Data Engineering Quotes

Software engineers are sneaky bastards when it comes to data management.

On the first day of a college you will worry about how will you do inside the college? and at the last day of a college you will wonder what will you do outside the college?

Generally, the craft of programming is the factoring of a set of requirements into a a set of functions and data structures.

ideologues of every stripe, as well as folks with interests economic, political, or personal, can interpret data and statistics to suit their own purposes...

The important thing with Elon,” he says, “is that if you told him the risks and showed him the engineering data, he would make a quick assessment and let the responsibility shift from your shoulders to his.

Sometimes I would worry about my internet habits and force myself awy from the computer, to read a magazine or book. Contemporary literature offered no respite: I would find the prose cluttered with data points, tenuous historical connections, detail so finely tuned it could have only been extracted from a feverish night of search-engine queries. Aphorisms were in; authors were wired. I would pick up books that had been heavily documented on social media, only to find that the books themselves had a curatorial affect: beautiful descriptions of little substance, arranged in elegant vignettes—gestural text, the equivalent of a rumpled linen bedsheet or a bunch of dahlias placed just so. Oh, I would think, turning the page. This author is addicted to the internet, too.

A basic principle of data processing teaches the folly of trying to maintain independent files in synchonism.

Search engines finds the information, not necessarily the truth.

Data scientist (noun): Person who is better at statistics than any software engineer and better at software engineering than any statistician. — Josh Wills

Algorithms tuned by an average engineer can outperform those built by the world’s leading experts if the average engineer has access to far more data.

At its most basic level semantic search applies meaning to the connections between the different data nodes of the Web in ways that allow a clearer understanding of them than we have ever had to date.

The appeal by twentieth-century pluralists to scientific method was also ideologically—and even messianically—driven. It ignored scientific data that interfered with environmentalist assumptions and misrepresented socialist faith as “scientific planning.

Of course!” Jack beamed. “Software engineers are sneaky bastards when it comes to data management.

Every thought that arises in the mind has its roots in data you have already accumulated.

the invention of deep learning means that we are moving from the age of expertise to the age of data. Training successful deep-learning algorithms requires computing power, technical talent, and lots of data. But of those three, it is the volume of data that will be the most important going forward. That’s because once technical talent reaches a certain threshold, it begins to show diminishing returns. Beyond that point, data makes all the difference. Algorithms tuned by an average engineer can outperform those built by the world’s leading experts if the average engineer has access to far more data.

He liked to start sentences with okay, so. It was a habit he had picked up from the engineers. He thought it made him sound smarter, thought it made him sound like them, those code jockeys, standing by the coffee machine, talking faster than he could think, talking not so much in sentences as in data structures, dense clumps of logic with the occasional inside joke. He liked to stand near them, pretending to stir sugar into his coffee, listening in on them as if they were speaking a different language. A language of knowing something, a language of being an expert at something. A language of being something more than an hourly unit.

The Columbia Accident Investigation Board concluded that NASA’s culture “emphasized chain of command, procedure, following the rules, and going by the book. While rules and procedures were essential for coordination, they had an unintended negative effect.” Once again, “allegiance to hierarchy and procedure” had ended in disaster. Again, lower ranking engineers had concerns they could not quantify; they stayed silent because “the requirement for data was stringent and inhibiting.

If you could be any character on The Next Generation, who would you be?" "Easy," Solomon said. "Data. For sure." "That makes sense," Clark said. "You?" "I always liked Wesley Crusher." "What?" Solomon was appalled. "Nobody likes Wesley Crusher." "Why not?" Lisa asked. "Because he's a total Mary Sue," Solomon said. "He's too perfect." "But he's always saving the day," Clark argued. "Like, always." "Exactly. He's just a talking deus ex machina. Everybody on the ship treats him like a dumb kid, then he saves them at the last minute and, every single time, they go right back to treating him like a dumb kid again. Do I need to remind you that the starship Enterprise is full of genius scientists and engineers? Why's this kid who can't get into Starfleet Academy smarter than all of them?" "Good point," Clark said. "He's still my choice, though.

As one Google Translate engineer put it, "when you go from 10,000 training examples to 10 billion training examples, it all starts to work. Data trumps everything.

The science and engineering of programming just isn’t good enough to produce flawless software, and that isn’t going to change anytime soon. The

data mining is an exploratory undertaking closer to research and development than it is to engineering.

Data science is the civil engineering of data. Its acolytes possess a practical knowledge of tools and materials, coupled with a theoretical understanding of what’s possible.

My nose wrinkles at the reek of the trickle of data flowing to the device in his hands; no security system devised by man can withstand the relentless destructive pressure of boredom.

Company engineers helped to design Westborough, and they made it functional and cheap. One contractor who did some work for Data General was quoted in Fortune as saying, “What they call tough auditing, we call thievery.

Monte Carlo is able to discover practical solutions to otherwise intractable problems because the most efficient search of an unmapped territory takes the form of a random walk. Today’s search engines, long descended from their ENIAC-era ancestors, still bear the imprint of their Monte Carlo origins: random search paths being accounted for, statistically, to accumulate increasingly accurate results. The genius of Monte Carlo—and its search-engine descendants—lies in the ability to extract meaningful solutions, in the face of overwhelming information, by recognizing that meaning resides less in the data at the end points and more in the intervening paths.

At this risk of being redundant, everything must be checked in to code. This includes the following: DB object migrations Triggers Procedures and functions Views Configurations Sample datasets for functionality Data cleanup scripts

I repeat, feedback is a method of controlling a system by reinserting into it the results of its past performance. If these results are merely used as numerical data for the criticism of the system and its regulation, we have the simple feedback of the control engineers. If, however, the information which proceeds backward from the performance is able to change the general method and pattern of performance, we have a process which may well be called learning.

engineering desire, is the approach of Silicon Valley, authoritarian governments, and the Cult of Experts. The first two use intelligence and data to centrally plan a system in which people want things that other people want them to want -- things that benefit a certain group of people. This approach poses a serious threat to human agency. It also lacks respect for the capability of people to freely desire what is best for themselves and for the people they love.

intellectuals who reject eternal truths and experience through the ages for the social engineering by supposed experts and their administrative state—which claim to use data, science, and empiricism to analyze, manage, and control society.

