Data Measurement Quotes

In a world without love, this is what people are to each other: values, benefits, and liabilities, numbers and data. We weigh, we quantify, we measure, and the soul is ground to dust.

We tend to overvalue the things we can measure and undervalue the things we cannot.

We don’t know what astatine looks like, because, as Lowe put it, “that stuff just doesn’t want to exist.” It’s so radioactive (with a half-life measured in hours) that any large piece of it would be quickly vaporized by its own heat. Chemists suspect that it has a black surface, but no one really knows. There’s no material safety data sheet for astatine. If there were, it would just be the word “NO” scrawled over and over in charred blood.

What gets measured (and clearly defined) does get done.

My father will pay,' [Julian] says after a beat. 'I'm valuable to the movement.' I don't say anything. In a world without love, this is what people are to each other: values, benefits and liabilities, numbers and data. We weigh, we quantify, we measure, and the soul is ground to dust.

Sometimes, curiosity overpowers the warning of danger. It just does. Especially, when the human brain doesn’t have enough memories to measure the level of danger. Because the brain lacks examples, past references. People call it experience. So, when the reference data is few, the only option is to get more of it. Curiosity is an inexperienced brain’s call to collect reference data. Right now, this very human curiosity burns her brain.

That the volume of information, of data, of judgements, of measurements, was too much, and there were too many people, and too many desires of too many people, and too many opinions of too many people, and too much pain from too many people, and having all of it constantly collated, collected, added and aggregated, and presented to her as if that all made it tidier and more manageable--it was too much.

The greatest risks are never the ones you can see and measure, but the ones you can’t see and therefore can never measure. The ones that seem so far outside the boundary of normal probability that you can’t imagine they could happen in your lifetime—even though, of course, they do happen, more often than you care to realize.

People often think that the best way to predict the future is by collecting as much data as possible before making a decision. But this is like driving a car looking only at the rearview mirror—because data is only available about the past.

Systemic disruption requires us to accept that there may be no measurable data to fully substantiate our understanding of those disruptions. Imaging and exploring the multiplicity of potential futures which may arise from disruptions is a creative exercise, not a number-crunching one.

Money is not a magic wand but a measuring stick, not wealth but a gauge of it.

Remember Goodhart’s law? “When a measure becomes a target, it ceases to be a good measure.

When a measure becomes a target, it ceases to be a good measure.”)

NASA are idiots. They want to send canned primates to Mars!" Manfred swallows a mouthful of beer, aggressively plonks his glass on the table: "Mars is just dumb mass at the bottom of a gravity well; there isn't even a biosphere there. They should be working on uploading and solving the nanoassembly conformational problem instead. Then we could turn all the available dumb matter into computronium and use it for processing our thoughts. Long-term, it's the only way to go. The solar system is a dead loss right now – dumb all over! Just measure the MIPS per milligram. If it isn't thinking, it isn't working. We need to start with the low-mass bodies, reconfigure them for our own use. Dismantle the moon! Dismantle Mars! Build masses of free-flying nanocomputing processor nodes exchanging data via laser link, each layer running off the waste heat of the next one in. Matrioshka brains, Russian doll Dyson spheres the size of solar systems. Teach dumb matter to do the Turing boogie!

Midas’s error was to mistake gold, wealth’s monetary measure, for wealth itself. But wealth is not a thing or a random sequence. It is inextricably rooted in hard won knowledge over extended time.

In this modern day, when only what we see is allowed to have certainity, and when scientific data seems to hold the trump card for truth, when only what can be measured exists, love defies all these strictures and dances joyfully before the eyes of human beings, teasing them with the promise of the unknown.

Silver noticed that the areas where Trump performed best made for an odd map. Trump performed well in parts of the Northeast and industrial Midwest, as well as the South. He performed notably worse out West. Silver looked for variables to try to explain this map. Was it unemployment? Was it religion? Was it gun ownership? Was it rates of immigration? Was it opposition to Obama? Silver found that the single factor that best correlated with Donald Trump’s support in the Republican primaries was that measure I had discovered four years earlier. Areas that supported Trump in the largest numbers were those that made the most Google searches for “nigger.

The flash opened up into something larger, an even more blasphemous notion that her brain contained too much. That the volume of information, of data, of judgments, of measurements, was too much, and there were too many people, and too many desires of too many people, and too many opinions of too many people, and too much pain from too many people, and having all of it constantly collated, collected, added and aggregated, and presented to her as if that all made it tidier and more manageable--it was too much.

In situations where there are no real feasible solutions to a problem, the gathering and publication of performance data serves as a form of virtue signaling. There is no real progress to show, but the effort demonstrated in gathering and publicizing the data satisfies a sense of moral earnestness. In lieu of real progress, the progress of measurement becomes a simulacrum of success.

Haven't you felt it? The loss of autonomy. The sense of being virtualized. The devices you use, the ones you carry everywhere, room to room, minute to minute, inescapably. Do you ever feel unfleshed? All the coded impulses you depend on to guide you. All the sensors in the room are watching you, listening to you, tracking your habits, measuring your capabilities. All the linked data designed to incorporate you into the megadata. Is there something that makes you uneasy? Do you think about the technovirus, all systems down, global implosion? Or is it more personal? Do you feel steeped in some horrific digital panic that's everywhere and nowhere?

You can’t manage what you can’t measure” is a maxim that is taught and believed by many in both the business and education sectors. But in fact, the phrase is ridiculous—something said by people who are unaware of how much is hidden. A large portion of what we manage can’t be measured, and not realizing this has unintended consequences. The problem comes when people think that data paints a full picture, leading them to ignore what they can’t see. Here’s my approach: Measure what you can, evaluate what you measure, and appreciate that you cannot measure the vast majority of what you do. And at least every once in a while, make time to take a step back and think about what you are doing.

If you don't want a man unhappy politically, don't give him two sides to a question to worry him; give him one. Better yet, give him none. Let him forget there is such a thing as war. If the government is inefficient, top-heavy, and tax-mad, better it be all those than that people worry over it. Peace, Montag. Give the people contests they win by remembering the words to more popular songs or the names of state capitals or how much corn Iowa grew last year. Cram them full of noncombustible data, chock them so damned full of 'facts' they feel stuffed, but absolutely 'brilliant' with information. Then they'll feel they're thinking, they'll get a sense of motion without moving. And they'll be happy, because facts of that sort don't change. Don't give them any slippery stuff like philosophy or sociology to tie things up with. That way lies melancholy. Any man who can take a TV wall apart and put it back together again, and most men can nowadays, is happier than any man who tries to slide-rule, measure and equate the universe, which just wont be measured or equated without making man feel bestial and lonely.

Like so many of the decisions to exclude women in the interests of simplicity, from architecture to medical research, this conclusion could only be reached in a culture that conceives of men as the default human and women as a niche aberration. To distort a reality you are supposedly trying to measure makes sense only if you don’t see women as essential. It makes sense only if you see women as an added extra, a complicating factor. It doesn’t make sense if you’re talking about half of the human race. It doesn’t make sense if you care about accurate data.

When we see animals in such vast numbers, their individuality is erased and our compassion is exhausted. I train my camera on this expanse of death and remember why there is no data on the marine animals we consume each year; we measure their deaths not individually but by the tonne.

I tended to be skeptical of anything that couldn't be measured, written down, and independently verified across a series of double-blind tests. But this was hard data. Lola's heart beat fastest for me.

That we are in the midst of crisis is now well understood. Our nation is at war, against a far-reaching network of violence and hatred. Our economy is badly weakened, a consequence of greed and irresponsibility on the part of some, but also our collective failure to make hard choices and prepare the nation for a new age. Homes have been lost; jobs shed; businesses shuttered. Our health care is too costly; our schools fail too many; and each day brings further evidence that the ways we use energy strengthen our adversaries and threaten our planet. These are the indicators of crisis, subject to data and statistics. Less measurable but no less profound is a sapping of confidence across our land — a nagging fear that America's decline is inevitable, and that the next generation must lower its sights. Today I say to you that the challenges we face are real. They are serious and they are many. They will not be met easily or in a short span of time. But know this, America — they will be met. On this day, we gather because we have chosen hope over fear, unity of purpose over conflict and discord. On this day, we come to proclaim an end to the petty grievances and false promises, the recriminations and worn out dogmas, that for far too long have strangled our politics.

