News 13.11.23: Five Essential Articles from Around the Web

News 13.11.23: Five Essential Articles from Around the Web
News 13.11.23: Five Essential Articles from Around the Web
News 13.11.23: Five Essential Articles from Around the Web

Generative artificial intelligence is a headspace and a technology—as much an event playing out in our minds as it is a material reality emerging at our fingertips. Fast and fluent, AI writing and image-making machines inspire in us visions of doomsday or a radiant posthuman future. They raise existential questions about themselves and ourselves. And, not least, they should lead us to reconsider certain neglected thinkers of recent intellectual history.

Consider a few of the bolder claims made by experts. Two years ago, Blaise Agüera y Arcas, vice president of Google Research, had already declared the end of the animal kingdom’s monopoly on language on the strength of Google’s experiments with large language models. LLMs, he argued, “illustrate for the first time the way that language understanding and intelligence can be dissociated from all the embodied and emotional characteristics we share with each other and with many other animals.”1 In a similar vein, the Stanford University computer scientist Christopher Manning has argued that if “meaning” constitutes “understanding of the network of connections between linguistic form and other things,” be they “objects in the world or other linguistic forms,” then “there can be no doubt” that LLMs can “learn meanings.”2 Again, the point is that humans have company. The philosopher Tobias Rees (among many others) has gone further, arguing that LLMs constitute a “far-reaching, epoch-making philosophical event” on par with the shift from the premodern conception of language as a divine gift to the modern notion of language as a distinctly human trait, even our defining one. On Rees’s telling, engineers at OpenAI, Google, and Facebook have become the new Descartes and Locke, “[rendering] untenable the idea that only humans have language” and thereby undermining the modern paradigm those philosophers inaugurated. LLMs, for Rees at least, signal modernity’s end.3

Rees calls the AI developers “philosophical laboratories” because “they disrupt the old concepts/ontologies we live by.”4 That characterization is somewhat misleading. Those disruptive engineers do not constitute a philosophical school in a traditional sense, since they aren’t advancing a positive philosophical program (such as explicit new theories of language or consciousness). And by their own admission, they lack important answers about how and why LLMs work. Yet unquestionably, the technology is blazing some kind of trail—whither, no one knows for sure—leaving us to philosophize in its wake, just as Manning, Agüera y Arcas, and Rees have done.

In this respect, current debates about writing machines are not as fresh as they seem. As is quietly acknowledged in the footnotes of scientific papers, much of the intellectual infrastructure of today’s advances was laid decades ago. In the 1940s, the mathematician Claude Shannon demonstrated that language use could be both described by statistics and imitated with statistics, whether those statistics were in human heads or a machine’s memory. Shannon, in other words, was the first statistical language modeler, which makes ChatGPT and its ilk his distant brainchildren. Shannon never tried to build such a machine, but some astute early readers of his work recognized that computers were primed to translate his paper-and-ink experiments into a powerful new medium. In writings now discussed largely in niche scholarly and computing circles, these readers imagined—and even made preliminary sketches of—machines that would translate Shannon’s proposals into reality. These readers likewise raised questions about the meaning of such machines’ outputs and wondered what the machines revealed about our capacity to write.

Read the rest of this article at: The Hedgehog Review

For the greater part of two decades, Sally Snowman has lived and worked contentedly on Little Brewster Island, a craggy patch of bare rock, crabgrass, concrete, and dilapidated buildings in Boston’s outer harbor. Under the auspices of the Coast Guard, she serves as the keeper, and the historian, of Boston Light. The lighthouse, opened in September, 1716, was the first in the American colonies, and Snowman is the last official keeper in the United States.

The lighthouse is a white tower, eighty-nine feet tall, whose east windows face across the North Atlantic toward the English coast, some three thousand miles away. Snowman, a plainspoken New Englander with mariner roots that reach back three centuries, maintains a crisp official manner while on duty. But sometimes, standing in the lantern room, she contemplates what it was like to undergo the voyage to the New World on a merchant’s galleon—made by hand from little more than oak, rope, tar, and flax cloth. Along with violent seasickness, passengers suffered from fever, dysentery, boils, scurvy, mouth rot, rat bites, and lice so copious that they could be scraped off the body. When gales raged, one emigrant wrote, people “cry and pray most piteously,” and “everyone believes that the ship will go to the bottom.” A woman on that crossing, incapacitated by a stalled labor, was shoved through a porthole into the sea. “It was a horrible trip,” Snowman said. “Imagine what they felt when they spotted the light.”

