Prediction Machines Quotes

We are narrow thinkers, we are noisy thinkers, and it is very easy to improve upon us.

Prediction Machines is not a recipe for success in the AI economy. Instead, we emphasize trade-offs. More data means less privacy. More speed means less accuracy. More autonomy means less control.

when your predictions are accurate enough—something happens. You cross a threshold where you should actually rethink your whole business model and product based on machine learning.…

the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction.

As AI takes over prediction, humans will do less of the combined prediction-judgment routine of decision making and focus more on the judgment role alone.

Having better prediction raises the value of judgment. After all, it doesn’t help to know the likelihood of rain if you don’t know how much you like staying dry or how much you hate carrying an umbrella. Prediction machines don’t provide judgment. Only humans do, because only humans can express the relative rewards from taking different actions. As AI takes over prediction, humans will do less of the combined prediction-judgment routine of decision making and focus more on the judgment role alone.

Consciousness, unprovable by scientific standards, is forever, then, the impossible phantom in the predictable biologic machine, and your every thought a genuine supernatural event. Your every thought is a ghost, dancing.

What will new AI technologies make so cheap? Prediction. Therefore, as economics tells us, not only are we going to start using a lot more prediction, but we are going to see it emerge in surprising new places.

Before machine learning, multivariate regression provided an efficient way to condition on multiple things, without the need to calculate dozens, hundreds, or thousands of conditional averages. Regression takes the data and tries to find the result that minimizes prediction mistakes, maximizing what is called “goodness of fit.

Value versus Cost Economists tend to focus on cost, and, as economists, we are as guilty of that as anyone. The entire premise of our first book, Prediction Machines, was that AI advances were going to dramatically reduce the cost of prediction, leading to a scale-up of its use. However, while that book suggested that the initial uses of AI would be where prediction was already occurring, either explicitly in, say, forecasting sales or the weather, or implicitly in classifying photos and language, we were mindful that the real opportunity would be the new applications and uses that were enabled when prediction costs fell low enough.

During the shopping process, Amazon’s AI offers suggestions of items that it predicts you will want to buy. The AI does a reasonable job. However, it is far from perfect. In our case, the AI accurately predicts what we want to buy about 5 percent of the time. We actually purchase about one of every twenty items it recommends. Considering the millions of items on offer, that’s not bad!

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.

It is the ability to make predictions about the future that is the crux of intelligence.

His weakness in this game, and in life, is that he's never prepared for how others will act. They are predetermined but too complex to solve or predict, and there are rules that he is just no good at applying.

Once upon a time on Tralfamadore there were creatures who weren’t anything like machines. They weren’t dependable. They weren’t efficient. They weren’t predictable. They weren’t durable. And these poor creatures were obsessed by the idea that everything that existed had to have a purpose, and that some purposes were higher than others. These creatures spent most of their time trying to find out what their purpose was. And every time they found out what seemed to be a purpose of themselves, the purpose seemed so low that the creatures were filled with disgust and shame. And, rather than serve such a low purpose, the creatures would make a machine to serve it. This left the creatures free to serve higher purposes. But whenever they found a higher purpose, the purpose still wasn’t high enough. So machines were made to serve higher purposes, too. And the machines did everything so expertly that they were finally given the job of finding out what the highest purpose of the creatures could be. The machines reported in all honesty that the creatures couldn’t really be said to have any purpose at all. The creatures thereupon began slaying each other, because they hated purposeless things above all else. And they discovered that they weren’t even very good at slaying. So they turned that job over to the machines, too. And the machines finished up the job in less time than it takes to say, “Tralfamadore.

If there is one thing developmental psychologists have learned over the years, it is that parents don’t have to be brilliant psychologists to succeed. They don’t have to be supremely gifted teachers. Most of the stuff parents do with flashcards and special drills and tutorials to hone their kids into perfect achievement machines don’t have any effect at all. Instead, parents just have to be good enough. They have to provide their kids with stable and predictable rhythms. They need to be able to fall in tune with their kids’ needs, combining warmth and discipline. They need to establish the secure emotional bonds that kids can fall back upon in the face of stress. They need to be there to provide living examples of how to cope with the problems of the world so that their children can develop unconscious models in their heads.

All reality is a game. Physics at its most fundamental, the very fabric of our universe, results directly from the interaction of certain fairly simple rules, and chance; the same description may be applied to the best, most elefant and both intellectually and aesthetically satisfying games. By being unknowable, by resulting from events which, at the sub-atomic level, cannot be fully predicted, the future remains makkeable, and retains the possibility of change, the hope of coming to prevail; victory, to use an unfashionable word. In this, the future is a game; time is one of the rules. Generally, all the best mechanistic games - those which can be played in any sense "perfectly", such as a grid, Prallian scope, 'nkraytle, chess, Farnic dimensions - can be traced to civilisations lacking a realistic view of the universe (let alone the reality). They are also, I might add, invariably pre-machine-sentience societies. The very first-rank games acknowledge the element of chance, even if they rightly restrict raw luck. To attempt to construct a game on any other lines, no matter how complicated and subtle the rules are, and regardless of the scale and differentiation of the playing volume and the variety of the powers and attibutes of the pieces, is inevitably to schackle oneself to a conspectus which is not merely socially but techno-philosophically lagging several ages behind our own. As a historical exercise it might have some value, As a work of the intellect, it's just a waste of time. If you want to make something old-fashioned, why not build a wooden sailing boat, or a steam engine? They're just as complicated and demanding as a mechanistic game, and you'll keep fit at the same time.

Part of why predicting the ending to our AI [artificial intelligence] story is so difficult is because this isn’t just a story about machines. It’s also a story about human beings, people with free wills that allows them to make their own choices and to shape their own destinies. Our AI future will be created by us, and it will reflect the choices we make and the actions we take.

You may think novelists always have fixed plans to which they work, so that the future predicted by Chapter One is always inexorably the actuality of Chapter Thirteen. But novelists write for countless different reasons: for money, for fame, for reviewers, for parents, for friends, for loved ones; for vanity, for pride, for curiosity, for amusement: as skilled furniture makers enjoy making furniture, as drunkards like drinking, as judges like judging, as Sicilians like emptying a shotgun into an enemy's back. I could fill a book with reasons, and they would all be true, though not true of all. Only one same reason is shared by all of us: we wish to create worlds as real as, but other than the world that is. Or was. This is why we cannot plan. We know a world is an organism, not a machine. We also know that a genuinely created world must be independent of its creator; a planned world (a world that fully reveals its planning) is a dead world. It is only when our characters and events begin to disobey us that they begin to live.

These late eclipses in the sun and moon portend no good to us: though the wisdom of nature can reason it thus and thus, yet nature finds itself scourged by the sequent effects: love cools, friendship falls off, brothers divide: in cities, mutinies; in countries, discord; in palaces, treason; and the bond cracked 'twixt son and father. This villain of mine comes under the prediction; there's son against father: the king falls from bias of nature; there's father against child. We have seen the best of our time: machinations, hollowness, treachery, and all ruinous disorders, follow us disquietly to our graves. Find out this villain, Edmund; it shall lose thee nothing; do it carefully. And the noble and true-hearted Kent banished! his offence, honesty! 'Tis strange.

Our brains are prediction machines optimized by experience,

It was a sombre snowy afternoon, and the gas-lamps were lit in the big reverberating station. As he paced the platform, waiting for the Washington express, he remembered that there were people who thought there would one day be a tunnel under the Hudson through which the trains of the Pennsylvania railway would run straight into New York. They were of the brotherhood of visionaries who likewise predicted the building of ships that would cross the Atlantic in five days, the invention of a flying machine, lighting by electricity, telephonic communication without wires, and other Arabian Nights marvels.

