Data Type Quotes

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.

Some people, from what I've seen, boo, when they lie, they become very still and centered and their gaze very concentrated and intense. They try to dominate the person they lie to. The person to whom they're lying. Another type becomes fluttery and insubstantial and punctuates his lie with little self-deprecating motions and sounds, as if credulity were the same as pity. Some bury the lie in so many digressions and asides that they like try to slip the lie in there through all the extraneous data like a tiny bug through a windowscreen ... Then there are what I might call your Kamikaze-style liars. These'll tell you a surreal and fundamentally incredible lie, and then pretend a crisis of conscience and retract the original lie, and then offer you the like they really want you to buy instead, so the real lie'll appear a some kind of concession, a settlement with through. That type's mercifully easy to see through ... Or then the type who sort of overelaborates on the lie, buttresses it with rococo formations of detail and amendment, and that's how you can always tell ... So Now I've established a subtype of the over-elaborator type. This is the liar who used to be an over-elaborator and but has somehow snapped to the fact that rococo elaborations give him away every time, so he changes and now lies tersely, sparely, seeming somehow bored, like what he's saying is too obviously true to waste time on.

In the first study, Grant and his colleagues analyzed data from one of the five biggest pizza chains in the United States. They discovered that the weekly profits of the stores managed by extroverts were 16 percent higher than the profits of those led by introverts—but only when the employees were passive types who tended to do their job without exercising initiative. Introverted leaders had the exact opposite results. When they worked with employees who actively tried to improve work procedures, their stores outperformed those led by extroverts by more than 14 percent.

Racism, at the individual level, can be seen as a predictive model whirring away in billions of human minds around the world. It is built from faulty, incomplete, or generalized data. Whether it comes from experience or hearsay, the data indicates that certain types of people have behaved badly. That generates a binary prediction that all people of that race will behave that same way. Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias.

genius is much more than high intelligence, innate talent, extraordinary work ethic, or uncanny luck, but rather a composite manifestation; a synthesis of very specific types of worldviews and behaviors. The more he looked at data through this lens, the more things started to make sense.

The paradox of the modern world is this: Not only do we do less, physically, than ever before, but we also almost never do nothing. Our bodies, deprived of large movements, are inundated with subtle-yet-continuous physical stimulation from noise, light, data, etc. This constant stream of input is a two-fold stressor, as not only is the frequency of certain environmentally induced loads extremely high, the types of input we are experiencing are unnatural.

as Jazaieri observes, “There’s no empirical evidence to suggest that beating ourselves up will actually help us change our behavior; in fact, some data suggests that this type of criticism can move us away from our goals rather than towards them.” Conversely, the more gently we speak to ourselves, the more we’ll do the same for others. So the next time you hear that harsh internal voice, pause, take a breath—and try again. Speak to yourself with the same tenderness you’d extend to a beloved child—literally using the same terms of endearment and amount of reassurance that you’d shower on an adorable three-year-old.

Whoever has the best algorithms and the most data wins. A new type of network effect takes hold: whoever has the most customers accumulates the most data, learns the best models, wins the most new customers, and so on in a virtuous circle (or a vicious one, if you’re the competition).

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.

Designing passive tracking apps as if women have pockets big enough to hold their phones is a perennial problem with an easy solution: include proper pockets in women’s clothing (she types, furiously, having just had her phone fall out of her pocket and smash on the floor for the hundredth time).

Excel suffers from an image problem. Most people assume that spreadsheet programs such as Excel are intended for accountants, analysts, financiers, scientists, mathematicians, and other geeky types. Creating a spreadsheet, sorting data, using functions, and making charts seems daunting, and best left to the nerds.

The realms of dating, marriage, and sex are all marketplaces, and we are the products. Some may bristle at the idea of people as products on a marketplace, but this is an incredibly prevalent dynamic. Consider the labor marketplace, where people are also the product. Just as in the labor marketplace, one party makes an offer to another, and based on the terms of this offer, the other person can choose to accept it or walk. What makes the dating market so interesting is that the products we are marketing, selling, buying, and exchanging are essentially our identities and lives. As with all marketplaces, every item in stock has a value, and that value is determined by its desirability. However, the desirability of a product isn’t a fixed thing—the desirability of umbrellas increases in areas where it is currently raining while the desirability of a specific drug may increase to a specific individual if it can cure an illness their child has, even if its wider desirability on the market has not changed. In the world of dating, the two types of desirability we care about most are: - Aggregate Desirability: What the average demand within an open marketplace would be for a relationship with a particular person. - Individual Desirability: What the desirability of a relationship with an individual is from the perspective of a specific other individual. Imagine you are at a fish market and deciding whether or not to buy a specific fish: - Aggregate desirability = The fish’s market price that day - Individual desirability = What you are willing to pay for the fish Aggregate desirability is something our society enthusiastically emphasizes, with concepts like “leagues.” Whether these are revealed through crude statements like, “that guy's an 8,” or more politically correct comments such as, “I believe she may be out of your league,” there is a tacit acknowledgment by society that every individual has an aggregate value on the public dating market, and that value can be judged at a glance. When what we have to trade on the dating market is often ourselves, that means that on average, we are going to end up in relationships with people with an aggregate value roughly equal to our own (i.e., individuals “within our league”). Statistically speaking, leagues are a real phenomenon that affects dating patterns. Using data from dating websites, the University of Michigan found that when you sort online daters by desirability, they seem to know “their place.” People on online dating sites almost never send a message to someone less desirable than them, and on average they reach out to prospects only 25% more desirable than themselves. The great thing about these markets is how often the average desirability of a person to others is wildly different than their desirability to you. This gives you the opportunity to play arbitrage with traits that other people don’t like, but you either like or don’t mind. For example, while society may prefer women who are not overweight, a specific individual within the marketplace may prefer obese women, or even more interestingly may have no preference. If a guy doesn’t care whether his partner is slim or obese, then he should specifically target obese women, as obesity lowers desirability on the open marketplace, but not from his perspective, giving him access to women who are of higher value to him than those he could secure within an open market.

Google gets $59 billion, and you get free search and e-mail. A study published by the Wall Street Journal in advance of Facebook’s initial public offering estimated the value of each long-term Facebook user to be $80.95 to the company. Your friendships were worth sixty-two cents each and your profile page $1,800. A business Web page and its associated ad revenue were worth approximately $3.1 million to the social network. Viewed another way, Facebook’s billion-plus users, each dutifully typing in status updates, detailing his biography, and uploading photograph after photograph, have become the largest unpaid workforce in history. As a result of their free labor, Facebook has a market cap of $182 billion, and its founder, Mark Zuckerberg, has a personal net worth of $33 billion. What did you get out of the deal? As the computer scientist Jaron Lanier reminds us, a company such as Instagram—which Facebook bought in 2012—was not valued at $1 billion because its thirteen employees were so “extraordinary. Instead, its value comes from the millions of users who contribute to the network without being paid for it.” Its inventory is personal data—yours and mine—which it sells over and over again to parties unknown around the world. In short, you’re a cheap date.

write whatever comes into your head, as fast as you can type, without reference to outlines, notes, data, books or any other aids. The object is to find out what you would like to say, what all your earlier work on the topic or project has already led you to believe.

Neuroimaging in the brain shows that once the areas of the brain that process incoming sensory data are sensitized to incoming data, that is, once the gating channels are opened more widely, the sections of the brain that gate that particular type of sensory data stay open. The baseline gating level increases even if the degree of sensory stimulus is not increased. The metaphysical background of the world begins to emerge into sensing on a regular basis.

My laboratory is a place where I write. I have become proficient at producing a rare species of prose capable of distilling ten years of work by five people into six published pages, written in a language that very few people can read and that no one ever speaks. This writing relates the details of my work with the precision of a laser scalpel, but its streamlined beauty is a type of artifice, a size-zero mannequin designed to showcase the glory of a dress that would be much less perfect on any real person. My papers do not display the footnotes that they have earned, the table of data that required painstaking months to redo when a graduate student quit, sneering on her way out that she didn’t want a life like mine. The paragraph that took five hours to write while riding on a plane, stunned with grief, flying to a funeral that I couldn’t believe was happening. The early draft that my toddler covered in crayon and applesauce while it was still warm from the printer. Although my publications contain meticulous details of the plants that did grow, the runs that went smoothly, and the data that materialized, they perpetrate a disrespectful amnesia against the entire gardens that rotted in fungus and dismay, the electrical signals that refused to stabilize, and the printer ink cartridges that we secured late at night through nefarious means. I

These new techno-religions can be divided into two main types: techno-humanism and data religion

will argue that the gender data gap is both a cause and a consequence of the type of unthinking that conceives of humanity as almost exclusively male.