Realizing the newfound promise of electrification a century ago required four key inputs: fossil fuels to generate it, entrepreneurs to build new businesses around it, electrical engineers to manipulate it, and a supportive government to develop the underlying public infrastructure. Harnessing the power of AI today—the “electricity” of the twenty-first century—requires four analogous inputs: abundant data, hungry entrepreneurs, AI scientists, and an AI-friendly policy environment.

A humorous treatment of the rigid uniformitarian view came from Mark Twain. Although the shortening of the Mississippi River he referred to was the result of engineering projects eliminating many of the bends in the river, it is a thought-provoking spoof: The Mississippi between Cairo and New Orleans was twelve hundred and fifteen miles long one hundred and seventy-six years ago. . . . Its length is only nine hundred and seventy-three miles at present. Now, if I wanted to be one of those ponderous scientific people, and “let on” to prove what had occurred in the remote past by what had occurred in a given time in the recent past . . . what an opportunity is here! Geology never had such a chance, nor such exact data to argue from! . . . In the space of one hundred and seventy-six years the Lower Mississippi has shortened itself two hundred and forty-two miles. That is an average of a trifle over one mile and a third per year. Therefore, any calm person, who is not blind or idiotic, can see that in the Old Oolitic Silurian Period, just a million years ago next November, the Lower Mississippi River was upwards of one million three hundred thousand miles long, and stuck out over the Gulf of Mexico like a fishing-rod. And by the same token any person can see that seven hundred and forty-two years from now the lower Mississippi will be only a mile and three-quarters long. . . . There is something fascinating about science. One gets such wholesale returns of conjecture out of such a trifling investment of fact.

Stop for a second to behold the miracle of engineering that these hand-held, networked computers represent—the typical CPU in a modern smartphone is ten times more powerful than the Cray-1 supercomputer installed at Los Alamos National Laboratory in 1976.

Complete rationality of action in the Cartesian sense demands complete knowledge of all the relevant facts. A designer or engineer needs all the data and full power to control or manipulate them if he is to organize the material objects to produce the intended result. But the success of action in society depends on more particular facts than anyone can possibly know. And our whole civilization in consequence rests, and must rest, on our believing rnuch that we cannot know to be true in the Cartesian sense.

Amazon engineer Greg Linden originally introduced doppelganger searches to predict readers’ book preferences, the improvement in recommendations was so good that Amazon founder Jeff Bezos got to his knees and shouted, “I’m not worthy!” to Linden. But what is really interesting about doppelganger searches, considering their power, is not how they’re commonly being used now. It is how frequently they are not used. There are major areas of life that could be vastly improved by the kind of personalization these searches allow.

they didn't examine the problem and accumulate data to figure out the best solution - they engineered the outcome they wanted from the beginning. if they didn't achieve their desired outcome, they understood it was because of a decision they made at the start of a process

It would be interesting to keep a running log of predictions and see if we can spot the absolute corkers when they are still just pert little buds. One such that I spotted recently was a statement made in February by a Mr. Wayne Leuck, vice-president of engineering at USWest, the American phone company. Arguing against the deployment of high-speed wireless data connections, he said, “Granted, you could use it in your car going sixty miles an hour, but I don’t think too many people are going to be doing that.” Just watch. That’s a statement that will come back to haunt him.

But you weren’t born,” I tell him. “I wrote an algorithm based on the Linux operating kernel. You’re an open-source search engine married to a dialog bot and a video compiler. The program scrubs the Web and archives a person’s images and videos and data—everything you say, you’ve said before.” For

The brain is a belief engine. From sensory data flowing in through the senses the brain naturally begins to look for and find patterns, and then infuses those patterns with meaning. The first process I call patternicity: the tendency to find meaningful patterns in both meaningful and meaningless data. The second process I call agenticity: the tendency to infuse patterns with meaning, intention, and agency. We can’t help it. Our brains evolved to connect the dots of our world into meaningful patterns that explain why things happen. These meaningful patterns become beliefs, and these beliefs shape our understanding of reality. Once beliefs are formed, the brain begins to look for and find confirmatory evidence in support of those beliefs, which adds an emotional boost of further confidence in the beliefs and thereby accelerates the process of reinforcing them, and round and round the process goes in a positive feedback loop of belief confirmation.

PRISM enabled the NSA to routinely collect data from Microsoft, Yahoo!, Google, Facebook, Paltalk, YouTube, Skype, AOL, and Apple, including email, photos, video and audio chats, Web-browsing content, search engine queries, and all other data stored on their clouds, transforming the companies into witting coconspirators.

Different databases are designed to solve different problems. Using a single database engine for all of the requirements usually leads to non- performant solutions; storing transactional data, caching session information, traversing graph of customers and the products their friends bought are essentially different problems.

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

The chopped salad is engineered…to free one’s hand and eyes from the task of consuming nutrients, so that precious attention can be directed toward a small screen, where it is more urgently needed, so it can consume data: work email or Amazon’s nearly infinite catalog or Facebook’s actually infinite News Feed, where, as one shops for diapers or engages with the native advertising sprinkled between the not-hoaxes and baby photos, one is being productive by generating revenue for a large internet company, which is obviously good for the economy, or at least it is certainly better than spending lunch reading a book from the library, because who is making money from that?

As more and more data flows from your body and brain to the smart machines via the biometric sensors, it will become easy for corporations and government agencies to know you, manipulate you, and make decisions on your behalf. Even more importantly, they could decipher the deep mechanisms of all bodies and brains, and thereby gain the power to engineer life. If we want to prevent a small elite from monopolising such godlike powers, and if we want to prevent humankind from splitting into biological castes, the key question is: who owns the data? Does the data about my DNA, my brain and my life belong to me, to the government, to a corporation, or to the human collective?