In science we may start with experimental results, data, observations, measurements, ‘facts’. We invent, if we can, a rich array of possible explanations and systematically confront each explanation with the facts.

Each of these men, philosophers taking ethical measure of an age in which machines think faster than humans but regularly prove at least as flawed, repeatedly smack into a phenomenon that never troubled their intellectual predecessors: although humans have obviously survived every pox and meteor that nature has tossed at it until now, technology is something we toss back at our own peril. "On the bright side, it hasn't killed us yet either," says Nick Bostrom, who, when not refining doomsday data, researches how to extend the human life span.

I post Instagram photos that I think of as testaments to my beauty and then obsessively check the likes to see if the internet agrees. I collect this data more than I want to admit, trying to measure my allure as objectively and brutally as possible. I want to calculate my beauty to protect myself, to understand exactly how much power and lovability I have.

You can’t build a house without nails and wood. If you don’t want a house built, hide the nails and wood. If you don’t want a man unhappy politically, don’t give him two sides to a question to worry him; give him one. Better yet, give him none. Let him forget there is such a thing as war. If the government is inefficient, top-heavy, and tax-mad, better it be all those than that people worry over it. Peace, Montag. Give the people contests they win by remembering the words to more popular songs or the names of state capitals or how much corn Iowa grew last year. Cram them full of noncombustible data, chock them so damned full of ‘facts’ they feel stuffed, but absolutely ‘brilliant’ with information. Then they’ll feel they’re thinking, they’ll get a sense of motion without moving. And they’ll be happy, because facts of that sort don’t change. Don’t give them any slippery stuff like philosophy or sociology to tie things up with. That way lies melancholy. Any man who can take a TV wall apart and put it back together again, and most men can nowadays, is happier than any man who tries to slide-rule, measure, and equate the universe, which just won't be measured or equated without making man feel bestial and lonely. I know, I've tried it; to hell with it.

In these cases, the mind knows what it's doing better than the guile, because the mind flows, the guile dams up, that is, the mind stride but the guile limps. And that's no guileless statement, however, and that's no Harvard like, as MIT will measure soon with computers and docks of Martian data.

With no universal measure for meaning to compare with the seemingly solid accounting for income, we fall into the data trap. Our larger culture, and our pesky parents, push us toward decisions that seem to score well but are blind to the most important elements of healthy careers and meaningful lives.

This is the world we live in, a world of safety and happiness and order, a world without love. A world where children crack their heads on stone fireplaces and nearly gnaw off their tongues and the parents are concerned. Not heartbroken, frantic, desperate. Concerned, as they are when you fail mathematics, as they are when they are late to pay their taxes. [...] That’s the thing: We didn’t really care. A world without love is also a world without stakes. [...] In a world without love, this is what people are to each other: values, benefits, and liabilities, numbers and data. We weigh, we quantify, we measure, and the soul is ground to dust.

I offered to pass along information about NEHSA to Heidi so she can let her patients know about it. I don’t have any scientific or clinical data to back this up, but I think snow-boarding is the most effective rehabilitative tool I’ve experienced. It forces me to focus on my abilities and not my disability, to overcome huge obstacles, both physical and psychological, to stay up on that board and get down the mountain in one piece. And each time I get down the mountain in one piece, I gain a real confidence and sense of independence I haven’t felt anywhere else since the accident, a sense of true well-being that stays with me well beyond the weekend. And whether snowboarding with NEHSA has a measurable and lasting therapeutic effect for people like me or not, it’s a lot more fun than drawing cats and picking red balls up off a tray

When a fruit salad, a lover, or a jazz trio is just too imperfect for our tastes, we stop eating, kissing, and listening. But the law of large numbers suggests that when a measurement is too imperfect for our tastes, we should not stop measuring. Quite the opposite - we should measure again and again until niggling imperfections yield to the onslaught of data.

Envisioning fungi as nanoconductors in mycocomputers, Gorman (2003) and his fellow researchers at Northwestern University have manipulated mycelia of Aspergillus niger to organize gold into its DNA, in effect creating mycelial conductors of electrical potentials. NASA reports that microbiologists at the University of Tennessee, led by Gary Sayler, have developed a rugged biological computer chip housing bacteria that glow upon sensing pollutants, from heavy metals to PCBs (Miller 2004). Such innovations hint at new microbiotechnologies on the near horizon. Working together, fungal networks and environmentally responsive bacteria could provide us with data about pH, detect nutrients and toxic waste, and even measure biological populations.

Were we dealing with a spectrum-based system that described male and female sexuality with equal accuracy, data taken from gay males would look similar to data taken from straight females—and yet this is not what we see in practice. Instead, the data associated with gay male sexuality presents a mirror image of data associated with straight males: Most gay men are as likely to find the female form aversive as straight men are likely to find the male form aversive. In gay females we observe a similar phenomenon, in which they mirror straight females instead of appearing in the same position on the spectrum as straight men—in other words, gay women are just as unlikely to find the male form aversive as straight females are to find the female form aversive. Some of the research highlighting these trends has been conducted with technology like laser doppler imaging (LDI), which measures genital blood flow when individuals are presented with pornographic images. The findings can, therefore, not be written off as a product of men lying to hide middling positions on the Kinsey scale due to a higher social stigma against what is thought of in the vernacular as male bisexuality/pansexuality. We should, however, note that laser Doppler imaging systems are hardly perfect, especially when measuring arousal in females. It is difficult to attribute these patterns to socialization, as they are observed across cultures and even within the earliest of gay communities that emerged in America, which had to overcome a huge amount of systemic oppression to exist. It’s a little crazy to argue that the socially oppressed sexuality of the early American gay community was largely a product of socialization given how much they had overcome just to come out. If, however, one works off the assumptions of our model, this pattern makes perfect sense. There must be a stage in male brain development that determines which set of gendered stimuli is dominant, then applies a negative modifier to stimuli associated with other genders. This stage does not apparently take place during female sexual development. 

Science likes to measure things, to test hypotheses and collect data. Until quite recently science wasn’t testing hypotheses about animal feelings. From the time Charles Darwin wrote his last book, The Expression of the Emotions in Man and Animals (1872) to about the time Neil Armstrong left footprints on the moon nearly a century later (1969), prevailing scientific dogma denied animals their hearts and minds. A nonhuman animal was viewed as merely a responder to external stimuli. The idea that a walrus made decisions, or that a parakeet felt emotions, was considered unscientific.

the founder embraced the button only when new data revealed it as a powerful source of behavioral surplus that helped to ratchet up the magnetism of the Facebook News Feed, as measured by the volume of comments.34

People often think that the best way to predict the future is by collecting as much data as possible before making a decision. But this…is like driving a car looking only at the rearview mirror-because data is only available about the past.

[John] Dalton was a man of regular habits. For fifty-seven years he walked out of Manchester every day; he measured the rainfall, the temperature—a singularly monotonous enterprise in this climate. Of all that mass of data, nothing whatever came. But of the one searching, almost childlike question about the weights that enter the construction of these simple molecules—out of that came modern atomic theory. That is the essence of science: ask an impertinent question, and you are on the way to the pertinent answer.

He handed Mae a piece of paper, on which he'd written, in crude all capitals, a list of assertions under the headline "The Rights of Humans in a Digital Age." Mae scanned it, catching passages: "We must all have the right to anonymity." "Not every human activity can be measured." "The ceaseless pursuit of data to quantify the value of any endeavour is catastrophic to true understanding." "The barrier between public and private must remain unbreachable." At the end she found one line, written in red ink: "We must all have the right to disappear.

