Data Standardization Quotes

Women have always worked. They have worked unpaid, underpaid, underappreciated, and invisibly, but they have always worked. But the modern workplace does not work for women. From its location, to its hours, to its regulatory standards, it has been designed around the lives of men and it is no longer fit for purpose. The world of work needs a wholesale redesign--of its regulations, of its equipment, of its culture--and this redesign must be led by data on female bodies and female lives. We have to start recognising that the work women do is not an added extra, a bonus that we could do without: women's work, paid and unpaid, is the backbone of our society and our economy. It's about time we started valuing it.

The five-star scale doesn’t really exist for humans; it exists for data aggregation systems, which is why it did not become standard until the internet era. Making conclusions about a book’s quality from a 175-word review is hard work for artificial intelligences, whereas star ratings are ideal for them.

Women tend to sit further forward than men when driving. This is because we are on average shorter. Our legs need to be closer to reach the pedals, and we need to sit more upright to see clearly over the dashboard.49 This is not, however, the ‘standard seating position’. Women are ‘out of position’ drivers.50 And our wilful deviation from the norm means that we are at greater risk of internal injury on frontal collisions.51

I find it hard to talk about myself. I'm always tripped up by the eternal who am I? paradox. Sure, no one knows as much pure data about me as me. But when I talk about myself, all sorts of other factors--values, standards, my own limitations as an observer--make me, the narrator, select and eliminate things about me, the narratee. I've always been disturbed by the thought that I'm not painting a very objective picture of myself. This kind of thing doesn't seem to bother most people. Given the chance, people are surprisingly frank when they talk about themselves. "I'm honest and open to a ridiculous degree," they'll say, or "I'm thin-skinned and not the type who gets along easily in the world." Or "I am very good at sensing others' true feelings." But any number of times I've seen people who say they've easily hurt other people for no apparent reason. Self-styled honest and open people, without realizing what they're doing, blithely use some self-serving excuse to get what they want. And those "good at sensing others' true feelings" are duped by the most transparent flattery. It's enough to make me ask the question: How well do we really know ourselves? The more I think about it, the more I'd like to take a rain check on the topic of me. What I'd like to know more about is the objective reality of things outside myself. How important the world outside is to me, how I maintain a sense of equilibrium by coming to terms with it. That's how I'd grasp a clearer sense of who I am.

We must surrender our skepticism only in the face of rock-solid evidence. Science demands a tolerance for ambiguity. Where we are ignorant, we withhold belief. Whatever annoyance the uncertainty engenders serves a higher purpose: It drives us to accumulate better data. This attitude is the difference between science and so much else. Science offers little in the way of cheap thrills. The standards of evidence are strict. But when followed they allow us to see far, illuminating even a great darkness.

Serving humanity intelligently is held up as the “gold standard” of AI based systems. But, with the emergence of new technologies and AI systems with bio-metric data storage, surveillance, tracking and big data analysis, humanity and the society is facing a threat today from evilly designed AI systems in the hands of monster governments and irresponsible people. Humanity is on the verge of digital slavery.

The human victims of WMDs, we’ll see time and again, are held to a far higher standard of evidence than the algorithms themselves.

And so, because business leadership is still so dominated by men, modern workplaces are riddled with these kind of gaps, from doors that are too heavy for the average woman to open with ease, to glass stairs and lobby floors that mean anyone below can see up your skirt, to paving that’s exactly the right size to catch your heels. Small, niggling issues that aren’t the end of the world, granted, but that nevertheless irritate. Then there’s the standard office temperature. The formula to determine standard office temperature was developed in the 1960s around the metabolic resting rate of the average forty-year-old, 70 kg man.1 But a recent study found that ‘the metabolic rate of young adult females performing light office work is significantly lower’ than the standard values for men doing the same type of activity. In fact, the formula may overestimate female metabolic rate by as much as 35%, meaning that current offices are on average five degrees too cold for women. Which leads to the odd sight of female office workers wrapped up in blankets in the New York summer while their male colleagues wander around in summer clothes.

He’s already run the standard battery of questions, checked the check boxes, computed the data: hears voices = schizophrenic; too agitated = paranoid; too bright = manic; too moody = bipolar; and of course everyone knows a depressive, a suicidal, and if you’re all-around too unruly or obstructive or treatment resistant like a superbug, you get slapped with a personality disorder, too. In Crote Six, they said I “suffer” from schizoaffective disorder. That’s like the sampler plate of diagnoses, Best of Everything. But I don’t want to suffer. I want to live.

The average female handspan is between seven and eight inches,2 which makes the standard forty-eight-inch keyboard something of a challenge. Octaves on a standard keyboard are 7.4 inches wide, and one study found that this keyboard disadvantages 87% of adult female pianists.3 Meanwhile, a 2015 study which compared the handspan of 473 adult pianists to their ‘level of acclaim’ found that all twelve of the pianists considered to be of international renown had spans of 8.8 inches or above.

During the dot-com bubble, most people did not use a persuasive theory to gauge whether stock prices were too high, too low, or just right. Instead, as they watched stock prices go up, they invented explanations to rationalize what was happening. They talked about Moore’s Law, smart kids, and Alan Greenspan. Data without theory.

The value for which P=0.05, or 1 in 20, is 1.96 or nearly 2; it is convenient to take this point as a limit in judging whether a deviation ought to be considered significant or not. Deviations exceeding twice the standard deviation are thus formally regarded as significant. Using this criterion we should be led to follow up a false indication only once in 22 trials, even if the statistics were the only guide available. Small effects will still escape notice if the data are insufficiently numerous to bring them out, but no lowering of the standard of significance would meet this difficulty.

Leyner's fiction is, in this regard, an eloquent reply to Gilder's prediction that our TV-culture problems can be resolved by the dismantling of images into discrete chunks we can recombine as we fancy. Leyner's world is a Gilder-esque dystopia. The passivity and schizoid decay still endure for Leyner in his characters' reception of images and waves of data. The ability to combine them only adds a layer of disorientation: when all experience can be deconstructed and reconfigured, there become simply too many choices. And in the absence of any credible, noncommercial guides for living, the freedom to choose is about as "liberating" as a bad acid trip: each quantum is as good as the next, and the only standard of an assembly's quality is its weirdness, incongruity, its ability to stand out from a crowd of other image-constructs and wow some Audience.

At the time, when decisions had to be made, she had truly wanted to stay home- she was, in a word, exchausted- though she had never wanted such a thing before. And, honestly, what a privilege. What a treat. She understood that she was just a privileged, overeducated lady in the middle of America living the dream of holding her baby twenty-four hours a day. According to basically everyone’s standards, she had nothing to complain about, ever, after that point and possibly even leading up to it. In fact, wasn’t it a bit, you know, hoity-toity, a but oblivious middle-class white lady of her, even to think about complaining? If she read the articles, examined the data, contemplated her lot in life, her place in society, her historical role in the oppression of everyone other than white men, she really had not even a sparse spot of yard on which to stand and emit one single strangled scream.

While those p-values have been the standard for decades, they were arbitrarily chosen, leading some modern data scientists to question their usefulness.

The five-star scale doesn’t really exist for humans; it exists for data aggregation systems, which is why it did not become standard until the internet era.

ARPA should not force the research computers at each site to handle the routing of data, Clark argued. Instead ARPA should design and give each site a standardized minicomputer that would do the routing.

Fortunately, our colleges and universities are fully cognizant of the problems I have been delineating and take concerted action to address them. Curricula are designed to give coherence to the educational experience and to challenge students to develop a strong degree of moral awareness. Professors, deeply involved with the enterprise of undergraduate instruction, are committed to their students' intellectual growth and insist on maintaining the highest standards of academic rigor. Career services keep themselves informed about the broad range of postgraduate options and make a point of steering students away from conventional choices. A policy of noncooperation with U.S. News has taken hold, depriving the magazine of the data requisite to calculate its rankings. Rather than squandering money on luxurious amenities and exorbitant administrative salaries, schools have rededicated themselves to their core missions of teaching and the liberal arts. I'm kidding, of course.

The first is that brains, by contrast to the kinds of program we typically run on our computers, do not use standardized data storage and representation formats. Rather, each brain develops its own idiosyncratic representations of higher-level content.

In the majority of schools, what's needed isn't more professional development on deconstructing standards or academic discourse or using data to drive instruction. What's needed is time, space, and attention to managing stress and cultivating resilience.

Amidst all this organic plasticity and compromise, though, the infrastructure fields could still stake out territory for a few standardized subsystems, identical from citizen to citizen. Two of these were channels for incoming data—one for gestalt, and one for linear, the two primary modalities of all Konishi citizens, distant descendants of vision and hearing. By the orphan's two-hundredth iteration, the channels themselves were fully formed, but the inner structures to which they fed their data, the networks for classifying and making sense of it, were still undeveloped, still unrehearsed. Konishi polis itself was buried two hundred meters beneath the Siberian tundra, but via fiber and satellite links the input channels could bring in data from any forum in the Coalition of Polises, from probes orbiting every planet and moon in the solar system, from drones wandering the forests and oceans of Earth, from ten million kinds of scape or abstract sensorium. The first problem of perception was learning how to choose from this superabundance.

Look, cell phone geolocation data shows very few clustering anomalies for this hour and climate. And that’s holding up pretty much across all major metro areas. It’s gone down six percentage points since news of the Karachi workshop hit the Web, and it’s trending downward. If people are protesting, they aren’t doing it in the streets.” He circled his finger over a few clusters of dots. “Some potential protest knots in Portland and Austin, but defiance-related tag cloud groupings in social media put us within the three-sigma rule—meaning roughly sixty-eight percent of the values lie within one standard deviation of the mean.