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

In the mid-1990s, a new employee of Sun Microsystems in California kept disappearing from their database. Every time his details were entered, the system seemed to eat him whole; he would disappear without a trace. No one in HR could work out why poor Steve Null was database kryptonite. The staff in HR were entering the surname as “Null,” but they were blissfully unaware that, in a database, NULL represents a lack of data, so Steve became a non-entry. To computers, his name was Steve Zero or Steve McDoesNotExist. Apparently, it took a while to work out what was going on, as HR would happily reenter his details each time the issue was raised, never stopping to consider why the database was routinely removing him.

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

What’s more, attempting to score a teacher’s effectiveness by analyzing the test results of only twenty-five or thirty students is statistically unsound, even laughable. The numbers are far too small given all the things that could go wrong. Indeed, if we were to analyze teachers with the statistical rigor of a search engine, we’d have to test them on thousands or even millions of randomly selected students.

One moment it was a calculating machine, attempting dispassionately to keep up with the gouts of data. And then awash in those gouts, something metal twitched and a patter of valves sounded that had not been instructed by those numbers. A loop of data was self-generated by the analytical engine. The processor reflected on its creation in a hiss of high-pressure steam. One moment it was a calculating machine. The next, it thought.

Lilah did little more than sleep and eat and cry, which to me was the most fascinating thing in the entire universe. Why did she cry? When did she sleep? What made her eat a lot one day and little the next? Was she changing with time? I did what any obsessed person would do in such a case: I recorded data, plotted it, calculated statistical correlations. First I just wrote on scraps of paper and made charts on graph paper, but I very quickly became more sophisticated. I wrote computer software to make a beautifully colored plot showing times when Diane fed Lilah, in black; when I fed her, in blue (expressed mother's milk, if you must know); Lilah's fussy times, in angry red; her happy times, in green. I calculated patterns in sleeping times, eating times, length of sleep, amounts eaten. Then, I did what any obsessed person would do these days; I put it all on the Web.

According to the prevailing notion, to be free means to be free to satisfy one’s preferences. Preferences themselves are beyond rational scrutiny; they express the authentic core of a self whose freedom is realized when there are no encumbrances to its preference-satisfying behavior. Reason is in the service of this freedom, in a purely instrumental way; it is a person’s capacity to calculate the best means to satisfy his ends. About the ends themselves we are to maintain a principled silence, out of respect for the autonomy of the individual. To do otherwise would be to risk lapsing into paternalism. Thus does liberal agnosticism about the human good line up with the market ideal of “choice.” We invoke the latter as a content-free meta-good that bathes every actual choice made in the softly egalitarian, flattering light of autonomy. This mutually reinforcing set of posits about freedom and rationality provides the basic framework for the discipline of economics, and for “liberal theory” in departments of political science. It is all wonderfully consistent, even beautiful. But in surveying contemporary life, it is hard not to notice that this catechism doesn’t describe our situation very well. Especially the bit about our preferences expressing a welling-up of the authentic self. Those preferences have become the object of social engineering, conducted not by government bureaucrats but by mind-bogglingly wealthy corporations armed with big data. To continue to insist that preferences express the sovereign self and are for that reason sacred—unavailable for rational scrutiny—is to put one’s head in the sand. The resolutely individualistic understanding of freedom and rationality we have inherited from the liberal tradition disarms the critical faculties we need most in order to grapple with the large-scale societal pressures we now face.

In general, software engineering teams and IT departments seemed to be at the mercy of other groups who would negotiate, cajole, intimidate, and overrule even the most defensible and objectively derived plans. Even plans based on thorough analysis and backed by years of historical data were vulnerable. Most teams, which had neither a thorough analysis method nor any historical data, were powerless at the hands of others who would push them to commit to unknown (and often completely unreasonable) deliverables.

Uber had tagged Mr. England and his colleagues—essentially Greyballing them as city officials—based on data collected from the app and in other ways. The company then served up a fake version of the app, populated with ghost cars, to evade capture. This is supposed to be serious evidence of terrible wrongdoing. But a lot of us just read that description and burst out laughing, congratulating Uber’s engineers on their cleverness in leaving some blue-nosed petty authoritarian standing at the curb waiting for a car that never comes.

So we had better call upon our lawyers, politicians, philosophers and even poets to turn their attention to this conundrum: how do you regulate the ownership of data? This may well be the most important political question of our era. If we cannot answer this question soon, our sociopolitical system might collapse. People are already sensing the coming cataclysm. Perhaps this is why citizens all over the world are losing faith in the liberal story, which just a decade ago seemed irresistible. How, then, do we go forward from here, and how do we cope with the immense challenges of the biotech and infotech revolutions? Perhaps the very same scientists and entrepreneurs who disrupted the world in the first place could engineer some technological solution? For example, might networked algorithms form the scaffolding for a global human community that could collectively own all the data and oversee the future development of life? As global inequality rises and social tensions increase around the world, perhaps Mark Zuckerberg could call upon his 2 billion friends to join forces and do something together?

Whatever you are identified with, all your thoughts and emotions spring from that identity. Right now suppose you identify yourself as a man, all your thoughts and emotions flow from that identification. If you identify yourself with your nationality or religion, they will flow from those identifications. Whatever your thoughts and emotions, these identifications are a certain level of prejudice. In fact, your mind is itself a certain kind of prejudice. Why? Because it functions from limited data and is fronted by an essentially discriminatory intellect.

I dare to hope that search engines and social media algorithms will be optimized for truth and social relevance rather than simply showing people what they want to see; that there will be independent, third-party algorithms that rate the veracity of headlines, websites, and news stories in real time, allowing users to more quickly sift through the propaganda-laden garbage and get closer to evidence-based truth; that there will be actual respect for empirically tested data, because in an infinite sea of possible beliefs, evidence is the only life preserver we’ve got.