[O]ur attitudes towards things like race or gender operate on two levels. First of all, we have our conscious attitudes. This is what we choose to believe. These are our stated values, which we use to direct our behavior deliberately . . . But the IAT [Implicit Association Test] measures something else. It measures our second level of attitude, our racial attitude on an unconscious level - the immediate, automatic associations that tumble out before we've even had time to think. We don't deliberately choose our unconscious attitudes. And . . . we may not even be aware of them. The giant computer that is our unconscious silently crunches all the data it can from the experiences we've had, the people we've met, the lessons we've learned, the books we've read, the movies we've seen, and so on, and it forms an opinion.

Direct marketers, of course, realize that measurement is the key to success. Figure out what works, and do it more! Mass marketers have always resisted this temptation. When my old company approached the head of one of the largest magazine publishers in the world and pitched a technology that would allow advertisers to track who saw their ads and responded to them, he was aghast. He realized that this sort of data could kill his business. He knew that his clients didn’t want the data because then their jobs would get a lot more complex. Measurement means admitting what’s broken so you can fix it. Mass-media advertising, whether it’s on TV or in print, is all about emotion and craft, not about fixing mistakes. One reason the Internet ad boomlet faded so fast is that it forced advertisers to measure – and to admit what was going wrong.

Today, in our society, in economics, and in finance, we place far too much trust in numbers. Numbers are not reality . At best, they are a pale reflection of reality. At worst, they’re a gross distortion of the truths we seek to measure. But the damage doesn’t stop there. Not only do we rely too heavily on historic economic and market data; our optimistic bias also leads us to misinterpret the data and give them credence that they rarely merit. By worshipping at the altar of numbers and by discounting the immeasurable, we have in effect created a numeric economy that can easily undermine the real one. Government:

Meanwhile, the extraordinary measures we take to stay abreast of each minuscule change to the data stream end up magnifying the relative importance of these blips to the real scheme of things. Investors trade, politicians respond, and friends judge based on the micromovements of virtual needles. By dividing our attention between our digital extensions, we sacrifice our connection to the truer present in which we are living. The tension between the faux present of digital bombardment and the true now of a coherently living human generated the second kind of present shock, what we're calling digiphrenia—digi for "digital," and phrenia for "disordered condition of mental activity.

I advise you to look for a chance to break away, to find a subject you can make your own. That is where the quickest advances are likely to occur, as measured by discoveries per investigator per year. Therein you have the best chance to become a leader and, as time passes, to gain growing freedom to set your own course. If a subject is already receiving a great deal of attention, if it has a glamorous aura, if its practitioners are prizewinners who receive large grants, stay away from that subject. Listen to the news coming from the hubbub, learn how and why the subject became prominent, but in making your own long-term plans be aware it is already crowded with talented people. You would be a newcomer, a private amid bemedaled first sergeants and generals. Take a subject instead that interests you and looks promising, and where established experts are not yet conspicuously competing with one another, where few if any prizes and academy memberships have been given, and where the annals of research are not yet layered with superfluous data and mathematical models.

almost all the increased child survival is achieved through preventive measures outside hospitals by local nurses, midwives, and well-educated parents. Especially mothers: the data shows that half the increase in child survival in the world happens because the mothers can read and write. More children now survive because they don’t get ill in the first place.

The importance of experimental proof, on the other hand, does not mean that without new experimental data we cannot make advances. It is often said that science takes steps forward only when there is new experimental data. If this were true, we would have little hope of finding the theory of quantum gravity before measuring something new, but this is patently not the case. Which new data were available to Copernicus? None. He had the same data as Ptolemy. Which new data did Newton have? Almost none. His real ingredients were Kepler's laws and Galileo's results. What new data did Einstein have to discover general relativity? None. His ingredients were special relativity and Newton's theory. It simply isn't true that physics only advances when it is afforded new data.

In 2005, a survey was conducted in thirty-four countries measuring the percentage of adults who accept evolution. The United States ranked thirty-third, just above Turkey. Meanwhile, high school students in the United States test below those of every European and Asian nation in their understanding of science and math. These data are unequivocal: we are building a civilization of ignorance.

Despite all technical change in the advanced countries, to this day India, with a much smaller cultivated area than the US, produces annually a larger total tonnage of cereals, root crops, oil crops, sugar crops, fruits and vegetables. The precise figures are 858 million tonnes in India and 676 million tonnes in the US in 2007, the latest year for which the data from the United Nations Food and Agriculture Organisation is available. As for China, its even more intensive cultivation, developed over centuries, and consequent high land productivity were legendary; Britain’s agricultural yields at that time, properly measured over the same production period, were pathetic in comparison. By 2007 China produced 1,308 million tonnes from an area substantially less than that of India and of the US.

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.

Among other things, HeartMath research tests theories about the electromagnetic field of the human heart using machines that measure faint magnetic fields, such as those that are often used in MRIs and cardiologic tests. Remarkably, the heart’s toroidally shaped electrical field is sixty times greater than that of the brain, and its magnetic field is 5,000 times greater than that of the brain. The heart generates the strongest electromagnetic field in the body, and its pumping action transmits powerful rhythmic information patterns containing neurological, hormonal, and electromagnetic data to the brain and throughout the rest of the body. The heart actually sends more information to the brain than the brain sends to the heart. In other words, the heart has a mind of its own. Studies reveal this electromagnetic field seems to pick up information in the surrounding environment and also broadcasts one’s emotional state out from the body. Their measurements reveal that the field is large enough to extend several feet (or more) outside our bodies. Positive moods such as gratitude, joy, and happiness correlate to a larger, more expanded heart field, while emotions such as greed, anger, or sadness correlate to a constricted heart field.

This is a promising new source of insight that can supplement survey data but can’t replace it for the foreseeable future. That’s because the tools have a ways to go before they can accurately gauge sentiment about specific customer interactions as precisely and consistently as a survey. You should consider this option when your measurement program matures, but start out with the tried-and-true approach of fielding surveys.

The bottom line here -- and I use the phrase with an eye to the mind-set that promotes these 'systems' -- is that I am increasingly devoting more time to the generation and recording of data and less time to the educational substance of what the data is supposed to measure. Think of it as a man who develops ever more elaborate schemes for counting his money, even as he forfeits more and more of his time for earning the money he counts.

The Measure of America, a report of the Social Science Research Council, ranks every state in the United States on its “human development.” Each rank is based on life expectancy, school enrollment, educational degree attainment, and median personal earnings. Out of the 50 states, Louisiana ranked 49th and in overall health ranked last. According to the 2015 National Report Card, Louisiana ranked 48th out of 50 in eighth-grade reading and 49th out of 50 in eighth-grade math. Only eight out of ten Louisianans have graduated from high school, and only 7 percent have graduate or professional degrees. According to the Kids Count Data Book, compiled by the Annie E. Casey Foundation, Louisiana ranked 49th out of 50 states for child well-being. And the problem transcends race; an average black in Maryland lives four years longer, earns twice as much, and is twice as likely to have a college degree as a black in Louisiana. And whites in Louisiana are worse off than whites in Maryland or anywhere else outside Mississippi. Louisiana has suffered many environmental problems too: there are nearly 400 miles of low, flat, subsiding coastline, and the state loses a football field–size patch of wetland every hour. It is threatened by rising sea levels and severe hurricanes, which the world’s top scientists connect to climate change.

The marginal gains approach is not just about mechanistic iteration. You need judgment and creativity to determine how to find solutions to what the data is telling you, but those judgments, in turn, are tested as part of the next optimization loop. Creativity not guided by a feedback mechanism is little more than white noise. Success is a complex interplay between creativity and measurement, the two operating together, the two sides of the optimization loop.

