Tech Nine Quotes

David Kohl, professor emeritus at Virginia Tech University, has found that individuals who write down their goals will have nine times the success of those who don’t put their goals on paper.

While plenty of smart people advocate AI for the public good, we are not yet discussing artificial intelligence as a public good. This is a mistake.

There would be no way to create a set of commandments for AI. We couldn’t write out all of the rules to correctly optimize for humanity, and that’s because while thinking machines may be fast and powerful, they lack flexibility.

Right now, there is no other country on Earth with as much data as China, as many people as China, and as many electronics per capita. No other country is positioned to have a bigger economy than America’s within our lifetimes. No other country has more potential to influence our planet’s ecosystem, climate, and weather patterns—leading to survival or catastrophe—than China. No other country bridges both the developed and developing world like China does.

Of course, I should have known the kids would pop out in the atmosphere of Roberta's office. That's what they do when Alice is under stress. They see a gap in the space-time continuum and slip through like beams of light through a prism changing form and direction. We had got into the habit in recent weeks of starting our sessions with that marble and stick game called Ker-Plunk, which Billy liked. There were times when I caught myself entering the office with a teddy that Samuel had taken from the toy cupboard outside. Roberta told me that on a couple of occasions I had shot her with the plastic gun and once, as Samuel, I had climbed down from the high-tech chairs, rolled into a ball in the corner and just cried. 'This is embarrassing,' I admitted. 'It doesn't have to be.' 'It doesn't have to be, but it is,' I said. The thing is. I never knew when the 'others' were going to come out. I only discovered that one had been out when I lost time or found myself in the midst of some wacky occupation — finger-painting like a five-year-old, cutting my arms, wandering from shops with unwanted, unpaid-for clutter. In her reserved way, Roberta described the kids as an elaborate defence mechanism. As a child, I had blocked out my memories in order not to dwell on anything painful or uncertain. Even as a teenager, I had allowed the bizarre and terrifying to seem normal because the alternative would have upset the fiction of my loving little nuclear family. I made a mental note to look up defence mechanisms, something we had touched on in psychology.

Mr. Duffy Napp has just transmitted a nine-word e-mail asking that I immediately send a letter of reference to your firm on his behalf; his request has summoned from the basement of my heart a star-spangled constellation of joy, so eager am I to see Mr. Napp well established at Maladin IT. As for the basis of our acquaintanceship: I am a professor in an English department whose members consult Tech Help—aka Mr. Napp—only in moments of desperation. For example, let us imagine that a computer screen, on the penultimate page of a lengthy document, winks coyly, twice, and before the “save” button can be deployed, adopts a Stygian façade. In such a circumstance one’s only recourse—unpalatable though it may be—is to plead for assistance from a yawning adolescent who will roll his eyes at the prospect of one’s limited capabilities and helpless despair. I often imagine that in olden days people like myself would crawl to the doorway of Tech Help on our knees, bearing baskets of food, offerings of the harvest, the inner organs of neighbors and friends— all in exchange for a tenuous promise from these careless and inattentive gods that the thoughts we entrusted to our computers will be restored unharmed. Colleagues have warned me that the departure of Mr. Napp, our only remaining Tech Help employee, will leave us in darkness. I am ready. I have girded my loins and dispatched a secular prayer in the hope that, given the abysmal job market, a former mason or carpenter or salesman—someone over the age of twenty-five—is at this very moment being retrained in the subtle art of the computer and will, upon taking over from Mr. Napp, refrain (at least in the presence of anxious faculty seeking his or her help) from sending text messages or videos of costumed dogs to both colleagues and friends. I can almost imagine it: a person who would speak in full sentences—perhaps a person raised by a Hutterite grandparent on a working farm.

Ironically, given the high-tech quality of the diagnostic and monitoring effort, the containment policies were based on traditional methods dating from the public health strategies against bubonic plague of the seventeenth century and the foundation of epidemiology as a discipline in the nineteenth century—case tracking, isolation, quarantine, the cancellation of mass gatherings, the surveillance of travelers, recommendations to increase personal hygiene, and barrier protection by means of masks, gowns, gloves, and eye protection. Although SARS affected twenty-nine countries and five continents, the containment operation successfully limited the outbreak primarily to hospital settings, with only sporadic community involvement. By July 5, 2003, WHO could announce that the pandemic was over.

