Data Modelling Quotes

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 point is, the brain talks to itself, and by talking to itself changes its perceptions. To make a new version of the not-entirely-false model, imagine the first interpreter as a foreign correspondent, reporting from the world. The world in this case means everything out- or inside our bodies, including serotonin levels in the brain. The second interpreter is a news analyst, who writes op-ed pieces. They read each other's work. One needs data, the other needs an overview; they influence each other. They get dialogues going. INTERPRETER ONE: Pain in the left foot, back of heel. INTERPRETER TWO: I believe that's because the shoe is too tight. INTERPRETER ONE: Checked that. Took off the shoe. Foot still hurts. INTERPRETER TWO: Did you look at it? INTERPRETER ONE: Looking. It's red. INTERPRETER TWO: No blood? INTERPRETER ONE: Nope. INTERPRETER TWO: Forget about it. INTERPRETER ONE: Okay. Mental illness seems to be a communication problem between interpreters one and two. An exemplary piece of confusion. INTERPRETER ONE: There's a tiger in the corner. INTERPRETER TWO: No, that's not a tiger- that's a bureau. INTERPRETER ONE: It's a tiger, it's a tiger! INTERPRETER TWO: Don't be ridiculous. Let's go look at it. Then all the dendrites and neurons and serotonin levels and interpreters collect themselves and trot over to the corner. If you are not crazy, the second interpreter's assertion, that this is a bureau, will be acceptable to the first interpreter. If you are crazy, the first interpreter's viewpoint, the tiger theory, will prevail. The trouble here is that the first interpreter actually sees a tiger. The messages sent between neurons are incorrect somehow. The chemicals triggered are the wrong chemicals, or the impulses are going to the wrong connections. Apparently, this happens often, but the second interpreter jumps in to straighten things out.

What does regression do? It finds a prediction based on the average of what has occurred in the past. For instance, if all you have to go on to determine whether it is going to rain tomorrow is what happened each day last week, your best guess might be an average. If it rained on two of the last seven days, you might predict that the probability of rain tomorrow is around two in seven, or 29 percent. Much of what we know about prediction has been making our calculations of the average better by building models that can take in more data about the context.

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.

Companies can learn a lot from biological systems. The human immune system for example is adaptive, redundant, diverse, modular, data-driven and network collaborative. A company that desires not just short term profit but also long term resilience should apply these features of the human immune system to it's business models and company structure.

It’s important to remember that big data all comes from the same place – the past. A new campaigning style, a single rogue variable or a ‘black swan’ event can throw the most perfectly calibrated model into chaos.

However, when you create a model from proxies, it is far simpler for people to game it. This is because proxies are easier to manipulate than the complicated reality they represent.

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.

Something as superfluous as "play" is also an essential feature of our consciousness. If you ask children why they like to play, they will say, "Because it's fun." But that invites the next question: What is fun? Actually, when children play, they are often trying to reenact complex human interactions in simplified form. Human society is extremely sophisticated, much too involved for the developing brains of young children, so children run simplified simulations of adult society, playing games such as doctor, cops and robber, and school. Each game is a model that allows children to experiment with a small segment of adult behavior and then run simulations into the future. (Similarly, when adults engage in play, such as a game of poker, the brain constantly creates a model of what cards the various players possess, and then projects that model into the future, using previous data about people's personality, ability to bluff, etc. The key to games like chess, cards, and gambling is the ability to simulate the future. Animals, which live largely in the present, are not as good at games as humans are, especially if they involve planning. Infant mammals do engage in a form of play, but this is more for exercise, testing one another, practicing future battles, and establishing the coming social pecking order rather than simulating the future.)

The lack of transparency regarding training data sources and the methods used can be problematic. For example, algorithmic filtering of training data can skew representations in subtle ways. Attempts to remove overt toxicity by keyword filtering can disproportionately exclude positive portrayals of marginalized groups. Responsible data curation requires first acknowledging and then addressing these complex tradeoffs through input from impacted communities.

So we pour in data from the past to fuel the decision-making mechanisms created by our models, be they linear or nonlinear. But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand.[...]It is in those outliers and imperfections that the wildness lurks.

I’ve said several times that the brain acts like a scientist. It forms hypotheses through prediction and tests them against the “data” of sensory input. It corrects its predictions by way of prediction error, like a scientist adjusts his or her hypotheses in the face of contrary evidence. When the brain’s predictions match the sensory input, this constitutes a model of the world in that instant, just like a scientist judges that a correct hypothesis is the path to scientific certainty.

Every piece of data ingested by a model plays a role in determining its behavior. The fairness, transparency, and representativeness of the data reflect directly in the LLMs' outputs. Ignoring ethical considerations in data sourcing can inadvertently perpetuate harmful stereotypes, misinformation, or gaps in knowledge. It can also infringe on the rights of data creators.

And in any case, while there is evidence that women can to a certain extent accept men as role models, men won’t do the same for women. Women will buy books by and about men, but men won’t buy books by and about women (or at least not many).

A computer model which manipulated data about itself and its “surroundings” in essentially the same way as an organic brain would have to possess essentially the same mental states. “Simulated consciousness” was as oxymoronic as “simulated addition.