High-quality and transparent data, clearly documented, timely rendered, and publicly available are the sine qua non of competent public health management. During a pandemic, reliable and comprehensive data are critical for determining the behavior of the pathogen, identifying vulnerable populations, rapidly measuring the effectiveness of interventions, mobilizing the medical community around cutting-edge disease management, and inspiring cooperation from the public. The shockingly low quality of virtually all relevant data pertinent to COVID-19, and the quackery, the obfuscation, the cherrypicking and blatant perversion would have scandalized, offended, and humiliated every prior generation of American public health officials. Too often, Dr. Fauci was at the center of these systemic deceptions. The “mistakes” were always in the same direction—inflating the risks of coronavirus and the safety and efficacy of vaccines in

We have to start accounting for the three themes that define women's relationship with that world. The first of these themes is the female body - or, to be precise - its invisibility. Routinely forgetting to accommodate the female body in design - whether medical, technological or architectural - has led to a world that is less hospitable and more dangerous for women to navigate. It leads to us injuring ourselves in jobs and cars that weren't designed for our bodies. It leads us to dying from drugs that don't work. It has led to the creation of a world where women just don't fit very well. There is an irony in how the female body is apparently invisible when it comes to collecting data, because when it comes to the second trend that defines women's lives, the visibility of the female body is key. That tend is male sexual violence against women - how we don't measure it, don't design our world to account for it, and in so doing, allow it to limit women's liberty.

If a model did anything too obviously bizarre—flooded the Sahara or tripled interest rates—the programmers would revise the equations to bring the output back in line with expectation. In practice, econometric models proved dismally blind to what the future would bring, but many people who should have known better acted as though they believed in the results. Forecasts of economic growth or unemployment were put forward with an implied precision of two or three decimal places. Governments and financial institutions paid for such predictions and acted on them, perhaps out of necessity or for want of anything better. Presumably they knew that such variables as “consumer optimism” were not as nicely measurable as “humidity” and that the perfect differential equations had not yet been written for the movement of politics and fashion. But few realized how fragile was the very process of modeling flows on computers, even when the data was reasonably trustworthy and the laws were purely physical, as in weather forecasting.

Quantum physics tells us that no matter how thorough our observation of the present, the (unobserved) past, like the future, is indefinite and exists only as a spectrum of possibilities. The universe, according to quantum physics, has no single past, or history. The fact that the past takes no definite form means that observations you make on a system in the present affect its past. That is underlined rather dramatically by a type of experiment thought up by physicist John Wheeler, called a delayed-choice experiment. Schematically, a delayed-choice experiment is like the double-slit experiment we just described, in which you have the option of observing the path that the particle takes, except in the delayed-choice experiment you postpone your decision about whether or not to observe the path until just before the particle hits the detection screen. Delayed-choice experiments result in data identical to those we get when we choose to observe (or not observe) the which-path information by watching the slits themselves. But in this case the path each particle takes—that is, its past—is determined long after it passed through the slits and presumably had to “decide” whether to travel through just one slit, which does not produce interference, or both slits, which does. Wheeler even considered a cosmic version of the experiment, in which the particles involved are photons emitted by powerful quasars billions of light-years away. Such light could be split into two paths and refocused toward earth by the gravitational lensing of an intervening galaxy. Though the experiment is beyond the reach of current technology, if we could collect enough photons from this light, they ought to form an interference pattern. Yet if we place a device to measure which-path information shortly before detection, that pattern should disappear. The choice whether to take one or both paths in this case would have been made billions of years ago, before the earth or perhaps even our sun was formed, and yet with our observation in the laboratory we will be affecting that choice. In

Our group has come together for one purpose,” Shoshana said. “We demand comprehensive action to ensure that Facebook cannot be weaponized to undermine the vote and with it American democracy.” We decided that instead of making broad, lofty demands, we would focus first on quickly actionable points,8 especially given the tight timeline and Trump’s increasingly unhinged behavior. We distilled them down to three demands of Facebook: to enforce its own policies and remove posts inciting violence; to ban ads that seek to delegitimize election results; and to take measures to prevent disinformation and misinformation about the election results. It was a sign of the times that within twenty-four hours, Facebook acted on all of them. It never admitted it, though. Instead, it attacked our members. In those months, much of what Rappler had discovered about Facebook and social media based on our own data and research, as well as many of our suspicions, was slowly being confirmed by reporters, whistleblowers, and even the companies themselves.

The problem is that there may not be any way to really prove animal consciousness with data. Clever experiments can show that animals perform behaviorally in ways that people behave when they are in a particular state of phenomenal consciousness. But we can create robots that behave the way humans behave when we are having a phenomenal experience. Consciousness is, and probably always will be, an inner experience that is unobservable to anyone other than the experiencing organism. And in the absence of verbal report, there is little to measure.

The general laws of migration hold that the greater the obstacles and the farther the distance traveled, the more ambitious the migrants. “It is the higher status segments of a population which are most residentially mobile,” the sociologists Karl and Alma Taeuber wrote in a 1965 analysis of census data on the migrants, published the same year as the Moynihan Report. “As the distance of migration increases,” wrote the migration scholar Everett Lee, “the migrants become an increasingly superior group.” Any migration takes some measure of energy, planning, and forethought. It requires not only the desire for something better but the willingness to act on that desire to achieve it. Thus the people who undertake such a journey are more likely to be either among the better educated of their homes of origin or those most motivated to make it in the New World, researchers have found. “Migrants who overcome a considerable set of intervening obstacles do so for compelling reasons, and such migrations are not taken lightly,” Lee wrote. “Intervening obstacles serve to weed out some of the weak or the incapable.” The

They asked forty-two experienced investors in the firm to estimate the fair value of a stock (the price at which the investors would be indifferent to buying or selling). The investors based their analysis on a one-page description of the business; the data included simplified profit and loss, balance sheet, and cash flow statements for the past three years and projections for the next two. Median noise, measured in the same way as in the insurance company, was 41%. Such large differences among investors in the same firm, using the same valuation methods, cannot be good news.

Our schools cannot be improved by the blind worship of data. Data are only as good as the measures used to create the numbers and good as the underlying activities. If the measures are shoddy, then the data will be shoddy. If the data reflect mainly the amount of time invested in test preparation activities, then the data are worthless. If the data are based on dumbed-down state tests, then the data are meaningless. A good accountability system, whether for schools, teachers, or students, must include a variety of measures, not only test scores...our schools should be “data-informed”, “not data driven".

We can begin to understand what this means by taking up the fourth principle: whenever possible, we should take measures to re-embody the information we think about. The pursuit of knowledge has frequently sought to disengage thinking from the body, to elevate ideas to a cerebral sphere separate from our grubby animal anatomy. Research on the extended mind counsels the opposite approach: we should be seeking to draw the body back into the thinking process. That may take the form of allowing our choices to be influenced by our interoceptive signals—a source of guidance we’ve often ignored in our focus on data-driven decisions. It might take the form of enacting, with bodily movements, the academic concepts that have become abstracted, detached from their origin in the physical world. Or it might take the form of attending to our own and others’ gestures, tuning back in to what was humanity’s first language, present long before speech. As we’ve seen from research on embodied cognition, at a deep level the brain still understands abstract concepts in terms of physical action, a fact reflected in the words we use (“reaching for a goal,” “running behind schedule”); we can assist the brain in its efforts by bringing the literal body back into the act of thinking.

New high-tech tools allow for more precise measuring and tracking, better sharing of information, and increased visibility of targeted populations. In a system dedicated to supporting poor and working-class people's self-determination, such diligence would guarantee that they attain all the benefits they are entitled to by law. In that context, integrated data and modernized administration would not necessarily result in bad outcomes for poor communities. But automated decision-making in our current welfare system acts a lot like older, atavistic forms of punishment and containment. It filters and diverts. It is a gatekeeper, not a facilitator.