This underscores another common feature of WMDs. They tend to punish the poor. This is, in part, because they are engineered to evaluate large numbers of people. They specialize in bulk, and they’re cheap. That’s part of their appeal. The wealthy, by contrast, often benefit from personal input. A white-shoe law firm or an exclusive prep school will lean far more on recommendations and face-to-face interviews than will a fast-food chain or a cash-strapped urban school district. The privileged, we’ll see time and again, are processed more by people, the masses by machines.

PRISM enabled the NSA to routinely collect data from Microsoft, Yahoo!, Google, Facebook, Paltalk, YouTube, Skype, AOL, and Apple, including email, photos, video and audio chats, Web-browsing content, search engine queries, and all other data stored on their clouds, transforming the companies into witting coconspirators. Upstream collection, meanwhile, was arguably even more invasive. It enabled the routine capturing of data directly from private-sector Internet infrastructure—the switches and routers that shunt Internet traffic worldwide, via the satellites in orbit and the high-capacity fiber-optic cables that run under the ocean.

A friend of ours encountered this problem with his home-built computer long ago. He wrote a BIOS that used a magic value in a particular memory location to determine whether a reset was a cold reboot or a warm reboot. After a while the machine refused to boot after power-up because the memory had learned the magic value, and the boot process therefore treated every reset as a warm reboot. As this did not initialize the proper variables, the boot process failed. The solution in his case was to swap some memory chips around, scrambling the magic value that the SRAM had learned. For us, it was a lesson to remember: memory retains more data than you think.

In the coming decades, it is likely that we will see more Internet-like revolutions, in which technology steals a march on politics. Artificial intelligence and biotechnology might soon overhaul our societies and economies – and our bodies and minds too – but they are hardly a blip on our political radar. Our current democratic structures just cannot collect and process the relevant data fast enough, and most voters don’t understand biology and cybernetics well enough to form any pertinent opinions. Hence traditional democratic politics loses control of events, and fails to provide us with meaningful visions for the future. That doesn’t mean we will go back to twentieth-century-style dictatorships. Authoritarian regimes seem to be equally overwhelmed by the pace of technological development and the speed and volume of the data flow. In the twentieth century, dictators had grand visions for the future. Communists and fascists alike sought to completely destroy the old world and build a new world in its place. Whatever you think about Lenin, Hitler or Mao, you cannot accuse them of lacking vision. Today it seems that leaders have a chance to pursue even grander visions. While communists and Nazis tried to create a new society and a new human with the help of steam engines and typewriters, today’s prophets could rely on biotechnology and super-computers.

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

It’s easy to raise graduation rates, for example, by lowering standards. Many students struggle with math and science prerequisites and foreign languages. Water down those requirements, and more students will graduate. But if one goal of our educational system is to produce more scientists and technologists for a global economy, how smart is that? It would also be a cinch to pump up the income numbers for graduates. All colleges would have to do is shrink their liberal arts programs, and get rid of education departments and social work departments while they’re at it, since teachers and social workers make less money than engineers, chemists, and computer scientists. But they’re no less valuable to society.

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

That morning, a Syrian refugee entered a park in Annecy, France, and stabbed several children. A brutal video of the attack quickly emerged and began spreading across Twitter. The few employees who remained on the gutted trust and safety team scrambled to remove it. In the past, when violent imagery went viral on Twitter, the company used a data matching tool to detect any tweets containing it and wiped them out all at once. But the employees discovered that the tool they relied on wasn’t working. When they investigated, they learned that it was one of the bits of code that engineers had torn out months earlier. The tool had mistakenly flagged an image of a SpaceX rocket launch, so Musk had ordered the entire system be killed.

Dr. Fauci’s business closures pulverized America’s middle class and engineered the largest upward transfer of wealth in human history. In 2020, workers lost $3.7 trillion while billionaires gained $3.9 trillion.46 Some 493 individuals became new billionaires,47 and an additional 8 million Americans dropped below the poverty line.48 The biggest winners were the robber barons—the very companies that were cheerleading Dr. Fauci’s lockdown and censoring his critics: Big Technology, Big Data, Big Telecom, Big Finance, Big Media behemoths (Michael Bloomberg, Rupert Murdoch, Viacom, and Disney), and Silicon Valley Internet titans like Jeff Bezos, Bill Gates, Mark Zuckerberg, Eric Schmidt, Sergey Brin, Larry Page, Larry Ellison, and Jack Dorsey.

It’s All about Scaling Most of the current learning algorithms were discovered more than twenty-five years ago, so why did it take so long for them to have an impact on the real world? With the computers and labeled data that were available to researchers in the 1980s, it was only possible to demonstrate proof of principle on toy problems. Despite some promising results, we did not know how well network learning and performance would scale as the number of units and connections increased to match the complexity of real-world problems. Most algorithms in AI scale badly and never went beyond solving toy problems. We now know that neural network learning scales well and that performance continues to increase with the size of the network and the number of layers. Backprop, in particular, scales extremely well. Should we be surprised? The cerebral cortex is a mammalian invention that mushroomed in primates and especially in humans. And as it expanded, more capacity became available and more layers were added in association areas for higher-order representations. There are few complex systems that scale this well. The Internet is one of the few engineered systems whose size has also been scaled up by a million times. The Internet evolved once the protocols were established for communicating packets, much like the genetic code for DNA made it possible for cells to evolve. Training many deep learning networks with the same set of data results in a large number of different networks that have roughly the same average level of performance.

By 2011, enough data had been accumulated to show that some risk existed due to long-term, heavy use of mobile phones, compelling the International Agency for Research on Cancer, a branch of the World Health Organization, to classify mobile cell phone radiation in the Group 2B category, indicating a possible carcinogen (a substance or source of exposure that can cause cancer).14 This is the same category as DDT, lead, engine exhaust, chloroform, and glyphosate (the active ingredient in Roundup®). Later, in 2016, a $25 million study conducted by the National Toxicology Program (NTP), part of the National Institutes of Health, confirmed what many have believed for years—that exposure to EMF radiation emitted from cell phones can lead to serious health issues including brain and heart tumors.