He said sometimes when you're young you have to think about things, because you're forming your value-sets and you keep coming up with Data Insufficient and finding holes in your programs. So you keep trying to do a fix on your sets. And the more powerful your mind is and the more intense your concentration is, the worse damage you can do to yourself, which is why, Justin says, Alphas always have trouble and some of them go way off and out-there, and why almost all Alphas are eccentric. But he says the best thing you can do if you're too bright for your own good is what the Testers do, be aware where you got which idea, keep a tab on everything, know how your ideas link up with each other and with your deep-sets and value-sets, so when you're forty or fifty or a hundred forty and you find something that doesn't work, you can still find all the threads and pull them. But that's not real easy unless you know what your value-sets are, and most CITs don't. CITs have a trouble with not wanting to know that kind of thing. Because some of them are real eetee once you get to thinking about how they link. Especially about sex and ego-nets. Justin says inflexibility is a trap and most Alpha types are inward-turned because they process so fast they're gone and thinking before a Gamma gets a sentence out. Then they get in the habit of thinking they thought of everything, but they don't remember everything stems from input. You may have a new idea, but it stems from input somebody gave you, and that could be wrong or your senses could have been lying to you. He says it can be an equipment-quality problem or a program-quality problem, but once an Alpha takes a falsehood for true, it's a personal problem.

This idea about crossing borders many times a day on the internet…Well, imagine there’s a blogger in Australia and they’ve written a nice article and actually they want to be paid a little bit of money when people read their thing. He’s not set up on Visa, you don’t want to type out all this stuff on a credit card. Surely, if you were to pay him 50p’s worth of bitcoin for this incredible article that he’s written, or a piece of data that he’s calculated that for some reason has value to you, it enables little transactions like that to happen on a vast scale. You can do it quickly and simply and get rid of all this noise in the middle. Ironically, I think cryptos are more likely to push the world towards paid content than the other way around – because they enable it in a way that wasn’t possible before.

In one experiment, CA would show people on online panels pictures of simple bar graphs about uncontroversial things (e.g., the usage rates of mobile phones or sales of a car type) and the majority would be able to read the graph correctly. However, unbeknownst to the respondents, the data behind these graphs had actually been derived from politically controversial topics, such as income inequality, climate change, or deaths from gun violence. When the labels of the same graphs were later switched to their actual controversial topic, respondents who were made angry by identity threats were more likely to misread the relabeled graphs that they had previously understood. What CA observed was that when respondents were angry, their need for complete and rational explanations was also significantly reduced. In particular, anger put people in a frame of mind in which they were more indiscriminately punitive, particularly to out-groups. They would also underestimate the risk of negative outcomes. This led CA to discover that even if a hypothetical trade war with China or Mexico meant the loss of American jobs and profits, people primed with anger would tolerate that domestic economic damage if it meant they could use a trade war to punish immigrant groups and urban liberals.

He may not be obsessive about it enough to log his data into a spreadsheet, but he’s mindful and aware of what he’s doing. He understands the mechanism behind charm and can often turn it on or off depending on what he wants. He has learned the type of humor and story-telling that gets a positive response in women. The last thing you can say about him was that he was born into the world with the “automatic” ability to fuck a lot of girls.

These new techno-religions can be divided into two main types: techno-humanism and data religion. Data religion argues that humans have completed their cosmic task and should now pass the torch on to entirely new kinds of entities.

neural mechanisms for filtering sensory data inflows exist in the neural networks for every type of sensory input that we experience, including our nonkinesthetic feeling sense (what I have called heart perception in The Secret Teachings of Plants,

in our data around 90 percent of males who carry Yamnaya ancestry have a Y-chromosome type of steppe origin that was absent in Iberia prior to that time. It is clear that there were extraordinary hierarchies and imbalances in power at work in the expansions from the steppe.

Facebook automatically catalogued every tiny action from its users, not just their comments and clicks but the words they typed and did not send, the posts they hovered over while scrolling and did not click, and the people's names they searched and did not befriend. They could use that data, for instance, to figure out who your closest friends were, defining the strength of the relationship with a constantly changing number between 0 and 1 they called a "friend coefficient". The people rated closest to 1 would always be at the top of your news feed.

Contemporary man, owing to certain, almost imperceptible conditions of ordinary life which are firmly rooted in modern civilisation and which seem to have become, so to speak, " inevitable " in daily life, has gradually deviated from the natural type he ought to have represented on account of the sum-total of the influences of place and environment in which he was born and reared and which, under normal conditions, without any artificial impediments, would have indicated by their very nature for each individual the lawful path of his development in that final normal type which he ought to have become even in his preparatory age.   Today, civilisation, with its unlimited scope in extending its influence, has wrenched man from the normal conditions in which he should be living.   It is, of course, true that modern civilisation has opened up for man new and vaster horizons in different technical, mechanical and many other so-called " sciences ", thereby enlarging his world perception, but civilisation has, instead of a balanced rising to a higher degree of development, developed only certain sides of his general being to the detriment of others, while, because of the absence of an harmonious education, certain faculties inherent in man have even been completely destroyed, depriving him in this way of the natural privileges of his type. In other words, by not educating the growing generation harmoniously, this civilisation, which should have been, according to common sense, in all respects like a good mother to man, has withheld from him what she should have given him ; and, it appears, that she has even taken from him the possibility of the progressive and balanced development of a new type, which development would have inevitably taken place if only in the course of time and according to the law of general human progress.   From this follows the indubitable fact, which can be clearly established, that, instead of an accomplished individual type, which historical data would show man to have been some centuries ago and one normally in communion with Nature and the environment generating him, there developed instead a being that was uprooted from the soil, unfit for life, and a stranger to all normal conditions of existence.

In the name of speed, Morse and Vail had realized that they could save strokes by reserving the shorter sequences of dots and dashes for the most common letters. But which letters would be used most often? Little was known about the alphabet’s statistics. In search of data on the letters’ relative frequencies, Vail was inspired to visit the local newspaper office in Morristown, New Jersey, and look over the type cases. He found a stock of twelve thousand E’s, nine thousand T’s, and only two hundred Z’s. He and Morse rearranged the alphabet accordingly. They had originally used dash-dash-dot to represent T, the second most common letter; now they promoted T to a single dash, thus saving telegraph operators uncountable billions of key taps in the world to come. Long afterward, information theorists calculated that they had come within 15 percent of an optimal arrangement for telegraphing English text.

Imagine a hundred million people clicking polls and typing in their favorite TV shows and products and political leanings, day after day. It’s the biggest data profile ever. And it’s voluntary. That’s the funny part. People resist a census, but give them a profile page and they’ll spend all day telling you who they are.

Although acting inconsistently with one’s own implicit interests and developmental trends can sometimes pay off, the data suggest that those who ignore their deeper impulses, curiosities, and values typically experience sub-optimal outcomes. For example, the latter types tend not to be the ones who make a mark on history.

Bullshit involves language, statistical figures, data graphics, and other forms of presentation intended to persuade or impress an audience by distracting, overwhelming, or intimidating them with a blatant disregard for truth, logical coherence, or what information is actually being conveyed. The key elements of this definition are that bullshit bears no allegiance to conveying the truth, and that the bullshitter attempts to conceal this fact behind some type of rhetorical veil. Sigmund Freud illustrated the concept about as well as one could imagine in a letter he wrote his fiancée, Martha Bernays, in 1884: So I gave my lecture yesterday.

The Sumerian writing system did so by combining two types of signs, which were pressed in clay tablets. One type of signs represented numbers. There were signs for 1, 10, 60, 600, 3,600 and 36,000. (The Sumerians used a combination of base-6 and base-10 numeral systems. Their base-6 system bestowed on us several important legacies, such as the division of the day into twenty-four hours and of the circle into 360 degrees.) The other type of signs represented people, animals, merchandise, territories, dates and so forth. By combining both types of signs the Sumerians were able to preserve far more data than any human brain could remember or any DNA chain could encode.