Avoid succumbing to the gambler’s fallacy or the base rate fallacy. Anecdotal evidence and correlations you see in data are good hypothesis generators, but correlation does not imply causation—you still need to rely on well-designed experiments to draw strong conclusions. Look for tried-and-true experimental designs, such as randomized controlled experiments or A/B testing, that show statistical significance. The normal distribution is particularly useful in experimental analysis due to the central limit theorem. Recall that in a normal distribution, about 68 percent of values fall within one standard deviation, and 95 percent within two. Any isolated experiment can result in a false positive or a false negative and can also be biased by myriad factors, most commonly selection bias, response bias, and survivorship bias. Replication increases confidence in results, so start by looking for a systematic review and/or meta-analysis when researching an area.

Developing and maintaining integrity require constant attention. John Weston, chairman and CEO of Automatic Data Processing, Inc., says, “I`ve always tried to live with the following simple rule: Don`t do what you wouldn`t feel comfortable reading about in the newspapers the next day.” That`s a good standard all of us should keep.

In emerging technologies, security is the biggest threat, and common standards for communication and safety are improving, which means that risks will be minimised. We can only hope that man with this technology can actually stop the destruction of our planet, make the population healthier, and create a better future for all of us.

perfectionism is a desperate attempt to live up to impossible standards. Perfectionism will do anything to protect those impossible standards. It can’t let you find out how impossible they are, especially with the cold eye of data, so it terrifies you into thinking that you’ll be crushed by disappointment if you peer behind that curtain. Data would tell you that your bank account is low, but you’re spending a lot more on coffee than you think. If you started making it at home, you could easily start saving for a vacation. You might even stop comparing yourself to the impossible financial standards of your friends online. You might make some reasonable goals and completely change the way you view money. You might even have fun.

facsimile science. (By this term I mean materials that carry the accoutrements of science—including in some cases peer review—but fail to adhere to accepted scientific standards such as methodological naturalism, complete and open reporting of data, and the willingness to revise assumptions in the light of data.)49 This is the problem of for-profit and predatory conferences and journals.

Our inherited desire to explain what we see fuels two kinds of cognitive errors. First, we are too easily seduced by patterns and by the theories that explain them. Second, we latch onto data that support our theories and discount contradicting evidence. We believe stories simply because they are consistent with the patterns we observe and, once we have a story, we are reluctant to let it go.

Even though bitcoin may not, after all, represent the potential for a new gold standard, its underlying technology will unbundle the roles of money. This can finally clarify and enable the necessary distinction between the medium of exchange and the measuring stick. Disaggregated will be all the GAFAM (Google, Apple, Facebook, Amazon, Microsoft conglomerates)—the clouds of concentrated computing and commerce.

But the story of BPA is not just about gender: it’s also about class. Or at least it’s about gendered class. Fearing a major consumer boycott, most baby-bottle manufacturers voluntarily removed BPA from their products, and while the official US line on BPA is that it is not toxic, the EU and Canada are on their way to banning its use altogether. But the legislation that we have exclusively concerns consumers: no regulatory standard has ever been set for workplace exposure.5 ‘It was ironic to me,’ says occupational health researcher Jim Brophy, ‘that all this talk about the danger for pregnant women and women who had just given birth never extended to the women who were producing these bottles. Those women whose exposures far exceeded anything that you would have in the general environment. There was no talk about the pregnant worker who is on the machine that’s producing this thing.

The master propagandist, like the advertising expert, avoids obvious emotional appeals and strives for a tone that is consistent with the prosaic quality of modern life—a dry, bland matter-of-factness. Nor does the propagandist circulate "intentionally biased" information. He knows that partial truths serve as more effective instruments of deception than lies. Thus he tries to impress the public with statistics of economic growth that neglect to give the base year from which growth is calculated, with accurate but meaningless facts about the standard of living—with raw and uninterpreted data, in other words, from which the audience is invited to draw the inescapable conclusion that things are getting better and the present régime therefore deserves the people's confidence, or on the other hand that things are getting worse so rapidly that the present régime should be given emergency powers to deal with the developing crisis.

Twenty minutes later, I was sitting in the federal building that housed the Department of Homeland Security, about fifteen stories up, locked in a standard federal issue interrogation room. Metal chair, metal table, big one-way mirror window, just like the movies. My arms were bound behind me with at least three flex-cuffs. The only addition to the room were the four tactical team members standing in each corner of the room, M4 rifles slung across their chests. Books, Splitter, Data and old Rattler himself, Agent Simmons.

Roy Jastram has produced a systematic study of the purchasing power of gold over the longest consistent datasets available.6 Observing English data from 1560 to 1976 to analyze the change in gold's purchasing power in terms of commodities, Jastram finds gold dropping in purchasing power during the first 140 years, but then remaining relatively stable from 1700 to 1914, when Britain went off the gold standard. For more than two centuries during which Britain primarily used gold as money, its purchasing power remained relatively constant, as did the price of wholesale commodities.

The World Bank provides data on broad money growth for 167 countries for the period between 1960 and 2015. The data for the annual average for all countries is plotted in Figure 6. While the data is not complete for all countries and all years, the average growth of money supply is 32.16% per year per country. The 32.16% figure does not include several hyperinflationary years during which a currency is completely destroyed and replaced by a new one, and so the results of this analysis cannot definitively tell us which currencies fared worst, as some of the most significant data cannot be compared.

The vast majority of scientists deserve our trust. But no matter how you slice it, scientific fraud isn’t rare. Hundreds of scientific papers get retracted every year, and while firm numbers are elusive, something like half of them are retracted due to fraud or other misconduct. Even big-name scientists transgress. Again, it’s unfair to condemn people from the past for failing to meet today’s standards, but historians have noted that Galileo, Newton, Bernoulli, Dalton, Mendel, and more all manipulated experiments and/or fudged data in ways that would have gotten them fired from any self-respecting lab today.

A scientist must put faith in the experimental data reported by other scientists, and in the institutions that sponsored those scientists, and in the standards by which those scientists received their credentials. A scientist must put faith in the authority of the journals that publish the results of various studies. Finally, but perhaps most fundamentally, a scientist must trust that empirical reality is indeed perceptible and measurable, and that the laws of cause and effect will apply universally. No scientific endeavor can proceed if the experimenter subjects every phenomenon to radical doubt, disqualifying his own observations as well as those of his peers. Polanyi concluded that science proceeds from a trust that is “fiduciary”—a word that derives from the Latin root meaning “faith-based.” Such faith is well placed and well founded, and it enables science to proceed apace; but, nonetheless, it is a species of faith, not an absolutely certain knowledge. “We must now recognize belief once more as the source of all knowledge,…” Polanyi said. “No intelligence, however critical or original, can operate outside such a fiduciary framework.” Secularism’s attempts to replace the authority of religion with a supposed “authority of experience and reason” has proven, in Polanyi’s words, “farcically inadequate

The formula to determine standard office temperature was developed in the 1960s around the metabolic resting rate of the average forty-year-old, 70 kg man.1 But a recent study found that ‘the metabolic rate of young adult females performing light office work is significantly lower’ than the standard values for men doing the same type of activity. In fact, the formula may overestimate female metabolic rate by as much as 35%, meaning that current offices are on average five degrees too cold for women. Which leads to the odd sight of female office workers wrapped up in blankets in the New York summer while their male colleagues wander around in summer clothes.

Every year or so I like to take a step back and look at a few key advertising, marketing, and media facts just to gauge how far removed from reality we advertising experts have gotten. These data represent the latest numbers I could find. I have listed the sources below. So here we go -- 10 facts, direct from the real world: E-commerce in 2014 accounted for 6.5 percent of total retail sales. 96% of video viewing is currently done on a television. 4% is done on a web device. In Europe and the US, people would not care if 92% of brands disappeared. The rate of engagement among a brand's fans with a Facebook post is 7 in 10,000. For Twitter it is 3 in 10,000. Fewer than one standard banner ad in a thousand is clicked on. Over half the display ads paid for by marketers are unviewable. Less than 1% of retail buying is done on a mobile device. Only 44% of traffic on the web is human. One bot-net can generate 1 billion fraudulent digital ad impressions a day. Half of all U.S online advertising - $10 billion a year - may be lost to fraud. As regular readers know, one of our favorite sayings around The Ad Contrarian Social Club is a quote from Noble Prize winning physicist Richard Feynman, who wonderfully declared that “Science is the belief in the ignorance of experts.” I think these facts do a pretty good job of vindicating Feynman.

Then there’s the standard office temperature. The formula to determine standard office temperature was developed in the 1960s around the metabolic resting rate of the average forty-year-old, 70 kg man. 1 But a recent study found that ‘the metabolic rate of young adult females performing light office work is significantly lower’ than the standard values for men doing the same type of activity. In fact, the formula may overestimate female metabolic rate by as much as 35%, meaning that current offices are on average five degrees too cold for women. Which leads to the odd sight of female office workers wrapped up in blankets in the New York summer while their male colleagues wander around in summer clothes.

It’s easy to raise graduation rates, for example, by lowering standards. Many students struggle with math and science prerequisites and foreign languages. Water down those requirements, and more students will graduate. But if one goal of our educational system is to produce more scientists and technologists for a global economy, how smart is that? It would also be a cinch to pump up the income numbers for graduates. All colleges would have to do is shrink their liberal arts programs, and get rid of education departments and social work departments while they’re at it, since teachers and social workers make less money than engineers, chemists, and computer scientists. But they’re no less valuable to society.