In 2019, Zach Goldberg, a political science PhD student at Georgia Tech, did a deep dive on LexisNexis, the world’s largest database of publicly available documents, including media reports. He found that over a nine-year period, the rate of news stories using progressive jargon associated with left-wing critical theory and social justice concepts shot into the stratosphere.18 What does this mean? That the mainstream media is framing the general public’s understanding of news and events according to what was until very recently a radical ideology confined to left-wing intellectual elites. It

the right words. In 2014, researchers at Georgia Tech published a study in which they examined over nine million words and phrases used on Kickstarter to determine which language leads to success.25 The most important lesson is that the words and phrases associated with reciprocity and authority produce the best responses, while projects that focus too much on the need for funds fail. The most successful

In this talk, I tell the story of how, when I was first a manager at New York Tech, I didn’t feel like a manager at all. And while I liked the idea of being in charge, I went to work every day feeling like something of a fraud. Even in the early years of Pixar, when I was the president, that feeling didn’t go away. I knew many presidents of other companies and had a good idea of their personality characteristics. They were aggressive and extremely confident. Knowing that I didn’t share many of those traits, again I felt like a fraud. In truth, I was afraid of failure. Not until about eight or nine years ago, I tell them, did the imposter feeling finally go away. I have several things to thank for that evolution: my experience of both weathering our failures and watching our films succeed; my decisions, post–Toy Story, to recommit myself to Pixar and its culture; and my enjoyment of my maturing relationship with Steve and John. Then, after fessing up, I ask the group, “How many of you feel like a fraud?” And without fail, every hand in the room shoots up. As managers, we all start off with a certain amount of trepidation. When we are new to the position, we imagine what the job is in order to get our arms around it, then we compare ourselves against our made-up model. But the job is never what we think it is. The trick is to forget our models about what we “should” be. A better measure of our success is to look at the people on our team and see how they are working together. Can they rally to solve key problems? If the answer is yes, you are managing well.

English mathematician Ada Lovelace and scientist Charles Babbage invented a machine called the “Difference Engine” and then later postulated a more advanced “Analytical Engine,” which used a series of predetermined steps to solve mathematical problems. Babbage hadn’t conceived that the machine could do anything beyond calculating numbers. It was Lovelace who, in the footnotes of a scientific paper she was translating, went off on a brilliant tangent speculating that a more powerful version of the Engine could be used in other ways.13 If the machine could manipulate symbols, which themselves could be assigned to different things (such as musical notes), then the Engine could be used to “think” outside of mathematics. While she didn’t believe that a computer would ever be able to create original thought, she did envision a complex system that could follow instructions and thus mimic a lot of what everyday people did. It seemed unremarkable to some at the time, but Ada had written the first complete computer program for a future, powerful machine—decades before the light bulb was invented. A

Turing figured out that a program and the data it used could be stored inside a computer—again, this was a radical proposition in the 1930s. Until that point, everyone agreed that the machine, the program, and the data were each independent. For the first time, Turing’s universal machine explained why all three were intertwined.

The tools and built environments of hair salons and the platforms powering the airline industry are examples of something called Conway’s law, which says that in absence of stated rules and instructions, the choices teams make tend to reflect the implicit values of their tribe.

In 1968, Melvin Conway, a computer programmer and high school math and physics teacher, observed that systems tend to reflect the people and values who designed them. Conway was specifically looking at how organizations communicate internally, but later Harvard and MIT studies proved his idea more broadly. Harvard Business School analyzed different codebases, looking at software that was built for the same purpose but by different kinds of teams: those that were tightly controlled, and those that were more ad-hoc and open source.10 One of their key findings: design choices stem from how their teams are organized, and within those teams, bias and influence tends to go overlooked.

Our fast, intuitive mind makes thousands of decisions autonomously all day long, and while it’s more energy efficient, it’s riddled with cognitive biases that affect our emotions, beliefs, and opinions. We make mistakes because of the fast side of our brain. We overeat, or drink to excess, or have unprotected sex. It’s that side of the brain that enables stereotyping

We are crossing a threshold into a new reality in which AI is generating its own programs, creating its own algorithms, and making choices without humans in the loop. At the moment, no one, in any country, has the right to interrogate an AI and see clearly how a decision was made.

Since AI isn’t being taught to make perfect decisions, but rather to optimize, our response to changing forces in society matter a lot. Our values are not immutable. This is what makes the problem of AI’s values so vexing. Building AI means predicting the values of the future. Our values aren’t static. So how do we teach machines to reflect our values without influencing them?

What’s not on the table, at the G-MAFIA or BAT, is optimizing for empathy. Take empathy out of the decision-making process, and you take away our humanity. Sometimes what might make no logical sense at all is the best possible choice for us at a particular moment. Like blowing off work to spend time with a sick family member, or helping someone out of a burning car, even if that action puts your own life in jeopardy.