A flexible mind changes itself and builds a better model as it gathers more data about reality.

as data becomes the new oil, many business models will be transformed from hardware to software to services.

At the same time, we have fallen for the idea that these services are ‘free’. In reality, we pay with our data into a business model of extracting human attention.

Big Tech firms, however, have no need to raise prices, because they have a business model by which they are not paid in money; they are paid in data, via a system of barter

Your job isn’t to build a product; it’s to de-risk a business model.

The philosophical implications of "predictive coding" are deep and strange. The model suggests that our perceptions of the world offer us not a literal transcription of reality but rather a seamless illusion woven from both the data of our senses and the models in our memories.

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

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

So why does the world appear stable to you when you’re looking at it? Why doesn’t it appear as jerky and nauseating as the poorly filmed video? Here’s why: your internal model operates under the assumption that the world outside is stable. Your eyes are not like video cameras – they simply venture out to find more details to feed into the internal model. They’re not like camera lenses that you’re seeing through; they’re gathering bits of data to feed the world inside your skull." The Brain: The Story of You - David Eagleman

The economist Robin Hanson estimates, based on historical economic and population data, a characteristic world economy doubling time for Pleistocene hunter–gatherer society of 224,000 years; for farming society, 909 years; and for industrial society, 6.3 years.3 (In Hanson’s model, the present epoch is a mixture of the farming and the industrial growth modes—the world economy as a whole is not yet growing at the 6.3-year doubling rate.) If

To create a model, then, we make choices about what’s important enough to include, simplifying the world into a toy version that can be easily understood and from which we can infer important facts and actions. We expect it to handle only one job and accept that it will occasionally act like a clueless machine, one with enormous blind spots.

A forecaster should almost never ignore data, especially when she is studying rare events like recessions or presidential elections, about which there isn’t very much data to begin with. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model—that she is interested in showing off rather than trying to be accurate.

Turns out, while all light pollution is bad for astrophysics, the low-pressure sodium lamps are least bad because their contamination can be easily subtracted from telescope data. In a model of cooperation, the entire city of Tucson, Arizona, the nearest large municipality to the Kitt Peak National Observatory, has, by agreement with the local astrophysicists, converted all its streetlights to low-pressure sodium lamps.

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.

Opaque and invisible models are the rule, and clear ones very much the exception. We’re modeled as shoppers and couch potatoes, as patients and loan applicants, and very little of this do we see—even in applications we happily sign up for. Even when such models behave themselves, opacity can lead to a feeling of unfairness.

The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.

So we pour in data from the past to fuel the decision-making mechanisms created by our models, be they linear or nonlinear. But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand.[...]Even though many economic and financial variables fall into distributions that approximate a bell curve, the picture is never perfect.[...]It is in those outliers and imperfections that the wildness lurks.

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?

The purpose of models is not to fit the data but to sharpen the questions.

All models are wrong, but some are useful.’ CHAPTER 6 Algorithms, Analytics and Prediction

In an optimization-based model, preferences or payoffs are fundamental. In a rule-based model, the behavior is fundamental. Behavioral rules can be fixed or adapt.

With limited training data, a more constrained model tends to perform better.

This is a point I’ll be returning to in future chapters: we’ve seen time and again that mathematical models can sift through data to locate people who are likely to face great challenges, whether from crime, poverty, or education. It’s up to society whether to use that intelligence to reject and punish them—or to reach out to them with the resources they need.

If we want the future to be better than the past, moral imagination is required, and that’s something only humans can provide [87]. Data and models should be our tools, not our masters.

If you look at this development from the perspective of a university president, it’s actually quite sad. Most of these people no doubt cherished their own college experience—that’s part of what motivated them to climb the academic ladder. Yet here they were at the summit of their careers dedicating enormous energy toward boosting performance in fifteen areas defined by a group of journalists at a second-tier newsmagazine. They were almost like students again, angling for good grades from a taskmaster. In fact, they were trapped by a rigid model, a WMD.

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

If every time new data comes along we have to add complexity to our model in order to accommodate it, this should be a hint that the model is fundamentally a failure. It becomes a blob of ‘silly putty’ that is malleable enough to fit any new data. This sort of model is not a proper basis for a hypothesis; it is merely a blank check to claim we understand something when we really do not.

It’s not in keeping with the scientific model to investigate a purportedly haunted location with the intent to prove that ghosts exist. The paranormal researcher should remain neutral and unbiased throughout the investigation and let the data prove a definitive conclusion, whether that’s the one they wanted or not. They should walk into an investigation thinking, “I will document what happens and then examine the data for conclusions.

We’ve seen time and again that mathematical models can sift through data to locate people who are likely to face great challenges, whether from crime, poverty, or educations. It’s up to society whether to use that intelligence to reject and punish them—or to reach out to them with the resources they need. We can use the scale and efficiency that make WMDs so pernicious in order to help people. It all depends on the objective we choose.

In a different direction, the necessity to model the analysis of noisy incomplete sensory data not by logic but by Bayesian inference first came to the forefront in the robotics community with their use of Kalman filters.