Haven't you felt it? The loss of autonomy. The sense of being virtualized. The devices you use, the ones you carry everywhere, room to room, minute to minute, inescapably. Do you ever feel unfleshed? All the coded impulses you depend on to guide you. All the sensors in the room that are watching you, listening to you, tracking your habits, measuring your capabilities. All the linked data designed to incorporate you into the megadata. Is there something that makes you uneasy? Do you think about the technovirus, all systems down, global implosion? Or is it more personal? Do you feel steeped in some horrific digital panic that's everywhere and nowhere?

Assessment can be either formal and/or informal measures that gather information. In education, meaningful assessment is data that guides and informs the teacher and/or stakeholders of students' abilities, strategies, performance, content knowledge, feelings and/or attitudes. Information obtained is used to make educational judgements or evaluative statements. Most useful assessment is data which is used to adjust curriculum in order to benefit the students. Assessment should be used to inform instruction. Diagnosis and assessment should document literacy in real-world contexts using data as performance indicators of students' growth and development.

Cram them full of noncombustible data, chock them so damned full of ‘facts’ they feel stuffed, but absolutely ‘brilliant’ with information. Then they’ll feel they’re thinking, they’ll get a sense of motion without moving. And they’ll be happy, because facts of that sort don’t change. Don’t give them any slippery stuff like philosophy or sociology to tie things up with. That way lies melancholy. Any man who can take a TV wall apart and put it back together again, and most men can, nowadays, is happier than any man who tries to slide rule, measure, and equate the universe, which just won’t be measured or equated without making man feel bestial and lonely.

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

An important attribute of metabolites is their close relationship to both the biological states of interest (i.e. disease status) and relevant genomic, transcriptomic, and proteomic variants causally related to the disease state. As such, metabo-profiles can be viewed as an intermediate measure that links pre-disposing genes and environmental exposures to a resulting disease state. Causal metabolites also typically have a stronger relationship (i.e. larger effect size) to the underlying genetics and the disease phenotype. Thus, the integration of metabolomic data into systems biology approaches may provide a missing link between genes and disease states.

This is another area where my thinking has changed over time. I used to prioritize nutrition over everything else, but I now consider exercise to be the most potent longevity “drug” in our arsenal, in terms of lifespan and healthspan. The data are unambiguous: exercise not only delays actual death but also prevents both cognitive and physical decline, better than any other intervention. We also tend to feel better when we exercise, so it probably has some harder-to-measure effect on emotional health as well. My hope is that you will understand not only the how but the why of various types of exercise, so you will be able to formulate a program that fits your own personal goals.

You need to make sure that each one of those steps is done more systematically and purposefully. For example, you should think through what questions are asked and how the different answers candidates give differentiate them in the ways that you are seeking to differentiate them. You should also save all of those answers so you can learn about how indicative they might be of subsequent behaviors and performance. I do not mean that the human dimension or art of the hiring process should be eliminated—the personal values and esprit de corps part of a relationship are critically important and can’t be fully measured by data. Sometimes the twinkle in the eye and the facial expressions are telling.

We can now see the continuum23 between macrohistory and microhistory. We are collecting the kinds of precise, quantitative, microhistorical measurements that typically led to the emergence of a new science…but at the scale of billions of people, and going into our second decade. So, another term for “Big Data” should be “Big History.” All data is a record of past events, sometimes the immediate past, sometimes the past of months or years ago, sometimes (in the case of Google Books or the Digital Michelangelo project) the past of decades or centuries ago. After all, what’s another word for data storage in a computer? Memory. Memory, as in the sense of human memory, and as in the sense of history.

Data show that the more patients actually know, the less they want of our treatments at the end of life. A study of 230 surrogate decision makers for patients on breathing machines demonstrated that the better the quality of clinician–family communication, the less life support was elected. Another study showed that people were less likely to want CPR after they learned what it actually entailed. Most people dramatically overestimate the likelihood of survival after CPR. When they learn the real numbers, they are less likely to want it by about 50 percent. In short, when people have a more robust understanding of the benefits and burdens of the treatment they are actually getting, they want less of it.

As black-box technologies become more widespread, there have been no shortage of demands for increased transparency. In 2016 the European Union's General Data Protection Regulation included in its stipulations the "right to an explanation," declaring that citizens have a right to know the reason behind the automated decisions that involve them. While no similar measure exists in the United States, the tech industry has become more amenable to paying lip service to "transparency" and "explainability," if only to build consumer trust. Some companies claim they have developed methods that work in reverse to suss out data points that may have triggered the machine's decisions—though these explanations are at best intelligent guesses. (Sam Ritchie, a former software engineer at Stripe, prefers the term "narratives," since the explanations are not a step-by-step breakdown of the algorithm's decision-making process but a hypothesis about reasoning tactics it may have used.) In some cases the explanations come from an entirely different system trained to generate responses that are meant to account convincingly, in semantic terms, for decisions the original machine made, when in truth the two systems are entirely autonomous and unrelated. These misleading explanations end up merely contributing another layer of opacity. "The problem is now exacerbated," writes the critic Kathrin Passig, "because even the existence of a lack of explanation is concealed.

Both measurement error and sampling error are unpredictable, but they’re predictably unpredictable. You can always expect data from different samples, measures or groups to have somewhat different characteristics – in terms of the averages, the highest and lowest scores, and practically everything else. So even though they’re normally a nuisance, measurement error and sampling error can be useful as a means of spotting fraudulent data. If a dataset looks too neat, too tidily similar across different groups, something strange might be afoot. As the geneticist J. B. S. Haldane put it, ‘man is an orderly animal’ who ‘finds it very hard to imitate the disorder of nature’, and that goes for fraudsters as much as for the rest of us.

The shopkeeper is very efficient, has an efficient home delivery system and knows the tastes and price considerations of his customers. But he is labelled ‘unorganized’ by our experts and national income data and his contribution thereby diminished. The footfalls in his shop cannot be measured using Western models [since there is no place to keep anybody’s foot inside his shop!] and so he is derided and abused. It is like clubbing housewives along with prostitutes in our Census data to show them that they are involved in ‘unproductive’ activities. These are economic constructs imposed by the west on the rest and it is a form of terminological terrorism which is mouthed ad-nauseam by our economists and policy planners without understanding their implications.

In their book American Grace: How Religion Divides and Unites Us, political scientists Robert Putnam and David Campbell analyzed a variety of data sources to describe how religious and nonreligious Americans differ. Common sense would tell you that the more time and money people give to their religious groups, the less they have left over for everything else. But common sense turns out to be wrong. Putnam and Campbell found that the more frequently people attend religious services, the more generous and charitable they become across the board.58 Of course religious people give a lot to religious charities, but they also give as much as or more than secular folk to secular charities such as the American Cancer Society.59 They spend a lot of time in service to their churches and synagogues, but they also spend more time than secular folk serving in neighborhood and civic associations of all sorts. Putnam and Campbell put their findings bluntly: By many different measures religiously observant Americans are better neighbors and better citizens than secular Americans—they are more generous with their time and money, especially in helping the needy, and they are more active in community life.60 Why are religious people better neighbors and citizens? To find out, Putnam and Campbell included on one of their surveys a long list of questions about religious beliefs (e.g., “Do you believe in hell? Do you agree that we will all be called before God to answer for our sins?”) as well as questions about religious practices (e.g., “How often do you read holy scriptures? How often do you pray?”). These beliefs and practices turned out to matter very little. Whether you believe in hell, whether you pray daily, whether you are a Catholic, Protestant, Jew, or Mormon … none of these things correlated with generosity. The only thing that was reliably and powerfully associated with the moral benefits of religion was how enmeshed people were in relationships with their co-religionists. It’s the friendships and group activities, carried out within a moral matrix that emphasizes selflessness. That’s what brings out the best in people. Putnam and Campbell reject the New Atheist emphasis on belief and reach a conclusion straight out of Durkheim: “It is religious belongingness that matters for neighborliness, not religious believing.”61