Prediction in a complex world is a chancy business. Every decision that a survival machine takes is a gamble, and it is the business of genes to program brains in advance so that on average they take decisions that pay off. The currency used in the casino of evolution is survival, strictly gene survival, but for many purposes individual survival is a reasonable approximation.

As the political prediction machine Nate Silver of 538.com tweeted in 2012, “If a place has sidewalks, it votes Democratic.

A good question is not concerned with a correct answer. A good question cannot be answered immediately. A good question challenges existing answers. A good question is one you badly want answered once you hear it, but had no inkling you cared before it was asked. A good question creates new territory of thinking. A good question reframes its own answers. A good question is the seed of innovation in science, technology, art, politics, and business. A good question is a probe, a what-if scenario. A good question skirts on the edge of what is known and not known, neither silly nor obvious. A good question cannot be predicted. A good question will be the sign of an educated mind. A good question is one that generates many other good questions. A good question may be the last job a machine will learn to do. A good question is what humans are for.  •

Just as electricity enabled decoupling the machine from the power source and thus facilitated shifting the value proposition from “lower fuel costs” to “vastly more productive factory design,” AI enables decoupling prediction from the other aspects of a decision and thus facilitates shifting the value proposition from “lower cost of prediction” to “vastly more productive systems.

If science could comprehend all phenomena so that eventually in a thoroughly rational society human beings became as predictable as cogs in a machine, then man, driven by this need to know and assert his freedom, would rise up and smash the machine. What the reformers of the Enlightenment, dreaming of a perfect organization of society, had overlooked, Dostoevski saw all too plainly with the novelist's eye: namely, that as modern society becomes more organized and hence more bureaucratized it piles up at its joints petty figures like that of the Underground Man, who beneath their nondescript surface are monsters of frustration and resentment.

Technology should be used to create a learning space—a breathable space that nurtures possibilities rather than merely fulfilling predictions.

Deep within our brains, as in theirs, our shadowy unconscious mind is continuously applying the lessons of our past experience to predict the consequences of our current circumstances. In fact, one way to characterize a brain is as a prediction machine.

If you wanted to predict how people would behave, Munger said, you only had to look at their incentives. FedEx couldn’t get its night shift to finish on time; they tried everything to speed it up but nothing worked—until they stopped paying night shift workers by the hour and started to pay them by the shift. Xerox created a new, better machine only to have it sell less well than the inferior older ones—until they figured out the salesmen got a bigger commission for selling the older one. “Well, you can say, ‘Everybody knows that,’ ” said Munger. “I think I’ve been in the top five percent of my age cohort all my life in understanding the power of incentives, and all my life I’ve underestimated it. And never a year passes but I get some surprise that pushes my limit a little

Pope Benedict XVI was the first to predict the crisis in the global financial system…Italian Finance Minister Giulio Tremonti said. “The prediction that an undisciplined economy would collapse by its own rules can be found” in an article written by Cardinal Joseph Ratzinger [in 1985], Tremonti said yesterday at Milan’s Cattolica University. —Bloomberg News, November 20, 2008

If science could comprehend all phenomena so that eventually in a thoroughly rational society human beings became as predictable as cogs in a machine, then man, driven by this need to know and assert his freedom, would rise up and smash the machine.

If you want one year of prosperity, grow grain. If you want ten years of prosperity, grow trees. If you want one hundred years of prosperity, grow people.

Indeed, the embeddings, simple as they are—just a row of numbers for each word, based on predicting nearby missing words in a text—seemed to capture a staggering amount of real-world information.

Arrange these threats in ascending order of deadliness: wolves, vending machines, cows, domestic dogs and toothpicks. I will save you the trouble: they have been ordered already. The number of deaths known to have been caused by wolves in North America in the twenty-first century is one: if averaged out, that would be 0.08 per year. The average number of people killed in the US by vending machines is 2.2 (people sometimes rock them to try to extract their drinks, with predictable results). Cows kill some twenty people in the US, dogs thirty-one. Over the past century, swallowing toothpicks caused the deaths of around 170 Americans a year. Though there are sixty thousand wolves in North America, the risk of being killed by one is almost nonexistent.

Without our flaws we would be like a well-oiled machine, and our actions and thoughts could be predicted through simulation, if we only had sufficient processing power. That will never happen. Our flaws are a variable outside the scope of such a calculation, and they drive us to great achievements or to utterly despicable deeds.

Fear of this uncertainty motivates people to spin their wheels for days considering all the possible outcomes, calculating them in a spreadsheet using utility cost analysis or some other fancy method that even the guy who invented it doesn't use. But all that analysis just keeps you on the sidelines. Often you're better off flipping a coin and moving in any clear direction. Once you start moving, you get new data regardless of where you're trying to go. And the new data makes the next decision and the next better than staying on the sidelines desperately trying to predict the future without that time machine.

I guess it's always uncomfortable to discover you're not as individual as you thought. But it really bothered me. From one perspective, I was an independent animal, exercising free will in order to elicit predictable reactions from an inert vending machine. But from another, the vending machine was choosing to withhold snacks in order to extract predictable, mechanical reactions from young men. I couldn't figure out any objective reason to consider one scenario more likely than the other.

Why do such bad questions get predictably asked? Maybe part of the problem is that we have learned to ask the wrong questions of ourselves. Our culture is steeped in a kind of pop psychology whose obsessive question is: Are you happy? We ask it so reflexively that i seems natural to wish that a pharmacist with a time machine could deliver a lifetime supply of antidepressants to Bloomsbury, so that an incomparable feminist prose stylist could be reoriented to produce litters of Woolf babies.

The military mind has one aim, and that is to make soldiers react as mechanically as possible. They want the same predictability in a man as they do in a telephone or a machine gun, and they train their soldiers to act as a unit, not as individuals.

The great masses, who have never been, in the history of mankind, more subject to hypnotic suggestion than they are right now, have become the puppets of the "public opinion" that is engineered by the newspapers in the service, it need hardly be emphasized, of the reigning powers of finance. What is printed in the morning editions of the big city newspapers is the opinion of nine out of ten readers by nightfall. The United States of America, whose more rapid "progress" enables us to predict the future on a daily basis, has pulled far ahead of the pack when it comes to standardizing thought, work, entertainment, etc. Thus, the United States in 1917 went to war against Germany in sincere indignation because the newspapers had told them that Prussian "militarism" was rioting in devilish atrocities as it attempted to conquer the world. Of course, these transparent lies were published in the daily rags because the ruling lords of Mammon knew that American intervention in Europe would fatten their coffers. Thus, whereas the Americans thought that they were fighting for such high-minded slogans as "liberty" and "justice," they were actually fighting to stuff the money bags of the big bankers. These "free citizens" are, in fact, mere marionettes; their freedom is imaginary, and a brief glance at American work-methods and leisure-time entertainments is enough to prove conclusively that l’homme machine is not merely imminent: it is already the American reality.

LeCun made an unexpected prediction about the effects all of this AI and machine learning technology would have on the job market. Despite being a technologist himself, he said that the people with the best chances of coming out ahead in the economy of the future were not programmers and data scientists, but artists and artisans.

Whatever input a brain region cannot explain is therefore passed on to the next level, which then attempts to make sense of it. We may conceive of the cortex as a massive hierarchy of predictive systems, each of which tries to explain the inputs and exchanges the remaining error messages with the others, in the hope that they may do a better job.

Do you have a sales process? If you don’t—get one. ANYTHING is better than no process.