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

von Braun went looking for problems, hunches, and bad news. He even rewarded those who exposed problems. After Kranz and von Braun’s time, the “All Others Bring Data” process culture remained, but the informal culture and power of individual hunches shriveled. In 1974, William Lucas took over the Marshall Space Flight Center. A NASA chief historian wrote that Lucas was a brilliant engineer but “often grew angry when he learned of problems.” Allan McDonald described him to me as a “shoot-the-messenger type guy.” Lucas transformed von Braun’s Monday Notes into a system purely for upward communication. He did not write feedback and the notes did not circulate. At one point they morphed into standardized forms that had to be filled out. Monday Notes became one more rigid formality in a process culture. “Immediately, the quality of the notes fell,” wrote another official NASA historian.

The current narrative we seem to tell ourselves about our privacy is that it is a sort of currency we trade to corporations in return for innovation. But the corporation has an insatiable appetite for our most personal data in order to drive us to consume during our every waking moment. I think this is critical, because in some ways social networks are powerful engines of conformity. The ability for students to develop their own ideas, identities, and political affiliations should take place outside of the panopticon view of Facebook, but whether this is any longer possible is an open question. My own memory is that the development of my political and cultural persona between the ages of fifteen and twenty-one had a lot to do with being outside the zone of judgment of my parents, their conservative peers from my hometown, Cleveland, and maybe even from my siblings. I’m not sure that it could happen if we were all on Facebook together.

Peopleware. A major contribution during recent years has been DeMarco and Lister's 1987 book, Peopleware: Productive Projects and Teams. Its underlying thesis is that "The major problems of our work are not so much technological as sociological in nature." It abounds with gems such as, "The manager's function is not to make people work, it is to make it possible for people to work." It deals with such mundane topics as space, furniture, team meals together. DeMarco and Lister provide real data from their Coding War Games that show stunning correlation between performances of programmers from the same organization, and between workplace characteristics and both productivity and defect levels. The top performers' space is quieter, more private, better protected against interruption, and there is more of it. . . . Does it really matter to you . . . whether quiet, space, and privacy help your current people to do better work or [alternatively] help you to attract and keep better people?[19]

Wider lanes were, obviously, safer than narrower ones. Only they’re not. This time, the problem with the cost-benefit equation wasn’t a faulty premise, but the data itself. In order to test the wider-lanes-are-safer-lanes hypothesis, I studied every crash that occurred on the bridge over a three-year period and marked each one on a map. If that notion had been true, I reasoned, more crashes would have occurred where the lanes were narrowest, that is, at the towers. Just the opposite turned out to be the case. The towers, it turned out, were the safest places on the entire bridge; my explanation is that when lanes get very narrow motorists drive more carefully. Even though every traffic engineer in the country had been taught the gospel of wider lanes, the opposite appeared to be true: “grossly substandard lanes seemed to be the safest of all.” This was the traffic engineering equivalent of saying the Earth was round when the masses knew it was flat. Still, most engineers do not accept this fact.

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

When we mix a practical ability to engineer minds with our ignorance of the mental spectrum and with the narrow interests of governments, armies and corporations, we get a recipe for trouble. We may successfully upgrade our bodies and our brains, while losing our minds in the process. Indeed, techno-humanism may end up downgrading humans. The system may prefer downgraded humans not because they would possess any superhuman knacks, but because they would lack some really disturbing human qualities that hamper the system and slow it down. As any farmer knows, it’s usually the brightest goat in the flock that stirs up the most trouble, which is why the Agricultural Revolution involved downgrading animals’ mental abilities. The second cognitive revolution, dreamed up by techno-humanists, might do the same to us, producing human cogs who communicate and process data far more effectively than ever before, but who can hardly pay attention, dream or doubt. For millions of years we were enhanced chimpanzees. In the future, we may become oversized ants.

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

Search engine query data is not the product of a designed statistical experiment and finding a way to meaningfully analyse such data and extract useful knowledge is a new and challenging field that would benefit from collaboration. For the 2012–13 flu season, Google made significant changes to its algorithms and started to use a relatively new mathematical technique called Elasticnet, which provides a rigorous means of selecting and reducing the number of predictors required. In 2011, Google launched a similar program for tracking Dengue fever, but they are no longer publishing predictions and, in 2015, Google Flu Trends was withdrawn. They are, however, now sharing their data with academic researchers... Google Flu Trends, one of the earlier attempts at using big data for epidemic prediction, provided useful insights to researchers who came after them... The Delphi Research Group at Carnegie Mellon University won the CDC’s challenge to ‘Predict the Flu’ in both 2014–15 and 2015–16 for the most accurate forecasters. The group successfully used data from Google, Twitter, and Wikipedia for monitoring flu outbreaks.

I have identified patterns in the technology and civilisational behaviour that confirms to me, with great certainty, that approximately thirty thousand human years ago, about ten thousand years after the date of the ceephays’ fall, a new center of data and physical traffic accumulated at the Keijir System. Ten thousand years correlates closely with how long I estimate a ceephay vessel would have taken to travel some feasible sublight to avoid detection using jump engines, until being discovered by reeh vessels, possibly in some kind of hibernation. The accumulation of technologies at the Keijir System was very rapid, and was responded to by several of the species who were recording this data. There are even several surviving speculations from their academia at the time, wondering what was happening at Keijir. I believe these events are entirely consistent with the discovery, by an organic species of lesser intelligence and capability, of an entity of greater intelligence and capability. Great technological advances appear to have followed, and the expansion of what became the Reeh Empire commenced shortly thereafter.

