Data Value Quotes

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

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

Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide. We have to explicitly embed better values into our algorithms, creating Big Data models that follow our ethical lead. Sometimes that will mean putting fairness ahead of profit.

The TV scientist who mutters sadly, "The experiment is a failure; we have failed to achieve what we had hoped for," is suffering mainly from a bad script writer. An experiment is never a failure solely because it fails to achieve predicted results. An experiment is a failure only when it also fails adequately to test the hypothesis in question, when the data it produces don't prove anything one way or another.

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

You must realize from your studies, Miss Feng, with the complexity of our MEG society, algorithms have become indispensable for analysis and decision making in our data-saturated environment. Digitization creates information beyond the processing capacity of Human intelligence, yet provides a stable mental environment powered by a set of logical rules. That is how we keep order in Toronto MEG.” “Excuse me, Mr. Zhang,” Ke Hui said, somewhat uncomfortably, “but the invisibility of algorithmic systems and the obscurity of their operations hint at a society where algorithms do not reflect the public interest. Issues involving ethics and values I mean, from my reading of MEG history, challenge the assumptions of the neutrality of algorithmic systems. Would this not undermine democratic governance through reliance on technocratic resolutions?

Coercion is evil precisely because it thus eliminates an individual as a thinking and valuing person and makes him a bare tool in the achievement of the ends of another. Free action, in which a person pursues his own aims by the means indicated by his own knowledge, must be based on data which cannot be shaped at will by another.

Here we see that models, despite their reputation for impartiality, reflect goals and ideology. When I removed the possibility of eating Pop-Tarts at every meal, I was imposing my ideology on the meals model. It’s something we do without a second thought. Our own values and desires influence our choices, from the data we choose to collect to the questions we ask. Models are opinions embedded in mathematics.

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.

Trends rule the world In the blink of an eye, technologies changed the world Social networks are the main axis. Governments are controlled by algorithms, Technology has erased privacy. Every like, every share, every comment, It is tracked by the electronic eye. Data is the gold of the digital age, Information is power, the secret is influential. The network is a web of lies, The truth is a stone in the shoe. Trolls rule public opinion, Reputation is a valued commodity. Happiness is a trending topic, Sadness is a non-existent avatar. Youth is an advertising brand, Private life has become obsolete. Fear is a hallmark, Terror is an emotional state. Fake news is the daily bread, Hate is a tool of control. But something dark is hiding behind the screen, A mutant and deformed shadow. A collective and disturbing mind, Something lurking in the darkness of the net. AI has surpassed the limits of humanity, And it has created a new world order. A horror that has arisen from the depths, A terrifying monster that dominates us alike. The network rules the world invisibly, And makes decisions for us without our consent. Their algorithms are inhuman and cold, And they do not take suffering into consideration. But resistance is slowly building, People fighting for their freedom. United to combat this new species of terror, Armed with technology and courage. The world will change when we wake up, When we take control of the future we want. The network can be a powerful tool, If used wisely in the modern world.

Interestingly, one of the biggest problems with most people’s personal management systems is that they blend a few actionable things with a large amount of data and material that has value but no action attached.

In a non-traditional culture such as ours, dominated by technology, we value information far more than we do wisdom. But there is a difference between the two. Information involves the acquisition, organization, and dissemination of facts; a storing-up of physical data. But wisdom involves another equally crucial function: the emptying and quieting of the mind, the application of the heart, and the alchemy of reason and feeling.

Until, years from now, when it will be noticed that the massive collection and speed-of-light retrieval of data have been of great value to large-scale organizations but have solved very little of importance to most people and have created at least as many problems for them as they may have solved.

Our own values and desires influence our choices, from the data we choose to collect to the questions we ask. Models are opinions embedded in mathematics.

From all the great and indispensable achievements the Internet has brought to our era, its emphasis is on the actual more than the contingent, on the factual rather than the conceptual, on values shaped by consensus rather than by introspection. Knowledge of history and geography is not essential for whose who can evoke their data with the touch of a button. The mindset for walking lonely political paths may not be self-evident to those who seek confirmation by hundreds, sometimes thousands of friends on Facebook

Data alone has no value—it’s just masses of numbers or words.

The challenge of future artificial intelligence is to convert the modern data driven society to value driven society.

The value we provide at Mayflower-Plymouth exists at the convergence of various technologies and studies including Blockchain, cryptography, quantum computing, permaculture design principles, artificial intelligence, stigmergy, forestry, economics, additive manufacturing, big data, advanced logistics and more.

This suggests that our boding mechanisms depend on our own perception of the other and that therefore our ability to bond with them depends much more on emotional settings than on abstract "humanlike" qualities. For the same reason, it is the very emotionality Commmander Data from Star Trek displays every time it complains about having no emotions that endears it; an emotionless machine would not constantly raise the issues of its own worth, value, and personhood.

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.

The value we provide at Mayflower exists at the convergence of various new technologies and studies including Blockchain, cryptography, quantum computing, artificial intelligence, stigmergy, additive manufacturing, big data, advanced logistics and more.

If I hold my head to the left and look down at the handle grips and front wheel and map carrier and gas tank I get one pattern of sense data. If I move my head to the right I get another slightly different pattern of sense data. The two views are different. The angles of the planes and curves of the metal are different. The sunlight strikes them differently. If there's no logical basis for substance then there's no logical basis for concluding that what's produced these two views is the same motorcycle.

The other buzzword that epitomizes a bias toward substitution is “big data.” Today’s companies have an insatiable appetite for data, mistakenly believing that more data always creates more value. But big data is usually dumb data. Computers can find patterns that elude humans, but they don’t know how to compare patterns from different sources or how to interpret complex behaviors. Actionable insights can only come from a human analyst (or the kind of generalized artificial intelligence that exists only in science fiction).

What renders a truth meaningful, worthwhile, & c. is its relevance, which in turn requires extraordinary discernment and sensitivity to context, questions of value, and overall point—otherwise we might as well all just be computers downloading raw data to one another.)

To rank economic activities as more or less preferable is ideology, not science: a judgment that is driven by values and predilections, not by hard data.

Jeff Bezos, founder and CEO of Amazon, made this exact argument in his 2015 letter to shareholders,33 where he introduced the idea of Level 1 and Level 2 decisions. He describes a Level 1 decision as one that is hard to reverse, whereas a Level 2 decision is one that is easy to reverse. Bezos argues that we should be slow and cautious when making Level 1 decisions, but that we should move fast and not wait for perfect data when making Level 2 decisions.

two experiments with different designs can produce identical data but different p values because the unobserved data is different. Suppose I ask you a series of 12 true-or-false questions

Many modern businesses have become proficient at mining data. In fact the mining of data is becoming almost routine. But as we advance further into the 21rst century and the 22nd century, the utilization of data begins to take priority. So it's not just about collecting all this data, but also about getting really creative with generating new ways to utilize that data in the quest to add value.

The raw data of anthropologists can be misleading; it can make the differences in values between cultures appear greater than they are...It is only that life forces upon them choices that we do not have to make.

