 468 backpacks for, 402, 426-27, 433 boulders examined in, 421-22, 427, 429, 432 cameras for, 407 circadian rhythms and, 414 deep core sample taken in, 432 — 433, 438-40 drill used in, 422-23, 432-33, 438-40 electrolyte solution for, 446-47, 475 exhaustion from, 423-24, 438, 446-47 exploration in, 409-10, 411-12, 417 first session for, 417-23 Galileo’s experiment repeated in, 442-43, 640 Genesis Rock found in, 430-31, 437, 453, 481, 558, 645 geological information from, 402, 405-6,418,419-21,423, 424-444 geologic traverses in, 417-18, 424, 433, 440-42 g-forces in, 415 gloves for, 422-23 at Hadley Delta mountain, 403, 412,414,415,424-29, 453, 466, 478, 479, 518, 521 at Hadley Rille, 438, 440-42, 450, 488 hammer used in, 421, 423, 443, 638 landmarks for, 412-15, 419 lunar maps for, 407, 420 Lunar Roving Vehicle in, 402, 406, 407,417-18, 422, 424, 425-27, 429, 436, 441,442, 443, 449, 639 at North Complex craters, 415, 438, 440, 442 oxygen supply for, 422, 426-27 penetrometer used in, 432 rake used in, 432, 441 reconnaissance in, 414-15 Apollo 15 moonwalks (cont.) rock samples in, 420-22, 423, 428-32, 433, 453, 523 second session for, 423-33 space suits for, 402, 415-16, 422-23, 426-27, 433, 446 at Spur crater, 427, 429-32, 453, 523, 558 Surface Geology Team for, 420 third session for, 438-44 timeline of, 432, 440, 442 TV transmissions from, 419, 421, 443 walkback limit for, 426-27, 431, 432 Apollo 16, 452-94, 598-99 Apollo 13 vs., 459, 468 Apollo 14 vs., 460, 470 Apollo 15 vs., 464, 468, 469, 474, 475, 478, 479, 481 backup crew for, 549 Casper in, 456-62, 481-85, 491- 492, 494 checklists for, 484 “circ” burn in, 458-59 crew morale in, 473, 475-77 Data Acquisition Camera (DAC) in, 483-84 Descartes highland as destination of, 364, 452-56, 463, 464, 466, 474, 479, 480, 481, 489, 490, 491,492-93, 507 descent orbit of, 456, 459 Duke in, 456, 458, 459, 460, 462-91,492, 493-94 earthrise witnessed by, 484 England in, 468, 469, 472-74, 475, 476, 480, 481, 488, 489, 490-91 flight plan for, 484-85, 490 geological information from, 394, 454-56, 463, 464-91 gimbal motors’ problem in, 456 — 460, 462, 484 Kraft in, 461, 462, 478 lunar observations in, 482-85, 493, 494 lunar orbit of, 483, 484, 490, 493, 494 McDivitt in, 454, 459, 460-62, 641 Mattingly in, 456-62, 464, 481 — 485, 490-92, 493, 494, 641 media coverage of, 490 Muehlberger in, 464-67, 470, 471, 472, 474, 475, 479, 481, 486-90 music on, 484 onboard computers for, 459 orange juice for, 475-76 Orion in, 456, 458, 459, 460, 469, 472, 474, 475 photographic mission of, 482-84 Powered Descent in, 456, 459, 462-63, 641 Saturn V booster for, 457, 461 simulation for, 457 sleeping in, 476-77, 483 solar corona observed in, 483-84 SPS engine for, 456-60, 462, 484, 641 telemetry from, 459, 461 trajectory of, 454-55 TV transmissions from, 467, 469, 471,487-88, 490 Young in, 456, 458, 460, 462-91, 493-94 Apollo 16 moonwalks, 469-90 Apollo Lunar Surface Experiment Package (ALSEP) in, 467-69, 470, 473, 477-78, 493-94 breccias found in, 470, 472, 474, 489-90, 492 at Cayley Plains, 468, 469, 470, 472, 474, 479, 481,483, 487, 490, 491 at Cincos craters, 479-80 extensions for, 487, 489 first session for, 469-74 at Flag crater, 470—71 geologic traverses in, 469-74 g-forces in, 469 House Rock found in, 488-90 Langseth’s heat-flow experiment in, 467-69, 473, 475, 477-78 Lunar Roving Vehicle in, 467, 472, 478, 479, 486, 487, 489 magnetometer in, 472 at North Ray crater, 486, 487-90 photomaps for, 466, 469 at Plum crater, 471-72 rock samples in, 463, 464, 470, 471-72, 474, 479-81,488-91, 523 second session for, 478-81 seismometer placed in, 493-94 at South Ray crater, 474, 479-481, 487 at Stone Mountain, 466, 475, 478-81, 521 Surface Geology Team for, 465, 471, 474, 479, 481, 490 third session for, 487-92 timeline of, 464-65 volcanic evidence in, 452, 455, 465, 466, 470, 473-75, 481, 483, 487, 490-91,492-93 walkback limit for, 489 Apollo 17, 495-551, 599 abort procedure for, 498, 499 America in, 498, 534-35, 545, 547, 548 Apollo 10 vs., 501-2, 511, 512 Apollo 11 vs., 511 Apollo 15 vs., 518, 521, 523, 536 backup crew for, 549-50 Cernan in, 449, 451, 498-505, 506, 508-47, 550, 566, 567-68, 643 Cernan-Schmitt relationship in, 510-14, 536, 636-37, 647 Challenger lunar module in, 504, 508-9, 514-15, 517, 519, 535, 540, 543-45, 546 checklists for, 520-21, 533 crew assigned to, 449-51 crew morale in, 503-5, 523-24 earthrise viewed from, 545-46 Evans in, 451, 498, 499-501, 531-35, 545, 548-50, 644 as final mission, 496, 504-5, 546, 547-48, 550-51 geological information from, 401, 505-7, 509-31 Gordon and, 449, 451, 498, 515 liftoff of, 495-96 lunar observations in, 534-35, 545, 644 Lunar Orbit Insertion (LOI) of, 511 media coverage of, 532 Muehlberger in, 522-23, 525, 527, 537, 540, 541 music on, 517 nighttime launch for, 495, 498, 500-501 Nixon and, 546, 645 onboard computers of, 508 Parker in, 511, 514, 520, 522, 525, 528, 530 Powered Descent in, 508-9, 643 rendezvous in, 544-45 Saturn V booster for, 498, 499, 500-501, 504, 511 Schmitt in, 397, 401, 449, 450-451,498, 500, 503-48, 550, 643 Silver in, 522-24, 527, 528, 644 Slayton in, 449, 450, 451, 502, 533, 550 sleeping in, 517, 540 space program and, 496-98, 504- 5, 509, 530-31, 534, 543, 546, 547-48, 550-51 splashdown of, 550-51 SPS engine for, 534, 547 Taurus-Littrow as destination of, 505- 8, 512, 516, 517, 518, 522, 525, 526, 529, 530, 539, 540, 543, 547 trajectory of, 507-8 TV transmissions from, 514, 530, 537-38, 543, 547 weather observations in, 512, 535 Apollo 17 moonwalks, 509-46 Apollo Lunar Surface Experiment Package (ALSEP) in, 513, 541 breccias collected in, 538-39 core samples in, 513, 515, 516, 525, 528-29 earth as viewed in, 510, 511-13 exploration in, 519-20, 540, 546 first session for, 509-14 Apollo 17 moonwalks (cont.) geologic traverses in, 513, 514, 515, 518-30, 535-44 g-forces in, 517, 541, 567 hammer used in, 542 Langseth’s heat-flow experiment in, 513 at Lara crater, 524-25, 526 Lunar Gravimeter placed in, 513 Lunar Roving Vehicle in, 511, 513,515,525,530,537,539, 541, 542, 543 at Nansen crater, 520-21, 527, 528, 541 at North Massif, 536-41 orange soil discovered in, 527 — 530, 644 oxygen supply for, 513, 514 photographic mission of, 520, 527, 530, 539-40 at Poppy crater, 508 rock samples in, 518, 521-23, 525, 538-39, 542, 543, 544 at Sculptured Hills, 536, 541 second session for, 517-31 at Shorty crater, 525-30, 644 at South Massif, 517-22 space