Two months in Shanghai, and what does she have to show for herself? She had been full of plans on the plane ride over, had studied her phrase book as if cramming for an exam, had been determined to refine her computational model with a new set of data, expecting insights and breakthroughs, plotting notes for a new article. Only the time has trickled away so quickly. She has meandered through the days chatting with James instead of gathering data. At night, she has gone out to dinners and bars. [James'] Chinese has not improved; her computational model has barely been touched. She does not know what she has been doing with herself, and now an airplane six days away is waiting for her.

Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.

The data on organised abuse has been simplified or distorted in an attempt force it to conform to mechanical psychological models of dissociative obedience or else to the psychiatric framework of ‘paedophilia’. Psychopathology alone is an inadequate explanation for environments in which sexual abuse has a social and symbolic function for groups of adults. Abusive groups do not emerge in a vacuum but rather they are formed within pre-existing social arrangements such as families, churches and schools.

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.

Thus, they do not need to understand the statistical and mathematical models in depth. However, marketers need to understand the fundamental ideas behind a predictive model so that they can guide the technical teams to select data to use and which patterns to find.

Thanks in part to the resulting high score on the evaluation, he gets a longer sentence, locking him away for more years in a prison where he’s surrounded by fellow criminals—which raises the likelihood that he’ll return to prison. He is finally released into the same poor neighborhood, this time with a criminal record, which makes it that much harder to find a job. If he commits another crime, the recidivism model can claim another success. But in fact the model itself contributes to a toxic cycle and helps to sustain it. That’s a signature quality of a WMD.

finance. What’s the expected amount of data? What’s the expected signal in the data? How much data do we have? What are the opportunities to use this model? What is the payoff for those opportunities? What’s the scale of this model if it works? What’s the probability that the idea is valid?

Research by media scholars Daniel Kreiss and Philip Howard indicates that the 2008 Obama campaign compiled significant data on more than 250 million Americans, including “a vast array of online behavioral and relational data collected from use of the campaign’s web site and third-party social media sites such as Facebook.…”96 Journalist Sasha Issenberg, who documented these developments in his book The Victory Lab, quotes one of Obama’s 2008 political consultants who likened predictive modeling to the tools of a fortune-teller: “We knew who… people were going to vote for before they decided.

Surveillance is the business model of the Internet for two primary reasons: people like free, and people like convenient. The truth is, though, that people aren’t given much of a choice. It’s either surveillance or nothing, and the surveillance is conveniently invisible so you don’t have to think about it.

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.

Today most of the debate on the cutting edge in macroeconomics would not call itself “Keynesian” or “monetarist” or any other label relating to a school of thought. The data are considered the ruling principle, and it is considered suspect to have too strong a loyalty to any particular model about the underlying structure of the economy.

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

In the same way, hybrid models of blended learning are not noticeably simpler for teachers than the existing system. On the contrary, in many cases they appear to require all the expertise of the traditional model plus new expertise in managing digital devices and in integrating data across all the supplemental online experiences in the teacher-directed rotation.

They somehow managed to persuade themselves that computer models constitute data. That very complicated guesses become facts. They made themselves believe they had the power to accurately model, not merely something as inconceivably complex as, say, a single zygote…but a national economy, a weather system, a planetary ecosphere, a multiplanet society—even a universe.

Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And

The difference between human dynamics and data mining boils down to this: Data mining predicts our behaviors based on records of our patterns of activity; we don't even have to understand the origins of the patterns exploited by the algorithm. Students of human dynamics, on the other hand, seek to develop models and theories to explain why, when, and where we do the things we do with some regularity.

As should be obvious by now, surveillance is the business model of the Internet. You create “free” accounts on Web sites such as Snapchat, Facebook, Google, LinkedIn, Foursquare, and PatientsLikeMe and download free apps like Angry Birds, Candy Crush Saga, Words with Friends, and Fruit Ninja, and in return you, wittingly or not, agree to allow these companies to track all your moves, aggregate them, correlate them, and sell them to as many people as possible at the highest price, unencumbered by regulation, decency, or ethical limitation. Yet so few stop and ask who else has access to all these data detritus and how it might be used against us. Dataveillance is the “new black,” and its uses, capabilities, and powers are about to mushroom in ways few consumers, governments, or technologists might have imagined.

In a provocative article in Wired, editor-in-chief Chris Anderson argued that huge databases render scientific theory itself obsolete. Why spend time formulating human-language hypotheses, after all, when you can quickly analyze trillions of bits of data and find the clusters and correlations? He quotes Peter Norvig, Google’s research director: “All models are wrong, and increasingly you can succeed without them.

But even when Facebook isn't deliberately exploiting its users, it is exploiting its users—its business model requires it. Even if you distance yourself from Facebook, you still live in the world that Facebook is shaping. Facebook, using our native narcissism and our desire to connect with other people, captured our attention and our behavioral data; it used this attention and data to manipulate our behavior, to the point that nearly half of America began relying on Facebook for news. Then, with the media both reliant on Facebook as a way of reaching readers and powerless against the platform's ability to suck up digital advertising revenue—it was like a paperboy who pocketed all the subscription money—Facebook bent the media's economic model to match its own practices: publications needed to capture attention quickly and consistently trigger high emotional responses to be seen at all. The result, in 2016, was an unending stream of Trump stories, both from the mainstream news and from the fringe outlets that were buoyed by Facebook's algorithm. What began as a way for Zuckerberg to harness collegiate misogyny and self-interest has become the fuel for our whole contemporary nightmare, for a world that fundamentally and systematically misrepresents human needs.