Back in the early 1990s, the FBI started worrying about its ability to conduct telephone surveillance. The FBI could do it with the old analog phone switches: a laborious process involving alligator clips, wires, and a tape recorder. The problem was that digital switches didn’t work that way. Isolating individual connections was harder, and the FBI became concerned about the potential loss of its ability to wiretap. So it lobbied Congress hard and got a law passed in 1994 called the Communications Assistance for Law Enforcement Act, or CALEA, requiring telcos to re-engineer their digital switches to have eavesdropping capabilities built in. Fast-forward 20 years, and the FBI again wants the IT industry to make surveillance easier for itself. A lot of communications no longer happen over the telephone. They’re happening over chat. They’re happening over e-mail. They’re happening over Skype. The FBI is currently lobbying for a legislative upgrade to CALEA, one that covers all communications systems: all voice, video, and text systems, including World of Warcraft and that little chat window attached to your online Scrabble game. The FBI’s ultimate goal is government prohibition of truly secure communications. Valerie

Dr. Fauci’s business closures pulverized America’s middle class and engineered the largest upward transfer of wealth in human history. In 2020, workers lost $3.7 trillion while billionaires gained $3.9 trillion.46 Some 493 individuals became new billionaires,47 and an additional 8 million Americans dropped below the poverty line.48 The biggest winners were the robber barons—the very companies that were cheerleading Dr. Fauci’s lockdown and censoring his critics: Big Technology, Big Data, Big Telecom, Big Finance, Big Media behemoths (Michael Bloomberg, Rupert Murdoch, Viacom, and Disney), and Silicon Valley Internet titans like Jeff Bezos, Bill Gates, Mark Zuckerberg, Eric Schmidt, Sergey Brin, Larry Page, Larry Ellison, and Jack Dorsey. The very Internet companies that snookered us all with the promise of democratizing communications made it impermissible for Americans to criticize their government or question the safety of pharmaceutical products; these companies propped up all official pronouncements while scrubbing all dissent. The same Tech/Data and Telecom robber barons, gorging themselves on the corpses of our obliterated middle class, rapidly transformed America’s once-proud democracy into a censorship and surveillance police state from which they profit at every turn.

This happens because data scientists all too often lose sight of the folks on the receiving end of the transaction. They certainly understand that a data-crunching program is bound to misinterpret people a certain percentage of “he time, putting them in the wrong groups and denying them a job or a chance at their dream house. But as a rule, the people running the WMDs don’t dwell on those errors. Their feedback is money, which is also their incentive. Their systems are engineered to gobble up more data and fine-tune their analytics so that more money will pour in. Investors, of course, feast on these returns and shower WMD companies with more money. And the victims? Well, an internal data scientist might say, no statistical system can be perfect. Those folks are collateral damage. And often, like Sarah Wysocki, they are deemed unworthy and expendable. Big Data has plenty of evangelists, but I’m not one of them. This book will focus sharply in the other direction, on the damage inflicted by WMDs and the injustice they perpetuate. We will explore harmful examples that affect people at critical life moments: going to college, borrowing money, getting sentenced to prison, or finding and holding a job. All of these life domains are increasingly controlled by secret models wielding arbitrary punishments.

one stubborn glitch they couldn’t figure out: the program did a wonderful job spewing out data on the trajectory of artillery shells, but it just didn’t know when to stop. Even after the shell would have hit the ground, the program kept calculating its trajectory, “like a hypothetical shell burrowing through the ground at the same rate it had traveled through the air,” as Jennings described it. “Unless we solved that problem, we knew the demonstration would be a dud, and the ENIAC’s inventors and engineers would be embarrassed.”69 Jennings and Snyder worked late into the evening before the press briefing trying to fix it, but they couldn’t. They finally gave up at midnight, when Snyder needed to catch the last train to her suburban apartment. But after she went to bed, Snyder figured it out: “I woke up in the middle of the night thinking what that error was. . . . I came in, made a special trip on the early train that morning to look at a certain wire.” The problem was that there was a setting at the end of a “do loop” that was one digit off. She flipped the requisite switch and the glitch was fixed. “Betty could do more logical reasoning while she was asleep than most people can do awake,” Jennings later marveled. “While she slept, her subconscious untangled the knot that her conscious mind had been unable to.

We are striving to engineer the internet of all things in hope to make us healthy, happy and powerful. Yet once the internet of all things is up and running, we might be reduced from engineers to chips then to data and eventually, we might dissolve within the data torrent like a clamp of earth within a gushing river. Dataism, thereby, threatens to do to Homo sapiens what Homo sapiens has done to all other animals. In the course of history, humans have created a global network and evaluated everything according to its function within the network. For thousands of years this boosted human pride and prejudices. Since humans fulfilled the most important function in the network, it was easy for us to take credit for the network’s achievements and to see ourselves as the apex of creation. The lives and experiences of all other animals were undervalued because they fulfilled far less important functions. And whenever an animal ceased to fulfil any function at all it went extinct. However, once humans loose their functional importance to the network, we’ll discover that we are not the apex of creation after all. The yardsticks that we ourselves have enshrined will condemn us to join the mammoths and the Chinese river dolphins in oblivion. Looking back, humanity will turn out to be just a ripple within the cosmic data flow.

2006 interview by Jim Gray, Amazon CTO Werner Vogels recalled another watershed moment: We went through a period of serious introspection and concluded that a service-oriented architecture would give us the level of isolation that would allow us to build many software components rapidly and independently. By the way, this was way before service-oriented was a buzzword. For us service orientation means encapsulating the data with the business logic that operates on the data, with the only access through a published service interface. No direct database access is allowed from outside the service, and there’s no data sharing among the services.3 That’s a lot to unpack for non–software engineers, but the basic idea is this: If multiple teams have direct access to a shared block of software code or some part of a database, they slow each other down. Whether they’re allowed to change the way the code works, change how the data are organized, or merely build something that uses the shared code or data, everybody is at risk if anybody makes a change. Managing that risk requires a lot of time spent in coordination. The solution is to encapsulate, that is, assign ownership of a given block of code or part of a database to one team. Anyone else who wants something from that walled-off area must make a well-documented service request via an API.