suits for, 542 Surface Geology Team for, 522 — 523, 525, 537, 540-41 third session for, 531, 535-44 timeline of, 504, 515, 521, 525, 541 volcanic evidence in, 506, 515, 527-30 walkback limit for, 519-20, 528, 644 Apollo 18, 283, 284, 401-2, 503, 506, 541, 578, 643 Apollo 19, 283, 506, 578, 643 Apollo 20, 232, 283, 285, 350 Armstrong, Jan, 19, 21, 161, 181, 202, 568 Armstrong, Neil, 138, 160-63, 168-69, 175, 586, 619-20 Aldrin and, 147-50, 173, 227, 569, 570, 618 Apollo 8 and, 82 in Apollo 11, 137, 138-39, 147- 150, 166, 183, 184-227, 250-251, 255, 291, 323, 390-91, 580,617-18, 623-24, 647 Apollo 17 and, 498, 504 Collins and, 168, 568 as first man on moon, 138, 147 — 150, 205-11,221,227, 569-70, 618 in Gemini project, 22, 51-52, 168-69, 170 geological training of, 179, 390 — 391 post-Apollo experience of, 565, 568-70, 582 as test pilot, 32, 138, 160-63, 165, 168, 169 Astronaut Office, 28, 30, 137, 282, 304, 449, 496 Shepard as head of, 44-45, 245, 291, 342-43, 350, 388, 389, 396-97, 611 astronauts, 4-5, 27-55, 114 aerospace design and, 16, 27, 31 biographical information on, 585-94 competition among, 29, 35, 42-49, 64 deaths of, 11-26, 51, 247-48, 443 as former test pilots, 21, 32, 34, 45, 47, 54, 115 geological training for, 389-410 income and perks of, 32, 41 lunar landing as goal of, 29-30, 54-55 marriages of, 349-50 medical evaluation of, 46-47 post-Apollo experiences of, 553 — 583 public image of, 349-50, 497-98 risks taken by, 20-23, 26, 30 rookie, 29, 32-37,41,43-49, 50, 52-53 scientists vs., 386-88, 389 selection process for, 1-7, 33-34, 35, 39-40, 50, 51, 136, 137-38, 176, 284, 342, 346-48 training of, 29-37, 45, 49 veteran, 28, 32-35, 37, 41 wives of, 64, 114, 115, 349-50, 416 see also individual astronauts Babbitt, Don, 17 Bales, Steve, 191, 194, 195, 196 Bassett, Charlie, 21, 45, 48, 51, 65 Bean, Alan, ix, 48, 243-48, 391, 586 in Apollo project, 53, 134, 245 — 248 Apollo 1 disaster and, 19 in Apollo 12, 234-41, 243-84, 371, 391, 580-81 Apollo 15 and, 448 Conrad and, 245, 246, 247, 248, 281 in Fourteen, 41, 48, 50, 51, 53, 245 as fourth man on moon, 263 post-Apollo experience of, 580— 583 Bean, Sue, 19 Bennett, Floyd, 413 Benware, Betty, 296, 310 Benware, Bob, 296 Beregovoy, Georgi, 77, 634 Bergman, Jules, 297 Berry, Chuck, 19, 98-99, 182-83, 245, 288, 307, 333, 334, 446 Bohm, David, 557 Borman, Frank, 32, 53, 60-61, 77-78, 123, 124, 125, 133, 586-87 Apollo 1 disaster and, 60, 61, 124, 611, 613 in Apollo 8, 60-62, 64, 65, 66-67, 70-71, 73, 74, 75, 77-134, 290, 389-90,614,615, 621-22 Apollo 11 and, 128, 137-38, 290 in Gemini project, 42, 49, 50, 62, 67-68, 80, 103, 128 at North American plant, 27, 60, 61, 124, 613-14 post-Apollo experience of, 291, 562-63 Borman, Susan, 122-25, 127, 133, 310, 311, 647 Bostick, Jerry, 191 Boudette, Gene, 466, 470, 493 Bourgin, Simon, 616 Brand, Vance, 317, 324, 325, 401 Bush, George, 577 Carlton, Bob, 191 Carpenter, Scott, 35 Carr, Jerry, 104, 106, 110, 115, 125, 202 Apollo 12 and, 238, 240-41, 260 Carrying the Fire (Collins), 568 Cemanr Barbara, 499 Cernan, Gene, 51, 147, 505, 587, 647 Apollo 1 disaster and, 15, 26 in Apollo 10, 136, 150-51, 152, 155-59, 165, 191, 250, 501-2, 511, 512 Apollo 14 and, 354, 368-69, 370, 377 in Apollo 17, 449, 451,498-505, 506, 508-47, 550, 566, 567-68, 643 as eleventh man on moon, 509 in Gemini project, 51, 156, 501, 502, 614 in helicopter crash, 449, 640-41 as last man on moon, 544 post-Apollo experience of, 565 — 568, 582 Schmitt and, 510-14, 536, 636-637, 647 Cernan, Tracy, 500, 501-2, 508, 540 Chaffee, Martha, 19 Chaffee, Roger, 12, 14, 17, 19, 21-26, 30, 637 Challenger disaster, 565, 569, 573 — 574 Charlesworth, Cliff, 208 Chauvin, Skip, 235, 236 Clarke, Arthur C., Ill, 291, 616, 624 Clinton, Bill, 577 Cohen, Aaron, 574 Collins, Mike, 45, 53, 65, 587 Apollo 1 disaster and, 19 Apollo 8 and, 86, 87, 89, 91, 98, 117, 126, 127, 174 in Apollo 11, 138, 148, 173, 174- Collins, Mike (cont.) 176, 177, 184-90, 192, 202, 219-27, 250, 395, 621 Armstrong and, 168, 568 in Gemini project, 48-49 post-Apollo experience of, 560 Collins, Pat, 176, 182, 202 Columbia space shuttle, 573 command module, Apollo: abort handle in, 73, 85, 87 Block I prototype for, 16, 17, 608 Block II prototype for, 16, 27, 60, 61, 608 design of, 12, 13-15, 16, 17, 23-25, 27, 60, 61, 73, 74 fireproofing of, 61, 82 hatch for, 14, 17, 24-25, 609-10 testing of, 60-62 Conrad, Charles “Pete,” ix, 3-7, 27, 29-37, 48, 54-55, 192, 587-88 Apollo 11 and, 136-37 in Apollo 12, 234-43, 246, 247, 248-84, 323, 351, 371, 391 Apollo 13 and, 296, 297, 310, 333 Bean and, 245, 246, 247, 248, 281 in Gemini project, 29, 41-43, 52, 54, 68, 242-43, 253, 279-280 geological training of, 391, 399, 405-6 Lovell and, 27, 36, 42, 65 in New 4, 31-32,35-37,41 post-Apollo experience of, 554 — 556, 580 as test pilot, 3-4, 5, 7, 27, 34, 36, 41, 55, 65 as third man on moon, 260-63 training of, 35-37, 41 Conrad, Jane, 261, 310 Cook, James, 411-12 Cooper, Gordon: in Apollo project, 347-48, 378, 449 in Gemini project, 42, 43, 279, 347 in Mercury project, 341, 347 in Original 7, 31, 44 cosmonauts, 57, 58, 77, 409-10, 443, 613, 634 Criswell, David, 576 Cronkite, Walter, 227 Cunningham, Claire, 48 Cunningham, Lo, 143 Cunningham, Walt, 47, 48, 53, 245, 610 in Apollo 7, 76, 77 in Fourteen, 47, 48, 50-51, 53, 245, 246 Dana, Bill, 163 De’Orsey, Leo, 342 Duke, Charlie, 158, 186, 191, 288, 345, 588 Apollo 11 and, 186, 191, 192, 195, 196, 197, 199, 202, 204, 220 Apollo 13 and, 308, 312 in Apollo 16, 456, 458, 459, 460, 462-91,492, 493-94 Apollo 17 and, 549-50 geological training of, 393-94 as tenth man on moon, 469 Duke, Dotty, 485-86 Duke, Mike, 392 Dwight, Ed, 611-12 Ehrlichman, John, 336 Eiermann, Horst, 497 Eisele, Donn, 76, 349 El-Baz, Farouk, 394-96, 434-35, 482-83, 484, 535, 639, 644 Elston, Don, 466, 470, 493 England, Tony, 468, 469, 472-74, 475, 476, 480, 481, 488, 489, 490-91 Engle, Joe, 370, 377, 449-50, 451, 503, 535 Evans, Jaime, 451, 499 Evans, Jan, 499-501,531-33 Evans, Jon, 534 Evans, Ron, 186, 499-500, 532-33, 551, 588 Apollo 11 and, 186, 222 Apollo 14 and, 360 in Apollo 17, 451, 498, 499-501, 531-35, 545, 548-50, 644 Eyles, Don, 358 Fallaci, Oriana, 261 Feltz, Charlie, 609 Fendell, Ed, 487, 522 Fourteen group, 41, 43-49, 50 Frank, Pete, 472 Freedom 7, 337-40, 352 Freedom space station, 577 Freeman, Ted, 21 Frick, Charles, 608-9 Frondell, Clifford, viii, 233 Frost, Robert, 582 Fullerton, Gordon, 535, 546 Gagarin, Yuri, 58, 340 