Big data is a type of supercomputing for commercial enterprises and governments that will make it possible to monitor a pandemic as it happens, anticipate where the next bank robbery will occur, optimize fast food supply chains, predict voter behavior on election day, and forecast the volatility of political uprisings while they are happening.

Connascence, in the context of software engineering, refers to the degree of coupling between software components. (Connascence.io hosts a handy reference to the various types of connascence.) Software components are connascent if a change in one would require the other(s) to be modified in order to maintain the overall correctness of the system.

In the name of speed, Morse and Vail had realized that they could save strokes by reserving the shorter sequences of dots and dashes for the most common letters. But which letters would be used most often? Little was known about the alphabet’s statistics. In search of data on the letters’ relative frequencies, Vail was inspired to visit the local newspaper office in Morristown, New Jersey, and look over the type cases.

The most convincing data concern rare humans with damage restricted to the amygdala, either due to a type of encephalitis or a congenital disorder called Urbach-Wiethe disease, or where the amygdala was surgically destroyed to control severe, drug-resistant seizures originating there.5 Such individuals are impaired in detecting angry facial expressions (while being fine at recognizing other emotional states—stay tuned).

After all, emotions are not some mystical phenomenon – they are the result of a biochemical process. Hence, in the not too distant future a machine-learning algorithm could analyse the biometric data streaming from sensors on and inside your body, determine your personality type and your changing moods, and calculate the emotional impact that a particular song – even a particular musical key – is likely to have on you.10

No one has been able to aggregate more intention data on what consumers like than Google. Google not only sees you coming, but sees where you’re going. When homicide investigators arrive at a crime scene and there is a suspect—almost always the spouse—they check the suspect’s search history for suspicious Google queries (like “how to poison your husband”). I suspect we’re going to find that U.S. agencies have been mining Google to understand the intentions of more than some shopper thinking about detergent, but cells looking for fertilizer to build bombs. Google controls a massive amount of behavioral data. However, the individual identities of users have to be anonymized and, to the best of our knowledge, grouped. People are not comfortable with their name and picture next to a list of all the things they have typed into the Google query box. And for good reasons. Take a moment to imagine your picture and your name above everything you have typed into that Google search box. You’ve no doubt typed in some crazy shit that you would rather other people not know. So, Google has to aggregate this data, and can only say that people of this age or people of this cohort, on average, type in these sorts of things into their Google search box. Google still has a massive amount of data it can connect, if not to specific identities, to specific groups.

The data that drives algorithms isn't just a few numbers now. It's monstrous tables of millions of numbers, thousands upon thousands of rows and columns of numbers....Matrices are created and refined by computers endlessly churning through Big Data's records on everyone, and everything they've done. No human can read those matrices, even with computers helping you interpret them they are simply too large and complex to fully comprehend. But the computers can use them, applying the appropriate matrix to show us the appropriate video that will eventually lead us to make an appropriate purchase. We are not living in "The Matrix," but there's still a matrix controlling us. What does this have to do with the rabbit hole of conspiracy theories? It has everything to do with it. These algorithms are quickly becoming the primary route down the rabbit hole. To a large extent this has already happened, but it's going to get far, far worse. Tufekci described what happened when she tried watching different types of content on YouTube. She started watching videos of Donald Trump rallies. 'I wanted to write about one of [Donald Trump]'s rallies, so I watched it a few times on YouTube. YouTube started recommending to me, and autoplaying to me, white supremacist videos, in increasing order of extremism. If I watched one, it served up one even more extreme. If you watch Hilary Clinton or Bernie Sanders content, YouTube recommends and autoplays [left-wing] conspiracy videos, and it goes downhill from there." Downhill, into the rabbit hole....Without human intervention the algorithm has evolved to a perfect a method of gently stepping up the intensity of the conspiracy videos that it shows you so that you don't get turned off, and so you continue to watch. They get more intense because the algorithm has found (not in any human sense, but found nonetheless) that the deeper it can guide people down the rabbit hole, the more revenue it can make.

When cheap-seat criticism becomes the loudest, most prevalent type of criticism we encounter, it pushes out the idea that thoughtful criticism and feedback can be and often are useful. We stop teaching people how to offer constructive, helpful feedback and critiques, and, in order to save ourselves, we shut down all incoming data. We start to exist in echo chambers where nothing we do or say is challenged. This is also dangerous. When we stop caring what people think, we lose our capacity for connection. But

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

It was a habit he developed, of incubating and maturing his thought upon a subject, and of then rushing into the type-writer with it.  That it did not see print was a matter a small moment with him.  The writing of it was the culminating act of a long mental process, the drawing together of scattered threads of thought and the final generalizing upon all the data with which his mind was burdened.  To write such an article was the conscious effort by which he freed his mind and made it ready for fresh material and problems.

One other thing. And this really matters for readers of this book. According to official Myers–Briggs documents, the test can ‘give you an insight into what kinds of work you might enjoy and be successful doing’. So if you are, like me, classified as ‘INTJ’ (your dominant traits are being introverted, intuitive and having a preference for thinking and judging), the best-fit occupations include management consultant, IT professional and engineer.30 Would a change to one of these careers make me more fulfilled? Unlikely, according to respected US psychologist David Pittenger, because there is ‘no evidence to show a positive relation between MBTI type and success within an occupation…nor is there any data to suggest that specific types are more satisfied within specific occupations than are other types’. Then why is the MBTI so popular? Its success, he argues, is primarily due to ‘the beguiling nature of the horoscope-like summaries of personality and steady marketing’.31 Personality tests have their uses, even if they do not reveal any scientific ‘truth’ about us. If we are in a state of confusion they can be a great emotional comfort, offering a clear diagnosis of why our current job may not be right, and suggesting others that might suit us better. They also raise interesting hypotheses that aid self-reflection: until I took the MBTI, I had certainly never considered that IT could offer me a bright future (by the way, I apparently have the wrong personality type to be a writer). Yet we should be wary about relying on them as a magic pill that enables us suddenly to hit upon a dream career. That is why wise career counsellors treat such tests with caution, using them as only one of many ways of exploring who you are. Human personality does not neatly reduce into sixteen or any other definitive number of categories: we are far more complex creatures than psychometric tests can ever reveal. And as we will shortly learn, there is compelling evidence that we are much more likely to find fulfilling work by conducting career experiments in the real world than by filling out any number of questionnaires.32

Grant had a theory about which kinds of circumstances would call for introverted leadership. His hypothesis was that extroverted leaders enhance group performance when employees are passive, but that introverted leaders are more effective with proactive employees. To test his idea, he and two colleagues, professors Francesca Gino of Harvard Business School and David Hofman of the Kenan-Flagler Business School at the University of North Carolina, carried out a pair of studies of their own. In the first study, Grant and his colleagues analyzed data from one of the five biggest pizza chains in the United States. They discovered that the weekly profits of the stores managed by extroverts were 16 percent higher than the profits of those led by introverts—but only when the employees were passive types who tended to do their job without exercising initiative. Introverted leaders had the exact opposite results. When they worked with employees who actively tried to improve work procedures, their stores outperformed those led by extroverts by more than 14 percent. In

For the third type of coping strategy, at the societal level, we need to ask how non-state actors (such as communities and nonprofit organizations) will respond to the consequences of the data revolution. We think a wave of civil-society organizations will emerge in the next decade designed to shield connected citizens from their governments and from themselves. Powerful lobbying groups will advocate content and privacy laws. Rights organizations that document repressive surveillance tactics will call for better citizen protection. There

Grant and his colleagues analyzed data from one of the five biggest pizza chains in the United States. They discovered that the weekly profits of the stores managed by extroverts were 16 percent higher than the profits of those led by introverts—but only when the employees were passive types who tended to do their job without exercising initiative. Introverted leaders had the exact opposite results. When they worked with employees who actively tried to improve work procedures, their stores outperformed those led by extroverts by more than 14 percent.