This creates a pernicious feedback loop. The policing itself spawns new data, which justifies more policing. And our prisons fill up with hundreds of thousands of people found guilty of victimless crimes. Most of them come from impoverished neighborhoods, and most are black or Hispanic. So even if a model is color blind, the result of it is anything but. In our largely segregated cities, geography is a highly effective proxy for race.

Eventually, the performance of a classifier, computational power as well as predictive power, depends heavily on the underlying data that are available for learning. The five main steps that are involved in training a machine learning algorithm can be summarized as follows: Selection of features. Choosing a performance metric. Choosing a classifier and optimization algorithm. Evaluating the performance of the model. Tuning the algorithm.

We think of our eyes as video cameras and our brains as blank tapes to be filled with percepts. Memory, in this flawed model, is simply rewinding the tape and playing it back in the theater of the mind. This is not at all what happens. The perceptual system, and the brain that analyzes its data, are deeply influenced by the beliefs it already holds. As a consequence, much of what passes before our eyes may be invisible to a brain focused on something else.

The fact that the information platform requires an extension of sensors means that it is countering the tendency towards a lean platform. These are not asset-less companies – far from it; they spend billions of dollars to purchase fixed capital and take other companies over. Importantly, ‘once we understand this [tendency], it becomes clear that demanding privacy from surveillance capitalists or lobbying for an end to commercial surveillance on the Internet is like asking Henry Ford to make each Model T by hand’.15 Calls for privacy miss how the suppression of privacy is at the heart of this business model. This tendency involves constantly pressing against the limits of what is socially and legally acceptable in terms of data collection. For the most part, the strategy has been to collect data, then apologise and roll back programs if there is an uproar, rather than consulting with users beforehand.16 This is why we will continue to see frequent uproars over the collection of data by these companies.

Most of us didn’t feel too enthusiastic about making a collapsar jump, either. We’d been assured that we wouldn’t even feel it happen, just free fall all the way. I wasn’t convinced. As a physics student, I’d had the usual courses in general relativity and theories of gravitation. We only had a little direct data at that time — Stargate was discovered when I was in grade school — but the mathematical model seemed clear enough. The collapsar Stargate was a perfect sphere about three kilometers in radius. It was suspended forever in a state of gravitational collapse that should have meant its surface was dropping toward its center at nearly the speed of light. Relativity propped it up, at least gave it the illusion of being there … the way all reality becomes illusory and observer-oriented when you study general relativity. Or Buddhism. Or get drafted. At any rate, there would be a theoretical point in space-time when one end of our ship was just above the surface of the collapsar, and the other end was a kilometer away (in our frame of reference). In any sane universe, this would set up tidal stresses and tear the ship apart, and we would be just another million kilograms of degenerate matter on the theoretical surface, rushing headlong to nowhere for the rest of eternity or dropping to the center in the next trillionth of a second. You pays your money and you takes your frame of reference. But they were right. We blasted away from Stargate 1, made a few course corrections and then just dropped, for about an hour.

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

Faced with this disparity, Netflix stopped asking people to tell them what they wanted to see in the future and started building a model based on millions of clicks and views from similar customers. The company began greeting its users with suggested lists of films based not on what they claimed to like but on what the data said they were likely to view. The result: customers visited Netflix more frequently and watched more movies. “The algorithms know you better than you know yourself,” says Xavier Amatriain, a former data scientist at Netflix.

Big data is based on the feedback economy where the Internet of Things places sensors on more and more equipment. More and more data is being generated as medical records are digitized, more stores have loyalty cards to track consumer purchases, and people are wearing health-tracking devices. Generally, big data is more about looking at behavior, rather than monitoring transactions, which is the domain of traditional relational databases. As the cost of storage is dropping, companies track more and more data to look for patterns and build predictive models".

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.

The future for ancient DNA laboratories that I find appealing is based on a model that has emerged among radiocarbon dating laboratories. For example, the Oxford Radiocarbon Accelerator Unit processes large numbers of samples for a fee, and uses this income stream to support a factory that churns out routine dates and produces data more cheaply, efficiently, and at higher quality than would be possible if its scientists limited themselves to their own questions. But its scientists then piggyback on the juggernaut of the radiocarbon dating factory they have built to do cutting-edge science,

Computer cores made if liquid crystal that can re-form itself into any configuration, creating the ultimate efficiency for any particular piece of cybernetic business that needs doing, shifting from storage of data to moving it to analyzing it and the altering to a form most efficient for acting on the analysis.Hearts that can make minds, from little bits if brightness in Cowboy's skull that let him move his panzer, to large models that create working analogs of the human brain, the vast artificial intelligences that keep things moving smoothly for the Orbitals and the governments of the planet. All in miniature potential, here in the cardboard box.

Computer cores made of liquid crystal that can re-form itself into any configuration, creating the ultimate efficiency for any particular piece of cybernetic business that needs doing, shifting from storage of data to moving it to analyzing it and the altering to a form most efficient for acting on the analysis. Hearts that can make minds, from little bits if brightness in Cowboy's skull that let him move his panzer, to large models that create working analogs of the human brain, the vast artificial intelligences that keep things moving smoothly for the Orbitals and the governments of the planet. All in miniature potential, here in the cardboard box.