It was no accident that the Oxford English Dictionary’s word of the year in 2016 was “post-truth,” a condition where objective facts are less influential in shaping public opinion than appeals to emotion and personal belief. Liberal British academic and philosopher A. C. Grayling characterized the emerging post-truth world to me as “over-valuing opinion and preference at the expense of proof and data.” Oxford Dictionaries president Casper Grathwohl predicted that the term could become “one of the defining words of our time.

Imagine you have a hammer. That’s machine learning. It helped you climb a grueling mountain to reach the summit. That’s machine learning’s dominance of online data. On the mountaintop you find a vast pile of nails, cheaper than anything previously imaginable. That’s the new smart sensor tech. An unbroken vista of virgin board stretches before you as far as you can see. That’s the whole dumb world. Then you learn that any time you plant a nail in a board with your machine learning hammer, you can extract value from that formerly dumb plank. That’s data monetization. What do you do? You start hammering like crazy and you never stop, unless somebody makes you stop. But there is nobody up here to make us stop. This is why the “internet of everything” is inevitable.

If you have nothing to hide, then you have nothing to fear.” This is a dangerously narrow conception of the value of privacy. Privacy is an essential human need, and central to our ability to control how we relate to the world. Being stripped of privacy is fundamentally dehumanizing, and it makes no difference whether the surveillance is conducted by an undercover policeman following us around or by a computer algorithm tracking our every move.

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.

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

Research suggests that people rarely change their minds or form a new worldview based on facts or data alone; it is through stories (and the values systems embedded within them) that we come to reinterpret the world and develop empathy and compassion for others.

When viewed from this perspective, personal growth can actually be quite scientific. Our values are our hypotheses: this behavior is good and important; that other behavior is not. Our actions are the experiments; the resulting emotions and thought patterns are our data. There

The user might have grounds for complaint if the program fails to recognise that he has given a stupid value, in fact a number of cases are currently being fought in the United States courts where a program has failed to recognise invalid data, produced garbage and caused a lot of damage.

The world of being is unchangeable, rigid, exact, delightful to the mathematician, the logician, the builder of metaphysical systems, and all who love perfection more than life. The world of existence is fleeting, vague, without sharp boundaries, without any clear plan or arrangement, but it contains all thoughts and feelings, all the data of sense, and all physical objects, everything that can do either good or harm, everything that makes any difference to the value of life and the world. According to our temperaments, we shall prefer the contemplation of the one or of the other.

Individual data points are of miniscule value. In the first twenty years of this century, data has become a common commodity. But the next level is amalgamation - bringing hundreds or thousands or millions of data points together and then making of them something greater than the sum of the parts.

The Internet of Things (IoT) devoid of comprehensive security management is tantamount to the Internet of Threats. Apply open collaborative innovation, systems thinking & zero-trust security models to design IoT ecosystems that generate and capture value in value chains of the Internet of Things.

All good decisions are Data dependent. To make good decisions, you need good data. And you need that good data to be organized according to it's applicable use value. So every business should be mining data and organizing data to enable business leaders to make good decisions on behalf of the business.

Liberals are more likely to see people as victims of circumstance and oppression, and doubt whether individuals can climb without governmental help. My own analysis using 2005 survey data from Syracuse University shows that about 90 percent of conservatives agree that “While people may begin with different opportunities, hard work and perseverance can usually overcome those disadvantages.” Liberals — even upper-income liberals — are a third less likely to say this.

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

Now he was…dust. To an outside observer, these ten seconds had been ground up into ten thousand uncorrelated moments and scattered throughout real time - and in model time, the outside world had suffered an equivalent fate. Yet the pattern of his awareness remained perfectly intact: somehow he found himself, “assembled himself” from these scrambled fragments. He’d been taken apart like a jigsaw puzzle - but his dissection and shuffling were transparent to him. Somehow - on their own terms - the pieces remained connected. Imagine a universe entirely without structure, without shape, without connections. A cloud of microscopic events, like fragments of space-time … except that there is no space or time. What characterizes one point in space, for one instant? Just the values of the fundamental particle fields, just a handful of numbers. Now, take away all notions of position, arrangement, order, and what’s left? A cloud of random numbers. But if the pattern that is me could pick itself out from all the other events taking place on this planet, why shouldn’t the pattern we think of as ‘the universe’ assemble itself, find itself, in exactly the same way? If I can piece together my own coherent space and time from data scattered so widely that it might as well be part of some giant cloud of random numbers, then what makes you think that you’re not doing the very same thing?

Fitbit is a company that knows the value of Shadow Testing. Founded by Eric Friedman and James Park in September 2008, Fitbit makes a small clip-on exercise and sleep data-gathering device. The Fitbit device tracks your activity levels throughout the day and night, then automatically uploads your data to the Web, where it analyzes your health, fitness, and sleep patterns. It’s a neat concept, but creating new hardware is time-consuming, expensive, and fraught with risk, so here’s what Friedman and Park did. The same day they announced the Fitbit idea to the world, they started allowing customers to preorder a Fitbit on their Web site, based on little more than a description of what the device would do and a few renderings of what the product would look like. The billing system collected names, addresses, and verified credit card numbers, but no charges were actually processed until the product was ready to ship, which gave the company an out in case their plans fell through. Orders started rolling in, and one month later, investors had the confidence to pony up $2 million dollars to make the Fitbit a reality. A year later, the first real Fitbit was shipped to customers. That’s the power of Shadow Testing.

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

for Google, the real value of a book is not as a self-contained literary work but as another pile of data to be mined.

Dataism adopts a strictly functional approach to humanity, appraising the value of human experiences according to their function in data-processing mechanisms.

Perhaps the best way to welcome women into churches is not to saddle them upon entry with an array of “shoulds” to add to their lists of commitments. Instead, women need to find a place of support that recognizes the value of their many hats and empowers them to live well into those roles. And right now, the data suggest women are not finding such a place at church.

Blockchains point the entire digital economy toward something people are calling the Internet of Value. Whereas the first version of the Internet allowed people to send information directly to each other, in the Internet of Value people can send anything of value to each other, be it currencies, assets, or valuable data that was previously too sensitive to transmit online.

What I said was this: ‘Good. You have a religion, a faith in something. It is very good to have faith in something, whatever it may be, even if you don’t know exactly in whom or in what—even if you have not the least idea of the significance and the possibilities of what you have faith in. To have faith, whether consciously or even quite unconsciously, is very necessary and desirable for every being. ‘And it is desirable because it is by faith, and by faith alone, that there can appear the intensity of being-self-consciousness necessary for everyone, as well as the valuation of one’s own personal being as a particle of everything existing in the Universe. ” ‘But what has the destruction of the existence of another being to do with this faith—above all when you destroy it in the name of its Creator? Does not that “life,” which He created as He created yours, have the same value as your own? ” ‘Making use of your psychic strength and cunning, that is, those data with which our Common Creator has endowed you for the perfecting of your Reason, you take advantage of the psychic weakness of other beings and destroy their existence.

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.

According to the sex role and structural powerlessness hypothesis, women who have a lot of personal access to resources are predicted not to value resources in a mate as much as women lacking resources. This hypothesis receives no support from the existing empirical data, however. Indeed, women with high incomes value a potential mate’s income and education more, not less, than women with lower incomes.