Kant had overlooked a fundamental and immediate type of data about ourselves: our own bodies and our own feelings. We can know ourselves from the inside, he insisted. We have direct, immediate knowledge, not dependent on our perceptions. Hence, he was the first philosopher to look at impulses and feelings from the inside, and for the rest of his career he wrote extensively about interior human concerns: sex, love, death, dreams, suffering, religion, suicide, relations with others, vanity, self-esteem. More than any other philosopher, he addressed those

In biology you become accustomed to different ways data can represent itself. Salmon returning upstream and herd animals have very linear patterns. Birds follow loops. I’m looking at another pattern. One that’s very familiar to me. It’s a predator’s circuit. I furiously type away, searching for the pattern imprinted on my memory. I find it. It’s not the same shape, but it has similar symmetry. I could write a formula for a fractal that would generate patterns just like these. But it’s not just a pattern, it’s a behavior. The behavior generating the pattern on my wall,

Judging types are in a hurry to make decisions. Perceiving types are not. This is why science doesn’t make any serious attempt to reach a final theory of everything. It always says, “Let’s do another experiment. And another. And another.” When will the experimentation ever end? When will scientists conclude that they have now collected easily enough data to now draw definitive conclusions? But they don’t want to draw any such conclusions. That’s not how they roll. Their method has no such requirement. That’s why many of them openly say that they do not want a final theory. It will stop them, they say, from “discovering” new things. Judging types like order and structure. They like decisions, conclusions, getting things done and reaching objectives. Perceiving types are doubtful and skeptical about all of that. They frequently refer to judging types as “judgmental”, which is literally perceived as a bad thing, “authoritarian”, “totalitarian”, “fascist”, “Nazi”, and so on. Perceiving types always want to have an open road ahead of them. They never want to actually arrive. Judging types cannot see the point of not wanting to reach your destination.

The word “collect” has a very special definition, according to the Department of Defense. It doesn’t mean collect; it means that a person looks at, or analyzes, the data. In 2013, Director of National Intelligence James Clapper likened the NSA’s trove of accumulated data to a library. All those books are stored on the shelves, but very few are actually read. “So the task for us in the interest of preserving security and preserving civil liberties and privacy is to be as precise as we possibly can be when we go in that library and look for the books that we need to open up and actually read.” Think of that friend of yours who has thousands of books in his house. According to this ridiculous definition, the only books he can claim to have collected are the ones he’s read. This is why Clapper asserts he didn’t lie in a Senate hearing when he replied “no” to the question “Does the NSA collect any type of data at all on millions or hundreds of millions of Americans?” From the military’s perspective, it’s not surveillance until a human being looks at the data, even if algorithms developed and implemented by defense personnel or contractors have analyzed it many times over.

A new social type was being created by the apartment building, a cool, unemotional personality impervious to the psychological pressures of high-rise life, with minimal needs for privacy, who thrived like an advanced species of machine in the neutral atmosphere. This was the sort of resident who was content to do nothing but sit in his over-priced apartment, watch television with the sound turned down, and wait for his neighbours to make a mistake. Perhaps the recent incidents represented a last attempt by Wilder and the airline pilots to rebel against this unfolding logic? Sadly, they had little chance of success, precisely because their opponents were people who were content with their lives in the high-rise, who felt no particular objection to an impersonal steel and concrete landscape, no qualms about the invasion of their privacy by government agencies and data-processing organizations, and if anything welcomed these invisible intrusions, using them for their own purposes. These people were the first to master a new kind of late twentieth-century life. They thrived on the rapid turnover of acquaintances, the lack of involvement with others, and the total self-sufficiency of lives which, needing nothing, were never disappointed. Alternatively,

tip for applying this learning: To get people to “fall in love” with your ideas, don’t solely rely on numbers and data. People can tune out this type of input relatively easily. But if you communicate with a story or experience, you create an emotion. Start your next meeting with a story instead of a spreadsheet. Make your audience feel as well as think. Connect emotionally with them by telling a personal anecdote that reinforces the point of your presentation. Or draw upon a nostalgic shared memory. Once you inspire emotion, your listener will be less likely to disengage, and more likely to remember and respond to your message.

As data analytics, superfast computers, digital technology, and other breakthroughs enabled by science play a bigger and bigger role in informing medical decision-making, science has carved out a new and powerful role as the steadfast partner of the business of medicine—which is also enjoying a new day in the sun. It may surprise some people to learn that the business of medicine is not a twenty-first-century invention. Health care has always been a business, as far back as the days when Hippocrates and his peers practiced medicine. Whether it was three goats, a gold coin, or a bank note, some type of payment was typically exchanged for medical services, and institutions of government or learning funded research. However, since the 1970s, business has been the major force directing the practice of medicine. Together, the business and science of medicine are the new kids on the block—the bright, shiny new things. Ideally, as I’ve suggested, the art, science, and business of medicine would work together in a harmonious partnership, each upholding the other and contributing all it has to offer to the whole. And sometimes (as we’ll find in later chapters) this partnership works well. When it does, the results are magnificent for patients and doctors, not to mention for scientists and investors.

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.

Google had a built-in disadvantage in the social networking sweepstakes. It was happy to gather information about the intricate web of personal and professional connections known as the “social graph” (a term favored by Facebook’s Mark Zuckerberg) and integrate that data as signals in its search engine. But the basic premise of social networking—that a personal recommendation from a friend was more valuable than all of human wisdom, as represented by Google Search—was viewed with horror at Google. Page and Brin had started Google on the premise that the algorithm would provide the only answer. Yet there was evidence to the contrary. One day a Googler, Joe Kraus, was looking for an anniversary gift for his wife. He typed “Sixth Wedding Anniversary Gift Ideas” into Google, but beyond learning that the traditional gift involved either candy or iron, he didn’t see anything creative or inspired. So he decided to change his status message on Google Talk, a line of text seen by his contacts who used Gmail, to “Need ideas for sixth anniversary gift—candy ideas anyone?” Within a few hours, he got several amazing suggestions, including one from a colleague in Europe who pointed him to an artist and baker whose medium was cake and candy. (It turned out that Marissa Mayer was an investor in the company.) It was a sobering revelation for Kraus that sometimes your friends could trump algorithmic search.

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.

Google searches, however, reveal that there was one line that did trigger the type of response then-president Obama might have wanted. He said, “Muslim Americans are our friends and our neighbors, our co-workers, our sports heroes and, yes, they are our men and women in uniform, who are willing to die in defense of our country.” After this line, for the first time in more than a year, the top Googled noun after “Muslim” was not “terrorists,” “extremists,” or “refugees.” It was “athletes,” followed by “soldiers.” And, in fact, “athletes” kept the top spot for a full day afterward. When we lecture angry people, the search data implies that their fury can grow. But subtly provoking people’s curiosity, giving new information, and offering new images of the group that is stoking their rage may turn their thoughts in different, more positive directions.

Equally important, statistical systems require feedback—something to tell them when they’re off track. Without feedback, however, a statistical engine can continue spinning out faulty and damaging analysis while never learning from its mistakes. Many of the WMDs I’ll be discussing in this book, including the Washington school district’s value-added model, behave like that. They define their own reality and use it to justify their results. This type of model is self-perpetuating, highly destructive—and very common. If the people being evaluated are kept in the dark, the thinking goes, they’ll be less likely to attempt to game the system. Instead, they’ll simply have to work hard, follow the rules, and pray that the model registers and appreciates their efforts. But if the details are hidden, it’s also harder to question the score or to protest against it.

Who among us can predict the future? Who would dare to? The answer to the first question is no one, really, and the answer to the second is everyone, especially every government and business on the planet. This is what that data of ours is used for. Algorithms analyze it for patterns of established behavior in order to extrapolate behaviors to come, a type of digital prophecy that’s only slightly more accurate than analog methods like palm reading. Once you go digging into the actual technical mechanisms by which predictability is calculated, you come to understand that its science is, in fact, anti-scientific, and fatally misnamed: predictability is actually manipulation. A website that tells you that because you liked this book you might also like books by James Clapper or Michael Hayden isn’t offering an educated guess as much as a mechanism of subtle coercion.

Prerequisites for sex: Men in general like to have sex to feel emotionally connected, and women need to feel emotionally connected to have sex. Almost 90 percent of the couples we interviewed agreed with that last sentence. We refer to this as women having more prerequisites for sex than men do. Women’s prerequisites aren’t always limited to emotional closeness; sometimes they are about feeling exhausted, distracted, not rested, or not good about herself or her body. Interestingly, the data shows that gay men have the most sex of any type of couple—two people with the fewest prerequisites—and lesbians have the least sex of any type of couple—two people with the most prerequisites. Sexual desire for women is a barometer for how the rest of her world is going. If she’s not rested, or happy, or healthy, or feeling supported or loved, she’s not going to feel like having sex.