Galileo Galilei, 442-43, 640 Garman, Jack, 195 Garriott, Owen, 387 Gemini project: Apollo project compared with, 16, 102, 128, 130, 148, 254 docking missions in, 43, 50, 51 medical experiments in, 46-47, 62 Mercury project compared with, 11, 22, 23, 28, 33 rendezvous flights in, 43, 49, 50, 54, 142, 168 safety of, 22-23 selection process for, 33-34, 35, 41 -43, 48-49, 50,51 spacecraft for, 16, 24, 92 space walks in, 50, 140, 144, 146-47, 206, 242-43 training for, 35-37, 41 Gemini 3, 42 Gemini 4, 42 Gemini 5, 29, 54, 253, 279-80, 347 Gemini 6, 42, 49 Gemini 7, 48-49, 50, 62, 67-68, 80, 128 Gemini 8, 22, 48, 49, 50, 51, 52, 168-69, 170, 399 Gemini 9, 48, 51, 501, 502,614 Gemini 10, 49, 51 Gemini 11, 49, 52, 54, 68, 242-43 Gemini 12, 51, 65, 103, 140, 144, 347 Gibson, Ed, 262, 268, 276, 387 Gilruth, Bob, 61, 178, 241, 285-86, 338, 406, 504, 612, 629 Glenn, John, 5, 31, 34-35,610 as first American in orbit, 5, 340, 579 Kennedy and, 610 in Mercury program, 5, 6, 163 Goddard, Robert, 79 Gold, Thomas, 180 Gooding, Jim, 645 Gordon, Dick, ix, 41, 45, 242-43, 389, 588-89 in Apollo 12, 235-43, 247, 248-249, 252, 253, 254, 256-57, 267-69, 280-84, 395 Apollo 15 and, 400-401,421 Apollo 17 and, 449, 451, 498, 515 in Gemini project, 49, 52, 68, 242-43 geological training of, 398, 408 Graveline, Duane, 387 Griffin, Gerry, 237-38, 312, 408, 428, 440, 513, 514, 542 Grissom, Betty, 19 Grissom, Gus: in Apollo 1 disaster, 12-26, 30, 53, 77, 348, 610-11 in Gemini project, 13, 22-23, 42, 339 in Mercury project, 12-13, 607-608 in Original 7, 12-14, 31, 33 Grumman Corporation, 56, 151, 155-56, 257, 304, 307, 406, 424, 504 Haise, Fred, 589, 629-30 in Apollo 13, 286, 288, 292-335, 351, 397, 629-31,646 Apollo 14 and, 358, 369-71 Apollo 16 and, 477, 549 geological training of, 393, 395, 396, 464 Haise, Mary, 333 Hamblin, Dora Jane, 633 Hammick, Jerry, 296-97 Harter, Alan, 18, 21-22 Hartzell, Lew, 78 Hasselblad cameras, 111, 119, 209-210, 225, 278, 363 Head, Jim, 450-51 Heinlein, Robert, 444 Henize, Karl, 435 Hillary, Edmund, 204, 623-24 Horz, Fred, 492 Hotz, Robert, 568-69 House, William, 343 Houston, Jean, 556 Hubble Space Telescope, 573, 577 Irwin, Jim, 249, 403-4, 416-17, 459, 589 Apollo 12 and, 249, 264 in Apollo 15, 400, 403-4, 405, 408, 409-51,497, 549, 638 as eighth man on moon, 423 geological training of, 398, 404-8 heart problem of, 446-47, 475 post-Apollo experience of, 557 — 559 Irwin, Mary, 403, 416 Jackson, Dale, 421-22, 540, 637 Johnson, Lyndon B., 49, 58, 78, 338 Kapryan, Walter, 235 Kelly, Fred, 18, 21-22 Kennedy, John F., vii, 1-2, 11, 26, 27, 31, 39, 43, 55, 58, 134, 135, 219, 231-32, 338, 340-41, 383, 384, 546, 576, 578, 610 Kennedy, Robert, 56 Kepler, Johannes, 165 Kerwin, Joe, 320-21, 332, 334, 387, 555 King, Elbert, 621 King, Jack, 498 King, Martin Luther, Jr., 56 Kissinger, Henry, 548 Komarov, Vladimir, 57, 77 Koppel, Ted, 565 Korean War, 12, 160, 168 Kraft, Christopher Columbus: Apollo 7 and, 76-77 Apollo 8 and, 59, 67-71, 77, 90, 103, 104, 105, 107, 124, 125, 126 Apollo 9 and, 140 Apollo 10 and, 151 Apollo 12 and, 241, 255, 257 Apollo 14 and, 345-46 Apollo 15 and, 442 Apollo 16 and, 461, 462, 478 Apollo 17 and, 504, 506, 550 as director of Flight Crew Operations, 59, 67-68, 170, 171, 406, 609, 630, 642 Kranz, Eugene F., viii, 170, 191 Apollo 11 and, 170-73, 176, 190-92, 194, 195 Apollo 13 and, 295, 299, 321, 325, 335 Gemini 8 aborted by, 52 Langseth, Mark, 467-69, 473, 475, 477-78, 513 Lawrence, Robert, 612 Leonov, Aleksey, 613 Liberty Bell 7, 13 Life, 31, 32, 33, 181, 342, 345, 352, 499, 610, 633 Lindbergh, Charles, 4, 31, 79-80, 231, 614 Logsden, John, 336, 625 Lorenzo, Frank, 562 Lousma, Jack, 293, 297, 299, 300, 302, 318, 319, 322-23, 327 Lovelace, Randy, 386 Lovell, Barbara, 114 Lovell, Jay, 115 Lovell, Jeffrey, 114, 310, 334 Lovell, Jim, 27,31,36,51,63-65, 79, 89, 503, 590 in Apollo 8, 60, 64, 65-66, 71, 73, 77, 78-134, 313, 563, 583, 616-17 in Apollo 13, 286-87, 289, 290-335, 348, 646 Apollo 15 and, 420 Apollo 17 and, 498, 528, 541 Conrad and, 27, 36, 42, 65 in Gemini project, 42, 48-49, 50, 51, 62, 65, 80, 102, 144 geological training of, 393-94, 395, 396, 464, 467 Lovell, Marilyn, 63-64, 114-16, 127, 286-87, 290, 296-97, 310-11,333-34, 335, 630 Lovell, Susan, 310 Low, George, 57, 58, 62, 82, 349, 506, 551 Luna 16 lunar probe, 409 Lunakhod 1 robot, 409 Lunar Landing Training vehicle (LLTV), 177-79,259, 463, 620 lunar module (LM): abort procedure for, 166-67, 172, 173, 192, 195, 196, 198, 199, 200 “ahead of the airplane” concept in, 165, 186 alarms of, 194-95, 196 ascent engine of, 56, 155, 159, 167, 202, 222-23, 279-80 ascent stage of, 167, 222-24, 305 “dead man’s curve” concept in, 167, 172, 199 delta-H information for, 194, 195 descent engine of, 158, 159, 164, 167, 193, 195, 200, 257, 302-3, 309-10, 314, 322, 323, 328, 358, 388, 406 design of, 34, 56-57, 151, 155-156,257, 304, 383, 388, 402, 406 footpads of, 168, 359, 368 fuel supply for, 179, 192, 198, 199, 200, 259, 353 hammocks in, 270, 271, 415 hatch of, 148-49, 207, 257, 260, 414-15 instrument panel for, 34, 163 — 164, 260 landing of, 113, 164-69, 209, 250-51 Landing Point Designator (LPD) for, 166, 196, 258 landmarks for, 113, 165-66, 201-2, 254-55, 258-59 life support systems of, 304-5, 312, 318, 319-20, 326 navigation system of, 166, 167, 196 nicknames for, 155, 257, 619 onboard computer of, 164, 166, 168, 191, 193-95, 196, 197, 201,251,256, 357-58,412, 413 oxygen supply of, 180, 207 pinpoint landing of, 250-51, 255, 257, 259-60, 282,351,367 pitchover maneuver for, 165, 193, 195-96,254, 258, 259, 412, 508 Powered Descent of, 164-69, 172, 176, 185, 190, 191-200, 202-3,219-20, 357-59, 406-407,412-14, 456, 508-9 radar of, 358-59 shutdown of, 199-200 simulator for, 136-37, 163-74, 176-77, 193-97, 249-50,258, 620 surface disturbed by, 198-99, 200-201,259-60 telescope of, 312-13 throttle of, 164, 165, 167 thrusters of, 159, 173, 189, 193, 197, 199 toggle switch in, 167, 197, 199 weight-saving program for, 151, 155-56 windows of, 195, 202, 207, 210, 257 Lunar Orbiter probes, 70, 179, 274, 394, 412, 435, 454, 482 Lunar Receiving Laboratory (LRL), 180-81,227,233, 365-67, 374, 430, 440, 447, 474, 492 Lunney, Glynn, 76, 299-300, 335 McAuliffe, Christa, 573 McCandless, Bruce, 150, 208, 210, 216, 217 McDivitt, Jim, 29, 33, 61, 62 in Apollo 9, 136, 137, 143, 144, 248-49, 291, 399, 461 Apollo 11 and, 137 Apollo 12 and, 138, 460 Apollo 15 and, 406, 407, 440, 442 McDivitt, Jim (cont.)