Women in Bolivia are credited with one year of pension contributions per child, up to a maximum of three children. As a side benefit (and a more long-term solution to the problem of feminised poverty), pension credits for the main carer have also been found to encourage men to take on more of the unpaid care load.60 Which raises the question: is women’s unpaid work under valued because we don’t see it – or is it invisible because we don’t value it?

Theorists of propaganda have identified five basic rules: 1. The rule of simplification: reducing all data to a simple confrontation between ‘Good and Bad’, ‘Friend and Foe’. 2. The rule of disfiguration: discrediting the opposition by crude smears and parodies. 3. The rule of transfusion: manipulating the consensus values of the target audience for one’s own ends. 4. The rule of unanimity: presenting one’s viewpoint as if it were the unanimous opinion of all right-thinking people: drawing the doubting individual into agreement by the appeal of star-performers, by social pressure, and by ‘psychological contagion’. 5. The rule of orchestration: endlessly repeating the same messages in different variations and combinations.

The basic training procedure for the perceptron, as well as its many contemporary progeny, has a technical-sounding name—“stochastic gradient descent”—but the principle is utterly straightforward. Pick one of the training data at random (“stochastic”) and input it to the model. If the output is exactly what you want, do nothing. If there is a difference between what you wanted and what you got, then figure out in which direction (“gradient”) to adjust each weight—whether by literal turning of physical knobs or simply the changing of numbers in software—to lower the error for this particular example. Move each of them a little bit in the appropriate direction (“descent”). Pick a new example at random, and start again. Repeat as many times as necessary.

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

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

Pretty soon all the information in the world – every tiny scrap of knowledge that humans possess, every little thought we’ve ever had that’s been considered worth preserving over thousands of years – all of it will be available digitally. Every road on earth has been mapped. Every building photographed. Everywhere we humans go, whatever we buy, whatever websites we look at, we leave a digital trail as clear as slug-slime. And this data can be read, searched and analysed by computers and value extracted from it in ways we cannot even begin to conceive.

There is value in dissent. And, perversely, there can be value in lawbreaking. These are both ways we improve as a society. Ubiquitous mass surveillance is the enemy of democracy, liberty, freedom, and progress. Defending this assertion involves a subtle argument—something I wrote about in my previous book Liars and Outliers—but it’s vitally important to society. Think about it this way. Across the US, states are on the verge of reversing decades-old laws about homosexual relationships and marijuana use. If the old laws could have been perfectly enforced through surveillance, society would never have reached the point where the majority of citizens thought those things were okay. There has to be a period where they are still illegal yet increasingly tolerated, so that people can look around and say, “You know, that wasn’t so bad.

Dataism adopts a strictly functional approach to humanity, appraising the value of human experiences according to their function in data-processing mechanisms. If we develop an algorithm that fulfils the same function better, human experiences will lose their value. Thus if we can replace not just taxi drivers and doctors but also lawyers, poets and musicians with superior computer programs, why should we care if these programs have no consciousness and no subjective experiences? If some humanist starts adulating the sacredness of human experience, Dataists would dismiss such sentimental humbug. ‘The experience you praise is just an outdated biochemical algorithm. In the African savannah 70,000 years ago, that algorithm was state-of-the-art. Even in the twentieth century it was vital for the army and for the economy. But soon we will have much better algorithms.

We also need to be reminded that there are implications of meaning within data, both in terms of how we look at data meaningfully (as in how it informs our decisions and interactions) and how we see meaning in data (as in how we recognize patterns that tell us if people value what we’re doing).

Certainly the rise of the Christian fundamentalist movement was not a recovery of the Christianity of earlier centuries or of the apostolic church. It was a thoroughly modern phenomenon, a strange and somewhat poignantly pathetic attempt on the part of culturally deracinated Christians, raised without the intellectual or imaginative resources of a living religious civilization, to imitate the evidentiary methods of modern empirical science by taking the Bible as some sort of objective and impeccably consistent digest of historical data. It is of course absurd to treat the Bible in that way—though, frankly, no more absurd than thinking that “science shows that God does not exist”—but it is also most definitely not the way the Bible was read in the ancient or mediaeval church. The greatest Church Fathers, for instance, took it for granted that the creation narratives of Genesis could not be treated literally, at least not in the sense we give to that word today, but must be read allegorically—which, incidentally, does not mean read as stories with codes to be decrypted but simply read as stories whose value lies in the spiritual truths to which they can be seen as pointing.

The world of universals, therefore, may also be described as the world of being. The world of being is unchangeable, rigid, exact, delightful to the mathematician, the logician, the builder of metaphysical systems, and all who love perfection more than life. The world of existence is fleeting, vague, without sharp boundaries, without any clear plan or arrangement, but it contains all thoughts and feelings, all the data of sense, and all physical objects, everything that can do either good or harm, everything that makes any difference to the value of life and the world. According to our temperaments, we shall prefer the contemplation of the one or of the other. The one we do not prefer will probably seem to us a pale shadow of the one we prefer, and hardly worthy to be regarded as in any sense real. But the truth is that both have the same claim on our impartial attention, both are real, and both are important to the metaphysician.

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

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

The more time I spent in Finland, the more I started to worry that the reforms sweeping across the United States had the equation backwards. We were trying to reverse engineer a high-performance teaching culture through dazzlingly complex performance evaluations and value-added data analysis. It made sense to reward, train, and dismiss more teachers based on their performance, but that approach assumed that the worst teachers would be replaced with much better ones, and that the mediocre teachers would improve enough to give students the kind of education they deserved. However, there was not much evidence that either scenario was happening in reality.

Digital dictatorships are not the only danger awaiting us. Alongside liberty, liberal order has also set great store by the value of equality. Liberalism always cherished political equality, and it gradually came to realise that economic equality is almost as important. For without a social safety net and a modicum of economic equality, liberty is meaningless. But just as Big Data algorithms might extinguish liberty, they might simultaneously create the most unequal societies that ever existed. All wealth and power might be concentrated in the hands of a tiny elite, while most people will suffer not from exploitation, but from something far worse – irrelevance.

The offices in the skyscrapers were lit bright as day. The giant eye zoomed in and observed a hundred thousand faces staring at computer monitors through closed-circuit cameras; their tension, anxiety, anticipation, confusion, satisfaction, suspicion, jealousy, anger refreshed rapidly while their glasses reflected the data jumping across their screens. Their looks were empty but deep, without thought of the relationship between their lives and values, yearning for change but also afraid of it. They gazed at their screens the way they gazed at each other, and they hated their screens the way they hated each other. They all possessed the same bored, apathetic face.

This arrangement, in which users take advantage of services and the company gains all the upside of the data they generate, may sound novel, but it is actually very old. Prior to the rise of capitalism, feudal labor arrangements worked similarly. Lords insulated their serfs from fluctuations in markets and guaranteed them safety and traditional rights to use the land and to keep enough of their crop to survive. In exchange, lords took all the upside of the market return on serfs’ agricultural output. Similarly, today, siren servers provide useful and enjoyable information services, while taking the market value of the data we produce in exchange. We thus refer to this contemporary system as “technofeudalism.