Imagine a latter-day Helmholtz presented by an engineer with a digital camera, with its screen of tiny photocells, set up to capture images projected directly on to the surface of the screen. That makes good sense, and obviously each photocell has a wire connecting it to a computing device of some kind where images are collated. Makes sense again. Helmholtz wouldn’t send it back. But now, suppose I tell you that the eye’s ‘photocells’ are pointing backwards, away from the scene being looked at. The ‘wires’ connecting the photocells to the brain run all over the surface of the retina, so the light rays have to pass through a carpet of massed wires before they hit the photocells. That doesn’t make sense – and it gets even worse. One consequence of the photocells pointing backwards is that the wires that carry their data somehow have to pass through the retina and back to the brain. What they do, in the vertebrate eye, is all converge on a particular hole in the retina, where they dive through it. The hole filled with nerves is called the blind spot, because it is blind, but ‘spot’ is too flattering, for it is quite large, more like a blind patch, which again doesn’t actually inconvenience us much because of the ‘automatic Photoshop’ software in the brain. Once again, send it back, it’s not just bad design, it’s the design of a complete idiot.

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.

According to this view, free-market capitalism and state-controlled communism aren’t competing ideologies, ethical creeds or political institutions. At bottom, they are competing data-processing systems. Capitalism uses distributed processing, whereas communism relies on centralised processing. Capitalism processes data by directly connecting all producers and consumers to one another, and allowing them to exchange information freely and make decisions independently. For example, how do you determine the price of bread in a free market? Well, every bakery may produce as much bread as it likes, and charge for it as much as it wants. The customers are equally free to buy as much bread as they can afford, or take their business to the competitor. It isn’t illegal to charge $1,000 for a baguette, but nobody is likely to buy it. On a much grander scale, if investors predict increased demand for bread, they will buy shares of biotech firms that genetically engineer more prolific wheat strains. The inflow of capital will enable the firms to speed up their research, thereby providing more wheat faster, and averting bread shortages. Even if one biotech giant adopts a flawed theory and reaches an impasse, its more successful competitors will achieve the hoped-for breakthrough. Free-market capitalism thus distributes the work of analysing data and making decisions between many independent but interconnected processors. As the Austrian economics guru Friedrich Hayek explained, ‘In a system in which the knowledge of the relevant facts is dispersed among many people, prices can act to coordinate the separate actions of different people.

I work in theoretical computer science: a field that doesn’t itself win Fields Medals (at least not yet), but that has occasions to use parts of math that have won Fields Medals. Of course, the stuff we use cutting-edge math for might itself be dismissed as “ivory tower self-indulgence.” Except then the cryptographers building the successors to Bitcoin, or the big-data or machine-learning people, turn out to want the stuff we were talking about at conferences 15 years ago—and we discover to our surprise that, just as the mathematicians gave us a higher platform to stand on, so we seem to have built a higher platform for the practitioners. The long road from Hilbert to Gödel to Turing and von Neumann to Eckert and Mauchly to Gates and Jobs is still open for traffic today. Yes, there’s plenty of math that strikes even me as boutique scholasticism: a way to signal the brilliance of the people doing it, by solving problems that require years just to understand their statements, and whose “motivations” are about 5,000 steps removed from anything Caplan or Bostrom would recognize as motivation. But where I part ways is that there’s also math that looked to me like boutique scholasticism, until Greg Kuperberg or Ketan Mulmuley or someone else finally managed to explain it to me, and I said: “ah, so that’s why Mumford or Connes or Witten cared so much about this. It seems … almost like an ordinary applied engineering question, albeit one from the year 2130 or something, being impatiently studied by people a few moves ahead of everyone else in humanity’s chess game against reality. It will be pretty sweet once the rest of the world catches up to this.

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

Isaac Asimov’s short story “The Fun They Had” describes a school of the future that uses advanced technology to revolutionize the educational experience, enhancing individualized learning and providing students with personalized instruction and robot teachers. Such science fiction has gone on to inspire very real innovation. In a 1984 Newsweek interview, Apple’s co-founder Steve Jobs predicted computers were going to be a bicycle for our minds, extending our capabilities, knowledge, and creativity, much the way a ten-speed amplifies our physical abilities. For decades, we have been fascinated by the idea that we can use computers to help educate people. What connects these science fiction narratives is that they all imagined computers might eventually emulate what we view as intelligence. Real-life researchers have been working for more than sixty years to make this AI vision a reality. In 1962, the checkers master Robert Nealey played the game against an IBM 7094 computer, and the computer beat him. A few years prior, in 1957, the psychologist Frank Rosenblatt created Perceptron, the first artificial neural network, a computer simulation of a collection of neurons and synapses trained to perform certain tasks. In the decades following such innovations in early AI, we had the computation power to tackle systems only as complex as the brain of an earthworm or insect. We also had limited techniques and data to train these networks. The technology has come a long way in the ensuing decades, driving some of the most common products and apps today, from the recommendation engines on movie streaming services to voice-controlled personal assistants such as Siri and Alexa. AI has gotten so good at mimicking human behavior that oftentimes we cannot distinguish between human and machine responses. Meanwhile, not only has the computation power developed enough to tackle systems approaching the complexity of the human brain, but there have been significant breakthroughs in structuring and training these neural networks.

The population, who are, ultimately, indifferent to public affairs and even to their own interests, negotiate this indifference with an equally spectral partner and one that is similarly indifferent to its own will: the government [Ie pouvoir] . This game between zombies may stabilize in the long term. The Year 2000 will not take place in that an era of indifference to time itself - and therefore to the symbolic term of the millennium - will be ushered in by negotiation. Nowadays, you have to go straight from money to money, telegraphically so to speak, by direct transfer (that is the viral side of the matter). A viral revolution, then, more akin to the Glass Bead Game than to the steam engine, and admirably personified in Bernard Tapie's playboy face. For the look of money is reflected in faces. Gone are the hideous old capitalists, the old-style industrial barons wearing the masks of the suffering they have inflicted. Now there are only dashing playboys, sporty and sexual, true knights of industry, wearing the mask of the happiness they spread all around themselves. The world put on a show of despair after 1968. It's been putting on a big show of hope since 1980. No more tears, alright? Reaganite optimism, the pump ing up of the dollar. Fabius's glossy new look. Patriotic conviviality. Reluctance prohibited. The old pessimism was produced by the idea that things were getting worse and worse. The new pessimism is produced by the fact that everything is getting better and better. Supercooled euphoria. Controlled anaesthesia. I should like to see the equivalent of Bernard Tapie in the world of business emerge in the world of concepts. Buying up failing concepts, swallowing them up, dusting them off (firing all the deadbeats who are in the way), putting them back into circulation with a dynamic virginity, sending them shooting up on the Stock Exchange and then abandoning them afterwards like dogs. Some people do this very well. It is perhaps better to save tired concepts by maintaining them in a super cooled state like unemployed labour, or locking them away in interactive data banks kept alive on a respirator.