This Will Make You Smarter: 150 New Scientific Concepts to Improve Your Thinking by John Brockman
23andMe, Albert Einstein, Alfred Russel Wallace, banking crisis, Barry Marshall: ulcers, Benoit Mandelbrot, Berlin Wall, biofilm, Black Swan, butterfly effect, Cass Sunstein, cloud computing, congestion charging, correlation does not imply causation, Daniel Kahneman / Amos Tversky, dark matter, data acquisition, David Brooks, delayed gratification, Emanuel Derman, epigenetics, Exxon Valdez, Flash crash, Flynn Effect, hive mind, impulse control, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Jaron Lanier, Johannes Kepler, John von Neumann, Kevin Kelly, lifelogging, mandelbrot fractal, market design, Mars Rover, Marshall McLuhan, microbiome, Murray Gell-Mann, Nicholas Carr, open economy, Pierre-Simon Laplace, place-making, placebo effect, pre–internet, QWERTY keyboard, random walk, randomized controlled trial, rent control, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Satyajit Das, Schrödinger's Cat, security theater, selection bias, Silicon Valley, Stanford marshmallow experiment, stem cell, Steve Jobs, Steven Pinker, Stewart Brand, the scientific method, Thorstein Veblen, Turing complete, Turing machine, twin studies, Vilfredo Pareto, Walter Mischel, Whole Earth Catalog, WikiLeaks, zero-sum game
Online companies, such as Amazon and Google, don’t anguish over how to design their Web sites. Instead, they conduct controlled experiments by showing different versions to different groups of users until they have iterated to an optimal solution. (And with the amount of traffic those sites receive, individual tests can be completed in seconds.) They are helped, of course, by the fact that the Web is particularly conducive to rapid data acquisition and product iteration. But they are helped even more by the fact that their leaders often have backgrounds in engineering or science and therefore adopt a scientific—which is to say, experimental—mind-set. Government policies—from teaching methods in schools to prison sentencing to taxation —would also benefit from more use of controlled experiments. This is where many people start to get squeamish.
Pinpoint: How GPS Is Changing Our World by Greg Milner
Ayatollah Khomeini, British Empire, creative destruction, data acquisition, Dava Sobel, different worldview, digital map, Edmond Halley, Eratosthenes, experimental subject, Flash crash, friendly fire, Hedy Lamarr / George Antheil, Internet of things, Isaac Newton, John Harrison: Longitude, Kevin Kelly, land tenure, lone genius, low earth orbit, Mars Rover, Mercator projection, place-making, polynesian navigation, precision agriculture, race to the bottom, Silicon Valley, Silicon Valley startup, skunkworks, smart grid, the map is not the territory
., 203 Santa Ana winds, 226–27 Santa Cruz District Office, 13 Santa Cruz Islands, 11 Santa Cruz Mountains, 208 Sardinia, 159 satellite laser ranging, 209 satellite navigation systems, xvii, 37–45, 53, 76 see also Global Positioning System (GPS) satellites, 25–45, 47, 108 Air Force, xiv artificial, 44, 252–53 geostationary orbits of, 141–42 GRACE, 231–32 launching of, 29–36, 43, 61, 62–63, 88, 95 orbit and velocity of, xviii, 30, 39, 43–45, 63, 216 Soviet and Russian, 30–37, 44, 75, 101–2 spy, 39 television, 35 testing of, 58–59 tracking of, 29–32, 36–45, 59 U.S., 42–45 WAAS, 141–42, 171 see also Global Positioning System (GPS), satellites of; GPS signals Saturn, 259 Saudi Arabia, 62–64, 69 SCADA (Supervisory Control and Data Acquisition), 159 Scalia, Antonin, 189–90 Schofield, Andrew, 168 Schriever Air Force Base, xiii–xv, xx, 75, 261 Master Control Station at, xix, xx, 62–63, 256 Schultz, Kenneth, 53 Schwartzkopf, H. Norman, Jr., 65–66 Schweinfurt, 49 Schwitzgebel, Ralph, 172–77, 194–95, 198–200 Schwitzgebel, Robert, 172, 174–77, 194–95, 198–200 science, 207 cognitive, 118, 131 computer, 36, 84, 124, 239 neuro-, 129 social, 118 Science Committee on Psychological Experimentation (SCOPE), 173–74 Scotland, 195, 205 Scott, Logan, 149–50, 167 Scripps Institution of Oceanography, 218, 220, 224 seafloor spreading, 207 Sea of Japan, 81 Seattle, Wash., 225 Seaworth, Troy, 101–4, 105 Seaworth Farms, 101–4, 105 seismic monitoring equipment, 217–20, 222–24 GPS-enabled, 203–4 semiconductors, 78 Senate, U.S., 60 Preparedness Committee of, 35 sensors, 122 Serbia, 71 sex offenders, 196 sextants, 5 Shacklett, Mary, 191 Shapiro, Irwin, 209 Sharp, Andrew, 12–13 Shaw, John, xiii sidereal compass, 6, 14 Sierra Nevada Mountains, 206 Silicon Valley, 77, 79, 96 Simpson, John, 28 621B program, 44, 53, 57 Skinner, B.
Sandworm: A New Era of Cyberwar and the Hunt for the Kremlin's Most Dangerous Hackers by Andy Greenberg
air freight, Airbnb, Bernie Sanders, bitcoin, blockchain, call centre, clean water, data acquisition, Donald Trump, Edward Snowden, global supply chain, hive mind, Julian Assange, Just-in-time delivery, Kickstarter, Mikhail Gorbachev, open borders, pirate software, pre–internet, profit motive, ransomware, RFID, speech recognition, Steven Levy, Stuxnet, undersea cable, uranium enrichment, Valery Gerasimov, WikiLeaks, zero day
And while it would have looked entirely unremarkable to the average person in the security industry, it immediately snapped Wilhoit’s mind to attention. Wilhoit had an unusual background for a security researcher. Just two years earlier, he’d left a job in St. Louis as manager of IT security for Peabody Energy, America’s largest coal company. So he knew his way around so-called industrial control systems, or ICS—also known in some cases as supervisory control and data acquisition, or SCADA, systems. That software doesn’t just push bits around, but instead sends commands to and takes in feedback from industrial equipment, a point where the digital and physical worlds meet. ICS software is used for everything from the ventilators that circulate air in Peabody’s mines to the massive washing basins that scrub its coal, to the generators that burn coal in power plants to the circuit breakers at the substations that feed electrical power to consumers.
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
The rest of the country would watch as the first responders scrambled to react and assess damage, but a subsequent barrage of cyber attacks could cripple the police, the fire department and emergency-information systems in those cities. If that’s not terrifying enough, while urban emergency efforts slow to a crawl amid massive physical destruction and loss of life, a sophisticated computer virus could attack the industrial control systems around the country that maintain critical infrastructure like water, power and oil and gas pipelines. Commandeering these systems, called supervisory control and data acquisition (SCADA) systems, would enable terrorists to do all manner of things: shut down power grids, reverse waste-water treatment plants, disable the heat-monitoring systems at nuclear power plants. (When the Stuxnet worm attacked Iranian nuclear facilities in 2012, it operated by compromising the industrial control processes in nuclear centrifuge operations.) Rest assured that it would be incredibly, almost unthinkably difficult to pull off this level of attack—commandeering one SCADA system alone would require detailed knowledge of the internal architecture, months of coding and precision timing.
Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots by John Markoff
"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game
Headless and motionless, the robots were undeniably spooky. Without skin, they were cybernetic skeleton-men assembled from an admixture of steel, titanium, and aluminum. Each was illuminated by an eerie blue LED glow that revealed a computer embedded in the chest that monitored its motor control. Each of the presently removed “heads” housed another computer that monitored the body’s sensor control and data acquisition. When they were fully equipped, the robots stood six feet high and weighed 330 pounds. When moving, they were not as lithe in real life as they were in videos, but they had an undeniable presence. It was the week before DARPA would announce that it had contracted Boston Dynamics, the company that Raibert had founded two decades earlier, to build “Atlas” robots as the common platform for a new category of Grand Challenge competitions.