Remember that some organizations, especially activist groups, have no obligation to rigorous, unbiased data. They are working to convince you to adopt their view of the world and thus aren't necessarily impartial [...] This type of bias or spin is common, and you need to be on the alert for it in the reports you read. In fact, bias is a major reason to get multiple kinds of trend data before drawing conclusions. Even if activist groups don't publish false information, they might leave out key data, which might lead you in another direction. If you read particularly alarming data, for example, a trend that says, "we're losing 10 percent of all bird species each year," you should make sure you verify it with other sources. In a world that moves as fast as ours does, sensational problems sometimes arise, but if it's really an issue, more than one expert will be covering it.

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.

Kant distinguished between two types of truths: (1) analytic propositions, which derive from logic and “reason itself” rather than from observing the world; for example, all bachelors are unmarried, two plus two equals four, and the angles of a triangle always add up to 180 degrees; and (2) synthetic propositions, which are based on experience and observations; for example, Munich is bigger than Bern, all swans are white. Synthetic propositions could be revised by new empirical evidence, but not analytic ones. We may discover a black swan but not a married bachelor or (at least so Kant thought) a triangle with 181 degrees. As Einstein said of Kant’s first category of truths: “This is held to be the case, for example, in the propositions of geometry and in the principle of causality. These and certain other types of knowledge… do not previously have to be gained from sense data, in other words they are a priori knowledge.” Einstein

When we use silence, we know exactly what’s going on in our minds and just don’t share it, but others don’t know what we hide at that moment. We know how we feel and think, but we refuse to share these thoughts. If you have a certain belief, you identify as this certain box they have created to fit you in along with other people who share similar beliefs. The mind does not like complexity and wants to keep receiving information simple. When you remain silent, this quick and simple process that the mind does becomes complex, because it needs more data. Silence requires more analytical thinking which is difficult for a mind that is preoccupied with thousands of tasks and projects to come across daily. That’s the easiest job the mind can do. Based on previous experiences and personality samples that match yours, they will assume that you identify as a certain type, have certain beliefs or share similar thoughts to other people of that sample.

Many countries have a long history of spying on foreign corporations for their own military and commercial advantage. The US claims that it does not engage in commercial espionage, meaning that it does not hack foreign corporate networks and pass that information on to US competitors for commercial advantage. But it does engage in economic espionage, by hacking into foreign corporate networks and using that information in government trade negotiations that directly benefit US corporate interests. Recent examples are the Brazilian oil company Petrobras and the European SWIFT international bank payment system. In fact, a 1996 government report boasted that the NSA claimed that the economic benefits of one of its programs to US industry “totaled tens of billions of dollars over the last several years.” You may or may not see a substantive difference between the two types of espionage. China, without so clean a separation between its government and its industries, does not.

In the quantum theory, you start with a symmetry, and then you break it with the Higgs boson to get the universe that we see all around us. Similarly, Guth then theorized that maybe there was a new type of Higgs boson (called the inflaton) that made inflation possible. As with the original Higgs boson, the universe started out in the false vacuum that gave us the era of rapid inflation. But then quantum bubbles occurred within the inflaton field. Inside the bubble, the true vacuum emerged, where the inflation had stopped. Our universe emerged as one of these bubbles. The universe slowed down within the bubble, giving us the present-day expansion. So far, inflation seems to fit the astronomical data. It is currently the leading theory. But it has unexpected consequences. If we invoke the quantum theory, it means that the Big Bang can happen again and again. New universes may be being born out of our universe all the time. This means that our universe is actually a single bubble in a bubble bath of universes. This creates a multiverse of parallel universes.

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?

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.

Every entry, whether revised or reviewed, goes through multiple editing passes. The definer starts the job, then it’s passed to a copy editor who cleans up the definer’s work, then to a bunch of specialty editors: cross-reference editors, who make sure the definer hasn’t used any word in the entry that isn’t entered in that dictionary; etymologists, to review or write the word history; dating editors, who research and add the dates of first written use; pronunciation editors, who handle all the pronunciations in the book. Then eventually it’s back to a copy editor (usually a different one from the first round, just to be safe), who will make any additional changes to the entry that cross-reference turned up, then to the final reader, who is, as the name suggests, the last person who can make editorial changes to the entry, and then off to the proofreader (who ends up, again, being a different editor from the definer and the two previous copy editors). After the proofreaders are done slogging through two thousand pages of four-point type, the production editors send it off to the printer or the data preparation folks, and then we get another set of dictionary pages (called page proofs) to proofread. This process happens continuously as we work through a dictionary, so a definer may be working on batches in C, cross-reference might be in W, etymology in T, dating and pronunciation in the second half of S, copy editors in P (first pass) and Q and R (second pass), while the final reader is closing out batches in N and O, proofreaders are working on M, and production has given the second set of page proofs to another set of proofreaders for the letter L. We all stagger our way through the alphabet until the last batch, which is inevitably somewhere near G, is closed. By the time a word is put in print either on the page or online, it’s generally been seen by a minimum of ten editors. Now consider that when it came to writing the Collegiate Dictionary, Eleventh Edition, we had a staff of about twenty editors working on it: twenty editors to review about 220,000 existing definitions, write about 10,000 new definitions, and make over 100,000 editorial changes (typos, new dates, revisions) for the new edition. Now remember that the 110,000-odd changes made were each reviewed about a dozen times and by a minimum of ten editors. The time given to us to complete the revision of the Tenth Edition into the Eleventh Edition so production could begin on the new book? Eighteen months.

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.

Although these digital tools can improve the diagnostic process and offer clinicians a variety of state-of-the-art treatment options, most are based on a reductionist approach to health and disease. This paradigm takes a divide-and-conquer approach to medicine, "rooted in the assumption that complex problems are solvable by dividing them into smaller, simpler, and thus more tractable units." Although this methodology has led to important insights and practical implications in healthcare, it does have its limitations. Reductionist thinking has led researchers and clinicians to search for one or two primary causes of each disease and design therapies that address those causes.... The limitation of this type of reasoning becomes obvious when one examines the impact of each of these diseases. There are many individuals who are exposed to HIV who do not develop the infection, many patients have blood glucose levels outside the normal range who never develop signs and symptoms of diabetes, and many patients with low thyroxine levels do not develop clinical hypothyroidism. These "anomalies" imply that there are cofactors involved in all these conditions, which when combined with the primary cause or causes bring about the clinical onset. Detecting these contributing factors requires the reductionist approach to be complemented by a systems biology approach, which assumes there are many interacting causes to each disease.

By failing to make the obvious connection between an openly misogynistic culture and the mysterious lack of women, Levy contributed to the myth of innately talented hackers being implicitly male. And, today, it’s hard to think of a profession more in thrall to brilliance bias than computer science. ‘Where are the girls that love to program?’ asked a high-school teacher who took part in a summer programme for advanced-placement computer-science teachers at Carnegie Mellon; ‘I have any number of boys who really really love computers,’ he mused. ‘Several parents have told me their sons would be on the computer programming all night if they could. I have yet to run into a girl like that.’ This may be true, but as one of his fellow teachers pointed out, failing to exhibit this behaviour doesn’t mean that his female students don’t love computer science. Recalling her own student experience, she explained how she ‘fell in love’ with programming when she took her first course in college. But she didn’t stay up all night, or even spend a majority of her time programming. ‘Staying up all night doing something is a sign of single-mindedness and possibly immaturity as well as love for the subject. The girls may show their love for computers and computer science very differently. If you are looking for this type of obsessive behavior, then you are looking for a typically young, male behavior. While some girls will exhibit it, most won’t.