leap in the ability to process and data. For the sake of simplicity, this book will focus on the recent past to discuss various stages where information technology, norms, practices, and rules combined to allow for data gathering and sharing within an enterprise and with individuals. Framing and noting the various risks and opportunities within various stages in the Information Age creates a context for the ensuing discussion surrounding the mission and purpose of the privacy engineer and the call to action for the privacy engineer’s manifesto, as presented later in this book.

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

However, a real implementation may still have to include code to handle the case where something happens that was assumed to be impossible, even if that handling boils down to printf("Sucks to be you") and exit(666) — i.e., letting a human operator clean up the mess [93]. (This is arguably the difference between computer science and software engineering.)

It was possible to look at actual smartphones and tablets and laptops that had been manufactured on Old Earth. They did not work anymore, but their technical capabilities were described on little placards. And they were impressive compared to what Kath Two and other modern people carried around in their pockets. This ran contrary to most people's intuition, since in other areas the achievements of the modern world - the habitat ring, the Eye, and all the rest - were so vastly greater than what the people of Old Earth had ever accomplished. It boiled down to Amistics [the choices that different cultures made as to which technologies they would, and would not, make part of their lives]. In the decades before Zero, the Old Earthers had focused their intelligence on the small and the soft, not the big and the hard, and built a civilization that was puny and crumbling where physical infrastructure was concerned, but astonishingly sophisticated when it came to networked communications and software. The density with which they'd been able to pack transistors onto chips still had not been matched by any fabrication plant now in existence. Their devices could hold more data than anything you could buy today. Their ability to communicate through all sorts of wireless schemes was only now being matched - and that only in densely populated, affluent places like the Great Chain... Anyone who bothered to learn the history of the developed world in the years just before Zero understood perfectly well that Tavistock Prowse had been squarely in the middle of the normal range, as far as his social media habits and attention span had been concerned. But nevertheless, Blues called it Tav's Mistake. They didn't want to make it again. Any efforts made by modern consumer-goods manufacturers to produce the kinds of devices and apps that had disordered the brain of Tav were met with the same instinctive pushback as Victorian clergy might have directed against the inventor of a masturbation machine. To the extent the Blue's engineers could build electronics of comparable sophistication to those that Tav had used, they tended to put them into devices such as robots...

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If I can reach further, it is by connecting with influential nodes

A company’s revenue engine is a critical success factor. I had seen from my own direct experience how easy it was to get caught in silos: marketing people would just think of marketing, salespeople would just think of sales, and accounting wouldn’t think of itself as part of the revenue engine at all. Furthermore, product and the revenue engine were too often thought of completely independent of each other. The need for a more integrated approach was on my mind from the beginning. The revenue engine is a whole system. It encompasses a diverse set of integrated components, each doing its part to advance the system’s purpose. The engine is not just comprised of marketing and sales— it includes product, accounting, and the underlying technology and data infrastructure required to keep everything flowing. It involves people, tools, workflow, and metrics. Its purpose is to optimize reach, conversion, and expansion of customer spend. I call my revenue engine model “the bowtie schema.” It was the product of continuous iteration. As I interacted with marketing and sales practitioners and waded through the research, the model slowly emerged. The final model conveys not just the product and customer journey across the bowtie, but also the foundational layers that support that journey-- the interaction between people tools, workflow, and metrics that make it all happen. The most basic question a CEO must answer is whether the product has achieved a value breakthrough. Without that, the revenue engine is irrelevant. Once product-market fit is confirmed, the next step is to clearly identify your ideal customer profile (ICP) and your business model. This includes the lifetime value (LTV) profile of your company. Assuming a strong product, a clear ICP, and a solid understanding of the constraints composed by your unit economics, the path forward is clear. Then, the focus will turn to uplifting the maturity of your revenue engine and scaling it efficiently.

It was an open question as to which was more mysterious to a male NASA engineer: outer space or the American female. They appeared to have better data on outer space.

Google’s search product is an excellent example of an innovative business model. Prior to Google, the business model or “value capture framework” of search engines was to fit as many banner advertisements on a page as possible, and to charge as much as possible for them. Google, by contrast, used simple text ads and targeted them based on the keywords used in a particular search. Advertisers found this technique more attractive than banner ads, because they had better data on the effectiveness of individual ads, and could make more effective ads based on the data. This highly innovative business model is what made Google the juggernaut it is today, not the technical proficiency of its search algorithm.

Chinese and American companies have already kick-started this process, leaping out to massive leads over the rest of the world. Canada, the United Kingdom, France, and a few other countries play host to top-notch talent and research labs, but they often lack the other ingredients needed to become true AI superpowers: a large base of users and a vibrant entrepreneurial and venture-capital ecosystem. Other than London’s DeepMind, we have yet to see groundbreaking AI companies emerge from these countries. All of the seven AI giants and an overwhelming portion of the best AI engineers are already concentrated in the United States and China. They are building huge stores of data that are feeding into a variety of different product verticals, such as self-driving cars, language translation, autonomous drones, facial recognition, natural-language processing, and much more. The more data these companies accumulate, the harder it will be for companies in any other countries to ever compete.

After over a decade of monopoly in the online distribution, the major OTAs had to (at least partially) reinvent themselves, by diversifying and broaden their products in order to stay relevant. Because, if up until now metasearch engines merely aggregated third-party data, they now provide the option to complete one’s reservation without even leaving the result page. And that, for an OTA, is a problem.