Without further adieu, this is the best question ever to use to open calls: “Did I catch you at a bad time?” Conversationally, it might

Noise in machine learning just means errors in the data, or random events that you can’t predict.

Lack of money is a common excuse for not being creative.

The future is so hard to predict. If I had a time machine, would it even make a difference to try to go back there and explain to my young self what was ahead?

Shifting from organic growth to proactive growth requires new habits, practices and systems, causing a lot of delays and frustrations.

I never make stupid mistakes. Only very, very clever ones.

There is ALWAYS a way to move forward, even without money.

Nothing so conclusively proves a man's ability to lead others as what he does from day to day to lead himself. ~Thomas J. Watson

Killer Salespeople Uncover True Problems Behind Desired Solutions

Customers don't care at all whether you close the deal or not. They care about improving their business. It’s easy to forget this in the heat of a sales cycle.

On successively higher levels of the hierarchy we find more complex, flexible and less predictable patterns of activity, while on successively lower levels we find more and more mechanised, stereotyped and predictable patterns. In the language of the physicist, a holon on a higher level of the hierarchy has more degrees of freedom than a holon on a lower level.

Thanks to this predictive learning mechanism, arbitrary signals can become the bearers of reward and trigger a dopamine response. This secondary reward effect has been demonstrated with money in humans and with the mere sight of a syringe in drug addicts. In both cases, the brain anticipates future rewards. As we saw in the first chapter, such a predictive signal is extremely useful for learning, because it allows the system to criticize itself and to foresee the success or failure of an action without having to wait for external confirmation.

The brain is an organ that builds models and makes creative predictions, but its models and predictions can as easily be specious as valid. Our brains are always looking at patterns and making analogies. If correct correlations cannot be found, the brain is more than happy to accept false ones. Pseudoscience, bigotry, faith, and intolerance are often rooted in false analogy.

Looking down from the heavens, she saw how small, and yet how important each human life is. Drops in the bucket of eternity. She saw her minute place in the organic machine of the Cosmos, witnessed the give and take and the slow, steady swinging of life's pendulum. The world relies on order, pattern, and repetition. The earth spins and swings around the sun with rational, mathematical predictability. But she also saw the chaotic nature of things. No matter what, you can never know with certainty what will happen. Lightening can strike, the ground can open up and swallow you, and the very air you breathe can tear your life away.

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.

Day by day, however, the machines are gaining ground upon us; day by day we are becoming more subservient to them; more men are daily bound down as slaves to tend them, more men are daily devoting the energies of their whole lives to the development of mechanical life. The upshot is simply a question of time, but that the time will come when the machines will hold the real supremacy over the world and its inhabitants is what no person of a truly philosophic mind can for a moment question.

Though it accounts for only 2 percent of the body’s mass, it uses up a fifth of all the oxygen we breathe, and it’s where a quarter of all our glucose gets burned. The brain is the most energetically expensive piece of equipment in our body, and has been ruthlessly honed by natural selection to be efficient at the tasks for which it evolved. One might say that the whole point of our nervous system, from the sensory organs that feed information to the glob of neurons that interprets it, is to develop a sense of what is happening in the present and what will happen in the future, so that we can respond in the best possible way. Strip away the emotions, the philosophizing, the neuroses, and the dreams, and our brains, in the most reductive sense, are fundamentally prediction and planning machines.

As soon as an Analytical Engine exists, it will necessarily guide the future course of the science. Whenever any result is sought by its aid, the question will then arise — by what course of calculation can these results be arrived at by the machine in the shortest time?

There are three phrases that make possible the world of writing about the world of not-yet (you can call it science fiction or speculative fiction; you can call it anything you wish) and they are simple phrases: What if . . . ? If only . . . If this goes on . . . “What if . . . ?” gives us change, a departure from our lives. (What if aliens landed tomorrow and gave us everything we wanted, but at a price?) “If only . . .” lets us explore the glories and dangers of tomorrow. (If only dogs could talk. If only I were invisible.) “If this goes on . . .” is the most predictive of the three, although it doesn’t try to predict an actual future with all its messy confusion. Instead, “If this goes on . . .” fiction takes an element of life today, something clear and obvious and normally something troubling, and asks what would happen if that thing, that one thing, became bigger, became all-pervasive, changed the way we thought and behaved. (If this goes on, all communication everywhere will be through text messages or computers, and direct speech between two people, without a machine, will be outlawed.)

We needed a man to repair the machines, to keep them going and everything. And the army was always going to send this fellow they had, but he was always delayed. Now, we always were in a hurry. Everything we did, we tried to do as quickly as possible. In this particular case, we worked out all the numerical steps that the machines were supposed to do—multiply this, and then do this, and subtract that. Then we worked out the program, but we didn’t have any machine to test it on. So we set up this room with girls in it. Each one had a Marchant: one was the multiplier, another was the adder. This one cubed—all she did was cube a number on an index card and send it to the next girl. We went through our cycle this way until we got all the bugs out. It turned out that the speed at which we were able to do it was a hell of a lot faster than the other way, where every single person did all the steps. We got speed with this system that was the predicted speed for the IBM machine. The only difference is that the IBM machines didn’t get tired and could work three shifts. But the girls got tired after a while.

At various times in the past, technological optimists have predicted that textile workers would benefit from factory automation, that women would be emancipated by washing machines and vacuum cleaners, and that racial discrimination would vanish in the age of computers. If only.

But all predictive models share the same objective: They consider the various factors of an individual in order to derive a single predictive score for that individual. This score is then used to drive an organizational decision, guiding which action to take. Before using a model, we’ve got to build it. Machine learning builds the predictive model:

Three Keys To Predictable Revenue Building a Sales Machine that creates ongoing, predictable revenue takes: Predictable Lead Generation, the most important thing for creating predictable revenue. A Sales Development Team that bridges the chasm between marketing and sales. Consistent Sales Systems, because without consistency you have no predictability.

As a thought experiment, von Neumann's analysis was simplicity itself. He was saying that the genetic material of any self-reproducing system, whether natural or artificial, must function very much like a stored program in a computer: on the one hand, it had to serve as live, executable machine code, a kind of algorithm that could be carried out to guide the construction of the system's offspring; on the other hand, it had to serve as passive data, a description that could be duplicated and passed along to the offspring. As a scientific prediction, that same analysis was breathtaking: in 1953, when James Watson and Francis Crick finally determined the molecular structure of DNA, it would fulfill von Neumann's two requirements exactly. As a genetic program, DNA encodes the instructions for making all the enzymes and structural proteins that the cell needs in order to function. And as a repository of genetic data, the DNA double helix unwinds and makes a copy of itself every time the cell divides in two. Nature thus built the dual role of the genetic material into the structure of the DNA molecule itself.

I returned slowly through the mists of winter. Time lay more thickly about me than the mists. I was so unused to moving through time that I felt like a man walking under water. Time exerted great pressure on my blood vessels and my eardrums, so that I suffered from terrible headaches, weakness and nausea. Time clogged the hooves of my mare until she lay down beneath me and died. Nebulous Time was now time past; I crawled like a worm on its belly through the clinging mud of common time and the bare trees showed only the dreary shapes of an eternal November of the heart, for now all changes would henceforth be, as they had been before, absolutely predictable. And so I identified at last the flavour of my daily bread; it was and would be that of regret.

How do we learn? Is there a better way? What can we predict? Can we trust what we’ve learned? Rival schools of thought within machine learning have very different answers to these questions. The main ones are five in number, and we’ll devote a chapter to each. Symbolists view learning as the inverse of deduction and take ideas from philosophy, psychology, and logic. Connectionists reverse engineer the brain and are inspired by neuroscience and physics. Evolutionaries simulate evolution on the computer and draw on genetics and evolutionary biology. Bayesians believe learning is a form of probabilistic inference and have their roots in statistics. Analogizers learn by extrapolating from similarity judgments and are influenced by psychology and mathematical optimization.