It has often been claimed that there has been very little change in the average real income of American households over a period of decades. It is an undisputed fact that the average real income—that is, money income adjusted for inflation—of American households rose by only 6 percent over the entire period from 1969 to 1996. That might well be considered to qualify as stagnation. But it is an equally undisputed fact that the average real income per person in the United States rose by 51 percent over that very same period.3 How can both these statistics be true? Because the average number of individuals per household has been declining over the years. Half the households in the United States contained six or more people in 1900, as did 21 percent in 1950. But, by 1998, only ten percent of American households had that many people.4 The average number of persons per household not only varies over time, it also varies from one racial or ethnic group to another at a given time, and varies from one income bracket to another. As of 2007, for example, black household income was lower than Hispanic household income, even though black per capita income was higher than Hispanic per capita income, because black households average fewer people than Hispanic households. Similarly, Asian American household income was higher than white household income, even though white per capita income was higher than Asian American per capita income, because Asian American households average more people.5 Income comparisons using household statistics are far less reliable indicators of standards of living than are individual income data because households vary in size while an individual always means one person. Studies of what people actually consume—that is, their standard of living—show substantial increases over the years, even among the poor,6 which is more in keeping with a 51 percent increase in real per capita income than with a 6 percent increase in real household income. But household income statistics present golden opportunities for fallacies to flourish, and those opportunities have been seized by many in the media, in politics, and in academia.

In their seminal work, The History of Science and Technology, Bunch and Hellemans compile a list of the 8,583 most important innovations and inventions in the history of science and technology. Physicist Jonathan Huebner17 analyzed all these events along with the years in which they happened and global population at that year, and measured the rate of occurrence of these events per year per capita since the Dark Ages. Huebner found that while the total number of innovations rose in the twentieth century, the number of innovations per capita peaked in the nineteenth century. A closer look at the innovations of the pre-1914 world lends support to Huebner's data. It is no exaggeration to say that our modern world was invented in the gold standard years preceding World War I.

G. Stanley Hall, a creature of his times, believed strongly that adolescence was determined – a fixed feature of human development that could be explained and accounted for in scientific fashion. To make his case, he relied on Haeckel's faulty recapitulation idea, Lombroso's faulty phrenology-inspired theories of crime, a plethora of anecdotes and one-sided interpretations of data. Given the issues, theories, standards and data-handling methods of his day, he did a superb job. But when you take away the shoddy theories, put the anecdotes in their place, and look for alternate explanations of the data, the bronze statue tumbles hard. I have no doubt that many of the street teens of Hall's time were suffering or insufferable, but it's a serious mistake to develop a timeless, universal theory of human nature around the peculiarities of the people of one's own time and place.

von Braun went looking for problems, hunches, and bad news. He even rewarded those who exposed problems. After Kranz and von Braun’s time, the “All Others Bring Data” process culture remained, but the informal culture and power of individual hunches shriveled. In 1974, William Lucas took over the Marshall Space Flight Center. A NASA chief historian wrote that Lucas was a brilliant engineer but “often grew angry when he learned of problems.” Allan McDonald described him to me as a “shoot-the-messenger type guy.” Lucas transformed von Braun’s Monday Notes into a system purely for upward communication. He did not write feedback and the notes did not circulate. At one point they morphed into standardized forms that had to be filled out. Monday Notes became one more rigid formality in a process culture. “Immediately, the quality of the notes fell,” wrote another official NASA historian.

Recent studies indicate that boys raised by women, including single women and lesbian couples, do not suffer in their adjustment; they are not appreciably less “masculine”; they do not show signs of psychological impairment. What many boys without fathers inarguably do face is a precipitous drop in their socioeconomic status. When families dissolve, the average standard of living for mothers and children can fall as much as 60 percent, while that of the man usually rises. When we focus on the highly speculative psychological effects of fatherlessness we draw away from concrete political concerns, like the role of increased poverty. Again, there are as yet no data suggesting that boys without fathers to model masculinity are necessarily impaired. Those boys who do have fathers are happiest and most well adjusted with warm, loving fathers, fathers who score high in precisely “feminine” qualities.

What are the health effects of the choice between austerity and stimulus? Today there is a vast natural experiment being conducted on the body economic. It is similar to the policy experiments that occurred in the Great Depression, the post-communist crisis in eastern Europe, and the East Asian Financial Crisis. As in those prior trials, health statistics from the Great Recession reveal the deadly price of austerity—a price that can be calculated not just in the ticks to economic growth rates, but in the number of years of life lost and avoidable deaths. Had the austerity experiments been governed by the same rigorous standards as clinical trials, they would have been discontinued long ago by a board of medical ethics. The side effects of the austerity treatment have been severe and often deadly. The benefits of the treatment have failed to materialize. Instead of austerity, we should enact evidence-based policies to protect health during hard times. Social protection saves lives. If administered correctly, these programs don’t bust the budget, but—as we have shown throughout this book—they boost economic growth and improve public health. Austerity’s advocates have ignored evidence of the health and economic consequences of their recommendations. They ignore it even though—as with the International Monetary Fund—the evidence often comes from their own data. Austerity’s proponents, such as British Prime Minister David Cameron, continue to write prescriptions of austerity for the body economic, in spite of evidence that it has failed. Ultimately austerity has failed because it is unsupported by sound logic or data. It is an economic ideology. It stems from the belief that small government and free markets are always better than state intervention. It is a socially constructed myth—a convenient belief among politicians taken advantage of by those who have a vested interest in shrinking the role of the state, in privatizing social welfare systems for personal gain. It does great harm—punishing the most vulnerable, rather than those who caused this recession.

The National Institute of Standards and Technology has provided a preliminary estimation that between 16,400 and 18,800 civilians were in the WTC complex as of 8:46 am on September 11. At most 2,152 individual died in the WTC complex who were not 1) fire or police first responders, 2) security or fire safety personnel of the WTC or individual companies, 3) volunteer civilians who ran to the WTC after the planes' impact to help others or, 4) on the two planes that crashed into the Twin Towers. Out of this total number of fatalities, we can account for the workplace location of 2,052 individuals, or 95.35 percent. Of this number, 1,942 or 94.64 percent either worked or were supposed to attend a meeting at or above the respective impact zones of the Twin Towers; only 110, or 5.36 percent of those who died, worked below the impact zone. While a given person's office location at the WTC does not definitively indicate where that individual died that morning or whether he or she could have evacuated, these data strongly suggest that the evacuation was a success for civilians below the impact zone.

There was little effort to conceal this method of doing business. It was common knowledge, from senior managers and heads of research and development to the people responsible for formulation and the clinical people. Essentially, Ranbaxy’s manufacturing standards boiled down to whatever the company could get away with. As Thakur knew from his years of training, a well-made drug is not one that passes its final test. Its quality must be assessed at each step of production and lies in all the data that accompanies it. Each of those test results, recorded along the way, helps to create an essential roadmap of quality. But because Ranbaxy was fixated on results, regulations and requirements were viewed with indifference. Good manufacturing practices were stop signs and inconvenient detours. So Ranbaxy was driving any way it chose to arrive at favorable results, then moving around road signs, rearranging traffic lights, and adjusting mileage after the fact. As the company’s head of analytical research would later tell an auditor: “It is not in Indian culture to record the data while we conduct our experiments.

I find it hard to talk about myself. I’m always tripped up by the eternal who am I? paradox. Sure, no one knows as much pure data about me as me. But when I talk about myself, all sorts of other factors—values, standards, my own limitations as an observer—make me, the narrator, select and eliminate things about me, the narratee. I’ve always been disturbed by the thought that I’m not painting a very objective picture of myself. This kind of thing doesn’t seem to bother most people. Given the chance, people are surprisingly frank when they talk about themselves. “I’m honest and open to a ridiculous degree,” they’ll say, or “I’m thin-skinned and not the type who gets along easily in the world.” Or “I am very good at sensing others’ true feelings.” But any number of times I’ve seen people who say they’re easily hurt hurt other people for no apparent reason. Self-styled honest and open people, without realizing what they’re doing, blithely use some self-serving excuse to get what they want. And those “good at sensing others’ true feelings” are duped by the most transparent flattery. It’s enough to make me ask the question: How well do we really know ourselves?

I find it hard to talk about myself. I’m always tripped up by the eternal who am I? paradox. Sure, no one knows as much pure data about me as me. But when I talk about myself, all sorts of other factors – values, standards, my own limitations as an observer – make me, the narrator, select and eliminate things about me, the narratee. I’ve always been disturbed by the thought that I’m not painting a very objective picture of myself. This kind of thing doesn’t seem to bother most people. Given the chance, they’re surprisingly frank when they talk about themselves. “I’m honest and open to a ridiculous degree,” they’ll say, or “I’m thin-skinned and not the type who gets along easily in the world,” or “I’m very good at sensing others’ true feelings.” But any number of times I’ve seen people who say they’re easily hurt or hurt other people for no apparent reason. Self-styled honest and open people, without realizing what they’re doing, blithely use some self-serving excuse to get what they want. And those who are “good at sensing others’ true feelings” are taken in by the most transparent flattery. It’s enough to make me ask the question: how well do we really know ourselves?

The human mind wants to “win” whatever game is being played. This pitfall is evident in many areas of life. We focus on working long hours instead of getting meaningful work done. We care more about getting ten thousand steps than we do about being healthy. We teach for standardized tests instead of emphasizing learning, curiosity, and critical thinking. In short, we optimize for what we measure. When we choose the wrong measurement, we get the wrong behavior. This is sometimes referred to as Goodhart’s Law. Named after the economist Charles Goodhart, the principle states, “When a measure becomes a target, it ceases to be a good measure.”9 Measurement is only useful when it guides you and adds context to a larger picture, not when it consumes you. Each number is simply one piece of feedback in the overall system. In our data-driven world, we tend to overvalue numbers and undervalue anything ephemeral, soft, and difficult to quantify. We mistakenly think the factors we can measure are the only factors that exist. But just because you can measure something doesn’t mean it’s the most important thing. And just because you can’t measure something doesn’t mean it’s not important at all.