When planners fail to account for gender, public spaces become male spaces by default. The reality is that half the global population has a female body. Half the global population has to deal on a daily basis with the sexualised menace that is visited on that body. The entire global population needs the care that, currently, is mainly carried out, unpaid, by women. These are not niche concerns, and if public spaces are truly to for everyone, we have to start accounting for the lives of the other half of the world. And, as we've seen, this isn't just a matter of justice: it's also a matter of simple economics. By accounting for women's care responsibilities in urban planning, we make it easier for women to engage fully in the paid workforce - and as we will see in the next chapter, this is a significant driver of GDP. By accounting for the sexual violence women face and introducing preventative measures - like providing enough single-sex public toilets we save money in the long run by reducing the significant economic cost of violence against women. When we account for female socialisation in the design of our open spaces and public activities, we again save money in the long run by ensuring women's long-term mental and physical health. - In short, designing the female half of the world out of our public spaces is not a matter of resources. It's a matter of priorities, and, currently, whether unthinkingly or not, we just aren't prioritising women. This is manifestly unjust, and economically illiterate. Women have an equal right to public resources: we must stop excluding them by design

As it became impossible to believe in the morality of the Soviet Union, a shrinking contingent of true believers shifted their devotions, first to communist China under Mao. But then came revelations of even worse horrors in China in the 1960s—including 30 million deaths between 1959 and 1961. Then Cuba was the great hope, and then Vietnam, then Cambodia, then Albania for awhile in the late 1970s, and then Nicaragua in the 1980s. But the data and the disappointments piled up, all dealing a solid and devastating blow to socialism’s ability to claim a moral sanction.[259] One such set of summary data is reproduced below in the form of a table comparing liberal democratic, authoritarian, and totalitarian governments in terms of one measure of morality: the number of their own citizens those governments have killed.

Has ‘Western civilization’ really made life better for everyone? This ultimately comes down to the question of how to measure human happiness, which is a notoriously difficult thing to do. About the only dependable way anyone has ever discovered to determine whether one way of living is really more satisfying, fulfilling, happy or otherwise preferable to any other is to allow people to fully experience both, give them a choice, then watch what they actually do. For instance, if Pinker is correct, then any sane person who had to choose between (a) the violent chaos and abject poverty of the ‘tribal’ stage in human development and (b) the relative security and prosperity of Western civilization would not hesitate to leap for safety.25 But empirical data is available here, and it suggests something is very wrong with Pinker’s conclusions.

Based on this data, Trichopoulou published a landmark article in the New England Journal of Medicine (NEJM) in 2003, in which she concluded that adhering to a “traditional Mediterranean diet,” which includes “a high intake of olive oil” was associated with a “significant and substantial reduction in overall mortality.” It is therefore a shock to find out that in this study, Trichopoulou never actually measured the olive oil consumption of her subjects. It was not an item on the food-frequency questionnaire she used, either as a foodstuff eaten directly or as a fat used in cooking. Instead, she “estimated” its use from the questionnaire’s list of cooked dishes, making assumptions about how Greeks might cook them.XVII This shortcoming is not mentioned in the NEJM paper, however, and “olive oil” is listed in the paper without any explanation of its derivation.XVIII

You want to know who you really are?’ asks Dataism. ‘Then forget about mountains and museums. Have you had your DNA sequenced? No?! What are you waiting for? Go and do it today. And convince your grandparents, parents and siblings to have their DNA sequenced too – their data is very valuable for you. And have you heard about these wearable biometric devices that measure your blood pressure and heart rate twenty-four hours a day? Good – so buy one of those, put it on and connect it to your smartphone. And while you are shopping, buy a mobile camera and microphone, record everything you do, and put in online. And allow Google and Facebook to read all your emails, monitor all your chats and messages, and keep a record of all your Likes and clicks. If you do all that, then the great algorithms of the Internet-of-All-Things will tell you whom to marry, which career to pursue and whether to start a war.

Incentives are the cornerstone of modern life. And understanding them - or, often, deciphering them - is the key to understanding a problem, and how it might be solved. Knowing what to measure, and how to measure it, can make a complicated world less so. There is nothing like the sheer power of numbers to scrub away lawyers of confusion and contradiction, especially with emotional, hot-button topics. The conventional wisdom is often wrong. And a blithe acceptance of it can lead to sloppy, wasteful, or even dangerous outcomes. Correlation does not equal causality. When two things travel together, it is tempting to assume that one causes the other. Married people, for instance, are demonstrably happier than single people; does this mean that marriage causes happiness? Not necessarily. The data suggest that happy people are more likely to get married in the first place. As one researcher memorably put it, "If you're grumpy, who the hell wants to marry you?

Here are some practical Dataist guidelines for you: ‘You want to know who you really are?’ asks Dataism. ‘Then forget about mountains and museums. Have you had your DNA sequenced? No?! What are you waiting for? Go and do it today. And convince your grandparents, parents and siblings to have their DNA sequenced too – their data is very valuable for you. And have you heard about these wearable biometric devices that measure your blood pressure and heart rate twenty-four hours a day? Good – so buy one of those, put it on and connect it to your smartphone. And while you are shopping, buy a mobile camera and microphone, record everything you do, and put in online. And allow Google and Facebook to read all your emails, monitor all your chats and messages, and keep a record of all your Likes and clicks. If you do all that, then the great algorithms of the Internet-of-All-Things will tell you whom to marry, which career to pursue and whether to start a war.’ But where do these great algorithms come from? This is the mystery of Dataism. Just as according to Christianity we humans cannot understand God and His plan, so Dataism declares that the human brain cannot fathom the new master algorithms. At present, of course, the algorithms are mostly written by human hackers. Yet the really important algorithms – such as the Google search algorithm – are developed by huge teams. Each member understands just one part of the puzzle, and nobody really understands the algorithm as a whole. Moreover, with the rise of machine learning and artificial neural networks, more and more algorithms evolve independently, improving themselves and learning from their own mistakes. They analyse astronomical amounts of data that no human can possibly encompass, and learn to recognise patterns and adopt strategies that escape the human mind. The seed algorithm may initially be developed by humans, but as it grows it follows its own path, going where no human has gone before – and where no human can follow.

Dr. Kary Mullis, who won the Nobel Prize in Chemistry for inventing PCR, stated publicly numerous times that his invention should never be used for the diagnosis of infectious diseases. In July of 1997, during an event called Corporate Greed and AIDS in Santa Monica CA, Dr. Mullis explained on video, “With PCR you can find almost anything in anybody. It starts making you believe in the sort of Buddhist notion that everything is contained in everything else, right? I mean, because if you can model amplify one single molecule up to something that you can really measure, which PCR can do, then there’s just very few molecules that you don’t have at least one single one of them in your body. Okay? So that could be thought of as a misuse of it, just to claim that it’s meaningful.” Mikki explained, “The major issue with PCR is that it’s easily manipulated. It functions through a cyclical process whereby each revolution amplifies magnification. On a molecular level, most of us already have trace amounts of genetic fragments similar to coronavirus within us. By simply over-cycling the process, a negative result can be flipped to a positive. Governing bodies such as the CDC and the WHO can control the number of cases by simply advising the medical industry to increase or decrease the cycle threshold (CT).” In August of 2020, the New York Times reported that “a CT beyond 34 revolutions very rarely detect live virus, but most often, dead nucleotides that are not even contagious. In compliance with guidance from the CDC and the WHO, many top US labs have been conducting tests at cycle thresholds of 40 or more. NYT examined data from Massachusetts, New York, and Nevada and determined that up to 90 percent of the individuals who tested positive carried barely any virus.”17 90 percent! In May of 2021, CDC changed the PCR cycle threshold from 40 to 28 or lower for those who have been vaccinated. This one adjustment of the numbers allowed the vaccine pushers to praise the vaccines as a big success.