As AI advances, a more robust personal data record will afford greater efficiencies to the Big Nine, and so they will nudge us to accept and adopt PDRs, even if we don’t entirely understand the implications of using them. Of course, in China, PDRs are already being piloted under the auspices of its social credit score.

The mantra is part of a troubling ideology that’s pervasive among the Big Nine: build it first, and ask for forgiveness later.

Like the Patriot Act, the true intentions were without the American people’s knowledge, and for those who had accurate theories about why and what Stanley Braithsworth was doing with his tech company, it was propagandized that it was for “the security of the American people.

One probable near-term outcome of AI and a through-line in all three of the scenarios is the emergence of what I’ll call a “personal data record,” or PDR. This is a single unifying ledger that includes all of the data we create as a result of our digital usage (think internet and mobile phones), but it would also include other sources of information: our school and work histories (diplomas, previous and current employers); our legal records (marriages, divorces, arrests); our financial records (home mortgages, credit scores, loans, taxes); travel (countries visited, visas); dating history (online apps); health (electronic health records, genetic screening results, exercise habits); and shopping history (online retailers, in-store coupon use). In China, a PDR would also include all the social credit score data described in the last chapter.

Skills are taught experientially—meaning that students studying AI don’t have their heads buried in books. In order to learn, they need lexical databases, image libraries, and neural nets. For a time, one of the more popular neural nets at universities was called Word2vec, and it was built by the Google Brain team. It was a two-layer system that processed text, turning words into numbers that AI could understand.17 For example, it learned that “man is to king as woman is to queen.” But the database also decided that “father is to doctor as mother is to nurse” and “man is to computer programmer as woman is to homemaker.”18 The very system students were exposed to was itself biased. If someone wanted to analyze the farther-reaching implications of sexist code, there weren’t any classes where that learning could take place.

Our mission, which we choose to accept, is to blow up the One Percent, who’ve earned their place in this world standing on the tired backs of the Ninety-nine. Step one: Destroy Big Tech. UrbanMyth is a wholly owned subsidiary of TallTale Media Corp., whose CEO was paid a seventeen-million-dollar bonus last year but denied workers the forty hours a week that would have entitled them to benefits. He wouldn’t let go of the brass ring, so it’s time to pry his greedy fingers off of it. Let this data dump be a warning to Big Tech everywhere—pay your workers a living wage or you’re next. Happy reading.

There were nine or ten years between the brat with a borrowed typewriter who lived at home with his mother and the stay-at-home (sometimes) father, and between Dublin’s hottest bass player and the self-employed tech consultant (or something), both waiting for their children to come out of their Educate Together school. —How many have you? —Just the one – yourself? —Three. —Jesus. —I know. Those nine or ten years yawned – a gulf, a different time and world. But the twenty years since feel like a couple of months.

Specifically, they argue that digital technology drives inequality in three different ways. First, by replacing old jobs with ones requiring more skills, technology has rewarded the educated: since the mid-1970s, salaries rose about 25% for those with graduate degrees while the average high school dropout took a 30% pay cut.45 Second, they claim that since the year 2000, an ever-larger share of corporate income has gone to those who own the companies as opposed to those who work there—and that as long as automation continues, we should expect those who own the machines to take a growing fraction of the pie. This edge of capital over labor may be particularly important for the growing digital economy, which tech visionary Nicholas Negroponte defines as moving bits, not atoms. Now that everything from books to movies and tax preparation tools has gone digital, additional copies can be sold worldwide at essentially zero cost, without hiring additional employees. This allows most of the revenue to go to investors rather than workers, and helps explain why, even though the combined revenues of Detroit’s “Big 3” (GM, Ford and Chrysler) in 1990 were almost identical to those of Silicon Valley’s “Big 3” (Google, Apple, Facebook) in 2014, the latter had nine times fewer employees and were worth thirty times more on the stock market.47 Figure 3.5: How the economy has grown average income over the past century, and what fraction of this income has gone to different groups. Before the 1970s, rich and poor are seen to all be getting better off in lockstep, after which most of the gains have gone to the top 1% while the bottom 90% have on average gained close to nothing.46 The amounts have been inflation-corrected to year-2017 dollars. Third, Erik and collaborators argue that the digital economy often benefits superstars over everyone else.

Data is analogous to our world’s oceans. It surrounds us, is an endless resource, and remains totally useless to us unless we desalinate it, treating and processing it for consumption. At