Around this time, a young man named Samuel Slater slipped through the tight protective net thrown by British authorities around their textile business. As a former apprentice to Sir Richard Arkwright, Slater had sworn that he would never reveal his boss’s trade secrets. Flouting this pledge, he sailed to New York and made contact with Moses Brown, a Rhode Island Quaker. Under Slater’s supervision, Brown financed a spinning mill in Rhode Island that replicated Arkwright’s mill. Hamilton received detailed reports of this triumph, and pretty soon milldams proliferated on New England’s rivers. With patriotic pride, Brown predicted to Hamilton that “mills and machines may be erected in different places, in one year, to make all the cotton yarn that may be wanted in the United States.” 29 Hamilton

What works to generate flows of new leads: Trial-and-error in lead generation (requires patience, experimentation, money). “Marketing through teaching” via regular webinars, white papers, email newsletters and live events, to establish yourself as the trusted expert in your space (takes lots of time to build predictable momentum). Patience in building great word-of-mouth (the highest value lead generation source, but hardest to influence). Cold Calling 2.0: By far the most predictable and controllable source of creating new pipeline, but it takes focus and expertise to do it well. Luckily, you are holding the guide to the process in your hands right now. Building an excited partner ecosystem (very high value, very long time-to-results). PR: It’s great when, once in awhile, it generates actual results!

New York City manages expertly, and with marvelous predictability, whatever it considers humanly important. Fax machines, computers, automated telephones and even messengers on bikes convey a million bits of data through Manhattan every day to guarantee that Wall Street brokers get their orders placed, confirmed, delivered, at the moment they demand. But leaking roofs cannot be fixed and books cannot be gotten into Morris High in time to meet the fall enrollment. Efficiency in educational provision for low-income children, as in health care and most other elementals of existence, is secreted and doled out by our municipalities as if it were a scarce resource. Like kindness, cleanliness and promptness of provision, it is not secured by gravity of need but by the cash, skin color and class status of the applicant.

There are many buzzwords that gloss over these operations and their economic origins: “ambient computing,” “ubiquitous computing,” and the “internet of things” are but a few examples. For now I will refer to this whole complex more generally as the “apparatus.” Although the labels differ, they share a consistent vision: the everywhere, always-on instrumentation, datafication, connection, communication, and computation of all things, animate and inanimate, and all processes—natural, human, physiological, chemical, machine, administrative, vehicular, financial. Real-world activity is continuously rendered from phones, cars, streets, homes, shops, bodies, trees, buildings, airports, and cities back to the digital realm, where it finds new life as data ready for transformation into predictions, all of it filling the ever-expanding pages of the shadow text.4

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.

The lesson of figure 5 is that predictable illusions inevitably occur if a judgment is based on an impression of cognitive ease or strain. Anything that makes it easier for the associative machine to run smoothly will also bias beliefs. A reliable way to make people believe in falsehoods is frequent repetition, because familiarity is not easily distinguished from truth. Authoritarian institutions and marketers have always known this fact.

Ray Kurzweil—the eccentric inventor, futurist, and guru-in-residence at Google—envisions a radical future in which humans and machines have fully merged. We will upload our minds to the cloud, he predicts, and constantly renew our bodies through intelligent nanobots released into our bloodstream. Kurzweil predicts that by 2029 we will have computers with intelligence comparable to that of humans (i.e., AGI), and that we will reach the singularity by 2045.

The PEOPLE are the grand inquest who have a RIGHT to judge of its merits. The hideous daemon of Aristocracy has hitherto had so much influence as to bar the channels of investigation, preclude the people from inquiry and extinguish every spark of liberal information of its qualities. At length the luminary of intelligence begins to beam its effulgent rays upon this important production; the deceptive mists cast before the eyes of the people by the delusive machinations of its INTERESTED advocates begins to dissipate, as darkness flies before the burning taper; and I dare venture to predict, that in spite of those mercenary dectaimers, the plan will have a candid and complete examination. Those furious zealots who are for cramming it down the throats of the people, without allowing them either time or opportunity to scan or weigh it in the balance of their understandings, bear the same marks in their features as those who have been long wishing to erect an aristocracy in THIS COMMONWEALTH [of Massachusetts].

A good question is like the one Albert Einstein asked himself as a small boy—“What would you see if you were traveling on a beam of light?” That question launched the theory of relativity, E=MC2, and the atomic age. A good question is not concerned with a correct answer. A good question cannot be answered immediately. A good question challenges existing answers. A good question is one you badly want answered once you hear it, but had no inkling you cared before it was asked. A good question creates new territory of thinking. A good question reframes its own answers. A good question is the seed of innovation in science, technology, art, politics, and business. A good question is a probe, a what-if scenario. A good question skirts on the edge of what is known and not known, neither silly nor obvious. A good question cannot be predicted. A good question will be the sign of an educated mind. A good question is one that generates many other good questions. A good question may be the last job a machine will learn to do. A good question is what humans are for.

In the early 1980s, managers at the National Aeronautics and Space Administration (NASA) estimated that the flights would be 99.999 percent reliable, which represents a failure rate of only 1 in 100,000. According to the physicist Richard Feynman, who was a member of the commission that investigated the January 1986 Challenger accident, in which the shuttle broke apart shortly into its flight, killing all seven astronauts on board, this “would imply that one could put a Shuttle up each day for 300 years expecting to lose only one.” He wondered, “What is the cause of management’s fantastic faith in the machinery?” Engineers, who were more familiar with the shuttle itself and with machines in general, predicted only a 99 percent success rate, or a failure every 100 launches. A range safety officer, who personally observed test firings during the developmental phase of the rocket motors, expected a failure rate of 1 in 25. The Challenger accident proved that estimate to be the actual failure rate, giving a success rate of 96 percent after exactly 25 launchings.

You were burning in the middle of the worst solar storm our records can remember. (...) Everyone else fled. All your companions and crew left you alone to wrestle with the storm. “You did not blame them. In a moment of crystal insight, you realized that they were cowards beyond mere cowardice: their dependence on their immortality circuits had made it so that they could not even imagine risking their lives. They were all alike in this respect. They did not know they were not brave; they could not even think of dying as possible; how could they think of facing it, unflinching? “You did not flinch. You knew you were going to die; you knew it when the Sophotechs, who are immune to pain and fear, all screamed and failed and vanished. “And you knew, in that moment of approaching death, with all your life laid out like a single image for you to examine in a frozen moment of time, that no one was immortal, not ultimately, not really. The day may be far away, it may be further away than the dying of the sun, or the extinction of the stars, but the day will come when all our noumenal systems fail, our brilliant machines all pass away, and our records of ourselves and memories shall be lost. “If all life is finite, only the grace and virtue with which it is lived matters, not the length. So you decided to stay another moment, and erect magnetic shields, one by one; to discharge interruption masses into the current, to break up the reinforcement patterns in the storm. Not life but honor mattered to you, Helion: so you stayed a moment after that moment, and then another. (...) “You saw the plasma erupting through shield after shield (...) Chaos was attempting to destroy your life’s work, and major sections of the Solar Array were evaporated. Chaos was attempting to destroy your son’s lifework, and since he was aboard that ship, outside the range of any noumenal circuit, it would have destroyed your son as well. “The Array was safe, but you stayed another moment, to try to deflect the stream of particles and shield your son; circuit after circuit failed, and still you stayed, playing the emergency like a raging orchestra. “When the peak of the storm was passed, it was too late for you: you had stayed too long; the flames were coming. But the radio-static cleared long enough for you to have last words with your son, whom you discovered, to your surprise, you loved better than life itself. In your mind, he was the living image of the best thing in you, the ideal you always wanted to achieve. “ ‘Chaos has killed me, son,’ you said. ‘But the victory of unpredictability is hollow. Men imagine, in their pride, that they can predict life’s each event, and govern nature and govern each other with rules of unyielding iron. Not so. There will always be men like you, my son, who will do the things no one else predicts or can control. I tried to tame the sun and failed; no one knows what is at its fiery heart; but you will tame a thousand suns, and spread mankind so wide in space that no one single chance, no flux of chaos, no unexpected misfortune, will ever have power enough to harm us all. For men to be civilized, they must be unlike each other, so that when chaos comes to claim them, no two will use what strategy the other does, and thus, even in the middle of blind chaos, some men, by sheer blind chance, if nothing else, will conquer. “ ‘The way to conquer the chaos which underlies all the illusionary stable things in life, is to be so free, and tolerant, and so much in love with liberty, that chaos itself becomes our ally; we shall become what no one can foresee; and courage and inventiveness will be the names we call our fearless unpredictability…’ “And you vowed to support Phaethon’s effort, and you died in order that his dream might live.