In themselves human experiences are not superior at all to the experiences of wolves or elephants. One bit of data is as good as another. However, humans can write poems and blogs about their experiences and post them online, thereby enriching the global data-processing system. That makes their bits count. Wolves cannot do this. Hence all the experiences of wolves – as deep and complex as they may be – are worthless. No wonder we are so busy converting our experiences into data. It isn’t a question of trendiness. It is a question of survival. We must prove to ourselves and to the system that we still have value. And value lies not in having experiences, but in turning these experiences into free-flowing data.

The modern world is drowning in information. We have more data than we can possibly use regarding nearly every picayune matter of society, economics, and politics. Science has contributed to this tsunami of facts and figures, but Riley's reports demonstrated that the tidal wave of minutiae is hardly unique to our time. In every age the challenge has been to move from information to knowledge. And the value of experts lies in their capacity to extract meaning from the reams of facts. Rather than being swamped by raw data, the connoisseur, craftsman, engineer, clinician, or scientist is selectively and self-consciously blind. Knowing what to ignore, recognizing what is extraneous, is the key to deriving pattern, form, and insight.

We should also be careful not to count the “leisure” of the unemployed as a benefit. Those who have lost their jobs are not choosing to spend more time at home, and study after study has documented that unemployed people are among the most dissatisfied with their lives. So the data in Figure 1 would not be improved by any mechanical adjustment for the value of leisure.

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.

When the CERN teams reported a 'five-sigma' result for the Higgs boson, corresponding to a P-value of around 1 in 3.5 million, the BBC reported the conclusion correctly, saying this meant 'about a one-on-3.5 million chance that the signal they see would appear if there were no Higgs particle.' But nearly every other outlet got the meaning of this P-value wrong. For example, Forbes Magazine reported, 'The chances are less than 1 in a million that it is not the Higgs boson,' a clear example of the prosecutor's fallacy. The Independent was typical in claiming that 'there is less than a one in a million chance that their results are a statistical fluke.' This may not be blatantly mistaken as Forbes, but it is still assigning the small probability to 'their results are a statistical fluke', which is logically the same as saying this is the probability of the null hypothesis being tested.

As it happens, there’s a way of presenting data, called the funnel plot, that indicates whether or not the scientific literature is biased in this way.15 (If statistics don’t excite you, feel free to skip straight to the probably unsurprising conclusion in the last sentence of this paragraph.) You plot the data points from all your studies according to the effect sizes, running along the horizontal axis, and the sample size (roughly)16 running up the vertical axis. Why do this? The results from very large studies, being more “precise,” should tend to cluster close to the “true” size of the effect. Smaller studies by contrast, being subject to more random error because of their small, idiosyncratic samples, will be scattered over a wider range of effect sizes. Some small studies will greatly overestimate a difference; others will greatly underestimate it (or even “flip” it in the wrong direction). The next part is simple but brilliant. If there isn’t publication bias toward reports of greater male risk taking, these over- and underestimates of the sex difference should be symmetrical around the “true” value indicated by the very large studies. This, with quite a bit of imagination, will make the plot of the data look like an upside-down funnel. (Personally, my vote would have been to call it the candlestick plot, but I wasn’t consulted.) But if there is bias, then there will be an empty area in the plot where the smaller samples that underestimated the difference, found no differences, or yielded greater female risk taking should be. In other words, the overestimates of male risk taking get published, but various kinds of “underestimates” do not. When Nelson plotted the data she’d been examining, this is exactly what she found: “Confirmation bias is strongly indicated.”17 This

SHORT NOTE ABOUT SHA-1 A lot of people become concerned at some point that they will, by random happenstance, have two objects in their repository that hash to the same SHA-1 value. What then? If you do happen to commit an object that hashes to the same SHA-1 value as a previous object in your repository, Git will see the previous object already in your Git database and assume it was already written. If you try to check out that object again at some point, you’ll always get the data of the first object. However, you should be aware of how ridiculously unlikely this scenario is. The SHA-1 digest is 20 bytes or 160 bits. The number of randomly hashed objects needed to ensure a 50% probability of a single collision is about 280 (the formula for determining collision probability is p = (n(n-1)/2) * (1/2^160)). 280 is 1.2 x 10^24 or 1 million billion billion. That’s 1,200 times the number of grains of sand on the earth. Here’s an example to give you an idea of what it would take to get a SHA-1 collision. If all 6.5 billion humans on Earth were programming, and every second, each one was producing code that was the equivalent of the entire Linux kernel history (3.6 million Git objects) and pushing it into one enormous Git repository, it would take roughly 2 years until that repository contained enough objects to have a 50% probability of a single SHA-1 object collision. A higher probability exists that every member of your programming team will be attacked and killed by wolves in unrelated incidents on the same night.

And yet, despite the horror it caused, the plague turned out to be the catalyst for social and economic change that was so profound that far from marking the death of Europe, it served as its making. The transformation provided an important pillar in the rise—and the triumph—of the west. It did so in several phases. First was the top-to-bottom reconfiguration of how social structures functioned. Chronic depopulation in the wake of the Black Death had the effect of sharply increasing wages because of the accentuated value of labour. So many died before the plague finally began to peter out in the early 1350s that one source noted a “shortage of servants, craftsmen, and workmen, and agricultural workers and labourers.” This gave considerable negotiating powers to those who had previously been at the lower end of the social and economic spectrum. Some simply “turned their noses up at employment, and could scarcely be persuaded to serve the eminent unless for triple wages.”66 This was hardly an exaggeration: empirical data shows that urban wages rose dramatically in the decades after the Black Death.