I find it hard to talk about myself. I'm always tripped up by the eternal who am I? paradox. Sure, no one knows as much pure data about me as me. But when I talk about myself, all sorts of other factors - values, standards, my own limitations as an observer - make me, the narrator, select and eliminate things about me, the narratee. I've always been disturbed by the thought that I'm not painting a very objective picture of myself. This kind of things doesn't seem to bother most people. Given the chance, people are surprisingly frank when they talk about themselves. "I'm honest and open to a ridiculous degree," they'll say, or "I'm thin-skinned and not the type who gets along easily in the world." Or "I'm very good at sensing others' true feelings." But any number of times I've seen people who say they're easily hurt or hurt other people for no apparent reason. Self-styled honest and open people, without realizing what they're doing, blithely use some self-serving excuse to get what they want. And those "good at sensing others' true feelings" are taken in by the most transparent flattery. It's enough to make me ask the question: how well do really know ourselves? The more I think about it, the more I'd like to take a rain check on the topic of me. What I'd like to know more about is the objective reality of things outside myself. How important the world outside is to me, how I maintain a sense of equilibrium by coming to terms with it. That's how I'd grasp a clearer sense of who I am. These are the kind of ideas I had running through my head when I was a teenager. Like a master builder stretches taut his string and lays one brick after another, I constructed this viewpoint - or philosophy of life, to put a bigger spin on it. Logic and speculation played a part in formulating this viewpoint, but for the most part it was based on my own experiences. And speaking of experience, a number of painful episodes taught me that getting this viewpoint of mine across to other people wasn't the easiest thing in the world. The upshot of all this is that when I was young I began to draw an invisible boundary between myself and other people. No matter who I was dealing with, I maintained a set distance, carefully monitoring the person's attitude so that they wouldn't get any closer. I didn't easily swallow what other people told me. My only passions were books and music. As you might guess, I led a lonely life.

The best entrepreneurs don’t just follow Moore’s Law; they anticipate it. Consider Reed Hastings, the cofounder and CEO of Netflix. When he started Netflix, his long-term vision was to provide television on demand, delivered via the Internet. But back in 1997, the technology simply wasn’t ready for his vision—remember, this was during the era of dial-up Internet access. One hour of high-definition video requires transmitting 40 GB of compressed data (over 400 GB without compression). A standard 28.8K modem from that era would have taken over four months to transmit a single episode of Stranger Things. However, there was a technological innovation that would allow Netflix to get partway to Hastings’s ultimate vision—the DVD. Hastings realized that movie DVDs, then selling for around $ 20, were both compact and durable. This made them perfect for running a movie-rental-by-mail business. Hastings has said that he got the idea from a computer science class in which one of the assignments was to calculate the bandwidth of a station wagon full of backup tapes driving across the country! This was truly a case of technological innovation enabling business model innovation. Blockbuster Video had built a successful business around buying VHS tapes for around $ 100 and renting them out from physical stores, but the bulky, expensive, fragile tapes would never have supported a rental-by-mail business.

Pull approaches differ significantly from push approaches in terms of how they organize and manage resources. Push approaches are typified by "programs" - tightly scripted specifications of activities designed to be invoked by known parties in pre-determined contexts. Of course, we don't mean that all push approaches are software programs - we are using this as a broader metaphor to describe one way of organizing activities and resources. Think of thick process manuals in most enterprises or standardized curricula in most primary and secondary educational institutions, not to mention the programming of network television, and you will see that institutions heavily rely on programs of many types to deliver resources in pre-determined contexts. Pull approaches, in contrast, tend to be implemented on "platforms" designed to flexibly accommodate diverse providers and consumers of resources. These platforms are much more open-ended and designed to evolve based on the learning and changing needs of the participants. Once again, we do not mean to use platforms in the literal sense of a tangible foundation, but in a broader, metaphorical sense to describe frameworks for orchestrating a set of resources that can be configured quickly and easily to serve a broad range of needs. Think of Expedia's travel service or the emergency ward of a hospital and you will see the contrast with the hard-wired push programs.

Once again, he was deducing a theory from principles and postulates, not trying to explain the empirical data that experimental physicists studying cathode rays had begun to gather about the relation of mass to the velocity of particles. Coupling Maxwell’s theory with the relativity theory, he began (not surprisingly) with a thought experiment. He calculated the properties of two light pulses emitted in opposite directions by a body at rest. He then calculated the properties of these light pulses when observed from a moving frame of reference. From this he came up with equations regarding the relationship between speed and mass. The result was an elegant conclusion: mass and energy are different manifestations of the same thing. There is a fundamental interchangeability between the two. As he put it in his paper, “The mass of a body is a measure of its energy content.” The formula he used to describe this relationship was also strikingly simple: “If a body emits the energy L in the form of radiation, its mass decreases by L/V 2.” Or, to express the same equation in a different manner: L=mV 2. Einstein used the letter L to represent energy until 1912, when he crossed it out in a manuscript and replaced it with the more common E. He also used V to represent the velocity of light, before changing to the more common c. So, using the letters that soon became standard, Einstein had come up with his memorable equation: E=mc2

Modern statistics is built on the idea of models — probability models in particular. [...] The standard approach to any new problem is to identify the sources of variation, to describe those sources by probability distributions and then to use the model thus created to estimate, predict or test hypotheses about the undetermined parts of that model. […] A statistical model involves the identification of those elements of our problem which are subject to uncontrolled variation and a specification of that variation in terms of probability distributions. Therein lies the strength of the statistical approach and the source of many misunderstandings. Paradoxically, misunderstandings arise both from the lack of an adequate model and from over reliance on a model. […] At one level is the failure to recognise that there are many aspects of a model which cannot be tested empirically. At a higher level is the failure is to recognise that any model is, necessarily, an assumption in itself. The model is not the real world itself but a representation of that world as perceived by ourselves. This point is emphasised when, as may easily happen, two or more models make exactly the same predictions about the data. Even worse, two models may make predictions which are so close that no data we are ever likely to have can ever distinguish between them. […] All model-dependant inference is necessarily conditional on the model. This stricture needs, especially, to be borne in mind when using Bayesian methods. Such methods are totally model-dependent and thus all are vulnerable to this criticism. The problem can apparently be circumvented, of course, by embedding the model in a larger model in which any uncertainties are, themselves, expressed in probability distributions. However, in doing this we are embarking on a potentially infinite regress which quickly gets lost in a fog of uncertainty.

Though Hoover conceded that some might deem him a “fanatic,” he reacted with fury to any violations of the rules. In the spring of 1925, when White was still based in Houston, Hoover expressed outrage to him that several agents in the San Francisco field office were drinking liquor. He immediately fired these agents and ordered White—who, unlike his brother Doc and many of the other Cowboys, wasn’t much of a drinker—to inform all of his personnel that they would meet a similar fate if caught using intoxicants. He told White, “I believe that when a man becomes a part of the forces of this Bureau he must so conduct himself as to remove the slightest possibility of causing criticism or attack upon the Bureau.” The new policies, which were collected into a thick manual, the bible of Hoover’s bureau, went beyond codes of conduct. They dictated how agents gathered and processed information. In the past, agents had filed reports by phone or telegram, or by briefing a superior in person. As a result, critical information, including entire case files, was often lost. Before joining the Justice Department, Hoover had been a clerk at the Library of Congress—“ I’m sure he would be the Chief Librarian if he’d stayed with us,” a co-worker said—and Hoover had mastered how to classify reams of data using its Dewey decimal–like system. Hoover adopted a similar model, with its classifications and numbered subdivisions, to organize the bureau’s Central Files and General Indices. (Hoover’s “Personal File,” which included information that could be used to blackmail politicians, would be stored separately, in his secretary’s office.) Agents were now expected to standardize the way they filed their case reports, on single sheets of paper. This cut down not only on paperwork—another statistical measurement of efficiency—but also on the time it took for a prosecutor to assess whether a case should be pursued.

In April, Dr. Vladimir (Zev) Zelenko, M.D., an upstate New York physician and early HCQ adopter, reproduced Dr. Didier Raoult’s “startling successes” by dramatically reducing expected mortalities among 800 patients Zelenko treated with the HCQ cocktail.29 By late April of 2020, US doctors were widely prescribing HCQ to patients and family members, reporting outstanding results, and taking it themselves prophylactically. In May 2020, Dr. Harvey Risch, M.D., Ph.D. published the most comprehensive study, to date, on HCQ’s efficacy against COVID. Risch is Yale University’s super-eminent Professor of Epidemiology, an illustrious world authority on the analysis of aggregate clinical data. Dr. Risch concluded that evidence is unequivocal for early and safe use of the HCQ cocktail. Dr. Risch published his work—a meta-analysis reviewing five outpatient studies—in affiliation with the Johns Hopkins Bloomberg School of Public Health in the American Journal of Epidemiology, under the urgent title, “Early Outpatient Treatment of Symptomatic, High-Risk COVID-19 Patients that Should be Ramped-Up Immediately as Key to Pandemic Crisis.”30 He further demonstrated, with specificity, how HCQ’s critics—largely funded by Bill Gates and Dr. Tony Fauci31—had misinterpreted, misstated, and misreported negative results by employing faulty protocols, most of which showed HCQ efficacy administered without zinc and Zithromax which were known to be helpful. But their main trick for ensuring the protocols failed was to wait until late in the disease process before administering HCQ—when it is known to be ineffective. Dr. Risch noted that evidence against HCQ used late in the course of the disease is irrelevant. While acknowledging that Dr. Didier Raoult’s powerful French studies favoring HCQ efficacy were not randomized, Risch argued that the results were, nevertheless, so stunning as to far outweigh that deficit: “The first study of HCQ + AZ [ . . . ] showed a 50-fold benefit of HCQ + AZ vs. standard of care . . . This is such an enormous difference that it cannot be ignored despite lack of randomization.”32 Risch has pointed out that the supposed need for randomized placebo-controlled trials is a shibboleth. In 2014 the Cochrane Collaboration proved in a landmark meta-analysis of 10,000 studies, that observational studies of the kind produced by Didier Raoult are equal

