Longform – Abnormal Capitalist

The Original Tiger Kings

Siegfried then took out one of the gold coins that waited in his pocket. He had thousands of them made: look for the magic that is all around you, they read on one side. Then he performed a little magic—close-up magic, quiet and simple, the way he once did, before everything else.

Surrounded by the cats who reminded him so much of his lost partner—the same animals whose hulking presence had helped turn their first day together and every day after into the most extraordinary existence for everyone in their sprawling, magical family—Siegfried heard time and again the same five words his father once said to him: “How did you do that?” He never answered. Instead, Siegfried would smile, press the coin into the hands of one of his guests, and float away, leaving his visitors to stare at one another in silence, and the last of Roy’s tigers to exalt in their wonder.

→ The Atlantic

The Day The Music Burned

The recordings that burned up in the Universal fire — like the songs that are blasting from car windows on the street outside your home, like all the records that you or I or anyone else has ever heard — represent a wonderment that we have come to take for granted. For most of human history, every word spoken, every song sung, was by definition ephemeral: Air vibrated and sound traveled in and out of earshot, never to be heard again. But technology gave humanity the means to catch sounds, to transform a soprano’s warble, a violin’s trill, Chuck Berry’s blaring guitar, into something permanent and repeatable, a sonic artifact to which listeners can return again and again.

→ The New York Times Magazine

The Life and Death of the Kid Who Hunted the Taliban

That night, as darkness enveloped the family’s three-story mud-brick compound, Wasil’s uncles shuffled Hamidullah’s bloodied corpse inside. The boy drew close, his cheeks wet with tears. In the low light, he could see the blood that stained his father’s clothes. He was a child, yes, but he knew enough of his world to realize, without even asking, who had killed his father. And he knew what it meant for him.

In the weeks that followed, Wasil’s anger hardened into a grim and brutal ambition—one that would launch him toward fame and then toward tragedy. “Teach me how to shoot,” Wasil said to his uncle Samad when he had resolved himself to retribution. “I want to kill my father’s killer.”

→ GQ

How to Hide $400 Million

This didn’t just threaten Oesterlund’s fortune. It also had the potential to carve open a portal into the world of offshore finance, a place that the global elite has spent hundreds of millions of dollars to build and defend. In the offshore archipelago, their interests are hidden behind shell companies and trusts, their anonymity guaranteed under the law, from Delaware to the Bahamas to the South Pacific. James S. Henry, a former chief economist at McKinsey, calls the offshore financial world the “economic equivalent of an astrophysical black hole,” holding at least $21 trillion of the world’s financial wealth, more than the gross domestic product of the United States.

→ The New York Times Magazine

The Great A.I. Awakening

And yet the rise of machine learning makes it more difficult for us to carve out a special place for us. If you believe, with Searle, that there is something special about human “insight,” you can draw a clear line that separates the human from the automated. If you agree with Searle’s antagonists, you can’t. It is understandable why so many people cling fast to the former view. At a 2015 M.I.T. conference about the roots of artificial intelligence, Noam Chomsky was asked what he thought of machine learning. He pooh-poohed the whole enterprise as mere statistical prediction, a glorified weather forecast. Even if neural translation attained perfect functionality, it would reveal nothing profound about the underlying nature of language. It could never tell you if a pronoun took the dative or the accusative case. This kind of prediction makes for a good tool to accomplish our ends, but it doesn’t succeed by the standards of furthering our understanding of why things happen the way they do. A machine can already detect tumors in medical scans better than human radiologists, but the machine can’t tell you what’s causing the cancer.

→ The New York Times