Computers were built in the late 1940s because mathematicians like John von Neumann thought that if you had a computer—a machine to handle a lot of variables simultaneously—you would be able to predict the weather. Weather would finally fall to human understanding. And men believed that dream for the next forty years. They believed that prediction was just a function of keeping track of things. If you knew enough, you could predict anything. That’s been a cherished scientific belief since Newton.” “And?” “Chaos theory throws it right out the window. It says that you can never predict certain phenomena at all. You can never predict the weather more than a few days away. All the money that has been spent on long-range forecasting—about half a billion dollars in the last few decades—is money wasted. It’s a fool’s errand. It’s as pointless as trying to turn lead into gold. We look back at the alchemists and laugh at what they were trying to do, but future generations will laugh at us the same way. We’ve tried the impossible—and spent a lot of money doing it. Because in fact there are great categories of phenomena that are inherently unpredictable.

that rotten feeling of antlike industry. There is really no need to belabor the point, since it is obvious to most of us these days that mathematics has taken possession, like a demon, of every aspect of our lives. Most of us may not believe in the story of a Devil to whom one can sell one’s soul, but those who must know something about the soul (considering that as clergymen, historians, and artists they draw a good income from it) all testify that the soul has been destroyed by mathematics and that mathematics is the source of an evil intelligence that while making man the lord of the earth has also made him the slave of his machines. The inner drought, the dreadful blend of acuity in matters of detail and indifference toward the whole, man’s monstrous abandonment in a desert of details, his restlessness, malice, unsurpassed callousness, money-grubbing, coldness, and violence, all so characteristic of our times, are by these accounts solely the consequence of damage done to the soul by keen logical thinking! Even back when Ulrich first turned to mathematics there were already those who predicted the collapse of European civilization because no human faith, no love, no simplicity, no goodness, dwelt any longer in man.

Sheepwalking I define “sheepwalking” as the outcome of hiring people who have been raised to be obedient and giving them a brain-dead job and enough fear to keep them in line. You’ve probably encountered someone who is sheepwalking. The TSA “screener” who forces a mom to drink from a bottle of breast milk because any other action is not in the manual. A “customer service” rep who will happily reread a company policy six or seven times but never stop to actually consider what the policy means. A marketing executive who buys millions of dollars’ worth of TV time even though she knows it’s not working—she does it because her boss told her to. It’s ironic but not surprising that in our age of increased reliance on new ideas, rapid change, and innovation, sheepwalking is actually on the rise. That’s because we can no longer rely on machines to do the brain-dead stuff. We’ve mechanized what we could mechanize. What’s left is to cost-reduce the manual labor that must be done by a human. So we write manuals and race to the bottom in our search for the cheapest possible labor. And it’s not surprising that when we go to hire that labor, we search for people who have already been trained to be sheepish. Training a student to be sheepish is a lot easier than the alternative. Teaching to the test, ensuring compliant behavior, and using fear as a motivator are the easiest and fastest ways to get a kid through school. So why does it surprise us that we graduate so many sheep? And graduate school? Since the stakes are higher (opportunity cost, tuition, and the job market), students fall back on what they’ve been taught. To be sheep. Well-educated, of course, but compliant nonetheless. And many organizations go out of their way to hire people that color inside the lines, that demonstrate consistency and compliance. And then they give these people jobs where they are managed via fear. Which leads to sheepwalking. (“I might get fired!”) The fault doesn’t lie with the employee, at least not at first. And of course, the pain is often shouldered by both the employee and the customer. Is it less efficient to pursue the alternative? What happens when you build an organization like W. L. Gore and Associates (makers of Gore-Tex) or the Acumen Fund? At first, it seems crazy. There’s too much overhead, there are too many cats to herd, there is too little predictability, and there is way too much noise. Then, over and over, we see something happen. When you hire amazing people and give them freedom, they do amazing stuff. And the sheepwalkers and their bosses just watch and shake their heads, certain that this is just an exception, and that it is way too risky for their industry or their customer base. I was at a Google conference last month, and I spent some time in a room filled with (pretty newly minted) Google sales reps. I talked to a few of them for a while about the state of the industry. And it broke my heart to discover that they were sheepwalking. Just like the receptionist at a company I visited a week later. She acknowledged that the front office is very slow, and that she just sits there, reading romance novels and waiting. And she’s been doing it for two years. Just like the MBA student I met yesterday who is taking a job at a major packaged-goods company…because they offered her a great salary and promised her a well-known brand. She’s going to stay “for just ten years, then have a baby and leave and start my own gig.…” She’ll get really good at running coupons in the Sunday paper, but not particularly good at solving new problems. What a waste. Step one is to give the problem a name. Done. Step two is for anyone who sees themselves in this mirror to realize that you can always stop. You can always claim the career you deserve merely by refusing to walk down the same path as everyone else just because everyone else is already doing it.

Science is a time machine, and it goes both ways. We are able to predict our future with increasing certainty. Our ability to act in response to these predictions will ultimately determine our fate. Science and reason make the darkness visible. I worry that lack of investment in science and a retreat from reason may prevent us from seeing further, or delay our reaction to what we see, making a meaningful response impossible. There are no simple fixes. Our civilisation is complex, our global political system is inadequate, our internal differences of opinion are deep-seated. I’d bet you think you’re absolutely right about some things and virtually everyone else is an idiot. Climate Change? Europe? God? America? The Monarchy? Same-sex Marriage? Abortion? Big Business? Nationalism? The United Nations? The Bank Bailout? Tax Rates? Genetically Modified Crops? Eating Meat? Football? X Factor or Strictly? The way forward is to understand and accept that there are many opinions, but only one human civilisation, only one Nature, and only one science. The collective goal of ensuring that there is never less than one human civilisation must surely override our personal prejudices. At least we have come far enough in 40,800 years to be able to state the obvious, and this is a necessary first step.

a simple, inspiring mission for Wikipedia: “Imagine a world in which every single person on the planet is given free access to the sum of all human knowledge. That’s what we’re doing.” It was a huge, audacious, and worthy goal. But it badly understated what Wikipedia did. It was about more than people being “given” free access to knowledge; it was also about empowering them, in a way not seen before in history, to be part of the process of creating and distributing knowledge. Wales came to realize that. “Wikipedia allows people not merely to access other people’s knowledge but to share their own,” he said. “When you help build something, you own it, you’re vested in it. That’s far more rewarding than having it handed down to you.”111 Wikipedia took the world another step closer to the vision propounded by Vannevar Bush in his 1945 essay, “As We May Think,” which predicted, “Wholly new forms of encyclopedias will appear, ready made with a mesh of associative trails running through them, ready to be dropped into the memex and there amplified.” It also harkened back to Ada Lovelace, who asserted that machines would be able to do almost anything, except think on their own. Wikipedia was not about building a machine that could think on its own. It was instead a dazzling example of human-machine symbiosis, the wisdom of humans and the processing power of computers being woven together like a tapestry.