if consumption by the one billion people in the developed countries declined, it is certainly nowhere close to doing so where the other six billion of us are concerned. If the rest of the world bought cars and trucks at the same per capita rate as in the United States, the world’s population of cars and trucks would be 5.5 billion. The production of global warming pollution and the consumption of oil would increase dramatically over and above today’s unsustainable levels. With the increasing population and rising living standards in developing countries, the pressure on resource constraints will continue, even as robosourcing and outsourcing reduce macroeconomic demand in developed countries. Around the same time that The Limits to Growth was published, peak oil production was passed in the United States. Years earlier, a respected geologist named M. King Hubbert collected voluminous data on oil production in the United States and calculated that an immutable peak would be reached shortly after 1970. Although his predictions were widely dismissed, peak production did occur exactly when he predicted it would. Exploration, drilling, and recovery technologies have since advanced significantly and U.S. oil production may soon edge back slightly above the 1970 peak, but the new supplies are far more expensive. The balance of geopolitical power shifted slightly after the 1970 milestone. Less than a year after peak oil production in the U.S., the Organization of Petroleum Exporting Countries (OPEC) began to flex its muscles, and two years later, in the fall of 1973, the Arab members of OPEC implemented the first oil embargo. Since those tumultuous years when peak oil was reached in the United States, energy consumption worldwide has doubled, and the growth rates in China and other emerging markets portend further significant increases. Although the use of coal is declining in the U.S., and coal-fired generating plants are being phased out in many other developed countries as well, China’s coal imports have already increased 60-fold over the past decade—and will double again by 2015. The burning of coal in much of the rest of the developing world has also continued to increase significantly. According to the International Energy Agency, developing and emerging markets will account for all of the net global increase in both coal and oil consumption through the next two decades. The prediction of global peak oil is fraught with

As Graedon scrutinized the FDA’s standards for bioequivalence and the data that companies had to submit, he found that generics were much less equivalent than commonly assumed. The FDA’s statistical formula that defined bioequivalence as a range—a generic drug’s concentration in the blood could not fall below 80 percent or rise above 125 percent of the brand name’s concentration, using a 90 percent confidence interval—still allowed for a potential outside range of 45 percent among generics labeled as being the same. Patients getting switched from one generic to another might be on the low end one day, the high end the next. The FDA allowed drug companies to use different additional ingredients, known as excipients, that could be of lower quality. Those differences could affect a drug’s bioavailability, the amount of drug potentially absorbed into the bloodstream. But there was another problem that really drew Graedon’s attention. Generic drug companies submitted the results of patients’ blood tests in the form of bioequivalence curves. The graphs consisted of a vertical axis called Cmax, which mapped the maximum concentration of drug in the blood, and a horizontal axis called Tmax, the time to maximum concentration. The resulting curve looked like an upside-down U. The FDA was using the highest point on that curve, peak drug concentration, to assess the rate of absorption into the blood. But peak drug concentration, the point at which the blood had absorbed the largest amount of drug, was a single number at one point in time. The FDA was using that point as a stand-in for “rate of absorption.” So long as the generic hit a similar peak of drug concentration in the blood as the brand name, it could be deemed bioequivalent, even if the two curves reflecting the time to that peak looked totally different. Two different curves indicated two entirely different experiences in the body, Graedon realized. The measurement of time to maximum concentration, the horizontal axis, was crucial for time-release drugs, which had not been widely available when the FDA first created its bioequivalence standard in 1992. That standard had not been meaningfully updated since then. “The time to Tmax can vary all over the place and they don’t give a damn,” Graedon emailed a reporter. That “seems pretty bizarre to us.” Though the FDA asserted that it wouldn’t approve generics with “clinically significant” differences in release rates, the agency didn’t disclose data filed by the companies, so it was impossible to know how dramatic the differences were.

Punishment is not care, and poverty is not a crime. We need to create safe, supportive pathways for reentry into the community for all people and especially young people who are left out and act out. Interventions like decriminalizing youthful indiscretions for juvenile offenders and providing foster children and their families with targeted services and support would require significant investment and deliberate collaboration at the community, state, and federal levels, as well as a concerted commitment to dismantling our carceral state. These interventions happen automatically and privately for young offenders who are not poor, whose families can access treatment and hire help, and who have the privilege of living and making mistakes in neighborhoods that are not over-policed. We need to provide, not punish, and to foster belonging and self-sufficiency for our neighbors’ kids. More, funded YMCAs and community centers and summer jobs, for example, would help do this. These kinds of interventions would benefit all the Carloses, Wesleys, Haydens, Franks, and Leons, and would benefit our collective well-being. Only if we consider ourselves bound together can we reimagine our obligation to each other as community. When we consider ourselves bound together in community, the radically civil act of redistributing resources from tables with more to tables with less is not charity, it is responsibility; it is the beginning of reparation. Here is where I tell you that we can change this story, now. If we seek to repair systemic inequalities, we cannot do it with hope and prayers; we have to build beyond the systems and begin not with rehabilitation but prevention. We must reimagine our communities, redistribute our wealth, and give our neighbors access to what they need to live healthy, sustainable lives, too. This means more generous social benefits. This means access to affordable housing, well-resourced public schools, affordable healthcare, jobs, and a higher minimum wage, and, of course, plenty of good food. People ask me what educational policy reform I would suggest investing time and money in, if I had to pick only one. I am tempted to talk about curriculum and literacy, or teacher preparation and salary, to challenge whether police belong in schools, to push back on standardized testing, or maybe debate vocational education and reiterate that educational policy is housing policy and that we cannot consider one without the other. Instead, as a place to start, I say free breakfast and lunch. A singular reform that would benefit all students is the provision of good, free food at school. (Data show that this practice yields positive results; but do we need data to know this?) Imagine what would happen if, across our communities, people had enough to feel fed.

The variation of place and time is also a problem, as we cannot simply accept that Biblical Hebrew, which had already ceased to be a living language, underwent a unified development in places as diverse as Alexandria and Palestine. Neither do we know if the data afforded by the transcriptions corresponds to the standard ... pronunciation of Hebrew in this period or to dialect or substandard forms. On top of all these difficulties is the fact that the transcriptions have to be studied in manuscripts that are frequently late and defective, presenting many variants and corruptions in names that the copyists found completely alien.

Before the program began, teachers were surveyed about their confidence implementing the new standards. Sixty-three percent said they were confident in their ability to teach them. By the end, that number had risen to 95 percent. Nearly all of the teachers reported that the training had led to changes in their classrooms. But Coggins says they hope to use more concrete data, like student test scores,

Do you think the United States is currently a united or a divided country? If you are like most people, you would say the United States is divided these days due to the high level of political polarization. You might even say the country is about as divided as it has ever been. America, after all, is now color-coded: red states are Republican; blue states are Democratic. But, in Uncharted, Aiden and Michel note one fascinating data point that reveals just how much more divided the United States once was. The data point is the language people use to talk about the country. Note the words I used in the previous paragraph when I discussed how divided the country is. I wrote, “The United States is divided.” I referred to the United States as a singular noun. This is natural; it is proper grammar and standard usage. I am sure you didn’t even notice. However, Americans didn’t always speak this way. In the early days of the country, Americans referred to the United States using the plural form. For example, John Adams, in his 1799 State of the Union address, referred to “the United States in their treaties with his Britanic Majesty.” If my book were written in 1800, I would have said, “The United States are divided.” This little usage difference has long been a fascination for historians, since it suggests there was a point when America stopped thinking of itself as a collection of states and started thinking of itself as one nation.

EFM certainly seems like a good idea. A machine that measures and records a baby’s response to contractions provides scientific data about a particular woman’s labor. Logic says—and many people assume—that EFM improves birth outcomes. Actually, three decades of research shows that EFM doesn’t improve birth outcomes. When EFM is used during labor, no fewer babies die and no fewer have problems at birth. However, more women have cesareans when EFM is used.21 If EFM doesn’t help babies and puts mothers at higher risk of surgical intervention, it is not safer care. In 1988, a Harvard Medical School report described EFM as a “failed technology” but also predicted that doctors wouldn’t stop using it because they fear being sued. Fear of malpractice litigation is pervasive in obstetrics. Doctors too often make patient-care decisions based on their fear of a lawsuit rather than on evidence-based standards of practice established by their profession.

In his eighteenth-century system of the world, Newton brought together two themes. Embodied in his calculus and physics, one Newtonian revelation rendered the physical world predictable and measurable. Another, less celebrated, was his key role in establishing a trustworthy gold standard, which made economic valuations as calculable and reliable as the physical dimensions of items in trade.

it would be good to include staleness measurements in the standard set of metrics for databases. Eventual consistency is a deliberately vague guarantee, but for operability it’s important to be able to quantify “eventual.

In 2006, Avinash Kaushik and Ronny Kohavi, two data analysis professionals who were then working at Intuit and Microsoft, respectively, came up with the acronym HiPPO to summarize the dominant decision-making style at most companies. It stands for “highest-paid person’s opinion.” We love this shorthand and use it a lot, because it vividly illustrates the standard partnership. Even when the decisions are not made by the highest-paid people, they’re often—too often—based on opinions, judgments, intuition, gut, and System 1. The evidence is clear that this approach frequently doesn’t work well, and that HiPPOs too often destroy value.