Hunter-gatherers who survive childhood typically live to be old: their most common age of death is between sixty-eight and seventy-two, and most become grandparents or even great-grandparents.70 They most likely die from gastrointestinal or respiratory infections, diseases such as malaria or tuberculosis, or from violence and accidents.71 Health surveys also indicate that most of the noninfectious diseases that kill or disable older people in developed nations are rare or unknown among middle-aged and elderly hunter-gatherers.72 These admittedly limited studies have found that hunter-gatherers rarely if ever get type 2 diabetes, coronary heart disease, hypertension, osteoporosis, breast cancer, asthma, and liver disease. They also don’t appear to suffer much from gout, myopia, cavities, hearing loss, collapsed arches, and other common ailments. To be sure, hunter-gatherers don’t live in perpetually perfect health, especially since tobacco and alcohol have become increasingly available to them, but the evidence suggests that they are healthy compared to many older Americans today despite never having received any medical care. In short, if you were to compare contemporary health data from people around the world with equivalent data from hunter-gatherers, you would not conclude that rising rates of common mismatch diseases such as heart disease and type 2 diabetes are straightforward, inevitable by-products of economic progress and increased longevity. Moreover,

Ultimately, the World Top Incomes Database (WTID), which is based on the joint work of some thirty researchers around the world, is the largest historical database available concerning the evolution of income inequality; it is the primary source of data for this book.24 The book’s second most important source of data, on which I will actually draw first, concerns wealth, including both the distribution of wealth and its relation to income. Wealth also generates income and is therefore important on the income study side of things as well. Indeed, income consists of two components: income from labor (wages, salaries, bonuses, earnings from nonwage labor, and other remuneration statutorily classified as labor related) and income from capital (rent, dividends, interest, profits, capital gains, royalties, and other income derived from the mere fact of owning capital in the form of land, real estate, financial instruments, industrial equipment, etc., again regardless of its precise legal classification). The WTID contains a great deal of information about the evolution of income from capital over the course of the twentieth century. It is nevertheless essential to complete this information by looking at sources directly concerned with wealth. Here I rely on three distinct types of historical data and methodology, each of which is complementary to the others.25 In the first place, just as income tax returns allow us to study changes in income inequality, estate tax returns enable us to study changes in the inequality of wealth.26 This

The largest and most rigorous study that is currently available in this area is the third one commissioned by the British Home Office (Kelly, Lovett, & Regan, 2005). The analysis was based on the 2,643 sexual assault cases (where the outcome was known) that were reported to British police over a 15-year period of time. Of these, 8% were classified by the police department as false reports. Yet the researchers noted that some of these classifications were based simply on the personal judgments of the police investigators, based on the victim’s mental illness, inconsistent statements, drinking or drug use. These classifications were thus made in violation of the explicit policies of their own police agencies. There searchers therefore supplemented the information contained in the police files by collecting many different types of additional data, including: reports from forensic examiners, questionnaires completed by police investigators, interviews with victims and victim service providers, and content analyses of the statements made by victims and witnesses. They then proceeded to evaluate each case using the official criteria for establishing a false allegation, which was that there must be either “a clear and credible admission by the complainant” or “strong evidential grounds” (Kelly, Lovett, & Regan,2005). On the basis of this analysis, the percentage of false reports dropped to 2.5%." Lonsway, Kimberly A., Joanne Archambault, and David Lisak. "False reports: Moving beyond the issue to successfully investigate and prosecute non-stranger sexual assault." The Voice 3.1 (2009): 1-11.

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.

In September 1999, the Department of Justice succeeded in denaturalizing 63 participants in Nazi acts of persecution; and in removing 52 such individuals from this country. This appears to be but a small portion of those who actually were brought here by our own government. A 1999 report to the Senate and the House said "that between 1945 and 1955, 765 scientists, engineers, and technicians were brought to the United States under Overcast, Paperclip, and similar programs. It has been estimated that at least half, and perhaps as many as 80 percent of all the imported specialists were former Nazi Party members." A number of these scientists were recruited to work for the Air Force's School of Aviation Medicine (SAM) at Brooks Air Force Base in Texas, where dozens of human radiation experiments were conducted during the Cold War. Among them were flash-blindness studies in connection with atomic weapons tests and data gathering for total-body irradiation studies conducted in Houston. The experiments for which Nazi investigators were tried included many related to aviation research. Hubertus Strughold, called "the father of space medicine," had a long career at the SAM, including the recruitment of other Paperclip scientists in Germany. On September 24, 1995 the Jewish Telegraphic Agency reported that as head of Nazi Germany's Air Force Institute for Aviation Medicine, Strughold particpated in a 1942 conference that discussed "experiments" on human beings. The experiments included subjecting Dachau concentration camp inmates to torture and death. The Edgewood Arsenal of the Army's Chemical Corps as well as other military research sites recruited these scientists with backgrounds in aeromedicine, radiobiology, and opthamology. Edgewood Arsenal, Maryland ended up conducting experiments on more than seven thousand American soldiers. Using Auschwitz experiments as a guide, they conducted the same type of poison gas experiments that had been done in the secret I.G. Farben laboratories.

Imagine yourself sitting at a computer, about to visit a website. You open a Web browser, type in a URL, and hit Enter. The URL is, in effect, a request, and this request goes out in search of its destination server. Somewhere in the midst of its travels, however, before your request gets to that server, it will have to pass through TURBULENCE, one of the NSA’s most powerful weapons. Specifically, your request passes through a few black servers stacked on top of one another, together about the size of a four-shelf bookcase. These are installed in special rooms at major private telecommunications buildings throughout allied countries, as well as in US embassies and on US military bases, and contain two critical tools. The first, TURMOIL, handles “passive collection,” making a copy of the data coming through. The second, TURBINE, is in charge of “active collection”—that is, actively tampering with the users. You can think of TURMOIL as a guard positioned at an invisible firewall through which Internet traffic must pass. Seeing your request, it checks its metadata for selectors, or criteria, that mark it as deserving of more scrutiny. Those selectors can be whatever the NSA chooses, whatever the NSA finds suspicious: a particular email address, credit card, or phone number; the geographic origin or destination of your Internet activity; or just certain keywords such as “anonymous Internet proxy” or “protest.” If TURMOIL flags your traffic as suspicious, it tips it over to TURBINE, which diverts your request to the NSA’s servers. There, algorithms decide which of the agency’s exploits—malware programs—to use against you. This choice is based on the type of website you’re trying to visit as much as on your computer’s software and Internet connection. These chosen exploits are sent back to TURBINE (by programs of the QUANTUM suite, if you’re wondering), which injects them into the traffic channel and delivers them to you along with whatever website you requested. The end result: you get all the content you want, along with all the surveillance you don’t, and it all happens in less than 686 milliseconds. Completely unbeknownst to you. Once the exploits are on your computer, the NSA can access not just your metadata, but your data as well. Your entire digital life now belongs to them.

Imagine yourself sitting at a computer, about to visit a website. You open a Web browser, type in a URL, and hit Enter. The URL is, in effect, a request, and this request goes out in search of its destination server. Somewhere in the midst of its travels, however, before your request gets to that server, it will have to pass through TURBULENCE, one of the NSA’s most powerful weapons. Specifically, your request passes through a few black servers stacked on top of one another, together about the size of a four-shelf bookcase. These are installed in special rooms at major private telecommunications buildings throughout allied countries, as well as in US embassies and on US military bases, and contain two critical tools. The first, TURMOIL, handles “passive collection,” making a copy of the data coming through. The second, TURBINE, is in charge of “active collection”—that is, actively tampering with the users. You can think of TURMOIL as a guard positioned at an invisible firewall through which Internet traffic must pass. Seeing your request, it checks its metadata for selectors, or criteria, that mark it as deserving of more scrutiny. Those selectors can be whatever the NSA chooses, whatever the NSA finds suspicious: a particular email address, credit card, or phone number; the geographic origin or destination of your Internet activity; or just certain keywords such as “anonymous Internet proxy” or “protest.” If TURMOIL flags your traffic as suspicious, it tips it over to TURBINE, which diverts your request to the NSA’s servers. There, algorithms decide which of the agency’s exploits—malware programs—to use against you. This choice is based on the type of website you’re trying to visit as much as on your computer’s software and Internet connection. These chosen exploits are sent back to TURBINE (by programs of the QUANTUM suite, if you’re wondering), which injects them into the traffic channel and delivers them to you along with whatever website you requested. The end result: you get all the content you want, along with all the surveillance you don’t, and it all happens in less than 686 milliseconds. Completely unbeknownst to you. Once the exploits are on your computer, the NSA can access not just your metadata, but your data as well. Your entire digital life now belongs to them.