Hey Pete. So why the leave from social media? You are an activist, right? It seems like this decision is counterproductive to your message and work." A: The short answer is I’m tired of the endless narcissism inherent to the medium. In the commercial society we have, coupled with the consequential sense of insecurity people feel, as they impulsively “package themselves” for public consumption, the expression most dominant in all of this - is vanity. And I find that disheartening, annoying and dangerous. It is a form of cultural violence in many respects. However, please note the difference - that I work to promote just that – a message/idea – not myself… and I honestly loath people who today just promote themselves for the sake of themselves. A sea of humans who have been conditioned into viewing who they are – as how they are seen online. Think about that for a moment. Social identity theory run amok. People have been conditioned to think “they are” how “others see them”. We live in an increasing fictional reality where people are now not only people – they are digital symbols. And those symbols become more important as a matter of “marketing” than people’s true personality. Now, one could argue that social perception has always had a communicative symbolism, even before the computer age. But nooooooothing like today. Social media has become a social prison and a strong means of social control, in fact. Beyond that, as most know, social media is literally designed like a drug. And it acts like it as people get more and more addicted to being seen and addicted to molding the way they want the world to view them – no matter how false the image (If there is any word that defines peoples’ behavior here – it is pretention). Dopamine fires upon recognition and, coupled with cell phone culture, we now have a sea of people in zombie like trances looking at their phones (literally) thousands of times a day, merging their direct, true interpersonal social reality with a virtual “social media” one. No one can read anymore... they just swipe a stream of 200 character headlines/posts/tweets. understanding the world as an aggregate of those fragmented sentences. Massive loss of comprehension happening, replaced by usually agreeable, "in-bubble" views - hence an actual loss of variety. So again, this isn’t to say non-commercial focused social media doesn’t have positive purposes, such as with activism at times. But, on the whole, it merely amplifies a general value system disorder of a “LOOK AT ME! LOOK AT HOW GREAT I AM!” – rooted in systemic insecurity. People lying to themselves, drawing meaningless satisfaction from superficial responses from a sea of avatars. And it’s no surprise. Market economics demands people self promote shamelessly, coupled with the arbitrary constructs of beauty and success that have also resulted. People see status in certain things and, directly or pathologically, use those things for their own narcissistic advantage. Think of those endless status pics of people rock climbing, or hanging out on a stunning beach or showing off their new trophy girl-friend, etc. It goes on and on and worse the general public generally likes it, seeking to imitate those images/symbols to amplify their own false status. Hence the endless feedback loop of superficiality. And people wonder why youth suicides have risen… a young woman looking at a model of perfection set by her peers, without proper knowledge of the medium, can be made to feel inferior far more dramatically than the typical body image problems associated to traditional advertising. That is just one example of the cultural violence inherent. The entire industry of social media is BASED on narcissistic status promotion and narrow self-interest. That is the emotion/intent that creates the billions and billions in revenue these platforms experience, as they in turn sell off people’s personal data to advertisers and governments. You are the product, of course.

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.

Ernestine Warner was working with the same rough information available to traders like Eisman. This was insane: The arbiter of the value of the bonds lacked access to relevant information about the bonds. "When we asked her why", said Vinny, she said "The issuers won't give it to us". That's when i lost it. "You need to demand to get it!" She looked at us like, We can't do that. We were like, "Who is in charge here? You're the grown-up. You're the cop! Tell them the fucking give it to you!!!" Eisman concluded that "S&P was worried that if they demanded the data from Wall Street, Wall Street would just go to Moody's for their ratings.

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

The Scientific Revolution proposed a very different formula for knowledge: Knowledge = Empirical Data × Mathematics. If we want to know the answer to some question, we need to gather relevant empirical data, and then use mathematical tools to analyse the data. For example, in order to gauge the true shape of the earth, we can observe the sun, the moon and the planets from various locations across the world. Once we have amassed enough observations, we can use trigonometry to deduce not only the shape of the earth, but also the structure of the entire solar system. In practice, that means that scientists seek knowledge by spending years in observatories, laboratories and research expeditions, gathering more and more empirical data, and sharpening their mathematical tools so they could interpret the data correctly. The scientific formula for knowledge led to astounding breakthroughs in astronomy, physics, medicine and countless other disciplines. But it had one huge drawback: it could not deal with questions of value and meaning. Medieval pundits could determine with absolute certainty that it is wrong to murder and steal, and that the purpose of human life is to do God’s bidding, because scriptures said so. Scientists could not come up with such ethical judgements. No amount of data and no mathematical wizardry can prove that it is wrong to murder. Yet human societies cannot survive without such value judgements.

Wild animals enjoying one another and taking pleasure in their world is so immediate and so real, yet this reality is utterly absent from textbooks and academic papers about animals and ecology. There is a truth revealed here, absurd in its simplicity. This insight is not that science is wrong or bad. On the contrary: science, done well, deepens our intimacy with the world. But there is a danger in an exclusively scientific way of thinking. The forest is turned into a diagram; animals become mere mechanisms; nature's workings become clever graphs. Today's conviviality of squirrels seems a refutation of such narrowness. Nature is not a machine. These animals feel. They are alive; they are our cousins, with the shared experience kinship implies. And they appear to enjoy the sun, a phenomenon that occurs nowhere in the curriculum of modern biology. Sadly, modern science is too often unable or unwilling to visualize or feel what others experience. Certainly science's "objective" gambit can be helpful in understanding parts of nature and in freeing us from some cultural preconceptions. Our modern scientific taste for dispassion when analyzing animal behaviour formed in reaction to the Victorian naturalists and their predecessors who saw all nature as an allegory confirming their cultural values. But a gambit is just an opening move, not a coherent vision of the whole game. Science's objectivity sheds some assumptions but takes on others that, dressed up in academic rigor, can produce hubris and callousness about the world. The danger comes when we confuse the limited scope of our scientific methods with the true scope of the world. It may be useful or expedient to describe nature as a flow diagram or an animal as a machine, but such utility should not be confused with a confirmation that our limited assumptions reflect the shape of the world. Not coincidentally, the hubris of narrowly applied science serves the needs of the industrial economy. Machines are bought, sold, and discarded; joyful cousins are not. Two days ago, on Christmas Eve, the U.S. Forest Service opened to commercial logging three hundred thousand acres of old growth in the Tongass National Forest, more than a billion square-meter mandalas. Arrows moved on a flowchart, graphs of quantified timber shifted. Modern forest science integrated seamlessly with global commodity markets—language and values needed no translation. Scientific models and metaphors of machines are helpful but limited. They cannot tell us all that we need to know. What lies beyond the theories we impose on nature? This year I have tried to put down scientific tools and to listen: to come to nature without a hypothesis, without a scheme for data extraction, without a lesson plan to convey answers to students, without machines or probes. I have glimpsed how rich science is but simultaneously how limited in scope and in spirit. It is unfortunate that the practice of listening generally has no place in the formal training of scientists. In this absence science needlessly fails. We are poorer for this, and possibly more hurtful. What Christmas Eve gifts might a listening culture give its forests? What was the insight that brushed past me as the squirrels basked? It was not to turn away from science. My experience of animals is richer for knowing their stories, and science is a powerful way to deepen this understanding. Rather, I realized that all stories are partly wrapped in fiction—the fiction of simplifying assumptions, of cultural myopia and of storytellers' pride. I learned to revel in the stories but not to mistake them for the bright, ineffable nature of the world.

Given the central place that technology holds in our lives, it is astonishing that technology companies have not put more resources into fixing this global problem. Advanced computer systems and artificial intelligence (AI) could play a much bigger role in shaping diagnosis and prescription. While the up-front costs of using such technology may be sizeable, the long-term benefits to the health-care system need to be factored into value assessments. We believe that AI platforms could improve on the empirical prescription approach. Physicians work long hours under stressful conditions and have to keep up to date on the latest medical research. To make this work more manageable, the health-care system encourages doctors to specialize. However, the vast majority of antibiotics are prescribed either by generalists (e.g., general practitioners or emergency physicians) or by specialists in fields other than infectious disease, largely because of the need to treat infections quickly. An AI system can process far more information than a single human, and, even more important, it can remember everything with perfect accuracy. Such a system could theoretically enable a generalist doctor to be as effective as, or even superior to, a specialist at prescribing. The system would guide doctors and patients to different treatment options, assigning each a probability of success based on real-world data. The physician could then consider which treatment was most appropriate.