Smart Finance’s deep-learning algorithms don’t just look to the obvious metrics, like how much money is in your WeChat Wallet. Instead, it derives predictive power from data points that would seem irrelevant to a human loan officer. For instance, it considers the speed at which you typed in your date of birth, how much battery power is left on your phone, and thousands of other parameters. What does an applicant’s phone battery have to do with creditworthiness? This is the kind of question that can’t be answered in terms of simple cause and effect. But that’s not a sign of the limitations of AI. It’s a sign of the limitations of our own minds at recognizing correlations hidden within massive streams of data. By training its algorithms on millions of loans—many that got paid back and some that didn’t—Smart Finance has discovered thousands of weak features that are correlated to creditworthiness, even if those correlations can’t be explained in a simple way humans can understand. Those offbeat metrics constitute what Smart Finance founder Ke Jiao calls “a new standard of beauty” for lending, one to replace the crude metrics of income, zip code, and even credit

The Web evolved in an unregulated environment with no coherent privacy standards about personal data. Consequently, the rights to such data are unclear and vary from site to site. Location data, medical sensor data, and the like are collected by heavily regulated industries with fairly clear data ownership rules and could be made more widely accessible by extending the New Deal on Data to existing frameworks. But what of the Wild Wild Web? Fortunately, existing Web companies are coming under pressure to conform to the higher standards imposed on regulated industries.

I’ve argued that the good data that effective metrics provide are essential to advancing the science at the heart of evidence-based medicine. But I’ve also argued that not all metrics or standards are created equal, and we should not equate metric-tracking with trust-building, because to do so misses a crucial point: What looks good on paper and what drives the best outcomes in practice can be two very different things. Too often, what looks good on paper is what is possible to measure, not necessarily what is actually the best approach to caring for patients. And when we consider the costs of abiding by and tracking and reporting all of these metrics—the four hours of physician time, the eight hours of care team time, the $8 billion we spend as a nation every year—it’s pretty clear that we’re interfering with those best, relationship-building approaches. Instead of spending so much more of our national time, resources, and attention in medicine on creating artificial metrics designed to incentivize good physician and provider behavior while unwittingly reinforcing bad behavior, let’s give the art of medicine the room it needs to build trusting relationships in the way that the best doctors and medical practices have always done so: honestly, naturally, compassionately, and with the best outcomes for the patient squarely in mind.

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The American City was not unlike the first great products of American industrialism itself: the Colt revolver and the Winchester rifle. Gun manufacturing taught American industry about mass production, standardization, and the virtues of interchangeable parts, and the American city that industrialism produced was itself a very big gun: standardized, hugely profitable, and morally indifferent about any victims.

One assumption that is already being shattered is the idea that only routine, semi-skilled jobs like taxi driving, food delivery, or household chores are susceptible. Even traditional professions like medicine and law are proving to be susceptible to platform models. We’ve already mentioned Medicast, which applies an Uber-like model to finding a doctor. Several platform companies are providing online venues where legal services are available with comparable ease, speed, and convenience. Axiom Law has built a $200 million platform business by using a combination of data-mining software and freelance law talent to provide legal guidance and services to business clients; InCloudCounsel claims it can process basic legal documents such as licensing forms and nondisclosure agreements at a savings of up to 80 percent compared with a traditional law firm.11 In the decades to come, it seems likely that the platform model will be applied—or at least tested—in virtually every market for labor and professional services. How will this trend impact the service industries—not to mention the working lives of hundreds of millions of people? One likely result will be an even greater stratification of wealth, power, and prestige among service providers. Routine and standardized tasks will move to online platforms, where an army of relatively low-paid, self-employed professionals will be available to handle them. Meanwhile, the world’s great law firms, medical centers, consulting partnerships, and accounting practices will not vanish, but their relative size and importance will shrink as much of the work they used to do migrates to platforms that can provide comparable services at a fraction of the cost and with far greater convenience. A surviving handful of world-class experts will increasingly focus on a tiny subset of the most highly specialized and challenging assignments, which they can tackle from anywhere in the world using online tools. Thus, at the very highest level of professional expertise, winner-take-all markets are likely to emerge, with (say) two dozen internationally renowned attorneys competing for the splashiest and most lucrative cases anywhere on the globe.

When scientific proposals are brought forward, they are not judged by hunches or gut feelings. Only one standard is relevant: a proposal's ability to explain or predict experimental data and astronomical observations. Therein lies the singular beauty of science. As we struggle toward deeper understanding, we must give our creative imagination ample room to explore. We must be willing to step outside conventional ideas and established frameworks. But unlike the wealth of other human activities through which the creative impulse is channeled, science supplies a final reckoning, a built-in assessment of what's right and what's not. A complication of scientific life in the late twentieth and early twenty-first centuries is that some of our theoretical ideas have soared past our ability to test or observe. String theory has for some time been the poster child for this situation; the possibility that we're part of a multiverse provides an even more sprawling example. I've laid out a general prescription for how a multiverse proposal might be testable, but at our current level of understanding none of the multiverse theories we've encountered yet meet the criteria. With ongoing research, this situation could greatly improve.

Rather, you take the data you have and randomly divide it into a training set, which you give to the learner, and a test set, which you hide from it and use to verify its accuracy. Accuracy on held-out data is the gold standard in machine learning. You can write a paper about a great new learning algorithm you’ve invented, but if your algorithm is not significantly more accurate than previous ones on held-out data, the paper is not publishable. Accuracy on previously unseen data is a pretty stringent test; so much so, in fact, that a lot of science fails it. That does not make it useless, because science is not just about prediction; it’s also about explanation and understanding. But ultimately, if your models don’t make accurate predictions on new data, you can’t be sure you’ve truly understood or explained the underlying phenomena. And for machine learning, testing on unseen data is indispensable because it’s the only way to tell whether the learner has overfit or not.

In the past, security guides often suggested that it was necessary to overwrite multiple times (or “passes”). This may be true to some extent for flash media, as described below, but is apparently no longer true for traditional magnetic hard drives. See National Institute of Standards and Technology, NIST Special Publication 800-88, Revision 1. “Guidelines for Media Sanitization” (Dec. 2014) (“For storage devices containing magnetic media, a single overwrite pass with a [fixed] pattern such as binary zeroes typically hinders recovery of data even if state of the art laboratory techniques are applied to attempt to retrieve the data.”)

In April 2012, The New York Times published a heart-wrenching essay by Claire Needell Hollander, a middle school English teacher in the New York City public schools. Under the headline “Teach the Books, Touch the Heart,” she began with an anecdote about teaching John Steinbeck’s Of Mice and Men. As her class read the end together out loud in class, her “toughest boy,” she wrote, “wept a little, and so did I.” A girl in the class edged out of her chair to get a closer look and asked Hollander if she was crying. “I am,” she said, “and the funny thing is I’ve read it many times.” Hollander, a reading enrichment teacher, shaped her lessons around robust literature—her classes met in small groups and talked informally about what they had read. Her students did not “read from the expected perspective,” as she described it. They concluded (not unreasonably) that Holden Caulfield “was a punk, unfairly dismissive of parents who had given him every advantage.” One student read Lady Macbeth’s soliloquies as raps. Another, having been inspired by Of Mice and Men, went on to read The Grapes of Wrath on his own and told Hollander how amazed he was that “all these people hate each other, and they’re all white.” She knew that these classes were enhancing her students’ reading levels, their understanding of the world, their souls. But she had to stop offering them to all but her highest-achieving eighth-graders. Everyone else had to take instruction specifically targeted to boost their standardized test scores. Hollander felt she had no choice. Reading scores on standardized tests in her school had gone up in the years she maintained her reading group, but not consistently enough. “Until recently, given the students’ enthusiasm for the reading groups, I was able to play down that data,” she wrote. “But last year, for the first time since I can remember, our test scores declined in relation to comparable schools in the city. Because I play a leadership role in the English department, I felt increased pressure to bring this year’s scores up. All the teachers are increasing their number of test-preparation sessions and practice tests, so I have done the same, cutting two of my three classic book groups and replacing them with a test preparation tutorial program.” Instead of Steinbeck and Shakespeare, her students read “watered-down news articles or biographies, bastardized novels, memos or brochures.” They studied vocabulary words, drilled on how to write sentences, and practiced taking multiple-choice tests. The overall impact of such instruction, Hollander said, is to “bleed our English classes dry.” So

Building an airplane was nothing compared to shepherding research through Langley's grueling review process. "Present your case, build, sell it,= so they believe it" --- that was the Langley way. The author of a NACA document --- a technical report was the most comprehensive and exacting, a technical memorandum slightly less formal --- faced a firing squad of four or five people, chosen for their expertise in the topic. After a presentation of findings, the committee, which had read and analyzed the report in advance, let loose a barrage of questions and comments. The committee was brusque, thorough, and relentless in rooting out inaccuracies, inconsistencies, incomprehensible statements, and illogical conclusions obscured by technical gibberish. And that was before subjecting the report to the style, clarity, grammar, and presentation standards that were Pearl Young's legacy, before the addition of the charts and fancy graphics that reduced the data sheet to a coherent, visually persuasive point. A final report might be months, even years, in the making.