Imagine you're sitting having dinner in a restaurant. At some point during the meal, your companion leans over and whispers that they've spotted Lady Gaga eating at the table opposite. Before having a look for yourself, you'll no doubt have some sense of how much you believe your friends theory. You'll take into account all of your prior knowledge: perhaps the quality of the establishment, the distance you are from Gaga's home in Malibu, your friend's eyesight. That sort of thing. If pushed, it's a belief that you could put a number on. A probability of sorts. As you turn to look at the woman, you'll automatically use each piece of evidence in front of you to update your belief in your friend's hypothesis Perhaps the platinum-blonde hair is consistent with what you would expect from Gaga, so your belief goes up. But the fact that she's sitting on her own with no bodyguards isn't, so your belief goes down. The point is, each new observations adds to your overall assessment. This is all Bayes' theorem does: offers a systematic way to update your belief in a hypothesis on the basis of the evidence. It accepts that you can't ever be completely certain about the theory you are considering, but allows you to make a best guess from the information available. So, once you realize the woman at the table opposite is wearing a dress made of meat -- a fashion choice that you're unlikely to chance up on in the non-Gaga population -- that might be enough to tip your belief over the threshold and lead you to conclude that it is indeed Lady Gaga in the restaurant. But Bayes' theorem isn't just an equation for the way humans already make decisions. It's much more important that that. To quote Sharon Bertsch McGrayne, author of The Theory That Would Not Die: 'Bayes runs counter to the deeply held conviction that modern science requires objectivity and precision. By providing a mechanism to measure your belief in something, Bayes allows you to draw sensible conclusions from sketchy observations, from messy, incomplete and approximate data -- even from ignorance.

The tragedy of Central Appalachia is that it is becoming more marginalized in American life just when the country needs more than ever what it has to offer. At a time when the bonds of community and family are visibly failing and people feel more alone than ever, and as they are bombarded from all sides with more demands, and with more "data" that they can possibly digest, Appalachia offers a model for a less frenetic and more measured way of life. People of Appalachian descent elsewhere in the nation-and they number many millions-still feel deep ties to some Appalachian hamlet or hollow as to an ancestral homeland, though they may never have even visited it. As they make their way in the big world of getting and spending they know that something valuable has been lost for all they may have gained. That less frenetic way of life is deeply embedded in Appalachian culture, which has proved incredibly tough and enduring. Yet Appalachia has now been so thoroughly bypassed and forgotten that it cannot give, because the rest of America will not take, what could be it's greatest gift.

If the law of gravitation be regarded as universal, the point may be stated as follows. The laws of motion require to be stated by reference to what have been called kinetic axes: these are in reality axes having no absolute acceleration and no absolute rotation. It is asserted, for example, when the third law is combined with the notion of mass, that, if m, m' be the masses of two particles between which there is a force, the component accelerations of the two particles due to this force are in the ratio m2 : m1. But this will only be true if the accelerations are measured relative to axes which themselves have no acceleration. We cannot here introduce the centre of mass, for, according to the principle that dynamical facts must be, or be derived from, observable data, the masses, and therefore the centre of mass, must be obtained from the acceleration, and not vice versâ. Hence any dynamical motion, if it is to obey the laws of motion, must be referred to axes which are not subject to any forces. But, if the law of gravitation be accepted, no material axes will satisfy this condition. Hence we shall have to take spatial axes, and motions relative to these are of course absolute motions. 465. In order to avoid this conclusion, C. Neumann* assumes as an essential part of the laws of motion the existence, somewhere, of an absolutely rigid “Body Alpha”, by reference to which all motions are to be estimated. This suggestion misses the essence of the discussion, which is (or should be) as to the logical meaning of dynamical propositions, not as to the way in which they are discovered. It seems sufficiently evident that, if it is necessary to invent a fixed body, purely hypothetical and serving no purpose except to be fixed, the reason is that what is really relevant is a fixed place, and that the body occupying it is irrelevant. It is true that Neumann does not incur the vicious circle which would be involved in saying that the Body Alpha is fixed, while all motions are relative to it; he asserts that it is rigid, but rightly avoids any statement as to its rest or motion, which, in his theory, would be wholly unmeaning. Nevertheless, it seems evident that the question whether one body is at rest or in motion must have as good a meaning as the same question concerning any other body; and this seems sufficient to condemn Neumann’s suggested escape from absolute motion.

Noah Kagan, a growth hacker at Facebook, the personal finance service Mint.com (which sold to Intuit for nearly $170 million), and the daily deal site AppSumo (which has more than eight hundred thousand users), explains it simply: “Marketing has always been about the same thing—who your customers are and where they are.”5 What growth hackers do is focus on the “who” and “where” more scientifically, in a more measurable way. Whereas marketing was once brand-based, with growth hacking it becomes metric and ROI driven. Suddenly, finding customers and getting attention for your product are no longer guessing games. But this is more than just marketing with better metrics; this is not just “direct marketing” with a new name. Growth hackers trace their roots back to programmers—and that’s how they see themselves. They are data scientists meets design fiends meets marketers. They welcome this information, process it and utilize it differently, and see it as desperately needed clarity in a world that has been dominated by gut instincts and artistic preference for too long. But they also add a strong acumen for strategy, for thinking big picture, and for leveraging platforms, unappreciated assets, and new ideas.

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

Science is getting knocked on all sides these days, not only from religious fundamentalists, but from all kinds of people who perceive science as arrogant, one-sided, and the source of the troubles that come with the technology it produces. It's true that individuL scientists can be so arrogant and narrowly focused, they're blind to any but their own truths, and that new discoveries bring new problems with them. Still, I don't know many people who would refuse a biopsy for a newly discovered lump because they think science needs to be taken down a peg or two. Religion gets knocked for the same kinds of reasons as science: for its arrogance, narowmindedness, and tendency to create more trouble than it's worth. Religion is also accused of concealing reality under a comforting blanket of measureless faith -- the flip side, perhaps of the scientist for whom nothing can be real until she has measured it. My own sojourn into religion convinced me that good religion reveals rather than conceals. Religion is the soul in search of itself and its relationship to the cosmos. This journey requires looking at all of it: the joy, the sorrow, the beauty and the horror of life. We hope for the best. We want meaning and love to exist not only in ourselves, but in the very soul of the universe. At times this great hope might tempt us to pick and choose only the data that supports our desires. But in religion as in boat-building, the design must be tested in all conditions. When I say that I'm trying to pay attention, and that paying attention means being willing to look at all of it, I think I'm trying for the same moment of clarity that Graham experienced when the wind blew all over his theory. Looking at all of it is what good science is about. I believe that it's also what good religion is about.

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.

Two observations take us across the finish line. The Second Law ensures that entropy increases throughout the entire process, and so the information hidden within the hard drives, Kindles, old-fashioned paper books, and everything else you packed into the region is less than that hidden in the black hole. From the results of Bekenstein and Hawking, we know that the black hole's hidden information content is given by the area of its event horizon. Moreover, because you were careful not to overspill the original region of space, the black hole's event horizon coincides with the region's boundary, so the black hole's entropy equals the area of this surrounding surface. We thus learn an important lesson. The amount of information contained within a region of space, stored in any objects of any design, is always less than the area of the surface that surrounds the region (measured in square Planck units). This is the conclusion we've been chasing. Notice that although black holes are central to the reasoning, the analysis applies to any region of space, whether or not a black hole is actually present. If you max out a region's storage capacity, you'll create a black hole, but as long as you stay under the limit, no black hole will form. I hasten to add that in any practical sense, the information storage limit is of no concern. Compared with today's rudimentary storage devices, the potential storage capacity on the surface of a spatial region is humongous. A stack of five off-the-shelf terabyte hard drives fits comfortable within a sphere of radius 50 centimeters, whose surface is covered by about 10^70 Planck cells. The surface's storage capacity is thus about 10^70 bits, which is about a billion, trillion, trillion, trillion, trillion terabytes, and so enormously exceeds anything you can buy. No one in Silicon Valley cares much about these theoretical constraints. Yet as a guide to how the universe works, the storage limitations are telling. Think of any region of space, such as the room in which I'm writing or the one in which you're reading. Take a Wheelerian perspective and imagine that whatever happens in the region amounts to information processing-information regarding how things are right now is transformed by the laws of physics into information regarding how they will be in a second or a minute or an hour. Since the physical processes we witness, as well as those by which we're governed, seemingly take place within the region, it's natural to expect that the information those processes carry is also found within the region. But the results just derived suggest an alternative view. For black holes, we found that the link between information and surface area goes beyond mere numerical accounting; there's a concrete sense in which information is stored on their surfaces. Susskind and 'tHooft stressed that the lesson should be general: since the information required to describe physical phenomena within any given region of space can be fully encoded by data on a surface that surrounds the region, then there's reason to think that the surface is where the fundamental physical processes actually happen. Our familiar three-dimensional reality, these bold thinkers suggested, would then be likened to a holographic projection of those distant two-dimensional physical processes. If this line of reasoning is correct, then there are physical processes taking place on some distant surface that, much like a puppeteer pulls strings, are fully linked to the processes taking place in my fingers, arms, and brain as I type these words at my desk. Our experiences here, and that distant reality there, would form the most interlocked of parallel worlds. Phenomena in the two-I'll call them Holographic Parallel Universes-would be so fully joined that their respective evolutions would be as connected as me and my shadow.