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.

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.

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

In sum, Jellinek's highly influential articles were based on questionnaires completed by 98 male members of A.A. Of the 158 questionnaires returned, Jellinek had eliminated 60, excluding the data from some A.A. members who had pooled and averaged their answers on a single questionnaire because they shared their newsletter. Jellinek also excluded all questionnaires filled out by women because their answers differed greatly from the men's. No wonder Jellinek spoke of the limitations of the data. And no wonder his data conformed so closely to the A.A. model. Even in 1960, Jellinek acknowledged the lack of any demonstrated scientific foundation for his proposals. Of the lack of evidence he remarked, "For the time being this may suffice, but not indefinitely." 16

Scalable Social Network Analysis. The SSNA would monitor telephone calls, conference calls, and ATM withdrawals, but it also sought to develop a far more invasive surveillance technology, one that could “capture human activities in surveillance environments.” The Activity Recognition and Monitoring program, or ARM, was modeled after England’s CCTV camera. Surveillance cameras would be set up across the nation, and through the ARM program, they would capture images of people as they went about their daily lives, then save these images to massive data storage banks for computers to examine. Using state-of-the-art facial recognition software, ARM would seek to identify who was behaving outside the computer’s pre-programmed threshold for “ordinary.” The parameters for “ordinary” remain classified.

Even working within the laws of physics, researchers with an anti-God bias often make blind leaps of faith to escape any evidence of God’s involvement in the universe. For centuries Christians were criticized for their God-of-the-gaps arguments. Sometimes that criticism was deserved. Christians tended to use gaps in understanding or data to build a case for God’s miraculous intervention. Then, when scientific discoveries uncovered a natural explanation for the “divine phenomenon,” ridicule was heaped not only on those proposing the divine explanation but also on belief in God’s existence. In the twenty-first century we see the reverse of the God-of-the-gaps arguments. Nontheists, confronted with problems when ample research leads to no natural explanations and instead points to the supernatural, utterly reject the possibility of the supernatural and insist on a natural explanation even if it means resorting to absurdity. For example, steady state models were supported by an imagined force of physics for which there was not one shred of observational or experimental evidence. The oscillating universe model depended on an imagined bounce mechanism for which there was likewise not one shred of observational or experimental evidence. Similar appeals to imagined forces and phenomena have been the basis for all the cosmological models proposed to avoid the big bang implications about God (see chs. 8 and 9). The disproof of these models and the ongoing appeal by nontheists to more and more bizarre unknowns and unknowables seem to reflect the growing strength of the case for theism (see chs. 8, 9, 13, and 16).

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.

VaR has been called “potentially catastrophic,” “a fraud,” and many other things not fit for a family book about statistics like this one. In particular, the model has been blamed for the onset and severity of the financial crisis. The primary critique of VaR is that the underlying risks associated with financial markets are not as predictable as a coin flip or even a blind taste test between two beers. The false precision embedded in the models created a false sense of security. The VaR was like a faulty speedometer, which is arguably worse than no speedometer at all. If you place too much faith in the broken speedometer, you will be oblivious to other signs that your speed is unsafe. In contrast, if there is no speedometer at all, you have no choice but to look around for clues as to how fast you are really going.

these models are constructed not just from data but from the choices we make about which data to pay attention to—and which to leave out. Those choices are not just about logistics, profits, and efficiency. They are fundamentally moral. If we back away from them and treat mathematical models as a neutral and inevitable force, like the weather or the tides, we abdicate our responsibility. And the result, as we’ve seen, is WMDs that treat us like machine parts in the workplace, that blackball employees and feast on inequities. We must come together to police these WMDs, to tame and disarm them. My hope is that they’ll be remembered, like the deadly coal mines of a century ago, as relics of the early days of this new revolution, before we learned how to bring fairness and accountability to the age of data. Math deserves much better than WMDs, and democracy does too.

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

Peter Thiel and Ken Howery at Founders Fund, however, reached out to their friends behind the scenes at Friendster. They dug into why users were leaving the site. Like other users, Thiel and Howery knew that Friendster crashed often. They also knew that the team behind Friendster had received, and ignored, crucial advice on how to scale their site—how to transform a system built for a few thousand users into one that could support millions of users. They asked for and received a copy of Friendster’s data on user retention. They were stunned by how long users stayed with the site, despite the irritating crashes. They concluded that users weren’t leaving because social networks were weak business models, like clothing brands. They were leaving because of a software glitch. It was a False Fail. Thiel wrote Zuckerberg a check for $500,000. Eight years later, he sold most of his stake in Facebook for roughly a billion dollars.

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

In the twenty-first century it sounds childish to compare the human psyche to a steam engine. Today we know of a far more sophisticated technology – the computer – so we explain the human psyche as if it were a computer processing data rather than a steam engine regulating pressure. But this new analogy may turn out to be just as naïve. After all, computers have no minds. They don’t crave anything even when they have a bug, and the Internet doesn’t feel pain even when authoritarian regimes sever entire countries from the Web. So why use computers as a model for understanding the mind? Well, are we really sure that computers have no sensations or desires? And even if they haven’t got any at present, perhaps once they become complex enough they might develop consciousness? If that were to happen, how could we ascertain it? When computers replace our bus driver, our teacher and our shrink, how could we determine whether they have feelings or whether they are just a collection of mindless algorithms? When

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.

