Why weren't they grateful?

Robert Caro looks back on The Power Broker 40 years after it was published.

Why weren’t they grateful? As I recalled that Exedra scene in 1969, as I was trying to organize my book, I suddenly knew, all in a moment, that that question would be its last line. For the book would have to answer that very question, would have to answer the riddle posed by the Moses Men: How could there not be gratitude, immense gratitude, to the man who had dreamed a great dream — of Jones Beach and a dozen other great parks, and of parkways to reach them — and who to create them had fought, and won, an epic battle against Long Island’s seemingly invincible robber barons? How could there not be gratitude to the man who had built mighty Triborough, far-­stretching Verrazano, who had made possible Lincoln Center and the United Nations? And yet there were ample answers to that question. Did I think in that moment of Robert Moses’ racism — unashamed, unapologetic? Convinced that African-Americans were inherently “dirty,” and that they don’t like cold water (“They simply didn’t like swimming unless it was red hot,” he explained to me confidentially one day), he kept the water temperature deliberately frigid in pools, like the ones at Jones Beach and Thomas Jefferson Park in Manhattan, that he didn’t want them to use. Did I think of the bridges he built that embodied racism in concrete? When he opened his Long Island parks during the 1930s, the only way for many poor people, particularly poor people of color, to reach them was by bus, so he built bridges over his parkways too low for buses to pass. Or of the “slum clearance” projects he built that seemingly created new slums as fast he was clearing the old, or of the public housing he placed in locations that cemented the division of New York by race and class? Did I think in that moment of the more than half a million people he dispossessed for his projects and expressways, using methods that led one observer to say that “he hounded them out like cattle”? Did I think of how he systematically starved New York’s subways and commuter lines for decades and blocked proposals to build new ones, exacerbating the region’s dependence on the automobile? I don’t remember exactly what I thought of when I remembered Robert Moses’ speech at the Exedra — only that in that moment, seeing the book’s last line, I suddenly saw the book whole, saw the shape of everything that would lead up to that line. I began organizing the book, the thoughts coming faster, I recall, than I could write. Over the next days, I outlined the book — in a quite detailed outline — from beginning to end. Some parts of what I wrote from the outline would later have to be truncated or cut out entirely so that the book could fit into one volume; aside from these deletions, “The Power Broker” as it was published follows that outline all the way through.

The New Yorker Story

This paragraph from Jonathan Franzen on the birth of the New Yorker story is spot on.

It was also in the fifties that “the New Yorker story” emerged, quite suddenly, as a distinct literary genus. What made a story New Yorker was its carefully wrought, many-comma’d prose; its long passages of physical description, the precision and the sobriety of which created a kind of negative emotional space, a suggestion of feeling without the naming of it; its well-educated white characters, who could be found experiencing the melancholies of affluence, the doldrums of suburban marriage, or the thrill or the desolation of adultery; and, above all, its signature style of ending, which was either elegantly oblique or frustratingly coy, depending on your taste. Outside the offices of The New Yorker, its fiction editors were rumored to routinely delete the final paragraph of any story accepted for publication.

Just hard enough

Jason Calacanis once asked Peter Thiel why Paypal produced so many great founders. What was it about Paypal? I loved Peter’s answer — Most people’s experiences with startups fall into one of two categories. Many work for startups that fail and learn that startups are impossible so they never try. Others work for startups like Google or Facebook and learn that startups are easy so they quit when it gets hard. Paypal was “just hard enough”. Early Paypal employees learned that startups are really hard, but it is still possible to succeed.
 

I don't know if that's true, but it sounds great. (source)

RankBrain

For the past few months, a “very large fraction” of the millions of queries a second that people type into the company’s search engine have been interpreted by an artificial intelligence system, nicknamed RankBrain, said Greg Corrado, a senior research scientist with the company, outlining for the first time the emerging role of AI in search.
 
RankBrain uses artificial intelligence to embed vast amounts of written language into mathematical entities -- called vectors -- that the computer can understand. If RankBrain sees a word or phrase it isn’t familiar with, the machine can make a guess as to what words or phrases might have a similar meaning and filter the result accordingly, making it more effective at handling never-before-seen search queries.
 
[...]
 
RankBrain is one of the “hundreds” of signals that go into an algorithm that determines what results appear on a Google search page and where they are ranked, Corrado said. In the few months it has been deployed, RankBrain has become the third-most important signal contributing to the result of a search query, he said.
 
[...]
 
So far, RankBrain is living up to its AI hype. Google search engineers, who spend their days crafting the algorithms that underpin the search software, were asked to eyeball some pages and guess which they thought Google’s search engine technology would rank on top. While the humans guessed correctly 70 percent of the time, RankBrain had an 80 percent success rate.
 

More on RankBrain here.

Machine learning is advancing fast. At its best it feels a bit like magic, and it's endlessly malleable. Think it's missing something of importance? Add it as a factor, or tune it up.

I suspect in my lifetime we'll have machine learning so good it will be largely incomprehensible to me. That is, it won't be understandable by using analogies to how humans think because it will be its own form of intelligence.