Building Habitats on the Moon: Engineering Approaches to Lunar Settlements by Haym Benaroya
3D printing, biofilm, Black Swan, Brownian motion, Buckminster Fuller, carbon-based life, centre right, clean water, Colonization of Mars, Computer Numeric Control, conceptual framework, data acquisition, Elon Musk, fault tolerance, gravity well, inventory management, Johannes Kepler, low earth orbit, orbital mechanics / astrodynamics, performance metric, RAND corporation, risk tolerance, Ronald Reagan, stochastic process, telepresence, telerobotics, the scientific method, urban planning, X Prize, zero-sum game
Artists can create powerful sceneries – not necessarily lunar landscapes – that provide a supportive ambiance to the inhabitants. Figure 4.1.The deployment of the United States flag on the surface of the Moon is captured on film during the first lunar landing mission, Apollo 11. Here, astronaut Neil A. Armstrong, commander, stands on the left at the flag’s staff. Astronaut Edwin E. Aldrin, Jr., lunar module pilot, is also pictured. The picture was taken from film exposed by the 16 mm Data Acquisition Camera that was mounted in the Lunar Module . While astronauts Armstrong and Aldrin descended in the Lunar Module “Eagle” to explore the Sea of Tranquility region of the Moon, astronaut Michael Collins, command module pilot, remained with the Command and Service Module “Columbia” in lunar orbit. (S69-40308, July 20, 1969. Courtesy NASA) Space architecture is not only for architects. Engineers also practice space architecture when they integrate human factors into structural designs.
Autonomous Driving: How the Driverless Revolution Will Change the World by Andreas Herrmann, Walter Brenner, Rupert Stadler
Airbnb, Airbus A320, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, cleantech, computer vision, conceptual framework, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, demand response, digital map, disruptive innovation, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, Lyft, manufacturing employment, market fundamentalism, Mars Rover, Masdar, megacity, Pearl River Delta, peer-to-peer rental, precision agriculture, QWERTY keyboard, RAND corporation, ride hailing / ride sharing, self-driving car, sensor fusion, sharing economy, Silicon Valley, smart cities, smart grid, smart meter, Steve Jobs, Tesla Model S, Tim Cook: Apple, uber lyft, upwardly mobile, urban planning, Zipcar
One of the most challenging and interesting applications for deep neural networks in the ﬁeld of self-driving cars is a complete end-to-end solution, which describes a neural-network architecture spreading all the way from 116 Autonomous Driving sensory detection to plan of action and covers the full spectrum of selfdriving. This solution could be very beneﬁcial due to the simplistic nature of data acquisition for training. Ideally, this network could learn to drive a car just by monitoring multiple human drivers and adopting the rules of driving; almost like a student driver learning the correct behaviour while observing an experienced driver, which means that no manual annotation of data like object locations or intent would be necessary. Data collection and especially data annotation is a very challenging and costly task for developing production grade deep neural networks for driver assistance and piloted driving systems.
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
Donaldson: So that experience helped shape your interest in the influencing role and communication role of the CTO, and you saw how you could apply those skills as you grew your career. Where did you go to school? Kaplow: For my undergraduate, I went to NYU [New York University] and I majored in physics and in history. I realized that physics, although I enjoyed it quite a bit, was not necessarily the long-term path, but it did give me the opportunity to work in the High Energy physics department. I supported some experiments, mostly on the technology side for data acquisition and analysis. I realized that physics wasn't really going to be my true love, but I enjoyed it immensely and had some great professors. I was on campus there for four years and then I moved to the computer science department at the Courant Institute of Mathematical Sciences, which was sort of a natural progression from the kind of computer work I was doing in the physics department. It's probably a little bit better known for math, but it is a very, very good computer science department and had some great teachers.
The Patient Will See You Now: The Future of Medicine Is in Your Hands by Eric Topol
23andMe, 3D printing, Affordable Care Act / Obamacare, Anne Wojcicki, Atul Gawande, augmented reality, bioinformatics, call centre, Clayton Christensen, clean water, cloud computing, commoditize, computer vision, conceptual framework, connected car, correlation does not imply causation, creative destruction, crowdsourcing, dark matter, data acquisition, disintermediation, disruptive innovation, don't be evil, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, information asymmetry, interchangeable parts, Internet of things, Isaac Newton, job automation, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, Lyft, Mark Zuckerberg, Marshall McLuhan, meta analysis, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, RAND corporation, randomized controlled trial, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Snapchat, social graph, speech recognition, stealth mode startup, Steve Jobs, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize
Only now that we can capture such panoromic data on each individual, and in populations of people, along with the ability to manage and process such enormous sets of data, are we in the enviable position of predicting illness—and maybe, just maybe, once we get good at it, even preventing diseases in some individuals from ever happening. FIGURE 13.4: Really big data from the individual and comparing that individual’s data with all of the Earth’s population (IoMT = Internet of Medical Things). The two levels of data acquisition, comparison, and machine learning—individual and population—are critical, across all of the components of one’s GIS. Predicting Disease: Who, When, How, Why, and What? First, let’s make sure we differentiate prediction from diagnosis. Online symptom checkers66 are getting increasing electronic traffic and attention on the Internet to help people “self” (computer-assisted) diagnose, but they don’t predict an illness.
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
Stuxnet, the virus that attacked Iran’s nuclear weapons plant at Natanz in 2010, was just the beginning. Widely believed to the product of a joint Israeli-American operation, Stuxnet was a clever piece of malicious software, or malware, that infected computers involved with monitoring and controlling industrial machinery and infrastructure. Known by the acronym SCADA (supervisory control and data acquisition) these computer systems are industrial-grade versions of the Arduinos discussed in chapter 4. At Natanz some six thousand centrifuges were being used to enrich uranium to bomb-grade purity. Security experts believe Stuxnet, carried in on a USB thumb drive, infected and took over the SCADA systems controlling the plant’s equipment. Working stealthily to knock the centrifuges off balance even as it reported to operators that all was normal, Stuxnet is believed to have put over a thousand machines out of commission, significantly slowing the refinement process, and the Iranian weapons program.40 The wide spread of Stuxnet was shocking.
The Transparent Society: Will Technology Force Us to Choose Between Privacy and Freedom? by David Brin
affirmative action, airport security, Ayatollah Khomeini, clean water, cognitive dissonance, corporate governance, data acquisition, death of newspapers, Extropian, Howard Rheingold, illegal immigration, informal economy, information asymmetry, Iridium satellite, Jaron Lanier, John Markoff, John von Neumann, Kevin Kelly, Marshall McLuhan, means of production, mutually assured destruction, offshore financial centre, open economy, packet switching, pattern recognition, pirate software, placebo effect, plutocrats, Plutocrats, prediction markets, Ralph Nader, RAND corporation, Robert Bork, Saturday Night Live, Search for Extraterrestrial Intelligence, Steve Jobs, Steven Levy, Stewart Brand, telepresence, trade route, Vannevar Bush, Vernor Vinge, Whole Earth Catalog, Whole Earth Review, Yogi Berra, zero-sum game, Zimmermann PGP
Alas, weʼll see in chapter 5 that this type of “solution” conflicts fundamentally with human nature. We are, at our core, information pack rats and inveterate correlators. We hunger for news, facts, and rumors—especially when they are forbidden! In this attribute, the rich and powerful, and major corporations, are no different from the rest of us. The predictable consequence? If one kind of data acquisition is made illegal, you can be certain that someone will be doing it anyway on the sly, and possibly turning its dissemination into yet another highly profitable criminal enterprise, one that must be policed by yet another bureaucracy. Here is a little philosophical exercise that can sometimes be instructive. When dealing with so-called obvious solutions to fundamental issues, always try to imagine what might happen if we extrapolate the recommended trend to some extreme degree.
Countdown to Zero Day: Stuxnet and the Launch of the World's First Digital Weapon by Kim Zetter
Ayatollah Khomeini, Brian Krebs, crowdsourcing, data acquisition, Doomsday Clock, drone strike, Edward Snowden, facts on the ground, Firefox, friendly fire, Google Earth, information retrieval, John Markoff, Julian Assange, Kickstarter, Loma Prieta earthquake, Maui Hawaii, MITM: man-in-the-middle, pre–internet, RAND corporation, Silicon Valley, skunkworks, smart grid, smart meter, South China Sea, Stuxnet, undersea cable, uranium enrichment, Vladimir Vetrov: Farewell Dossier, WikiLeaks, Y2K, zero day
WHEN THE SYMANTEC researchers discovered in August 2010 that Stuxnet was designed for physical sabotage of Siemens PLCs, they weren’t the only ones who had no idea what a PLC was. Few people in the world had ever heard of the devices—this, despite the fact that PLCs are the components that regulate some of the most critical facilities and processes in the world. PLCs are used with a variety of automated control systems that include the better-known SCADA system (Supervisory Control and Data Acquisition) as well as distributed control systems and others that keep the generators, turbines, and boilers at power plants running smoothly.2 The systems also control the pumps that transmit raw sewage to treatment plants and prevent water reservoirs from overflowing, and they open and close the valves in gas pipelines to prevent pressure buildups that can cause deadly ruptures and explosions, such as the one that killed eight people and destroyed thirty-eight homes in San Bruno, California, in 2010.