He was known by three names. The official records have the first one: Marcos Maria Ribeira. And his official data. Born 1929. Died 1970. Worked in the steel foundry. Perfect safety record. Never arrested. A wife, six children. A model citizen, because he never did anything bad enough to go on the public record. The second name he had was Marcao. Big Marcos. Because he was a giant of a man. Reached his adult size early in his life. How old was he when he reached two meters? Eleven? Definitely by the time he was twelve. His size and strength made him valuable in the foundry,where the lots of steel are so small that much of the work is controlled by hand and strength matters. People's lives depended on Marcao's strength. His third name was Cao. Dog. That was the name you used for him when you heard his wife, Novinha, had another black eye, walked with a limp, had stitches in her lip. He was an animal to do that to her. Not that any of you liked Novinha. Not that cold woman who never gave any of you good morning. But she was smaller than he was, and she was the mother of his children, and when he beat her, he deserved the name of Cao. Tell me, is this the man you knew? Spent more hours in the bars than anyone but never made any friends there, never the camaraderie of alcohol for him. You couldn't even tell how much he had been drinking. He was surly and short-tempered before he had a drink and he was surly and short-tempered right before he passed out-nobody could tell the difference. You never heard of him having a friend, and none of you was ever glad to see him come into a room. That's the man you knew, most of you. Cao. Hardly a man at all. A few men, the men from the foundry in Bairro das Fabricados, knew him as a strong arm as they could trust. They knew he never said he could do more than he could do and he always did what he said he would do. You could count on him. So, within the walls of the foundry, he had their respect. But when you walked out of the door, you treated him like everybody else-ignored him, thought little of him. Some of you also know something else that you never talk about much. You know you gave him the name Cao long before he earned it. You were ten, eleven, twelve years old. Little boys. He grew so tall. It made you ashamed to be near him. And afraid, because he made you feel helpless. So you handled him the way human beings always handle things that are bigger than they are. You banded together. Like hunters trying to bring down a mastodon. Like bullfighters trying to weaken a giant bull to prepare it for the kill. Pokes, taunts, teases. Keep him turning around. He can't guess where the next blow was coming from. Prick him with barbs that stay under his skin. Weaken him with pain. Madden him. Because big as he is, you can make him do things. You can make him yell. You can make him run. You can make him cry. See? He's weaker than you after all. There's no blame in this. You were children then, and children are cruel without knowing better. You wouldn't do that now. But now that I've reminded you, you can clearly see an answer. You called him a dog, so he became one. For the rest of his life, hurting helpless people. Beating his wife. Speaking so cruelly and abusively to his son, Miro, that it drove the boy out of his house. He was acting the way you treated him, becoming what you told him he was. But the easy answer isn't true. Your torments didn't make him violent - they made him sullen. And when you grew out of tormenting him, he grew out of hating you. He wasn't one to bear a grudge. His anger cooled and turned into suspicion. He knew you despised him; he learned to live without you. In peace. So how did he become the cruel man you knew him to be? Think a moment. Who was it that tasted his cruelty? His wife. His children. Some people beat their wife and children because they lust for power, but are too weak or stupid to win power in the world.

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

Changing what we think is always a sticky process, especially when it comes to religion. When new information becomes available, we cringe under an orthodox mindset, particularly when we challenge ideas and beliefs that have been “set in stone” for decades. Thomas Kuhn coined the term paradigm shift to represent this often-painful transition to a new way of thinking in science. He argued that “normal science” represented a consensus of thought among scientists when certain precepts were taken as truths during a given period. He believed that when new information emerges, old ideas clash with new ones, causing a crisis. Once the basic truths are challenged, the crisis ends in either revolution (where the information provides new understanding) or dismissal (where the information is rejected as unsound). The information age that we live in today has likely surprised all of us as members of the LDS Church at one time or another as we encounter new ideas that revise or even contradict our previous understanding of various aspects of Church history and teachings. This experience is similar to that of the Copernican Revolution, which Kuhn uses as one of his primary examples to illustrate how a paradigm shift works. Using similar instruments and comparable celestial data as those before them, Copernicus and others revolutionized the heavens by describing the earth as orbiting the sun (heliocentric) rather than the sun as orbiting the earth (geocentric). Because the geocentric model was so ingrained in the popular (and scientific!) understanding, the new, heliocentric idea was almost impossible to grasp. Paradigm shifts also occur in religion and particularly within Mormonism. One major difference between Kuhn’s theory of paradigm shift and the changes that occur within Mormonism lies in the fact that Mormonism privileges personal revelation, which is something that cannot be institutionally implemented or decreed (unlike a scientific law). Regular members have varying degrees of religious experience, knowledge, and understanding dependent upon many factors (but, importantly, not “faithfulness” or “worthiness,” or so forth). When members are faced with new information, the experience of processing that information may occur only privately. As such, different members can have distinct experiences with and reactions to the new information they receive. This short preface uses the example of seer stones to examine the idea of how new information enters into the lives of average Mormons. We have all seen or know of friends or family who experience a crisis of faith upon learning new information about the Church, its members, and our history. Perhaps there are those reading who have undergone this difficult and unsettling experience. Anyone who has felt overwhelmed at the continual emergence of new information understands the gravity of these massive paradigm shifts and the potentially significant impact they can have on our lives. By looking at just one example, this preface will provide a helpful way to think about new information and how to deal with it when it arrives.