Babel led to an explosion in the number of languages. That was part of Enki's plan. Monocultures, like a field of corn, are susceptible to infections, but genetically diverse cultures, like a prairie, are extremely robust. After a few thousand years, one new language developed - Hebrew - that possessed exceptional flexibility and power. The deuteronomists, a group of radical monotheists in the sixth and seventh centuries B.C., were the first to take advantage of it. They lived in a time of extreme nationalism and xenophobia, which made it easier for them to reject foreign ideas like Asherah worship. They formalized their old stories into the Torah and implanted within it a law that insured its propagation throughout history - a law that said, in effect, 'make an exact copy of me and read it every day.' And they encouraged a sort of informational hygiene, a belief in copying things strictly and taking great care with information, which as they understood, is potentially dangerous. They made data a controlled substance... [and] gone beyond that. There is evidence of carefully planned biological warfare against the army of Sennacherib when he tried to conquer Jerusalem. So the deuteronomists may have had an en of their very own. Or maybe they just understood viruses well enough that they knew how to take advantage of naturally occurring strains. The skills cultivated by these people were passed down in secret from one generation to the next and manifested themselves two thousand years later, in Europe, among the Kabbalistic sorcerers, ba'al shems, masters of the divine name. In any case, this was the birth of rational religion. All of the subsequent monotheistic religions - known by Muslims, appropriately, as religions of the Book - incorporated those ideas to some extent. For example, the Koran states over and over again that it is a transcript, an exact copy, of a book in Heaven. Naturally, anyone who believes that will not dare to alter the text in any way! Ideas such as these were so effective in preventing the spread of Asherah that, eventually, every square inch of the territory where the viral cult had once thrived was under the sway of Islam, Christianity, or Judaism. But because of its latency - coiled about the brainstem of those it infects, passed from one generation to the next - it always finds ways to resurface. In the case of Judaism, it came in the form of the Pharisees, who imposed a rigid legalistic theocracy on the Hebrews. With its rigid adherence to laws stored in a temple, administered by priestly types vested with civil authority, it resembled the old Sumerian system, and was just as stifling. The ministry of Jesus Christ was an effort to break Judaism out of this condition... an echo of what Enki did. Christ's gospel is a new namshub, an attempt to take religion out of the temple, out of the hands of the priesthood, and bring the Kingdom of God to everyone. That is the message explicitly spelled out by his sermons, and it is the message symbolically embodied in the empty tomb. After the crucifixion, the apostles went to his tomb hoping to find his body and instead found nothing. The message was clear enough; We are not to idolize Jesus, because his ideas stand alone, his church is no longer centralized in one person but dispersed among all the people.

For example, consider a stack (which is a first-in, last-out list). You might have a program that requires three different types of stacks. One stack is used for integer values, one for floating-point values, and one for characters. In this case, the algorithm that implements each stack is the same, even though the data being stored differs. In a non-object-oriented language, you would be required to create three different sets of stack routines, with each set using different names. However, because of polymorphism, in Java you can create one general set of stack routines that works for all three specific situations. This way, once you know how to use one stack, you can use them all. More generally, the concept of polymorphism is often expressed by the phrase “one interface, multiple methods.” This means that it is possible to design a generic interface to a group of related activities. Polymorphism helps reduce complexity by allowing the same interface to be used to specify a general class of action.

Abstraction is the notion of looking at a group of “somethings” (such as cars, invoices, or executing computer programs) and realizing that they have common themes. You can then ignore the unimportant differences and just record the key data items that characterize the thing (e.g., license number, amount due, or address space boundaries). When you do this, it is called “abstraction”, and the types of data that you store are “abstract data types”. Abstraction sounds like a tough mathematical concept, but don’t be fooled—it’s actually a simplification.

How can this type of data be made to tell a reliable story? By subjecting it to the economist’s favorite trick: regression analysis. No, regression analysis is not some forgotten form of psychiatric treatment. It is a powerful—if limited—tool that uses statistical techniques to identify otherwise elusive correlations.

retrieve?� When it comes to databases, chances are you’ll need to retrieve your data as often than you’ll need to insert it. That’s where this chapter comes in: you’ll meet the powerful SELECT statement and learn how to gain access to that important information you’ve been putting in your tables. You’ll even learn how to use WHERE, AND, and OR to selectively get to your data and even avoid displaying the data that you don’t need. I’m a star! Date or no date? 54 A better SELECT 57 What the * is that? 58 How to query your data types 64 More punctuation problems 65 Unmatched single quotes 66 Single quotes are special characters 67 INSERT data with single quotes in it 68 SELECT specific columns to limit results 73 SELECT specific columns for faster results 73 Combining your queries 80 Finding numeric values 83 Smooth Comparison Operators

a digital design engineer, you would spend long hours going through the TTL Data Book familiarizing yourself with the types of TTL chips that were available. Once you knew all your tools, you could actually build the computer I showed in Chapter 17 out of TTL chips. Wiring the chips together is a lot easier than wiring individual transistors

Seminars allow you to deliver your stadium pitch to multiple people at one time. The purpose is to create more buyers, but the deciding factor as to if your seminar will swim or tank is your title and pitch. Seminars are over 500% more successful when you use market data over product data. Market data is factual information that can be found by anyone doing research. Product data is information developed by your company or yourself to sell your product.                  Prospects are only interested in product data when they are looking to purchase your type of product at that moment. Everyone is interested in market data if you could show them how they can benefit from the information.

The Android sensor framework lets you access many types of sensors. Some of these sensors are hardware-based and some are software-based. Hardware-based sensors are physical components built into a handset or tablet device. They derive their data by directly measuring specific environmental properties, such as acceleration, geomagnetic field strength, or angular change. Software-based sensors are not physical devices, although they mimic hardware-based sensors. Software-based sensors derive their data from one or more of the hardware-based sensors and are sometimes called virtual sensors or synthetic sensors. The linear acceleration sensor and the gravity sensor are examples of software-based

The adjective “efficient” in “efficient markets” refers to how investors use information. In an efficient market, every titbit of new information is processed correctly and immediately by investors. As a result, market prices react instantly and appropriately to any relevant news about the asset in question, whether it is a share of stock, a corporate bond, a derivative, or some other vehicle. As the saying goes, there are no $100 bills left on the proverbial sidewalk for latecomers to pick up, because asset prices move up or down immediately. To profit from news, you must be jackrabbit fast; otherwise, you’ll be too late. This is one rationale for the oft-cited aphorism “You can’t beat the market.” An even stronger form of efficiency holds that market prices do not react to irrelevant news. If this were so, prices would ignore will-o’-the-wisps, unfounded rumors, the madness of crowds, and other extraneous factors—focusing at every moment on the fundamentals. In that case, prices would never deviate from fundamental values; that is, market prices would always be “right.” Under that exaggerated form of market efficiency, which critics sometimes deride as “free-market fundamentalism,” there would never be asset-price bubbles. Almost no one takes the strong form of the efficient markets hypothesis (EMH) as the literal truth, just as no physicist accepts Newtonian mechanics as 100 percent accurate. But, to extend the analogy, Newtonian physics often provides excellent approximations of reality. Similarly, economists argue over how good an approximation the EMH is in particular applications. For example, the EMH fits data on widely traded stocks rather well. But thinly traded or poorly understood securities are another matter entirely. Case in point: Theoretical valuation models based on EMH-type reasoning were used by Wall Street financial engineers to devise and price all sorts of exotic derivatives. History records that some of these calculations proved wide of the mark.

To execute their innovation processes successfully, companies must obtain three distinct types of data. They must know which jobs their customers are trying to get done (that is, the tasks or activities customers are trying to carry out); the outcomes customers are trying to achieve (that is, the metrics customers use to define the successful execution of a job); and the constraints that may prevent customers from adopting or using a new product or service.

Four reasons have been cited for maintaining accurate inventory records: 1. To provide data for cost control 2. To assist in identifying purchasing needs 3. To provide accurate information on type and quantity of food and supplies on hand 4. To monitor usage of products and prevent theft and pilferage

That type of analysis could include data from training staffs and coaching staffs, performance data, and medical data.

Very few companies know how to exploit the data already embedded in their core operating systems. THE SOLUTION Evidence-based, data-driven decision making provides the answer, but it requires a big cultural shift and four changes in how operations are managed. Who Benefits from Big Data? 496 words Big data is big business. The IT research firm Gartner estimates that total software, social media, and IT services spending related to big data and analytics topped $28 billion worldwide in 2012. All estimates predict rapid growth. In addition to vendors, at least three types of organizations are harvesting value from big data.