Mastering Blockchain, Second Edition by Imran Bashir
3D printing, altcoin, augmented reality, autonomous vehicles, bitcoin, blockchain, business process, carbon footprint, centralized clearinghouse, cloud computing, connected car, cryptocurrency, data acquisition, Debian, disintermediation, disruptive innovation, distributed ledger, domain-specific language, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, Firefox, full stack developer, general-purpose programming language, gravity well, interest rate swap, Internet of things, litecoin, loose coupling, MITM: man-in-the-middle, MVC pattern, Network effects, new economy, node package manager, Oculus Rift, peer-to-peer, platform as a service, prediction markets, QR code, RAND corporation, Real Time Gross Settlement, reversible computing, RFC: Request For Comment, RFID, ride hailing / ride sharing, Satoshi Nakamoto, single page application, smart cities, smart contracts, smart grid, smart meter, supply-chain management, transaction costs, Turing complete, Turing machine, web application, x509 certificate
AI is a field of computer science that endeavors to build intelligent agents that can make rational decisions based on the scenarios and environment that they observe around them. Machine learning plays a vital role in AI by making use of raw data as a learning resource. A key requirement in AI-based systems is the availability of authentic data that can be used for machine learning and model building. The explosion of data coming out IoT devices, smartphone's, and other data acquisition means that AI and machine learning is becoming more and more powerful. There is, however, a requirement of authenticity of data. Once consumers, producers, and other entities are on a blockchain, the data that is generated as a result of interaction between these entities can be readily used as an input to machine learning engines with a guarantee of authenticity. This is where AI converges with blockchains.
To Save Everything, Click Here: The Folly of Technological Solutionism by Evgeny Morozov
3D printing, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, augmented reality, Automated Insights, Berlin Wall, big data - Walmart - Pop Tarts, Buckminster Fuller, call centre, carbon footprint, Cass Sunstein, choice architecture, citizen journalism, cloud computing, cognitive bias, creative destruction, crowdsourcing, data acquisition, Dava Sobel, disintermediation, East Village, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, Firefox, Francis Fukuyama: the end of history, frictionless, future of journalism, game design, Gary Taubes, Google Glasses, illegal immigration, income inequality, invention of the printing press, Jane Jacobs, Jean Tirole, Jeff Bezos, jimmy wales, Julian Assange, Kevin Kelly, Kickstarter, license plate recognition, lifelogging, lone genius, Louis Pasteur, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, moral panic, Narrative Science, Nelson Mandela, Nicholas Carr, packet switching, PageRank, Parag Khanna, Paul Graham, peer-to-peer, Peter Singer: altruism, Peter Thiel, pets.com, placebo effect, pre–internet, Ray Kurzweil, recommendation engine, Richard Thaler, Ronald Coase, Rosa Parks, self-driving car, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, Slavoj Žižek, smart meter, social graph, social web, stakhanovite, Steve Jobs, Steven Levy, Stuxnet, technoutopianism, the built environment, The Chicago School, The Death and Life of Great American Cities, the medium is the message, The Nature of the Firm, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, transaction costs, urban decay, urban planning, urban sprawl, Vannevar Bush, WikiLeaks
Presumably, even if they had infinite shelf space, museums would not abandon the idea of curation. The latter is a deliberate commitment, not a technological constraint stemming from a lack of resources. But Gordon Bell’s one-man museum, while nominally promising to turn its heroes into curators, rejects the very selectionist spirit of curatorial work; like the self-trackers and data miners we met in the previous chapter, Gordon Bell opts for preemptive data acquisition, hoping that one day it will provide him not just with the right answers but also with the right questions. Or perhaps it will just tell him where his car keys are—and who among us relishes the time spent crawling under the table searching for them? But wearing a gadget like a SenseCam around your neck may also help you find the greatest keys of all: those to your inner self. Thus, in Your Life, Uploaded, his book-length manifesto on the benefits of lifelogging, Bell assures us that it will yield “enhanced self-insight, the ability to relive one’s own life story in Proustian detail, the freedom to memorize less and think creatively more, and even a measure of earthly immortality by being cyberized.”
Analysis of Financial Time Series by Ruey S. Tsay
Asian financial crisis, asset allocation, Bayesian statistics, Black-Scholes formula, Brownian motion, business cycle, capital asset pricing model, compound rate of return, correlation coefficient, data acquisition, discrete time, frictionless, frictionless market, implied volatility, index arbitrage, Long Term Capital Management, market microstructure, martingale, p-value, pattern recognition, random walk, risk tolerance, short selling, statistical model, stochastic process, stochastic volatility, telemarketer, transaction costs, value at risk, volatility smile, Wiener process, yield curve
M. (1994), “Threshold heteroscedastic models,” Journal of Economic Dynamics and Control, 18, 931–955. Analysis of Financial Time Series. Ruey S. Tsay Copyright 2002 John Wiley & Sons, Inc. ISBN: 0-471-41544-8 CHAPTER 5 High-Frequency Data Analysis and Market Microstructure High-frequency data are observations taken at fine time intervals. In finance, they often mean observations taken daily or at a finer time scale. These data have become available primarily due to advances in data acquisition and processing techniques, and they have attracted much attention because they are important in empirical study of market microstructure. The ultimate high-frequency data in finance are the transaction-by-transaction or trade-by-trade data in security markets. Here time is often measured in seconds. The Trades and Quotes (TAQ) database of the New York Stock Exchange (NYSE) contains all equity transactions reported on the Consolidated Tape from 1992 to present, which includes transactions on NYSE, AMEX, NASDAQ, and the regional exchanges.
Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, longitudinal study, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game
In general, the worse our scanning equipment and the feebler our computers, the less we could rely on simulating low-level chemical and electrophysiological brain processes, and the more theoretical understanding would be needed of the computational architecture that we are seeking to emulate in order to create more abstract representations of the relevant functionalities.25 Conversely, with sufficiently advanced scanning technology and abundant computing power, it might be possible to brute-force an emulation even with a fairly limited understanding of the brain. In the unrealistic limiting case, we could imagine emulating a brain at the level of its elementary particles using the quantum mechanical Schrödinger equation. Then one could rely entirely on existing knowledge of physics and not at all on any biological model. This extreme case, however, would place utterly impracticable demands on computational power and data acquisition. A far more plausible level of emulation would be one that incorporates individual neurons and their connectivity matrix, along with some of the structure of their dendritic trees and maybe some state variables of individual synapses. Neurotransmitter molecules would not be simulated individually, but their fluctuating concentrations would be modeled in a coarse-grained manner. To assess the feasibility of whole brain emulation, one must understand the criterion for success.
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
The Third Pillar: How Markets and the State Leave the Community Behind by Raghuram Rajan
activist fund / activist shareholder / activist investor, affirmative action, Affordable Care Act / Obamacare, airline deregulation, Albert Einstein, Andrei Shleifer, banking crisis, barriers to entry, basic income, battle of ideas, Bernie Sanders, blockchain, borderless world, Bretton Woods, British Empire, Build a better mousetrap, business cycle, business process, capital controls, Capital in the Twenty-First Century by Thomas Piketty, central bank independence, computer vision, conceptual framework, corporate governance, corporate raider, corporate social responsibility, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, data acquisition, David Brooks, Deng Xiaoping, desegregation, deskilling, disruptive innovation, Donald Trump, Edward Glaeser, facts on the ground, financial innovation, financial repression, full employment, future of work, global supply chain, high net worth, housing crisis, illegal immigration, income inequality, industrial cluster, intangible asset, invention of the steam engine, invisible hand, Jaron Lanier, job automation, John Maynard Keynes: technological unemployment, joint-stock company, Joseph Schumpeter, labor-force participation, low skilled workers, manufacturing employment, market fundamentalism, Martin Wolf, means of production, moral hazard, Network effects, new economy, Nicholas Carr, obamacare, Productivity paradox, profit maximization, race to the bottom, Richard Thaler, Robert Bork, Robert Gordon, Ronald Reagan, Sam Peltzman, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, South China Sea, South Sea Bubble, Stanford marshmallow experiment, Steve Jobs, superstar cities, The Future of Employment, The Wealth of Nations by Adam Smith, trade liberalization, trade route, transaction costs, transfer pricing, Travis Kalanick, Tyler Cowen: Great Stagnation, universal basic income, Upton Sinclair, Walter Mischel, War on Poverty, women in the workforce, working-age population, World Values Survey, Yom Kippur War, zero-sum game
Once the individual controls her data, she will have the option to sell portions of it to firms, or enter into longer-term arrangements where firms would provide her services in return for the use of her data. Some of what is implicit today would become explicit, the difference is it would be controlled by the user. Indeed, new technologies like blockchains will help decentralize this process, and bargaining bots can help routinize data acquisition for a fee when corporations need vast amounts of data to train their artificial-intelligence applications.15 Another important source of power that e-platforms or social media have is their ownership of the network. If an individual leaves a network, her access to it, and to the many relationships she has built on it, are cut off. This causes many to stay on even if they dislike the network and its services.