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

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

This extreme situation in which all data is processed and all decisions are made by a single central processor is called communism. In a communist economy, people allegedly work according to their abilities, and receive according to their needs. In other words, the government takes 100 per cent of your profits, decides what you need and then supplies these needs. Though no country ever realised this scheme in its extreme form, the Soviet Union and its satellites came as close as they could. They abandoned the principle of distributed data processing, and switched to a model of centralised data processing. All information from throughout the Soviet Union flowed to a single location in Moscow, where all the important decisions were made. Producers and consumers could not communicate directly, and had to obey government orders. For instance, the Soviet economics ministry might decide that the price of bread in all shops should be exactly two roubles and four kopeks, that a particular kolkhoz in the Odessa oblast should switch from growing wheat to raising chickens, and that the Red October bakery in Moscow should produce 3.5 million loaves of bread per day, and not a single loaf more. Meanwhile the Soviet science ministry forced all Soviet biotech laboratories to adopt the theories of Trofim Lysenko – the infamous head of the Lenin Academy for Agricultural Sciences. Lysenko rejected the dominant genetic theories of his day. He insisted that if an organism acquired some new trait during its lifetime, this quality could pass directly to its descendants. This idea flew in the face of Darwinian orthodoxy, but it dovetailed nicely with communist educational principles. It implied that if you could train wheat plants to withstand cold weather, their progenies will also be cold-resistant. Lysenko accordingly sent billions of counter-revolutionary wheat plants to be re-educated in Siberia – and the Soviet Union was soon forced to import more and more flour from the United States.

All addictions — whether to drugs or to nondrug behaviours — share the same brain circuits and brain chemicals. On the biochemical level the purpose of all addictions is to create an altered physiological state in the brain. This can be achieved in many ways, drug taking being the most direct. So an addiction is never purely “psychological” all addictions have a biological dimension. And here a word about dimensions. As we delve into the scientific research, we need to avoid the trap of believing that addiction can be reduced to the actions of brain chemicals or nerve circuits or any other kind of neurobiological, psychological or sociological data. A multilevel exploration is necessary because it’s impossible to understand addiction fully from any one perspective, no matter how accurate. Addiction is a complex condition, a complex interaction between human beings and their environment. We need to view it simultaneously from many different angles — or, at least, while examining it from one angle, we need to keep the others in mind. Addiction has biological, chemical, neurological, psychological, medical, emotional, social, political, economic and spiritual underpinnings — and perhaps others I haven’t thought about. To get anywhere near a complete picture we must keep shaking the kaleidoscope to see what other patterns emerge. Because the addiction process is too multifaceted to be understood within any limited framework, my definition of addiction made no mention of “disease.” Viewing addiction as an illness, either acquired or inherited, narrows it down to a medical issue. It does have some of the features of illness, and these are most pronounced in hardcore drug addicts like the ones I work with in the Downtown Eastside. But not for a moment do I wish to promote the belief that the disease model by itself explains addiction or even that it’s the key to understanding what addiction is all about. Addiction is “all about” many things. Note, too, that neither the textbook definitions of drug addiction nor the broader view we’re taking here includes the concepts of physical dependence or tolerance as criteria for addiction. Tolerance is an instance of “give an inch, take a mile.” That is, the addict needs to use more and more of the same substance or engage in more and more of the same behaviour, to get the same rewarding effects. Although tolerance is a common effect of many addictions, a person does not need to have developed a tolerance to be addicted.

In 1950, a thirty-year-old scientist named Rosalind Franklin arrived at King’s College London to study the shape of DNA. She and a graduate student named Raymond Gosling created crystals of DNA, which they bombarded with X-rays. The beams bounced off the crystals and struck photographic film, creating telltale lines, spots, and curves. Other scientists had tried to take pictures of DNA, but no one had created pictures as good as Franklin had. Looking at the pictures, she suspected that DNA was a spiral-shaped molecule—a helix. But Franklin was relentlessly methodical, refusing to indulge in flights of fancy before the hard work of collecting data was done. She kept taking pictures. Two other scientists, Francis Crick and James Watson, did not want to wait. Up in Cambridge, they were toying with metal rods and clamps, searching for plausible arrangements of DNA. Based on hasty notes Watson had written during a talk by Franklin, he and Crick put together a new model. Franklin and her colleagues from King’s paid a visit to Cambridge to inspect it, and she bluntly told Crick and Watson they had gotten the chemistry all wrong. Franklin went on working on her X-ray photographs and growing increasingly unhappy with King’s. The assistant lab chief, Maurice Wilkins, was under the impression that Franklin was hired to work directly for him. She would have none of it, bruising Wilkins’s ego and leaving him to grumble to Crick about “our dark lady.” Eventually a truce was struck, with Wilkins and Franklin working separately on DNA. But Wilkins was still Franklin’s boss, which meant that he got copies of her photographs. In January 1953, he showed one particularly telling image to Watson. Now Watson could immediately see in those images how DNA was shaped. He and Crick also got hold of a summary of Franklin’s unpublished research she wrote up for the Medical Research Council, which guided them further to their solution. Neither bothered to consult Franklin about using her hard-earned pictures. The Cambridge and King’s teams then negotiated a plan to publish a set of papers in Nature on April 25, 1953. Crick and Watson unveiled their model in a paper that grabbed most of the attention. Franklin and Gosling published their X-ray data in another paper, which seemed to readers to be a “me-too” effort. Franklin died of cancer five years later, while Crick, Watson, and Wilkins went on to share the Nobel prize in 1962. In his 1968 book, The Double Helix, Watson would cruelly caricature Franklin as a belligerent, badly dressed woman who couldn’t appreciate what was in her pictures. That bitter fallout is a shame, because these scientists had together discovered something of exceptional beauty. They had found a molecular structure that could make heredity possible.