Rather than taking supply and demand as a given, the new breed of microeconomist helps nudge the two into line. In the early days of Airbnb, an online market for home rentals, its economists pored over customer data to spot market weaknesses. Costly enhancements, including professional photographs of every listing, are rigorously tested to make sure they work before being rolled out. The company also guides users uncertain about the right rate to charge for a listing. At Uber, a taxi service, prices surge during peak hours, pulling more drivers onto the road. At Poynt, a Silicon Valley startup offering a new type of cashless

Most mods are single-player only mods. Knowing how to install single player mods helps in installing multiplayer mods. You must first download the mod that you want. Go to a reliable website and download. If the mod that you want is missing and cannot be found, this usually means that it is discontinued.   Windows   First you will need an archive utility application, such as WinZip, WinRAR, 7-Zip, or something similar.   Locate you Minecraft application. Go to the start menu, and type “minecraft” in the search bar. Click on this option to open the folder in a new window.   Your Minecraft application data can be found within your .minecraft folder.   Back-up your Minecraft save files before installing any mod. To do this simply copy your saves folder and paste it into another folder. Copy the previous saves folder back into your .minecraft folder to restore.   Extract the mod you downloaded with WinRAR or any archive utility application.   Locate the minecraft.jar file. This file can be found in the bin folder in .minecraft.   Back-up your minecraft.jar file. Copy minecraft.jar in the same folder as the mods.   Open the minecraft.jar file with WinRAR.   Copy all the mod files into the minecraft.jar file and select "Add and replace files”   Lastly delete the folder named META-INF.

this point the value in f would be a map whose keys are strings and whose values are themselves stored as empty interface values: map[string]interface{}{        “Name”: “Wednesday”,        “Age”: 6,        “Parents”: []interface{}{            “Gomez”,            “Morticia”,        }, } To access this data we can use a type assertion to access f’s underlying map[string]interface{}: m := f.(map[string]interface{}) We can then iterate through the map

In Depth Types of Effect Size Indicators Researchers use several different statistics to indicate effect size depending on the nature of their data. Roughly speaking, these effect size statistics fall into three broad categories. Some effect size indices, sometimes called dbased effect sizes, are based on the size of the difference between the means of two groups, such as the difference between the average scores of men and women on some measure or the differences in the average scores that participants obtained in two experimental conditions. The larger the difference between the means, relative to the total variability of the data, the stronger the effect and the larger the effect size statistic. The r-based effect size indices are based on the size of the correlation between two variables. The larger the correlation, the more strongly two variables are related and the more of the total variance in one variable is systematic variance related to the other variable. A third category of effect sizes index involves the odds-ratio, which tells us the ratio of the odds of an event occurring in one group to the odds of the event occurring in another group. If the event is equally likely in both groups, the odds ratio is 1.0. An odds ratio greater than 1.0 shows that the odds of the event is greater in one group than in another, and the larger the odds ratio, the stronger the effect. The odds ratio is used when the variable being measured has only two levels. For example, imagine doing research in which first-year students in college are either assigned to attend a special course on how to study or not assigned to attend the study skills course, and we wish to know whether the course reduces the likelihood that students will drop out of college. We could use the odds ratio to see how much of an effect the course had on the odds of students dropping out. You do not need to understand the statistical differences among these effect size indices, but you will find it useful in reading journal articles to know what some of the most commonly used effect sizes are called. These are all ways of expressing how strongly variables are related to one another—that is, the effect size. Symbol Name d Cohen’s d g Hedge’s g h 2 eta squared v 2 omega squared r or r 2 correlation effect size OR odds ratio The strength of the relationships between variables varies a great deal across studies. In some studies, as little as 1% of the total variance may be systematic variance, whereas in other contexts, the proportion of the total variance that is systematic variance may be quite large,

From the data presented in the preceding chapters and in this comparison of the primitive and modernized dietaries it is obvious that there is great need that the grains eaten shall contain all the minerals and vitamins which Nature has provided that they carry. Important data might be presented to illustrate this phase in a practical way. In Fig. 95 will be seen three rats all of which received the same diet, except for the type of bread. The first rat (at the left) received whole-wheat products freshly ground, the center one received a white flour product and the third (at the right) a bran and middlings product. The amounts of each ash, of calcium as the oxide, and of phosphorus as the pentoxide; and the amounts of iron and copper present in the diet of each group are shown by the height of the columns beneath the rats. Clinically it will be seen that there is a marked difference in the physical development Qf these rats. Several rats of the same age were in each cage. The feeding was started after weaning at about twenty-three days of age. The rat at the left was on the entire grain product. It was fully developed. The rats in this cage reproduced normally at three months of age. The rats in this first cage had very mild dispositions and could be picked up by the ear or tail without danger of their biting. The rats represented by the one in the center cage using white flour were markedly undersized. Their hair came out in large patches and they had very ugly dispositions, so ugly that they threatened to spring through the cage wall at us when we came to look at them. These rats had tooth decay and they were not able to reproduce. The rats in the next cage (illustrated by the rat to the right) which were on the bran and middlings mixture did not show tooth decay, but were considerably undersized, and they lacked energy. The flour and middlings for the rats in cages two and three were purchased from the miller and hence were not freshly ground. The wheat given to the first group was obtained whole and ground while fresh in a hand mill. It is of interest that notwithstanding the great increase in ash, calcium, phosphorus, iron and copper present in the foods of the last group, the rats did not mature normally, as did those in the first group. This may have been

When running a Python program, the interpreter spends most of its time figuring out what low-level operation to perform, and extracting the data to give to this low-level operation. Given Python’s design and flexibility, the Python interpreter always has to determine the low-level operation in a completely general way, because a variable can have any type at any time. This is known as dynamic dispatch, and for many reasons, fully general dynamic dispatch is slow.[5] For example, consider what happens when the Python runtime evaluates a + b: The interpreter inspects the Python object referred to by a for its type, which requires at least one pointer lookup at the C level. The interpreter asks the type for an implementation of the addition method, which may require one or more additional pointer lookups and internal function calls. If the method in question is found, the interpreter then has an actual function it can call, implemented either in Python or in C. The interpreter calls the addition function and passes in a and b as arguments. The addition function extracts the necessary internal data from a and b, which may require several more pointer lookups and conversions from Python types to C types. If successful, only then can it perform the actual operation that adds a and b together. The result then must be placed inside a (perhaps new) Python object and returned. Only then is the operation complete. The situation for C is very different. Because C is compiled and statically typed, the C compiler can determine at compile time what low-level operations to perform and what low-level data to pass as arguments. At runtime, a compiled C program skips nearly all steps that the Python interpreter must perform. For something like a + b with a and b both being fundamental numeric types, the compiler generates a handful of machine code instructions to load the data into registers, add them, and store the result.

scripting language is a programming language that provides you with the ability to write scripts that are evaluated (or interpreted) by a runtime environment called a script engine (or an interpreter). A script is a sequence of characters that is written using the syntax of a scripting language and used as the source for a program executed by an interpreter. The interpreter parses the scripts, produces intermediate code, which is an internal representation of the program, and executes the intermediate code. The interpreter stores the variables used in a script in data structures called symbol tables. Typically, unlike in a compiled programming language, the source code (called a script) in a scripting language is not compiled but is interpreted at runtime. However, scripts written in some scripting languages may be compiled into Java bytecode that can be run by the JVM. Java 6 added scripting support to the Java platform that lets a Java application execute scripts written in scripting languages such as Rhino JavaScript, Groovy, Jython, JRuby, Nashorn JavaScript, and so on. Two-way communication is supported. It also lets scripts access Java objects created by the host application. The Java runtime and a scripting language runtime can communicate and make use of each other’s features. Support for scripting languages in Java comes through the Java Scripting API. All classes and interfaces in the Java Scripting API are in the javax.script package. Using a scripting language in a Java application provides several advantages: Most scripting languages are dynamically typed, which makes it simpler to write programs. They provide a quicker way to develop and test small applications. Customization by end users is possible. A scripting language may provide domain-specific features that are not available in Java. Scripting languages have some disadvantages as well. For example, dynamic typing is good to write simpler code; however, it turns into a disadvantage when a type is interpreted incorrectly and you have to spend a lot of time debugging it. Scripting support in Java lets you take advantage of both worlds: it allows you to use the Java programming language for developing statically typed, scalable, and high-performance parts of the application and use a scripting language that fits the domain-specific needs for other parts. I will use the term script engine frequently in this book. A script engine is a software component that executes programs written in a particular scripting language. Typically, but not necessarily, a script engine is an implementation of an interpreter for a scripting language. Interpreters for several scripting languages have been implemented in Java. They expose programming interfaces so a Java program may interact with them.