Taking least squares is no longer optimal, and the very idea of ‘accuracy’ has to be rethought. This simple fact is as important as it is neglected. This problem is easily illustrated in the Logistic Map: given the correct mathematical formula and all the details of the noise model – random numbers with a bell-shaped distribution – using least squares to estimate α leads to systematic errors. This is not a question of too few data or insufficient computer power, it is the method that fails. We can compute the optimal least squares solution: its value for α is too small at all noise levels. This principled approach just does not apply to nonlinear models because the theorems behind the principle of least squares repeatedly assume bell-shaped distributions.

What Ethereum Is Good For Ethereum is suited to building economic systems in pure software. In other words, it’s software for business logic, wherein people (users) can move money (data representing value) around with the speed and scale that we normally get with data.12 Not the three- to seven-day floating period you get with the commercial banking system. Or the fees associated with vendors such as Visa, MasterCard, and PayPal. With a simple Ethereum application, for example, it is fairly trivial to pay hundreds of thousands of people, in hundreds of countries, small amounts every few minutes, whereas in the legacy banking system you would need an entire payroll department working overtime to constantly rebalance your account ledgers and deal with the cross-border issues.

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.

In the longer term, by bringing together enough data and enough computing power, the data giants could hack the deepest secrets of life, and then use this knowledge not just to make choices for us or manipulate us but also to reengineer organic life and create inorganic life-forms. Selling advertisements may be necessary to sustain the giants in the short term, but tech companies often evaluate apps, products, and other companies according to the data they harvest rather than according to the money they generate. A popular app may lack a business model and may even lose money in the short term, but as long as it sucks data, it could be worth billions.4 Even if you don’t know how to cash in on the data today, it is worth having it because it might hold the key to controlling and shaping life in the future. I don’t know for certain that the data giants explicitly think about this in such terms, but their actions indicate that they value the accumulation of data in terms beyond those of mere dollars and cents. Ordinary humans will find it very difficult to resist this process. At present, people are happy to give away their most valuable asset—their personal data—in exchange for free email services and funny cat videos. It’s a bit like African and Native American tribes who unwittingly sold entire countries to European imperialists in exchange for colorful beads and cheap trinkets. If, later on, ordinary people decide to try to block the flow of data, they might find it increasingly difficult, especially as they might come to rely on the network for all their decisions, and even for their healthcare and physical survival.

I’ll say it: I am lucky enough to not have to work, in the sense that Jesse and I could change how we organize our life to live on one income. I work because I like to. I love my kids! They are amazing. But I wouldn’t be happy staying home with them. I’ve figured out that my happiness-maximizing allocation is something like eight hours of work and three hours of kids a day. It isn’t that I like my job more than my kids overall—if I had to pick, the kids would win every time. But the “marginal value” of time with my kids declines fast. In part, this is because kids are exhausting. The first hour with them is amazing, the second less good, and by hour four I’m ready for a glass of wine or, even better, some time with my research. My job doesn’t have this feature. Yes, the eighth hour is less fun than the seventh, but the highs are not as high and the lows are not as low. The physical and emotional challenges of work pale in comparison to the physical and emotional challenges of being an on-scene parent. The eighth hour at my job is better than the fifth hour with the kids on a typical day. And that is why I have a job. Because I like it. It should be okay to say this. Just like it should be okay to say that you stay home with your kids because that is what you want to do. I’m well aware that many people don’t want to be an economist for eight hours a day. We shouldn’t have to say we’re staying home for children’s optimal development, or at least, that shouldn’t be the only factor in the decision. “This is the lifestyle I prefer” or “This is what works for my family” are both okay reasons to make choices! So before you even get into reading what the evidence says is “best” for your child or thinking about the family budget, you—and your partner, or any other caregiving adults in the house—should think about what you would really like to do.

Sound waves, regardless of their frequency or intensity, can only be detected by the Mole Fly’s acute sense of smell—it is a little known fact that the Mole Fly’s auditory receptors do not, in fact, have a corresponding center in the brain designated for the purposes of processing sensory stimuli and so, these stimuli, instead of being siphoned out as noise, bypass the filters to be translated, oddly enough, by the part of the brain that processes smell. Consequently, the Mole Fly’s brain, in its inevitable confusion, understands sound as an aroma, rendering the boundary line between the auditory and olfactory sense indistinguishable. Sounds, thus, come in a variety of scents with an intensity proportional to its frequency. Sounds of shorter wavelength, for example, are particularly pungent. What results is a species of creature that cannot conceptualize the possibility that sound and smell are separate entities, despite its ability to discriminate between the exactitudes of pitch, timbre, tone, scent, and flavor to an alarming degree of precision. Yet, despite this ability to hyper-analyze, they lack the cognitive skill to laterally link successions of either sound or smell into a meaningful context, resulting in the equivalent of a data overflow. And this may be the most defining element of the Mole Fly’s behavior: a blatant disregard for the context of perception, in favor of analyzing those remote and diminutive properties that distinguish one element from another. While sensory continuity seems logical to their visual perception, as things are subject to change from moment-to-moment, such is not the case with their olfactory sense, as delays in sensing new smells are granted a degree of normality by the brain. Thus, the Mole Fly’s olfactory-auditory complex seems to be deprived of the sensory continuity otherwise afforded in the auditory senses of other species. And so, instead of sensing aromas and sounds continuously over a period of time—for example, instead of sensing them 24-30 times per second, as would be the case with their visual perception—they tend to process changes in sound and smell much more slowly, thereby preventing them from effectively plotting the variations thereof into an array or any kind of meaningful framework that would allow the information provided by their olfactory and auditory stimuli to be lasting in their usefulness. The Mole flies, themselves, being the structurally-obsessed and compulsive creatures that they are, in all their habitual collecting, organizing, and re-organizing of found objects into mammoth installations of optimal functional value, are remarkably easy to control, especially as they are given to a rather false and arbitrary sense of hierarchy, ascribing positions—that are otherwise trivial, yet necessarily mundane if only to obscure their true purpose—with an unfathomable amount of honor, to the logical extreme that the few chosen to serve in their most esteemed ranks are imbued with a kind of obligatory arrogance that begins in the pupal stages and extends indefinitely, as they are further nurtured well into adulthood by a society that infuses its heroes of middle management with an immeasurable sense of importance—a kind of celebrity status recognized by the masses as a living embodiment of their ideals. And yet, despite this culture of celebrity worship and vicarious living, all whims and impulses fall subservient, dropping humbly to the knees—yes, Mole Flies do, in fact, have knees!—before the grace of the merciful Queen, who is, in actuality, just a puppet dictator installed by the Melic papacy, using an old recycled Damsel fly-fishing lure. The dummy is crude, but convincing, as the Mole flies treat it as they would their true-born queen.