The US traded its manufacturing sector’s health for its entertainment industry, hoping that Police Academy sequels could take the place of the rustbelt. The US bet wrong. But like a losing gambler who keeps on doubling down, the US doesn’t know when to quit. It keeps meeting with its entertainment giants, asking how US foreign and domestic policy can preserve its business-model. Criminalize 70 million American file-sharers? Check. Turn the world’s copyright laws upside down? Check. Cream the IT industry by criminalizing attempted infringement? Check. It’ll never work. It can never work. There will always be an entertainment industry, but not one based on excluding access to published digital works. Once it’s in the world, it’ll be copied. This is why I give away digital copies of my books and make money on the printed editions: I’m not going to stop people from copying the electronic editions, so I might as well treat them as an enticement to buy the printed objects. But there is an information economy. You don’t even need a computer to participate. My barber, an avowed technophobe who rebuilds antique motorcycles and doesn’t own a PC, benefited from the information economy when I found him by googling for barbershops in my neighborhood. Teachers benefit from the information economy when they share lesson plans with their colleagues around the world by email. Doctors benefit from the information economy when they move their patient files to efficient digital formats. Insurance companies benefit from the information economy through better access to fresh data used in the preparation of actuarial tables. Marinas benefit from the information economy when office-slaves look up the weekend’s weather online and decide to skip out on Friday for a weekend’s sailing. Families of migrant workers benefit from the information economy when their sons and daughters wire cash home from a convenience store Western Union terminal. This stuff generates wealth for those who practice it. It enriches the country and improves our lives. And it can peacefully co-exist with movies, music and microcode, but not if Hollywood gets to call the shots. Where IT managers are expected to police their networks and systems for unauthorized copying – no matter what that does to productivity – they cannot co-exist. Where our operating systems are rendered inoperable by “copy protection,” they cannot co-exist. Where our educational institutions are turned into conscript enforcers for the record industry, they cannot co-exist. The information economy is all around us. The countries that embrace it will emerge as global economic superpowers. The countries that stubbornly hold to the simplistic idea that the information economy is about selling information will end up at the bottom of the pile. What country do you want to live in?

What are these substances? Medicines or drugs or sacramental foods? It is easier to say what they are not. They are not narcotics, nor intoxicants, nor energizers, nor anaesthetics, nor tranquilizers. They are, rather, biochemical keys which unlock experiences shatteringly new to most Westerners. For the last two years, staff members of the Center for Research in Personality at Harvard University have engaged in systematic experiments with these substances. Our first inquiry into the biochemical expansion of consciousness has been a study of the reactions of Americans in a supportive, comfortable naturalistic setting. We have had the opportunity of participating in over one thousand individual administrations. From our observations, from interviews and reports, from analysis of questionnaire data, and from pre- and postexperimental differences in personality test results, certain conclusions have emerged. (1) These substances do alter consciousness. There is no dispute on this score. (2) It is meaningless to talk more specifically about the “effect of the drug.” Set and setting, expectation, and atmosphere account for all specificity of reaction. There is no “drug reaction” but always setting-plus-drug. (3) In talking about potentialities it is useful to consider not just the setting-plus-drug but rather the potentialities of the human cortex to create images and experiences far beyond the narrow limitations of words and concepts. Those of us on this research project spend a good share of our working hours listening to people talk about the effect and use of consciousness-altering drugs. If we substitute the words human cortex for drug we can then agree with any statement made about the potentialities—for good or evil, for helping or hurting, for loving or fearing. Potentialities of the cortex, not of the drug. The drug is just an instrument. In analyzing and interpreting the results of our studies we looked first to the conventional models of modern psychology—psychoanalytic, behavioristic—and found these concepts quite inadequate to map the richness and breadth of expanded consciousness. To understand our findings we have finally been forced back on a language and point of view quite alien to us who are trained in the traditions of mechanistic objective psychology. We have had to return again and again to the nondualistic conceptions of Eastern philosophy, a theory of mind made more explicit and familiar in our Western world by Bergson, Aldous Huxley, and Alan Watts. In the first part of this book Mr. Watts presents with beautiful clarity this theory of consciousness, which we have seen confirmed in the accounts of our research subjects—philosophers, unlettered convicts, housewives, intellectuals, alcoholics. The leap across entangling thickets of the verbal, to identify with the totality of the experienced, is a phenomenon reported over and over by these persons.