The Data Encryption Standard (DES) has been by far the most popular block cipher for most of the last 30 years. Even though it is nowadays not considered secure against a determined attacker because the DES key space is too small, it is still used in legacy applications. Furthermore, encrypting data three times in a row with DES — a process referred to as 3DES or triple DES — yields a very secure cipher which is still widely used today

Know Singapore’s Credit Bureau to Get License Money Lender Approval Do you ever wonder how a licensed money lender like banks get the information they need to decide whether they will approve your loan or not? In this article, you’ll know the Credit Bureau Singapore (CBS) role on the moneylenders’ process of lending money. History of CBS Association of Banks in Singapore (ABS) and DBIC Holdings owns CBS. It was founded on November 15, 2002, and its key role is to serve as a financial risk management tool for financial institutions. Among CBS founders’ are Citibank, United Overseas Bank (UOB), Development Bank of Singapore (DBS), Oversea-Chinese Banking Corporation (OCBC), American Express, ANZ, Maybank, HSBC and Standard Chartered Bank. Key Role of CBS on Licensed Money Lender Loan Approval The CBS is a private company established to help financial companies and credit card institutions to evaluate the threats and opportunities of giving credit to possible or current customers. To put simply, when you apply for a loan, the CBS gives the licensed moneylender your credit report. This credit report reflects your credit information such as credit history, repayment track, and in some cases default records, lawsuit, and bankruptcy reports. This valuable information is collected from financial institutions and other public data resources (like subpoena and data of bankruptcy) which is part of CBS. The Banking Act allows the CB to get such customer’s confidential data and produce a “complete risk profile.” CBS follows a stringent code of conduct to protect the consumer’s data privacy. Only the official members of CBS can access and use the credit information. Licensed money lender should not disclose any information about their clients’ credit background to any third party. The CB also cannot collect customer’s personal data such as contact numbers, home address, credit limit, and salary. Now that you finally know who helps licensed money lender to decide your loan’s approval, you should now know that borrowing money is not as simple as it sounds. Multiple agencies are working together to check whether you are worthy of the money.

Know Singapore’s Credit Bureau to Get License Money Lender Approval Do you ever wonder how a licensed money lender like banks get the information they need to decide whether they will approve your loan or not? In this article, you’ll know the Credit Bureau Singapore (CBS) role on the moneylenders’ process of lending money. History of CBS Association of Banks in Singapore (ABS) and DBIC Holdings owns CBS. It was founded on November 15, 2002, and its key role is to serve as a financial risk management tool for financial institutions. Among CBS founders’ are Citibank, United Overseas Bank (UOB), Development Bank of Singapore (DBS), Oversea-Chinese Banking Corporation (OCBC), American Express, ANZ, Maybank, HSBC and Standard Chartered Bank. Key Role of CBS on Licensed Money Lender Loan Approval The CBS is a private company established to help financial companies and credit card institutions to evaluate the threats and opportunities of giving credit to possible or current customers. To put simply, when you apply for a loan, the CBS gives the licensed moneylender your credit report. This credit report reflects your credit information such as credit history, repayment track, and in some cases default records, lawsuit, and bankruptcy reports. This valuable information is collected from financial institutions and other public data resources (like subpoena and data of bankruptcy) which is part of CBS. The Banking Act allows the CB to get such customer’s confidential data and produce a “complete risk profile.” CBS follows a stringent code of conduct to protect the consumer’s data privacy. Only the official members of CBS can access and use the credit information. Licensed money lender should not disclose any information about their clients’ credit background to any third party. The CB also cannot collect customer’s personal data such as contact numbers, home address, credit limit, and salary. Now that you finally know who helps licensed money lender to decide your loan’s approval, you should now know that borrowing money is not as simple as it sounds. Multiple agencies are working together to check whether you are worthy of the money.

Empirical logic achieved a signal triumph in the Old Testament, where survivals from the early proto-logical stage are very few and far between. With it man reached a point where his best judgments about his relation to God, his fellow men and the world, were in most respects not appreciably inferior to ours. In fundamental ethical and spiritual matters we have not progressed at all beyond the empirico-logical world of the Old Testament or the unrivalled fusion of proto-logical intuition, 64 [see Coomaraswamy, Review of Religion, 1942, p. 138, paragraph 3] empirico-logical wisdom and logical deduction which we find in the New Testament. In fact a very large section of modern religion, literature and art actually represents a pronounced retrogression when compared with the Old Testament. For example, astrology, spiritism and kindred divagations, which have become religion to tens of millions of Europeans and Americans, are only the outgrowth of proto-logical interpretation of nature, fed by empirico-logical data and covered with a spurious shell of Aristotelian logic and scientific induction. Plastic and graphic art has swung violently away from logical perspective and perceptual accuracy, and has plunged into primordial depths of conceptual drawing and intuitive imagery. While it cannot be denied that this swing from classical art to conceptual and impressionistic art has yielded some valuable results, it is also true that it represents a very extreme retrogression into the proto-logical past. Much of the poetry, drama and fiction which has been written during the past half-century is also a reversion from classical and logical standards of morality and beauty into primitive savagery or pathological abnormality. Some of it has reached such paralogical levels of sophistication that it has lost all power to furnish any standards at all to a generation which has deliberately tried to abandon its entire heritage from the past. All systematic attempts to discredit inherited sexual morality, to substitute dream-states for reflection, and to replace logical writing by jargon, are retreats into the jungle from which man emerged through long and painful millennia of disillusionment. With the same brains and affective reactions as those which our ancestors possessed two thousand years ago, increasing sophistication has not been able to teach us any sounder fundamental principles of life than were known at that time. . . . Unless we can continue along the pathway of personal morality and spiritual growth which was marked out for civilized man by the founders of the Judaeo-Christian tradition, more than two thousand years ago, our superior skill in modifying and even in transforming the material world about us can lead only to repeated disasters, each more terrible than its predecessor. (Archaeology and the Religion of Israel, 5th Ed. New York: Doubleday Anchor, 31-33.)

Fiscal Numbers (the latter uniquely identifies a particular hospitalization for patients who might have been admitted multiple times), which allowed us to merge information from many different hospital sources. The data were finally organized into a comprehensive relational database. More information on database merger, in particular, how database integrity was ensured, is available at the MIMIC-II web site [1]. The database user guide is also online [2]. An additional task was to convert the patient waveform data from Philips’ proprietary format into an open-source format. With assistance from the medical equipment vendor, the waveforms, trends, and alarms were translated into WFDB, an open data format that is used for publicly available databases on the National Institutes of Health-sponsored PhysioNet web site [3]. All data that were integrated into the MIMIC-II database were de-identified in compliance with Health Insurance Portability and Accountability Act standards to facilitate public access to MIMIC-II. Deletion of protected health information from structured data sources was straightforward (e.g., database fields that provide the patient name, date of birth, etc.). We also removed protected health information from the discharge summaries, diagnostic reports, and the approximately 700,000 free-text nursing and respiratory notes in MIMIC-II using an automated algorithm that has been shown to have superior performance in comparison to clinicians in detecting protected health information [4]. This algorithm accommodates the broad spectrum of writing styles in our data set, including personal variations in syntax, abbreviations, and spelling. We have posted the algorithm in open-source form as a general tool to be used by others for de-identification of free-text notes [5].

The beauty of the normal distribution - its Michael Jordan power, finesse, and elegance - comes from the fact that we know by definition exactly what proportion of the observations in a normal distribution lie within one standard deviation of the mean (68.2 percent), within two standard deviations of the mean (95.4 percent), within three standard deviations of the mean (99.7 percent), and so on.

But if we have carefully chosen indicators that characterize an administrative unit and watch them closely, we are ready to apply the methods of factory control to administrative work. We can use de facto standards, inferred from the trend data, to forecast the number of people needed to accomplish various anticipated tasks. By rigorous application of the principles of forecasting, manpower can be reassigned from one area to another, and the headcount made to match the forecasted growth or decline in administrative activity. Without rigor, the staffing of administrative units would always be left at its highest level and, given Parkinson’s famous law, people would find ways to let whatever they’re doing fill the time available for its completion.

>>> fill = {"name": "Blitz", "age": "30"} >>> g = PersonDetailsForm(fill) >>> g.is_valid() True >>> g.cleaned_data {'age': 30, 'name': 'Blitz'}

SOA Microservices Inter-service communication Smart pipes, such as Enterprise Service Bus, using heavyweight protocols, such as SOAP and the other WS* standards. Dumb pipes, such as a message broker, or direct service-to-service communication, using lightweight protocols such as REST or gRPC Data Global data model and shared databases Data model and database per service Typical service Larger monolithic application Smaller service

NEW BIBLIOGRAPHIC FRAMEWORK To sustain broader partnerships—and to be seen in the non-library specific realm of the Internet—metadata in future library systems will undoubtedly take on new and varied forms. It is essential that future library metadata be understood and open to general formats and technology standards that are used universally. Libraries should still define what data is gathered and what is essential for resource use, keeping in mind the specific needs of information access and discovery. However, the means of storage and structure for this metadata must not be proprietary to library systems. Use of the MARC standard format has locked down library bibliographic information. The format was useful in stand-alone systems for retrieval of holdings in separate libraries, but future library systems will employ non-library-specific formats enabling the discovery of library information by any other system desiring to access the information. We can expect library systems to ingest non-MARC formats such as Dublin Core; likewise, we can expect library discovery interfaces to expose metadata in formats such as Microdata and other Semantic Web formats that can be indexed by search engines. Adoption of open cloud-based systems will allow library data and metadata to be accessible to non-library entities without special arrangements. Libraries spent decades creating and storing information that was only accessible, for the most part, to others within the same profession. Libraries have begun to make partnerships with other non-library entities to share metadata in formats that can be useful to those entities. OCLC has worked on partnerships with Google for programs such as Google Books, where provided library metadata can direct users back to libraries. ONIX for Books, the international standard for electronic distribution of publisher bibliographic data, has opened the exchange of metadata between publishers and libraries for the enhancements of records on both sides of the partnership. To have a presence in the web of information available on the Internet is the only means by which any data organization will survive in the future. Information access is increasingly done online, whether via computer, tablet, or mobile device. If library metadata does not exist where users are—on the Internet—then libraries do not exist to those users. Exchanging metadata with non-library entities on the Internet will allow libraries to be seen and used. In addition to adopting open systems, libraries will be able to collectively work on implementation of a planned new bibliographic framework when using library platforms. This new framework will be based on standards relevant to the web of linked data rather than standards proprietary to libraries