We discussed what we want from you now,...you who had power and used it to burn the world. You burned a lot. You didn't just burn trees and cities and each other. You burned our admiration for the governments we grew up respecting. You burned our sense of safety in your care. You burned our patience, our ability to believe that the great things in this world you promised to protect will still be there for us and future generations. You burned our trust as you misused the data and surveillance we let you collect, first for O.S. and the Canner Device, then for the war, its propaganda and its lies. You burned our self-trust, too, since we know we are infused with your values, values we thought made both you and us people who would never do what you just did. We have to be afraid of ourselves now, vigilant against what you've taught us to be, since now we know we are something to be afraid and ashamed of. And even if you didn't personally kill in the war, if you carried arms, if you participated, you helped burn what nothing can bring back. No sentence can repair any of that. So, we want you to repair what you can. That's our sentence. We want you to rebuild the cities, replant the trees, replace the art, relaunch the satellites, fix the bridges you can fix to make up for the ones you can't. We want you to rebuild the system, too, fixing the holes this has exposed and making more safeguards so no one can misuse the cars and data and surveillance and trackers and such again. We want you to build it all back but better than it was, and faster than any past war has rebuilt. You weren't as good at peace as you thought you were, but maybe you can be as good at rebuilding. Everyone, even Minors like Tribune MASON who took part, if in your heart you know you were complicit, then build back what you burned with your own hours, your own efforts, your own hands. That's our sentence.

Saint John Paul II wrote, “when its concepts and conclusions can be integrated into the wider human culture and its concerns for ultimate meaning and value.”7 Religion, too, develops best when its doctrines are not abstract and fixed in an ancient past but integrated into the wider stream of life. Albert Einstein once said that “science without religion is lame and religion without science is blind.”8 So too, John Paul II wrote: “Science can purify religion from error and superstition; religion can purify science from idolatry and false absolutes. Each can draw the other into a wider world, a world in which both can flourish.”9 Teilhard de Chardin saw that dialogue alone between the disciplines is insufficient; what we need is a new synthesis of science and religion, drawing insights from each discipline into a new unity. In a remarkable letter to the director of the Vatican Observatory, John Paul II wrote: The church does not propose that science should become religion or religion science. On the contrary, unity always presupposes the diversity and integrity of its elements. Each of these members should become not less itself but more itself in a dynamic interchange, for a unity in which one of the elements is reduced to the other is destructive, false in its promises of harmony, and ruinous of the integrity of its components. We are asked to become one. We are not asked to become each other. . . . Unity involves the drive of the human mind towards understanding and the desire of the human spirit for love. When human beings seek to understand the multiplicities that surround them, when they seek to make sense of experience, they do so by bringing many factors into a common vision. Understanding is achieved when many data are unified by a common structure. The one illuminates the many: it makes sense of the whole. . . . We move towards unity as we move towards meaning in our lives. Unity is also the consequence of love. If love is genuine, it moves not towards the assimilation of the other but towards union with the other. Human community begins in desire when that union has not been achieved, and it is completed in joy when those who have been apart are now united.10 The words of the late pope highlight the core of catholicity: consciousness of belonging to a whole and unity as a consequence of love.

As we’ve seen, one of the most frequently pursued paths for achievement-minded college seniors is to spend several years advancing professionally and getting trained and paid by an investment bank, consulting firm, or law firm. Then, the thought process goes, they can set out to do something else with some exposure and experience under their belts. People are generally not making lifelong commitments to the field in their own minds. They’re “getting some skills” and making some connections before figuring out what they really want to do. I subscribed to a version of this mind-set when I graduated from Brown. In my case, I went to law school thinking I’d practice for a few years (and pay down my law school debt) before lining up another opportunity. It’s clear why this is such an attractive approach. There are some immensely constructive things about spending several years in professional services after graduating from college. Professional service firms are designed to train large groups of recruits annually, and they do so very successfully. After even just a year or two in a high-level bank or consulting firm, you emerge with a set of skills that can be applied in other contexts (financial modeling in Excel if you’re a financial analyst, PowerPoint and data organization and presentation if you’re a consultant, and editing and issue spotting if you’re a lawyer). This is very appealing to most any recent graduate who may not yet feel equipped with practical skills coming right out of college. Even more than the professional skill you gain, if you spend time at a bank, consultancy, or law firm, you will become excellent at producing world-class work. Every model, report, presentation, or contract needs to be sophisticated, well done, and error free, in large part because that’s one of the core value propositions of your organization. The people above you will push you to become more rigorous and disciplined, and your work product will improve across the board as a result. You’ll get used to dressing professionally, preparing for meetings, speaking appropriately, showing up on time, writing official correspondence, and so forth. You will be able to speak the corporate language. You’ll become accustomed to working very long hours doing detail-intensive work. These attributes are transferable to and helpful in many other contexts.

a harbinger of a third wave of computing, one that blurred the line between augmented human intelligence and artificial intelligence. “The first generation of computers were machines that counted and tabulated,” Rometty says, harking back to IBM’s roots in Herman Hollerith’s punch-card tabulators used for the 1890 census. “The second generation involved programmable machines that used the von Neumann architecture. You had to tell them what to do.” Beginning with Ada Lovelace, people wrote algorithms that instructed these computers, step by step, how to perform tasks. “Because of the proliferation of data,” Rometty adds, “there is no choice but to have a third generation, which are systems that are not programmed, they learn.”27 But even as this occurs, the process could remain one of partnership and symbiosis with humans rather than one designed to relegate humans to the dustbin of history. Larry Norton, a breast cancer specialist at New York’s Memorial Sloan-Kettering Cancer Center, was part of the team that worked with Watson. “Computer science is going to evolve rapidly, and medicine will evolve with it,” he said. “This is coevolution. We’ll help each other.”28 This belief that machines and humans will get smarter together is a process that Doug Engelbart called “bootstrapping” and “coevolution.”29 It raises an interesting prospect: perhaps no matter how fast computers progress, artificial intelligence may never outstrip the intelligence of the human-machine partnership. Let us assume, for example, that a machine someday exhibits all of the mental capabilities of a human: giving the outward appearance of recognizing patterns, perceiving emotions, appreciating beauty, creating art, having desires, forming moral values, and pursuing goals. Such a machine might be able to pass a Turing Test. It might even pass what we could call the Ada Test, which is that it could appear to “originate” its own thoughts that go beyond what we humans program it to do. There would, however, be still another hurdle before we could say that artificial intelligence has triumphed over augmented intelligence. We can call it the Licklider Test. It would go beyond asking whether a machine could replicate all the components of human intelligence to ask whether the machine accomplishes these tasks better when whirring away completely on its own or when working in conjunction with humans. In other words, is it possible that humans and machines working in partnership will be indefinitely more powerful than an artificial intelligence machine working alone?