Digital Accounting: The Effects of the Internet and Erp on Accounting by Ashutosh Deshmukh
accounting loophole / creative accounting, AltaVista, business continuity plan, business intelligence, business process, call centre, computer age, conceptual framework, corporate governance, data acquisition, dumpster diving, fixed income, hypertext link, interest rate swap, inventory management, iterative process, late fees, money market fund, new economy, New Journalism, optical character recognition, packet switching, performance metric, profit maximization, semantic web, shareholder value, six sigma, statistical model, supply-chain management, supply-chain management software, telemarketer, transaction costs, value at risk, web application, Y2K
Finally, maintenance of existing products, after-sales service and management of long-term assets can also be performed. Detailed functionalities in these areas are briefly discussed below. • Life cycle data management: Life Cycle Data Management (LCDM) involves managing data for products and assets from design to retirement phases. LCDM integrates with a wide range of CAD tools to support online and offline design of products. LCDM also supports Supervisory Control and Data Acquisition (SCADA) tools, Geographic Information Systems (GIS) and Microsoft Office applications. LCDM has an XML interface to quickly connect with third-party tools. LCDM offers handling of different product features and requirement documents, bill-of-materials, routing of materials, CAD models and other technical documentation over the Internet and intranet. These functionalities can be used for online design of products, managing changes to existing products, releasing product changes to engineering and production lines, and supporting decisions concerning discontinuation of products
Area 51: An Uncensored History of America's Top Secret Military Base by Annie Jacobsen
Albert Einstein, anti-communist, Berlin Wall, cuban missile crisis, data acquisition, drone strike, Maui Hawaii, mutually assured destruction, operation paperclip, orbital mechanics / astrodynamics, Project Plowshare, RAND corporation, Ronald Reagan, South China Sea, uranium enrichment, urban sprawl, zero day
Into this category fit the covert operations and dirty tricks of Dick Bissell’s Directorate of Plans.” 12. called Teak and Orange: Film footage viewed at the Atomic Testing Museum, Las Vegas. 13. which is exactly where the ozone layer lies: Hoerlin, “United States High-Altitude Test,” 43. 14. “The impetus for these tests”: Ibid., 47. 15. his rationale: Ground stations were supposed to measure acoustic waves that would happen as a result of the blast but Teak detonated seven miles laterally off course to the south and the communication systems were knocked out. Orange detonated four miles higher than it was supposed to and “the deviations affected data acquisitions.” 16. The animals’ heads had been locked in gadgets: Oral history interview with Air Force colonel John Pickering, 52. Film footage viewed at the Atomic Testing Museum, Las Vegas. 17. “Teak and Orange events would ‘burn a hole’ into the natural ozone layer”: Hoerlin, “United States High-Altitude Test,” 43. 18. Von Braun can be seen examining the Redstone rocket: Teak shot film footage viewed at the Atomic Testing Museum library, Las Vegas. 19. left the island before the second test: Interview with Al O’Donnell; Neufeld, Von Braun, 332. 20. to dash up to Hitler’s lair: Neufeld, Von Braun, 127. 21. project called Operation Argus commenced: Final Review of Argus Fact Sheet, 16 Apr. 82.
In the Age of the Smart Machine by Shoshana Zuboff
affirmative action, American ideology, blue-collar work, collective bargaining, computer age, Computer Numeric Control, conceptual framework, data acquisition, demand response, deskilling, factory automation, Ford paid five dollars a day, fudge factor, future of work, industrial robot, information retrieval, interchangeable parts, job automation, lateral thinking, linked data, Marshall McLuhan, means of production, old-boy network, optical character recognition, Panopticon Jeremy Bentham, post-industrial society, RAND corporation, Shoshana Zuboff, social web, The Wealth of Nations by Adam Smith, Thorstein Veblen, union organizing, zero-sum game
1 5 Technology radically alters the context of what is possible. How different it is for a high-level executive to sit in his or her office and use a desktop terminal to view summaries of operating data or to review exceptions to optimal ranges of performance. It is still reasonable to argue that such an executive should not spend time responding to data that others are better positioned to manage. Once the awkwardness of the data acquisition process is eliminated, however, the immediate face validity of such arguments is diminished. 16 As the material circumstances of the enterprise evolve, so do the de- mands on the manager. In a highly informated organization such as Cedar Bluff, the autonomy of the data base and the extent of both vertical and horizontal channels of access to the data added considerable weight to the already severe pressures that middle managers felt.
House of Suns by Alastair Reynolds
We have long been wary of data contamination.’ While I was distracted, three of the arms had reached out and made contact with my suit. I was being hauled in slyly, as if they did not want me to realise what was happening. I flinched and jerked myself free. ‘May I ask some questions, curator?’ ‘There is never any harm in asking.’ But there was, I thought. There was potential harm in the most innocent act of data acquisition, as even the curator had acknowledged. ‘There’s quite a lot about the Vigilance we don’t know.’ ‘Many of your kind have been here already. Did they not satisfy your curiosity?’ ‘There are still some pieces missing from the picture.’ ‘And you think you will make a difference?’ ‘It’s my duty to try. My duty to the Line and the Commonality.’ ‘Then far be it from me to stand in the way of your enquiries, shatterling.’
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
For a sneak peek of what this dystopian world without computers and electricity looks like, one need only turn on the television for a taste of techno-Armageddon-cum-zombie apocalypse from shows such as The Walking Dead or from films like Planet of the Apes and Live Free or Die Hard. Hollywood machinations aside, our computer-based critical information infrastructures are increasingly under attack and deeply vulnerable to systemic failure—the impact from which could be truly catastrophic. Much of the world’s critical infrastructures utilize supervisory control and data acquisition (SCADA) systems to function. SCADA systems “automatically monitor and adjust switching, manufacturing, and other process control activities, based on digitized feedback data gathered by sensors.” These are specialized, and often older, computer systems that control physical pieces of equipment that do everything from route trains along their tracks to distribute power throughout a city. Increasingly, SCADA systems are being connected to the broader Internet, with significant implications for our common security.
The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball, Margy Ross
active measures, Albert Einstein, business intelligence, business process, call centre, cloud computing, data acquisition, discrete time, inventory management, iterative process, job automation, knowledge worker, performance metric, platform as a service, side project, zero-sum game
Hub-and-Spoke Corporate Information Factory Inmon Architecture The hub-and-spoke Corporate Information Factory (CIF) approach is advocated by Bill Inmon and others in the industry. Figure 1.9 illustrates a simplified version of the CIF, focusing on the core elements and concepts that warrant discussion. Figure 1.9 Simplified illustration of the hub-and-spoke Corporate Information Factory architecture. With the CIF, data is extracted from the operational source systems and processed through an ETL system sometimes referred to as data acquisition. The atomic data that results from this processing lands in a 3NF database; this normalized, atomic repository is referred to as the Enterprise Data Warehouse (EDW) within the CIF architecture. Although the Kimball architecture enables optional normalization to support ETL processing, the normalized EDW is a mandatory construct in the CIF. Like the Kimball approach, the CIF advocates enterprise data coordination and integration.
The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil
additive manufacturing, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business cycle, business intelligence, c2.com, call centre, carbon-based life, cellular automata, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, coronavirus, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, George Gilder, Gödel, Escher, Bach, informal economy, information retrieval, invention of the telephone, invention of the telescope, invention of writing, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Marshall McLuhan, Mikhail Gorbachev, Mitch Kapor, mouse model, Murray Gell-Mann, mutually assured destruction, natural language processing, Network effects, new economy, Norbert Wiener, oil shale / tar sands, optical character recognition, pattern recognition, phenotype, premature optimization, randomized controlled trial, Ray Kurzweil, remote working, reversible computing, Richard Feynman, Robert Metcalfe, Rodney Brooks, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, semantic web, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Y2K, Yogi Berra
Poggio and E. Bizzi, "Generalization in Vision and Motor Control," Nature 431 (2004): 768–74. 77. R. Llinas and J. P. Welsh, "On the Cerebellum and Motor Learning," Current Opinion in Neurobiology 3.6 (December 1993): 958–65; E. Courchesne and G. Allen, "Prediction and Preparation, Fundamental Functions of the Cerebellum," Learning and Memory 4.1 (May–June 1997): 1-35; J. M. Bower, "Control of Sensory Data Acquisition," International Review of Neurobiology 41 (1997): 489–513. 78. J. Voogd and M. Glickstein, "The Anatomy of the Cerebellum," Trends in Neuroscience 21.9 (September 1998): 370–75; John C. Eccles, Masao Ito, and János Szentágothai, The Cerebellum as a Neuronal Machine (New York: Springer-Verlag, 1967); Masao Ito, The Cerebellum and Neural Control (New York: Raven, 1984). 79. N. Bernstein, The Coordination and Regulation of Movements (New York: Pergamon Press, 1967). 80.