The Library of Congress, with other partners, continues to work on a new bibliographic framework (BIBFRAME). This framework will be an open-storage format based on newer technology, such as XML. A framework is merely a holder of content, and a more open framework will allow for easier access to stored metadata. While resource description and access (RDA) is a movement to rewrite cataloging rules, BIBFRAME is a movement to develop a new storage medium. The new storage framework may still use RDA as a means of describing content metadata, but it will move storage away from MARC to a new format based on standardized non-library technology. This new framework will encompass several important characteristics. It will transition storage of library metadata to an open format that is accessible for use by external systems, using standard technology employed outside of libraries. This will allow for libraries to share metadata with each other and with the rest of the Semantic Web. The new framework will also allow for the storage of both old and new metadata formats so that libraries may move forward without reworking existing records. Finally, the new framework will make use of formal metadata structure, as the benefit of named metadata fields has more power for search and discovery than the simple keyword searching employed by much of the Internet. Library metadata will become more important once its organized fields of information can be accessed by any standard non-library system. Embracing a new storage format for bibliographic metadata is much like adoption of a new computer storage format, such as moving your data storage from CD-ROM to an external USB hard drive; the metadata that libraries have created for decades will not be lost but will be converted to a new, more accessible, storage format, sustaining access to the information. Although these benefits may be seen by some, it can be expected that there may be resistance to changes in format as well. It will be no small undertaking to define how libraries will move forward and to then provide means for libraries to transition to new formats. Whatever transitions may be adopted, it will be important that libraries not abandon a structured metadata entry form in lieu of complete keyword formatting.

Let us look at the correlation between temperature, humidity and wind speed and all other features. Since the data also contains categorical features, we cannot only use the Pearson correlation coefficient, which only works if both features are numerical. Instead, I train a linear model to predict, for example, temperature based on one of the other features as input. Then I measure how much variance the other feature in the linear model explains and take the square root. If the other feature was numerical, then the result is equal to the absolute value of the standard Pearson correlation coefficient. But this model-based approach of “variance-explained” (also called ANOVA, which stands for ANalysis Of VAriance) works even if the other feature is categorical. The “variance-explained” measure lies always between 0 (no association) and 1 (temperature can be perfectly predicted from the other feature). We calculate the explained variance of temperature, humidity and wind speed with all the other features. The higher the explained variance (correlation), the more (potential) problems with PD plots. The following figure visualizes how strongly the weather features are correlated with other features.

On the other hand, smaller samples obviously make for larger standard errors and therefore a larger confidence interval (or “margin of sampling error,” to use the polling lingo).

You should see that a bigger sample makes for a shrinking standard error, which is how large national polls can end up with shockingly accurate results.

suspects.85 What legislative reforms of policing are crucial? Five are especially important: (1) expand the liability standards for police officers and the departments that employ them; (2) outlaw particularly dangerous police practices; (3) authorize suits against federal law enforcement officials who violate the Constitution; (4) mandate data collection about policing; and (5) increase transparency as to policing

Unfortunately, the SQL standard’s definition of isolation levels is flawed—it is ambiguous, imprecise, and not as implementation-independent as a standard should be [28]. Even though several databases implement repeatable read, there are big differences in the guarantees they actually provide, despite being ostensibly standardized [23]. There has been a formal definition of repeatable read in the research literature [29, 30], but most implementations don’t satisfy that formal definition. And to top it off, IBM DB2 uses “repeatable read” to refer to serializability [8]. As a result, nobody really knows what repeatable read means.

[원료약품분량] 이 약 1정(126mg) 중 졸피뎀 타르타르산염 (EP) [성상] 백색 장방형의 필름코팅제 [효능효과] 불면증 [용법용량] 까톡【pak6】텔레:【JRJR331】텔레:【TTZZZ6】라인【TTZZ6】 졸피뎀(Zolpidem) 은 불면증이나 앰비엔(Ambien), 앰비엔 CR(Ambien CR), 인터메조(Intermezzo), 스틸넉스(Stilnox), 스틸넉트(Stilnoct), 서블리넉스(Sublinox), 하이프너젠(Hypnogen), 조네이딘(Zonadin), Sanval, Zolsana and Zolfresh 등은 졸피뎀의 시판되는 품명이다. 1) 이 약은 작용발현이 빠르므로, 취침 바로 직전에 경구투여한다. In addition to "I love you" used to date in Korean, there are old words such as "goeda" [3], "dada" [4], and "alluda" [5]. In Chinese characters, 愛(ae) and 戀(yeon) have the meaning of love. In Chinese characters, 戀 mainly means love in a relationship, and 愛 means more comprehensive love than that. In the case of Jeong, the meaning is more comprehensive than Ae or Yeon, and it is difficult to say the word love. In the case of Japanese, it is divided into two types: 愛 (あい) and 恋 (いこ) [6]. There are two main views on etymology. First of all, there is a hypothesis that the combination of "sal" in "live" or "sard" and the suffix "-ang"/"ung" was changed to "love" from the Middle Ages, but "love" clearly appears as a form of "sudah" in the Middle Ages, so there is a problem that the vowels do not match at all. Although "Sarda" was "Sanda," the vowels match, but the gap between "Bulsa" and "I love you" is significant, and "Sanda" and "Sanda Lang," which were giants, have a difference in tone, so it is difficult to regard it as a very reliable etymology. Next, there is a hypothesis that it originated from "Saryang," which means counting the other person. It is a hypothesis argued by Korean language scholars such as Yang Ju-dong, and at first glance, it can be considered that "Saryang," which means "thinking and counting," has not much to do with "love" in meaning. In addition, some criticize the hypothesis, saying that the Chinese word Saryang itself is an unnatural coined word that means nothing more than "the amount of thinking." However, in addition to the meaning of "Yang," there is a meaning of "hearida," and "Saryang" is also included in the Standard Korean Dictionary and the Korean-Chinese Dictionary as a complex verb meaning "think and count." In addition, as will be described later, Saryang is an expression whose history is long enough to be questioned in the Chinese conversation book "Translation Noguldae" in the early 16th century, so the criticism cannot be considered to be consistent with the facts. In addition, if you look at the medieval Korean literature data, you can find new facts. 2) 성인의 1일 권장량은 10mg이며, 이러한 권장량을 초과하여서는 안된다. 노인 또는 쇠약한 환자들의 경우, 이 약의 효과에 민감할 수 있기 때문에, 권장량을 5mg으로 하며, 1일 10mg을 초과하지 않는다. ^^바로구입가기^^ ↓↓아래 이미지 클릭↓↓ 까톡【pak6】텔레:【JRJR331】텔레:【TTZZZ6】라인【TTZZ6】 3) 간 손상으로 이 약의 대사 및 배설이 감소될 수 있으므로, 노인 환자들에서처럼 특별한 주 의와 함께 용량을 5mg에서 시작하도록 한다. 4) 65세 미만의 성인의 경우, 약물의 순응도가 좋으면서 임상적 반응이 불충분한 경우 용량을 10mg까지 증량할 수 있다. 5) 치료기간은 보통 수 일에서 2주, 최대한 4주까지 다양하며, 용량은 임상적으로 적절한 경우 점진적으로 감량해가도록 한다. 6) 다른 수면제들과 마찬가지로, 장기간 사용은 권장되지 않으며, 1회 치료기간은 4주를 넘지 않도록 한다.

For too long we have positioned women as a deviation from standard humanity and this is why they have been allowed to become invisible. It's time for a change in perspective. It's time for women to be seen.

One of Clark’s earliest statements on the proper nature of economics argued that economics should be “based on a foundation of terms, conceptions, standards of measurement, and assumptions which is sufficiently realistic, comprehensive, and unbiased” to provide a basis for the analysis and discussion of practical issues (Clark 1919, p. 280). Relevance to practical issues, accuracy of data, and comprehensiveness, in the sense of not excluding any evidence relevant to the problem at hand, were the characteristics of a scientific approach to economics that Clark most frequently stressed (Clark 1924, p. 74).

So when you come across some observations that do not fit the standard explanation, let your mind wander to see whether some radically different interpretation might do a better job. Perhaps you will think of something that will fit both the new data and the old data and thereby supplant the standard expla- nation. Toy with different perspectives. Look for the unusual. Try consciously to innovate. Train yourself to imagine new schemes and innovative ways to fit the pieces together. Seek the joy of discovery. Always test your new thoughts against the facts, of course, in rigorous, cold-blooded, unemotional scientific manner. But play the great game of the visionary and the innovator as well.

Nevertheless, they felt a powerful urge to impart their wisdom to their friends at ARPA. Thanks to the legal beagles’ strictures, they were reduced to getting their points across by a weird pantomime of asking inscrutable but cunningly pointed questions. “Somebody would be talking about the design for some element and we’d drop all these hints,” Shoch recalled. “We’d say, ‘You know, that’s interesting, but what happens if this error message comes back, and what happens if that’s followed by a delayed duplicate that was slowed down in its response from a distant gateway when the flow control wouldn’t take it but it worked its way back and got here late? What do you do then?’ There would be this pause and they’d say, ‘You’ve tried this!’ And we’d reply, ‘Hey, we never said that!’” Eventually they managed to communicate enough of Pup’s architecture for it to become a crucial part of the ARPANET standard known as TCP/IP, which to this date is what enables data packets to pass gracefully across the global data network known as the Internet—with a capital “I.