Supervised learning algorithms typically require stationary features. The reason is that we need to map a previously unseen (unlabeled) observation to a collection of labeled examples, and infer from them the label of that new observation. If the features are not stationary, we cannot map the new observation to a large number of known examples. But stationary does not ensure predictive power. Stationarity is a necessary, non-sufficient condition for the high performance of an ML algorithm. The problem is, there is a trade-off between stationarity and memory. We can always make a series more stationary through differentiation, but it will be at the cost of erasing some memory, which will defeat the forecasting purpose of the ML algorithm.

Meanwhile, in Europe, the Renaissance continued, and I began to see the full scope of the Second Insight. The power of the church to define reality was diminishing, and Europeans were feeling as though they were awakening to look at life anew. Through the courage of countless individuals, all inspired by their intuitive memories, the scientific method was embraced as a democratic process of exploring and coming to understand the world in which humans found themselves. This method—exploring some aspect of the natural world, drawing conclusions, then offering this view to others—was thought of as the consensus-building process through which we would be able, finally, to understand mankind’s real situation on this planet, including our spiritual nature. But those in the church, entrenched in Fear, sought to squelch this new science. As political forces lined up on both sides, a compromise was reached. Science would be free to explore the outer, material world, but must leave spiritual phenomena to the dictates of the still-influential churchmen. The entire inner world of experience—our higher perceptual states of beauty and love, intuitions, coincidences, interpersonal phenomena, even dreams—all were, at first, off limits to the new science. Despite these restrictions, science began to map out and describe the operation of the physical world, providing information rich in ways to increase trade and utilize natural resources. Human economic security increased, and slowly we began to lose our sense of mystery and our heartfelt questions about the purpose of life. We decided it was purposeful enough just to survive and build a better, more secure world for ourselves and our children. Gradually we entered the consensus trance that denied the reality of death and created the illusion that the world was explained and ordinary and devoid of mystery. In spite of our rhetoric, our once-strong intuition of a spiritual source was being pushed farther into the background. In this growing materialism, God could only be viewed as a distant Deist’s God, a God who merely pushed the universe into being and then stood back to let it run in a mechanical sense, like a predictable machine, with every effect having a cause, and unconnected events happening only at random, by chance alone.

Yet our world of abundance, with seas of wine and alps of bread, has hardly turned out to be the ebullient place dreamt of by our ancestors in the famine-stricken years of the Middle Ages. The brightest minds spend their working lives simplifying or accelerating functions of unreasonable banality. Engineers write theses on the velocities of scanning machines and consultants devote their careers to implementing minor economies in the movements of shelf-stackers and forklift operators. The alcohol-inspired fights that break out in market towns on Saturday evenings are predictable symptoms of fury at our incarceration. They are a reminder of the price we pay for our daily submission at the altars of prudence and order - and of the rage that silently accumulates beneath a uniquely law-abiding and compliant surface.

A moth is such a simple machine in the animal world - the go-kart to the modern car - and it takes a lot of glitches to prevent it going. It's this intriguing simplicity, the idea that you could pull it into its constituent parts and put it back together in the same rainy day, that if you pulled back the skin, you could watch the inner workings, that makes a moth such an absorbing creature to study. Moths have a universal character: there are no individuals. Each reacts to a precise condition or stimulus in a predictable and replicable way. They are pre-programmed robots, unable to learn from experience. For instance, we know they will allways react to a smell, a pheromone or a particular spectrum of light in the same way. I can mimic the scent of a flower so that a moth will direct itself towards that scent ...

If you haven’t sent them an email yet, send an email as soon as you leave them the voicemail—give them more than one way to get back to you. Example 1: “Hi John, this is Aaron Ross from Salesforce.com. My number is 555-555-5555. John, I sent you an email a couple of days ago and hadn’t heard back, and I was hoping you could give me a quick courtesy response. I’ll resend it here in a minute. Again, Aaron Ross, 555-555-5555. Thank you and have a great day.” Example 2: “Hi John, this is Aaron Ross from Salesforce.com. My number is 555-555-5555. John, I’m calling to follow up on the email I sent you, I’d love to hear either way if you can please help me out or not. Again, Aaron Ross, 555-555-5555. Thank you and have a great day.” Example 3: (the mysterious version): “Hi John, this is Aaron Ross following up. My number is 555-555-5555. I’m free after 3pm today. Again, Aaron Ross, from Salesforce.com, 555-555-5555. Thanks and have a great day.

But what separates human consciousness from the consciousness of animals? Humans are alone in the animal kingdom in understanding the concept of tomorrow. Unlike animals, we constantly ask ourselves “What if?” weeks, months, and even years into the future, so I believe that Level III consciousness creates a model of its place in the world and then simulates it into the future, by making rough predictions. We can summarize this as follows: Human consciousness is a specific form of consciousness that creates a model of the world and then simulates it in time, by evaluating the past to simulate the future. This requires mediating and evaluating many feedback loops in order to make a decision to achieve a goal. By the time we reach Level III consciousness, there are so many feedback loops that we need a CEO to sift through them in order to simulate the future and make a final decision. Accordingly, our brains differ from those of other animals, especially in the expanded prefrontal cortex, located just behind the forehead, which allows us to “see” into the future. Dr. Daniel Gilbert, a Harvard psychologist, has written, “The greatest achievement of the human brain is its ability to imagine objects and episodes that do not exist in the realm of the real, and it is this ability that allows us to think about the future. As one philosopher noted, the human brain is an ‘anticipation machine,’ and ‘making the future’ is the most important thing it does.” Using brain scans, we can even propose a candidate for the precise area of the brain where simulation of the future takes place. Neurologist Michael Gazzaniga notes that “area 10 (the internal granular layer IV), in the lateral prefrontal cortex, is almost twice as large in humans as in apes. Area 10 is involved with memory and planning, cognitive flexibility, abstract thinking, initiating appropriate behavior, and inhibiting inappropriate behavior, learning rules, and picking out relevant information from what is perceived through the senses.” (For this book, we will refer to this area, in which decision making is concentrated, as the dorsolateral prefrontal cortex, although there is some overlap with other areas of the brain.)

A good question is worth a million good answers. A good question is like the one Albert Einstein asked himself as a small boy—“What would you see if you were traveling on a beam of light?” That question launched the theory of relativity, E=MC2, and the atomic age. A good question is not concerned with a correct answer. A good question cannot be answered immediately. A good question challenges existing answers. A good question is one you badly want answered once you hear it, but had no inkling you cared before it was asked. A good question creates new territory of thinking. A good question reframes its own answers. A good question is the seed of innovation in science, technology, art, politics, and business. A good question is a probe, a what-if scenario. A good question skirts on the edge of what is known and not known, neither silly nor obvious. A good question cannot be predicted. A good question will be the sign of an educated mind. A good question is one that generates many other good questions. A good question may be the last job a machine will learn to do. A good question is what humans are for.