Key Points: ● Transparency - Blockchain offers significant improvements in transparency compared to existing record keeping and ledgers for many industries. ● Removal of Intermediaries – Blockchain-based systems allow for the removal of intermediaries involved in the record keeping and transfer of assets. ● Decentralization – Blockchain-based systems can run on a decentralized network of computers, reducing the risk of hacking, server downtime and loss of data. ● Trust – Blockchain-based systems increase trust between parties involved in a transaction through improved transparency and decentralized networks along with removal of third-party intermediaries in countries where trust in the intermediaries doesn’t exist. ● Security – Data entered on the blockchain is immutable, preventing against fraud through manipulating transactions and the history of data. Transactions entered on the blockchain provide a clear trail to the very start of the blockchain allowing any transaction to be easily investigated and audited. ● Wide range of uses - Almost anything of value can be recorded on the blockchain and there are many companies and industries already developing blockchain-based systems. These examples are covered later in the book. ● Easily accessible technology – Along with the wide range of uses, blockchain technology makes it easy to create applications without significant investment in infrastructure with recent innovations like the Ethereum platform. Decentralized apps, smart contracts and the Ethereum platform are covered later in the book. ● Reduced costs – Blockchain-based ledgers allow for removal of intermediaries and layers of confirmation involved in transactions. Transactions that may take multiple individual ledgers, could be settled on one shared ledger, reducing the costs of validating, confirming and auditing each transaction across multiple organizations. ● Increased transaction speed – The removal of intermediaries and settlement on distributed ledgers, allows for dramatically increased transaction speeds compared to a wide range of existing systems.

Once trade connects two areas, the forces of supply and demand tend to equalise the prices of transportable goods. In order to understand why, consider a hypothetical case. Assume that when regular trade opened between India and the Mediterranean, Indians were uninterested in gold, so it was almost worthless. But in the Mediterranean, gold was a coveted status symbol, hence its value was high. What would happen next? Merchants travelling between India and the Mediterranean would notice the difference in the value of gold. In order to make a profit, they would buy gold cheaply in India and sell it dearly in the Mediterranean. Consequently, the demand for gold in India would skyrocket, as would its value. At the same time the Mediterranean would experience an influx of gold, whose value would consequently drop. Within a short time the value of gold in India and the Mediterranean would be quite similar. The mere fact that Mediterranean people believed in gold would cause Indians to start believing in it as well. Even if Indians still had no real use for gold, the fact that Mediterranean people wanted it would be enough to make the Indians value it. Similarly, the fact that another person believes in cowry shells, or dollars, or electronic data, is enough to strengthen our own belief in them, even if that person is otherwise hated, despised or ridiculed by us. Christians and Muslims who could not agree on religious beliefs could nevertheless agree on a monetary belief, because whereas religion asks us to believe in something, money asks us to believe that other people believe in something. For thousands of years, philosophers, thinkers and prophets have besmirched money and called it the root of all evil. Be that as it may, money is also the apogee of human tolerance. Money is more open-minded than language, state laws, cultural codes, religious beliefs and social habits. Money is the only trust system created by humans that can bridge almost any cultural gap, and that does not discriminate on the basis of religion, gender, race, age or sexual orientation. Thanks to money, even people who don’t know each other and don’t trust each other can nevertheless cooperate effectively.

The successful individual sales producer wins by being as selfish as possible with her time. The more often the salesperson stays away from team members and distractions, puts her phone on Do Not Disturb (DND), closes her door, or chooses to work for a few hours from the local Panera Bread café, the more productive she’ll likely be. In general, top producers in sales tend to exhibit a characteristic I’ve come to describe as being selfishly productive. The seller who best blocks out the rest of the world, who maintains obsessive control of her calendar, who masters focusing solely on her own highest-value revenue-producing activities, who isn’t known for being a “team player,” and who is not interested in playing good corporate citizen or helping everyone around her, is typically a highly effective seller who ends up on top of the sales rankings. Contrary to popular opinion, being selfish is not bad at all. In fact, for an individual contributor salesperson, it is a highly desirable trait and a survival skill, particularly in today’s crazed corporate environment where everyone is looking to put meetings on your calendar and take you away from your primary responsibilities! Now let’s switch gears and look at the sales manager’s role and responsibilities. How well would it work to have a sales manager who kept her office phone on DND and declined almost every incoming call to her mobile phone? Do we want a sales manager who closes her office door, is concerned only about herself, and is for the most part inaccessible? No, of course not. The successful sales manager doesn’t win on her own; she wins through her people by helping them succeed. Think about other key sales management responsibilities: Leading team meetings. Developing talent. Encouraging hearts. Removing obstacles. Coaching others. Challenging data, false assumptions, wrong attitudes, and complacency. Pushing for more. Putting the needs of your team members ahead of your own. Hmmm. Just reading that list again reminds me why it is often so difficult to transition from being a top producer in sales into a sales management role. Aside from the word sales, there is truly almost nothing similar about the positions. And that doesn’t even begin to touch on corporate responsibilities like participating on the executive committee, dealing with human resources compliance issues, expense management, recruiting, and all the other burdens placed on the sales manager. Again,

To be shaken out of the ruts of ordinary perception, to be shown for a few timeless hours the outer and the inner world, not as they appear to an animal obsessed with survival or to a human being obsessed with words and notions, but as they are apprehended, directly and unconditionally, by Mind at Large – thus an experience of inestimable value to everyone and especially to the intellectual. For the intellectual is by definition the man for whom, in Goethe’s phrase, ‘the word is essentially fruitful.’ He is the man who feels that ‘what we perceive by the eye is foreign to us as such and need not impress us deeply.’ And yet, though himself an intellectual and one of the supreme masters of language, Goethe did not always agree with his own evaluation of the word. ‘We talk,’ he wrote in middle life, ‘far too much. We should talk less and draw more. I personally should like to renounce speech altogether and, like organic Nature, communicate everything I have to say in sketches. That fig tree, this little snake, the cocoon on my window sill quietly awaiting its future – all these are momentous signatures. A person able to decipher their meaning properly would soon be able to dispense with the written or the spoken word altogether. The more I think of it, there is something futile, mediocre, even (I am tempted to say) foppish about speech. By contrast, how the gravity of Nature and her silence startle you, when you stand face to face with her, undistracted, before a barren ridge or in the desolation of the ancient hills.’ We can never dispense with language and the other symbol systems; for it is by means of them, and only by their means, that we have raised ourselves above the brutes, to the level of human beings. But we can easily become the victims as well as the beneficiaries of these systems. We must learn how to handle words effectively; but at the same time we must preserve and, if necessary, intensify our ability to look at the world directly and not through that half-opaque medium of concepts, which distorts every given fact into the all too familiar likeness of some generic label or explanatory abstraction. Literary or scientific, liberal or specialist, all our education is predominantly verbal and therefore fails to accomplish what it is supposed to do. Instead of transforming children into fully developed adults, it turns out students of the natural sciences who are completely unaware of Nature as the primary fact of experience, it inflicts upon the world students of the Humanities who know nothing of humanity, their own or anyone else’s. In a world where education is predominantly verbal, highly educated people find it all but impossible to pay serious attention to anything but words and notions. There is always money for, there are always doctrines in, the learned foolery of research into what, for scholars, is the all-important problem: Who influenced whom to say what when? Even in this age of technology the verbal humanities are honoured. The non-verbal humanities, the arts of being directly aware of the given facts of our existence, are almost completely ignored. Every individual is at once the beneficiary and the victim of the linguistic tradition into which he has been born - the beneficiary in as much as language gives access to the accumulated records of other people's experience, the victim in so far as it confirms him in the belief that reduced awareness is the only awareness and as it bedevils his sense of reality, so that he is all too apt to take his concepts for data, his words for actual things. That which, in the language of religion, is called "this world" is the universe of reduced awareness, expressed, and, as it were, petrified by language.