The first cut at the problem—the simplest but still eye-opening—is to ask how much income would have to be transferred from rich countries to poor countries to lift all of the world’s extreme poor to an income level sufficient to meet basic needs. Martin Ravallion and his colleagues on the World Bank’s poverty team have gathered data to address this question, at least approximately. The World Bank estimates that meeting basic needs requires $1.08 per day per person, measured in 1993 purchasing-power adjusted prices. Using household surveys, the Ravallion team has calculated the numbers of poor people around the world who live below that threshold, and the average incomes of those poor. According to the Bank’s estimates, 1.1 billion people lived below the $1.08 level as of 2001, with an average income of $0.77 per day, or $281 per year. More important, the poor had a shortfall relative to basic needs of $0.31 per day ($1.08 minus $0.77), or $113 per year. Worldwide, the total income shortfall of the poor in 2001 was therefore $113 per year per person multiplied by 1.1 billion people, or $124 billion. Using the same accounting units (1993 purchasing power adjusted U.S. dollars), the income of the twenty-two donor countries of the Development Assistance Committee (DAC) in 2001 was $20.2 trillion. Thus a transfer of 0.6 percent of donor income, amounting to $124 billion, would in theory raise all 1.1 billion of the world’s extreme poor to the basic-needs level. Notably, this transfer could be accomplished within the 0.7 percent of the GNP target of the donor countries. That transfer would not have been possible in 1980, when the numbers of the extreme poor were larger (1.5 billion) and the incomes of the rich countries considerably smaller. Back in 1981, the total income gap was around $208 billion (again, measured in 1993 purchasing power prices) and the combined donor country GNP was $13.2 trillion. Then it would have required 1.6 percent of donor income in transfers to raise the extreme poor to the basic-needs level.

Obama is also directing the U.S. government to invest billions of dollars in solar and wind energy. In addition, he is using bailout leverage to compel the Detroit auto companies to build small, “green” cars, even though no one in the government has investigated whether consumers are interested in buying small, “green” cars—the Obama administration just believes they should. All these measures, Obama recognizes, are expensive. The cap and trade legislation is estimated to impose an $850 billion burden on the private sector; together with other related measures, the environmental tab will exceed $1 trillion. This would undoubtedly impose a significant financial burden on an already-stressed economy. These measures are billed as necessary to combat global warming. Yet no one really knows if the globe is warming significantly or not, and no one really knows if human beings are the cause of the warming or not. For years people went along with Al Gore’s claim that “the earth has a fever,” a claim illustrated by misleading images of glaciers disappearing, oceans swelling, famines arising, and skies darkening. Apocalypse now! Now we know that the main body of data that provided the basis for these claims appears to have been faked. The Climategate scandal showed that scientists associated with the Intergovernmental Panel on Climate Change were quite willing to manipulate and even suppress data that did not conform to their ideological commitment to global warming.3 The fakers insist that even if you discount the fakery, the data still show.... But who’s in the mood to listen to them now? Independent scientists who have reviewed the facts say that average global temperatures have risen by around 1.3 degrees Fahrenheit in the past 100 years. Lots of things could have caused that. Besides, if you project further back, the record shows quite a bit of variation: periods of warming, followed by periods of cooling. There was a Medieval Warm Period around 1000 A.D., and a Little Ice Age that occurred several hundred years later. In the past century, the earth warmed slightly from 1900 to 1940, then cooled slightly until the late 1970s, and has resumed warming slightly since then. How about in the past decade or so? Well, if you count from 1998, the earth has cooled in the past dozen years. But the statistic is misleading, since 1998 was an especially hot year. If you count from 1999, the earth has warmed in the intervening period. This statistic is equally misleading, because 1999 was a cool year. This doesn’t mean that temperature change is in the eye of the beholder. It means, in the words of Roy Spencer, former senior scientist for climate studies at NASA, that “all this temperature variability on a wide range of time scales reveals that just about the only thing constant in climate is change.”4

How Google Works (Schmidt, Eric) - Your Highlight on Location 3124-3150 | Added on Sunday, April 5, 2015 10:35:40 AM In late 1999, John Doerr gave a presentation at Google that changed the company, because it created a simple tool that let the founders institutionalize their “think big” ethos. John sat on our board, and his firm, Kleiner Perkins, had recently invested in the company. The topic was a form of management by objectives called OKRs (to which we referred in the previous chapter), which John had learned from former Intel CEO Andy Grove.173 There are several characteristics that set OKRs apart from their typical underpromise-and-overdeliver corporate-objective brethren. First, a good OKR marries the big-picture objective with a highly measurable key result. It’s easy to set some amorphous strategic goal (make usability better … improve team morale … get in better shape) as an objective and then, at quarter end, declare victory. But when the strategic goal is measured against a concrete goal (increase usage of features by X percent … raise employee satisfaction scores by Y percent … run a half marathon in under two hours), then things get interesting. For example, one of our platform team’s recent OKRs was to have “new WW systems serving significant traffic for XX large services with latency < YY microseconds @ ZZ% on Jupiter.”174 (Jupiter is a code name, not the location of Google’s newest data center.) There is no ambiguity with this OKR; it is very easy to measure whether or not it is accomplished. Other OKRs will call for rolling out a product across a specific number of countries, or set objectives for usage (e.g., one of the Google+ team’s recent OKRs was about the daily number of messages users would post in hangouts) or performance (e.g., median watch latency on YouTube videos). Second—and here is where thinking big comes in—a good OKR should be a stretch to achieve, and hitting 100 percent on all OKRs should be practically unattainable. If your OKRs are all green, you aren’t setting them high enough. The best OKRs are aggressive, but realistic. Under this strange arithmetic, a score of 70 percent on a well-constructed OKR is often better than 100 percent on a lesser one. Third, most everyone does them. Remember, you need everyone thinking in your venture, regardless of their position. Fourth, they are scored, but this scoring isn’t used for anything and isn’t even tracked. This lets people judge their performance honestly. Fifth, OKRs are not comprehensive; they are reserved for areas that need special focus and objectives that won’t be reached without some extra oomph. Business-as-usual stuff doesn’t need OKRs. As your venture grows, the most important OKRs shift from individuals to teams. In a small company, an individual can achieve incredible things on her own, but as the company grows it becomes harder to accomplish stretch goals without teammates. This doesn’t mean that individuals should stop doing OKRs, but rather that team OKRs become the more important means to maintain focus on the big tasks. And there’s one final benefit of an OKR-driven culture: It helps keep people from chasing competitors. Competitors are everywhere in the Internet Century, and chasing them (as we noted earlier) is the fastest path to mediocrity. If employees are focused on a well-conceived set of OKRs, then this isn’t a problem. They know where they need to go and don’t have time to worry about the competition. ==========