KNEE SURGERY I’D FIRST HURT MY KNEES IN FALLUJAH WHEN THE WALL FELL on me. Cortisone shots helped for a while, but the pain kept coming back and getting worse. The docs told me I needed to have my legs operated on, but doing that would have meant I would have to take time off and miss the war. So I kept putting it off. I settled into a routine where I’d go to the doc, get a shot, go back to work. The time between shots became shorter and shorter. It got down to every two months, then every month. I made it through Ramadi, but just barely. My knees started locking and it was difficult to get down the stairs. I no longer had a choice, so, soon after I got home in 2007, I went under the knife. The surgeons cut my tendons to relieve pressure so my kneecaps would slide back over. They had to shave down my kneecaps because I had worn grooves in them. They injected synthetic cartilage material and shaved the meniscus. Somewhere along the way they also repaired an ACL. I was like a racing car, being repaired from the ground up. When they were done, they sent me to see Jason, a physical therapist who specializes in working with SEALs. He’d been a trainer for the Pittsburgh Pirates. After 9/11, he decided to devote himself to helping the country. He chose to do that by working with the military. He took a massive pay cut to help put us back together. I DIDN’T KNOW ALL THAT THE FIRST DAY WE MET. ALL I WANTED to hear was how long it was going to take to rehab. He gave me a pensive look. “This surgery—civilians need a year to get back,” he said finally. “Football players, they’re out eight months. SEALs—it’s hard to say. You hate being out of action and will punish yourselves to get back.” He finally predicted six months. I think we did it in five. But I thought I would surely die along the way. JASON PUT ME INTO A MACHINE THAT WOULD STRETCH MY knee. Every day I had to see how much further I could adjust it. I would sweat up a storm as it bent my knee. I finally got it to ninety degrees. “That’s outstanding,” he told me. “Now get more.” “More?” “More!” He also had a machine that sent a shock to my muscle through electrodes. Depending on the muscle, I would have to stretch and point my toes up and down. It doesn’t sound like much, but it is clearly a form of torture that should be outlawed by the Geneva Convention, even for use on SEALs. Naturally, Jason kept upping the voltage. But the worst of all was the simplest: the exercise. I had to do more, more, more. I remember calling Taya many times and telling her I was sure I was going to puke if not die before the day was out. She seemed sympathetic but, come to think of it in retrospect, she and Jason may have been in on it together. There was a stretch where Jason had me doing crazy amounts of ab exercises and other things to my core muscles. “Do you understand it’s my knees that were operated on?” I asked him one day when I thought I’d reached my limit. He just laughed. He had a scientific explanation about how everything in the body depends on strong core muscles, but I think he just liked kicking my ass around the gym. I swear I heard a bullwhip crack over my head any time I started to slack. I always thought the best shape I was ever in was straight out of BUD/S. But I was in far better shape after spending five months with him. Not only were my knees okay, the rest of me was in top condition. When I came back to my platoon, they all asked if I had been taking steroids.

The new prophets were men of a modest humane disposition: they brought life back to the village scale and the normal human dimensions; and out of this weakness they made a new kind of strength, not recognized in the palace or the marketplace. These meek, withdrawn, low-keyed, outwardly humble men appeared alone, or with a handful of equally humble followers, unarmed, unprotected. They did not look for institutional support: on the contrary, they dared to condemn and defy those in established positions, even predicting their downfall if they continued their established practices: "Mene, mene, tekel upharsin." "Thou art weighed in the balances and art found wanting." Even more intransigently than kings, the Axial prophets dared depart from customary usages and traditions, not only those of civilization, but the sexual cults, with their orgies and sacrifices that derived from neolithic practices. For them, nothing was sacred that did not lead to a higher life; and by higher they meant emancipated from both materialistic display and animal urgencies. Against the personified corporate power of kingship they stood for the precise opposite: the power of personality in each living soul.

July I watch eagerly a certain country graveyard that I pass in driving to and from my farm. It is time for a prairie birthday, and in one corner of this graveyard lives a surviving celebrant of that once important event. It is an ordinary graveyard, bordered by the usual spruces, and studded with the usual pink granite or white marble headstones, each with the usual Sunday bouquet of red or pink geraniums. It is extraordinary only in being triangular instead of square, and in harboring, within the sharp angle of its fence, a pin-point remnant of the native prairie on which the graveyard was established in the 1840’s. Heretofore unreachable by scythe or mower, this yard-square relic of original Wisconsin gives birth, each July, to a man-high stalk of compass plant or cutleaf Silphium, spangled with saucer-sized yellow blooms resembling sunflowers. It is the sole remnant of this plant along this highway, and perhaps the sole remnant in the western half of our county. What a thousand acres of Silphiums looked like when they tickled the bellies of the buffalo is a question never again to be answered, and perhaps not even asked. This year I found the Silphium in first bloom on 24 July, a week later than usual; during the last six years the average date was 15 July. When I passed the graveyard again on 3 August, the fence had been removed by a road crew, and the Silphium cut. It is easy now to predict the future; for a few years my Silphium will try in vain to rise above the mowing machine, and then it will die. With it will die the prairie epoch. The Highway Department says that 100,000 cars pass yearly over this route during the three summer months when the Silphium is in bloom. In them must ride at least 100,000 people who have ‘taken’ what is called history, and perhaps 25,000 who have ‘taken’ what is called botany. Yet I doubt whether a dozen have seen the Silphium, and of these hardly one will notice its demise. If I were to tell a preacher of the adjoining church that the road crew has been burning history books in his cemetery, under the guise of mowing weeds, he would be amazed and uncomprehending. How could a weed be a book? This is one little episode in the funeral of the native flora, which in turn is one episode in the funeral of the floras of the world. Mechanized man, oblivious of floras, is proud of his progress in cleaning up the landscape on which, willy-nilly, he must live out his days. It might be wise to prohibit at once all teaching of real botany and real history, lest some future citizen suffer qualms about the floristic price of his good life. * * *

The main ones are the symbolists, connectionists, evolutionaries, Bayesians, and analogizers. Each tribe has a set of core beliefs, and a particular problem that it cares most about. It has found a solution to that problem, based on ideas from its allied fields of science, and it has a master algorithm that embodies it. For symbolists, all intelligence can be reduced to manipulating symbols, in the same way that a mathematician solves equations by replacing expressions by other expressions. Symbolists understand that you can’t learn from scratch: you need some initial knowledge to go with the data. They’ve figured out how to incorporate preexisting knowledge into learning, and how to combine different pieces of knowledge on the fly in order to solve new problems. Their master algorithm is inverse deduction, which figures out what knowledge is missing in order to make a deduction go through, and then makes it as general as possible. For connectionists, learning is what the brain does, and so what we need to do is reverse engineer it. The brain learns by adjusting the strengths of connections between neurons, and the crucial problem is figuring out which connections are to blame for which errors and changing them accordingly. The connectionists’ master algorithm is backpropagation, which compares a system’s output with the desired one and then successively changes the connections in layer after layer of neurons so as to bring the output closer to what it should be. Evolutionaries believe that the mother of all learning is natural selection. If it made us, it can make anything, and all we need to do is simulate it on the computer. The key problem that evolutionaries solve is learning structure: not just adjusting parameters, like backpropagation does, but creating the brain that those adjustments can then fine-tune. The evolutionaries’ master algorithm is genetic programming, which mates and evolves computer programs in the same way that nature mates and evolves organisms. Bayesians are concerned above all with uncertainty. All learned knowledge is uncertain, and learning itself is a form of uncertain inference. The problem then becomes how to deal with noisy, incomplete, and even contradictory information without falling apart. The solution is probabilistic inference, and the master algorithm is Bayes’ theorem and its derivates. Bayes’ theorem tells us how to incorporate new evidence into our beliefs, and probabilistic inference algorithms do that as efficiently as possible. For analogizers, the key to learning is recognizing similarities between situations and thereby inferring other similarities. If two patients have similar symptoms, perhaps they have the same disease. The key problem is judging how similar two things are. The analogizers’ master algorithm is the support vector machine, which figures out which experiences to remember and how to combine them to make new predictions.