Masterminds of Programming: Conversations With the Creators of Major Programming Languages by Federico Biancuzzi, Shane Warden
Benevolent Dictator For Life (BDFL), business intelligence, business process, cellular automata, cloud computing, commoditize, complexity theory, conceptual framework, continuous integration, data acquisition, domain-specific language, Douglas Hofstadter, Fellow of the Royal Society, finite state, Firefox, follow your passion, Frank Gehry, general-purpose programming language, Guido van Rossum, HyperCard, information retrieval, iterative process, John von Neumann, Larry Wall, linear programming, loose coupling, Mars Rover, millennium bug, NP-complete, Paul Graham, performance metric, Perl 6, QWERTY keyboard, RAND corporation, randomized controlled trial, Renaissance Technologies, Ruby on Rails, Sapir-Whorf hypothesis, Silicon Valley, slashdot, software as a service, software patent, sorting algorithm, Steve Jobs, traveling salesman, Turing complete, type inference, Valgrind, Von Neumann architecture, web application
He is a member of the editorial board of the Journal of Universal Computer Science. James Gosling received a B.Sc. in computer science from the University of Calgary, Canada in 1977. He received a Ph.D. in computer science from Carnegie-Mellon University in 1983. The title of his thesis was “The Algebraic Manipulation of Constraints.” He is currently a VP & Fellow at Sun Microsystems. He has built satellite data acquisition systems, a multiprocessor version of Unix, several compilers, mail systems, and window managers. He has also built a WYSIWYG text editor, a constraint-based drawing editor and a text editor called Emacs for Unix systems. At Sun, his early activity was as lead engineer of the NeWS window system. He did the original design of the Java programming language and implemented its original compiler and virtual machine.
Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris
active measures, Andrei Shleifer, asset allocation, automated trading system, barriers to entry, Bernie Madoff, business cycle, buttonwood tree, buy and hold, compound rate of return, computerized trading, corporate governance, correlation coefficient, data acquisition, diversified portfolio, fault tolerance, financial innovation, financial intermediation, fixed income, floating exchange rates, High speed trading, index arbitrage, index fund, information asymmetry, information retrieval, interest rate swap, invention of the telegraph, job automation, law of one price, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market clearing, market design, market fragmentation, market friction, market microstructure, money market fund, Myron Scholes, Nick Leeson, open economy, passive investing, pattern recognition, Ponzi scheme, post-materialism, price discovery process, price discrimination, principal–agent problem, profit motive, race to the bottom, random walk, rent-seeking, risk tolerance, risk-adjusted returns, selection bias, shareholder value, short selling, Small Order Execution System, speech recognition, statistical arbitrage, statistical model, survivorship bias, the market place, transaction costs, two-sided market, winner-take-all economy, yield curve, zero-coupon bond, zero-sum game
Analysts who use price benchmarks obtained long after the trade may learn about these issues, but they also measure whether portfolio managers made good portfolio composition decisions. Analysts who use price benchmarks obtained long before managers decided to trade are immune to the split order problem, but they also measure the degree to which their portfolio composition decisions depend on past prices. No price benchmark is perfect for estimating implicit transaction costs. Analysts must make trade-offs between estimation cost and various estimator properties. When data acquisition costs are no consideration, the implementation shortfall is the best transaction cost estimator. It is not subject to any of the biases discussed above. When the costs of collecting data are significant, traders may prefer other estimators. Retail traders typically use the effective spread transaction cost estimator because it is generally unbiased for small orders. Institutional investment sponsors often use VWAP when they do not know when their managers ordered their trades.
The Quest: Energy, Security, and the Remaking of the Modern World by Daniel Yergin
"Robert Solow", addicted to oil, Albert Einstein, Asian financial crisis, Ayatollah Khomeini, banking crisis, Berlin Wall, bioinformatics, borderless world, BRICs, business climate, carbon footprint, Carmen Reinhart, cleantech, Climategate, Climatic Research Unit, colonial rule, Colonization of Mars, corporate governance, cuban missile crisis, data acquisition, decarbonisation, Deng Xiaoping, Dissolution of the Soviet Union, diversification, diversified portfolio, Elon Musk, energy security, energy transition, Exxon Valdez, facts on the ground, Fall of the Berlin Wall, fear of failure, financial innovation, flex fuel, global supply chain, global village, high net worth, hydraulic fracturing, income inequality, index fund, informal economy, interchangeable parts, Intergovernmental Panel on Climate Change (IPCC), James Watt: steam engine, John von Neumann, Kenneth Rogoff, life extension, Long Term Capital Management, Malacca Straits, market design, means of production, megacity, Menlo Park, Mikhail Gorbachev, Mohammed Bouazizi, mutually assured destruction, new economy, Norman Macrae, North Sea oil, nuclear winter, off grid, oil rush, oil shale / tar sands, oil shock, Paul Samuelson, peak oil, Piper Alpha, price mechanism, purchasing power parity, rent-seeking, rising living standards, Robert Metcalfe, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, Sand Hill Road, shareholder value, Silicon Valley, Silicon Valley startup, smart grid, smart meter, South China Sea, sovereign wealth fund, special economic zone, Stuxnet, technology bubble, the built environment, The Nature of the Firm, the new new thing, trade route, transaction costs, unemployed young men, University of East Anglia, uranium enrichment, William Langewiesche, Yom Kippur War
The tools available to the cyberattacker are extensive. They can mobilize networks of computers to mount a “bot attack” aimed at denial of service, shutting down systems. They can introduce malware—malicious software—that will cause systems to malfunction. Or they can seek, from remote locations, to take control of and disrupt systems. One point of entry is through the ubiquitous SCADA systems, the supervisory control and data acquisition computer systems that monitor and control every kind of industrial process. Originally, they were site specific, but now they are connected into larger information networks. Malicious intruders may gain access through a thumb drive and a desktop computer. A multitude of new entry points are provided by the proliferation of wireless devices and possibly by the smart meters that are part of the smart grid and that provide two-way communications between homes and the electrical distribution system.11 A test at a national laboratory in 2007 showed what happened when a hacker infiltrated an electric system.
Code Complete (Developer Best Practices) by Steve McConnell
Ada Lovelace, Albert Einstein, Buckminster Fuller, call centre, continuous integration, data acquisition, database schema, don't repeat yourself, Donald Knuth, fault tolerance, Grace Hopper, haute cuisine, if you see hoof prints, think horses—not zebras, index card, inventory management, iterative process, Larry Wall, loose coupling, Menlo Park, Perl 6, place-making, premature optimization, revision control, Sapir-Whorf hypothesis, slashdot, sorting algorithm, statistical model, Tacoma Narrows Bridge, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Turing machine, web application
Program Design Program design includes the major strokes of the design for a single program, mainly the way in which a program is divided into classes. Some program designs make it difficult to write a high-performance system. Others make it hard not to. Cross-Reference For details on designing performance into a program, see the "Additional Resources" section at the end of this chapter. Consider the example of a real-world data-acquisition program for which the highlevel design had identified measurement throughput as a key product attribute. Each measurement included time to make an electrical measurement, calibrate the value, scale the value, and convert it from sensor data units (such as millivolts) into engineering data units (such as degrees Celsius). In this case, without addressing the risk in the high-level design, the programmers would have found themselves trying to optimize the math to evaluate a 13th-order polynomial in software—that is, a polynomial with 14 terms, including variables raised to the 13th power.
Great North Road by Peter F. Hamilton
airport security, business process, corporate governance, data acquisition, dematerialisation, family office, illegal immigration, invention of the telescope, inventory management, plutocrats, Plutocrats, stem cell, the map is not the territory, undersea cable
Jede had returned to Newcastle midmorning and dumped the van in a GSW. Five minutes after he walked away it had burst into flame, much to the delight of the local feral youths. After that, there’d been no contact between Sherman and Aldred. Sherman had gone about his usual dark business with care. The file Linsell assembled, containing calls about secondary money transfers, tox procurement, corporate data acquisition, and two blackmail scams being set up, would have been enough for the city prosecutor to obtain a twenty-year sentence. Linsell wanted something else. Sherman had to be holding Umbreit’s family somewhere. It was their hold over him, the leverage to force him to build whatever it was they had him doing in the farmhouse barn. Ralph and Linsell were desperate to find them. Neither Clayton nor anyone at Jupiter could even guess what was being constructed at that remote location, nor why Aldred was apparently going rogue.