Gadgetry will continue to relieve mankind of tedious jobs. Kitchen units will be devised that will prepare ‘automeals,’ heating water and converting it to coffee; toasting bread; frying, poaching or scrambling eggs, grilling bacon, and so on. Breakfasts will be ‘ordered’ the night before to be ready by a specified hour the next morning. Communications will become sight-sound and you will see as well as hear the person you telephone. The screen can be used not only to see the people you call but also for studying documents and photographs and reading passages from books. Synchronous satellites, hovering in space will make it possible for you to direct-dial any spot on earth, including the weather stations in Antarctica. [M]en will continue to withdraw from nature in order to create an environment that will suit them better. By 2014, electroluminescent panels will be in common use. Ceilings and walls will glow softly, and in a variety of colors that will change at the touch of a push button. Robots will neither be common nor very good in 2014, but they will be in existence. The appliances of 2014 will have no electric cords, of course, for they will be powered by long- lived batteries running on radioisotopes. “[H]ighways … in the more advanced sections of the world will have passed their peak in 2014; there will be increasing emphasis on transportation that makes the least possible contact with the surface. There will be aircraft, of course, but even ground travel will increasingly take to the air a foot or two off the ground. [V]ehicles with ‘Robot-brains’ … can be set for particular destinations … that will then proceed there without interference by the slow reflexes of a human driver. [W]all screens will have replaced the ordinary set; but transparent cubes will be making their appearance in which three-dimensional viewing will be possible. [T]he world population will be 6,500,000,000 and the population of the United States will be 350,000,000. All earth will be a single choked Manhattan by A.D. 2450 and society will collapse long before that! There will, therefore, be a worldwide propaganda drive in favor of birth control by rational and humane methods and, by 2014, it will undoubtedly have taken serious effect. Ordinary agriculture will keep up with great difficulty and there will be ‘farms’ turning to the more efficient micro-organisms. Processed yeast and algae products will be available in a variety of flavors. The world of A.D. 2014 will have few routine jobs that cannot be done better by some machine than by any human being. Mankind will therefore have become largely a race of machine tenders. Schools will have to be oriented in this direction…. All the high-school students will be taught the fundamentals of computer technology will become proficient in binary arithmetic and will be trained to perfection in the use of the computer languages that will have developed out of those like the contemporary “Fortran". [M]ankind will suffer badly from the disease of boredom, a disease spreading more widely each year and growing in intensity. This will have serious mental, emotional and sociological consequences, and I dare say that psychiatry will be far and away the most important medical specialty in 2014. [T]he most glorious single word in the vocabulary will have become work! in our a society of enforced leisure.

[Magyar] had an intense dislike for terms like 'illiberal,' which focused on traits the regimes did not possess--like free media or fair elections. This he likened to trying to describe an elephant by saying that the elephant cannot fly or cannot swim--it says nothing about what the elephant actually is. Nor did he like the term 'hybrid regime,' which to him seemed like an imitation of a definition, since it failed to define what the regime was ostensibly a hybrid of. Magyar developed his own concept: the 'post-communist mafia state.' Both halves of the designation were significant: 'post-communist' because "the conditions preceding the democratic big bang have a decisive role in the formation of the system. Namely that it came about on the foundations of a communist dictatorship, as a product of the debris left by its decay." (quoting Balint Magyar) The ruling elites of post-communist states most often hail from the old nomenklatura, be it Party or secret service. But to Magyar this was not the countries' most important common feature: what mattered most was that some of these old groups evolved into structures centered around a single man who led them in wielding power. Consolidating power and resources was relatively simple because these countries had just recently had Party monopoly on power and a state monopoly on property. ... A mafia state, in Magyar's definition, was different from other states ruled by one person surrounded by a small elite. In a mafia state, the small powerful group was structured just like a family. The center of the family is the patriarch, who does not govern: "he disposes--of positions, wealth, statuses, persons." The system works like a caricature of the Communist distribution economy. The patriarch and his family have only two goals: accumulating wealth and concentrating power. The family-like structure is strictly hierarchical, and membership in it can be obtained only through birth or adoption. In Putin's case, his inner circle consisted of men with whom he grew up in the streets and judo clubs of Leningrad, the next circle included men with whom he had worked with in the KGB/FSB, and the next circle was made up of men who had worked in the St. Petersburg administration with him. Very rarely, he 'adopted' someone into the family as he did with Kholmanskikh, the head of the assembly shop, who was elevated from obscurity to a sort of third-cousin-hood. One cannot leave the family voluntarily: one can only be kicked out, disowned and disinherited. Violence and ideology, the pillars of the totalitarian state, became, in the hands of the mafia state, mere instruments. The post-communist mafia state, in Magyar's words, is an "ideology-applying regime" (while a totalitarian regime is 'ideology-driven'). A crackdown required both force and ideology. While the instruments of force---the riot police, the interior troops, and even the street-washing machines---were within arm's reach, ready to be used, ideology was less apparently available. Up until spring 2012, Putin's ideological repertoire had consisted of the word 'stability,' a lament for the loss of the Soviet empire, a steady but barely articulated restoration of the Soviet aesthetic and the myth of the Great Patriotic War, and general statements about the United States and NATO, which had cheated Russia and threatened it now. All these components had been employed during the 'preventative counter-revolution,' when the country, and especially its youth, was called upon to battle the American-inspired orange menace, which threatened stability. Putin employed the same set of images when he first responded to the protests in December. But Dugin was now arguing that this was not enough. At the end of December, Dugin published an article in which he predicted the fall of Putin if he continued to ignore the importance of ideas and history.

Ken MacLeod, a Scottish science fiction author, describes the Singularity as “the Rapture for nerds” and in the same way Christians are divided into preterist, premillennialist, and postmillennialist camps regarding the timing of the Parousia,39 Apocalyptic Techno-Heretics can be divided into three sects, renunciationist, apotheosan, and posthumanist. Whereas renunciationists foresee a dark future wherein humanity is enslaved or even eliminated by its machine masters and await the Singularity with the same sort of resignation that Christians who don’t buy into Rapture doctrine anticipate the Tribulation and the Antichrist, apotheosans anticipate a happy and peaceful amalgamation into a glorious, godlike hive mind of the sort envisioned by Isaac Asimov in his Foundation novels. Posthumanists, meanwhile, envision a detente between Man and Machine, wherein artificial intelligence will be wedded to intelligence amplification and other forms of technobiological modification to transform humanity and allow it to survive and perhaps even thrive in the Posthuman Era .40 Although it is rooted entirely in science and technology,41 there are some undeniable religious parallels between the more optimistic visions of the Singularity and conventional religious faith. Not only is there a strong orthogenetic element inherent in the concept itself, but the transhuman dream of achieving immortality through uploading one’s consciousness into machine storage and interacting with the world through electronic avatars sounds suspiciously like shedding one’s physical body in order to walk the streets of gold with a halo and a harp. Furthermore, the predictions of when this watershed event is expected to occur rather remind one of Sir Isaac Newton’s tireless attempts to determine the precise date of the Eschaton, which he finally concluded would take place sometime after 2065, only thirty years after Kurzweil expects the Singularity. So, if they’re both correct, at least Mankind can console itself that the Machine Age will be a short one.