We met for an excursion to the lighthouse one morning in August, at a marina in North Weymouth where Snowman keeps a banged-up Maritime skiff. “Bring rain gear,” she’d e-mailed. “40% chance of rain. Seas 2 ft . . . could be a bit bumpy.” A slight woman with a tanned, friendly face, she greeted me on the gangway in Coast Guard blue: ball cap, fleece, and drip-dry cargo pants. The crew—her brother-in-law Jack Richardson and her husband, Jay Thomson—was preparing for departure. Snowman met Thomson in 1993, when he attended an advanced training session that she led for the Coast Guard Auxiliary, the service’s volunteer corps. His T-shirt identified him in white lettering: “KEEPER’S HUSBAND.”

Read the rest of this article at: The New Yorker

News 13.11.23: Five Essential Articles from Around the Web

Taylor Swift’s new album, 1989, has sold over 1.5 million copies so far—and not just to teenage girls. A Time reporter admitted his whole office was upset they couldn’t stream the album on Spotify. “Saturday Night Live” suggests a “cure” for adult Swift fans.

Swift may have cross-generational appeal, but there’s a difference between how kids and adults respond to music. In a 2013 paper in the Journal of Personality and Social Psychology, a team of psychologists, led by Arielle Bonneville-Roussy at the University of Cambridge, designed a study to look at how our music-listening habits and attitudes toward music change over the course of our lives.

Other researchers had observed correlations between various personality traits and taste in music: preference for classical music and jazz is positively associated with openness, imagination, liberal values, and verbal ability; preference for “intense” music like heavy metal and punk is correlated with sociability and physical attractiveness.

Read the rest of this article at: The New Republic

News 13.11.23: Five Essential Articles from Around the Web

In 2019, Debra Halsch was diagnosed with smoldering multiple myeloma, a rare blood and bone marrow disorder that can develop into a type of blood cancer. Her doctors recommended chemotherapy, she said, but she feared the taxing side effects the drugs might wreak on her body. Instead, the life coach from Piermont, New York tried meditation.

A friend had told Halsch, now 57, about Joe Dispenza, who holds week-long meditation retreats that regularly attract thousands of people and carry a $2,299 price tag. Halsch signed up for one in Cancun, Mexico and soon became a devotee. She now meditates for at least two hours a day and says her health has improved as a result.

Dispenza, a chiropractor who has written various self-help books, has said he believes the mind can heal the body. After all, he says he healed himself back in 1986, when a truck hit him while he was bicycling, breaking six vertebrae. Instead of surgery, Dispenza says he spent hours each day recreating his spine in his mind, visualizing it healthy and healed. After 11 weeks, the story goes, he was back on his feet.

Halsch said she believes she can do the same for her illness. “If our thoughts and emotions can make our bodies sick, they can make us well, too,” she said.

In an email to Undark, Rhadell Hovda, chief operating officer for Dispenza’s parent company, Encephalon, Inc., emphasized that Dispenza does not claim meditation can treat or cure cancer. However, he does “follow the evidence when it is presented,” and has encountered people at workshops and retreats “who claimed to have healed from many conditions.”

Read the rest of this article at: Undark

In your brain, neurons are arranged in networks big and small. With every action, with every thought, the networks change: neurons are included or excluded, and the connections between them strengthen or fade. This process goes on all the time—it’s happening now, as you read these words—and its scale is beyond imagining. You have some eighty billion neurons sharing a hundred trillion connections or more. Your skull contains a galaxy’s worth of constellations, always shifting.

Geoffrey Hinton, the computer scientist who is often called “the godfather of A.I.,” handed me a walking stick. “You’ll need one of these,” he said. Then he headed off along a path through the woods to the shore. It wound across a shaded clearing, past a pair of sheds, and then descended by stone steps to a small dock. “It’s slippery here,” Hinton warned, as we started down.

New knowledge incorporates itself into your existing networks in the form of subtle adjustments. Sometimes they’re temporary: if you meet a stranger at a party, his name might impress itself only briefly upon the networks in your memory. But they can also last a lifetime, if, say, that stranger becomes your spouse. Because new knowledge merges with old, what you know shapes what you learn. If someone at the party tells you about his trip to Amsterdam, the next day, at a museum, your networks may nudge you a little closer to the Vermeer. In this way, small changes create the possibility for profound transformations.

“We had a bonfire here,” Hinton said. We were on a ledge of rock jutting out into Ontario’s Georgian Bay, which stretches to the west into Lake Huron. Islands dotted the water; Hinton had bought this one in 2013, when he was sixty-five, after selling a three-person startup to Google for forty-four million dollars. Before that, he’d spent three decades as a computer-science professor at the University of Toronto—a leading figure in an unglamorous subfield known as neural networks, which was inspired by the way neurons are connected in the brain. Because artificial neural networks were only moderately successful at the tasks they undertook—image categorization, speech recognition, and so on—most researchers considered them to be at best mildly interesting, or at worst a waste of time. “Our neural nets just couldn’t do anything better than a child could,” Hinton recalled. In the nineteen-eighties, when he saw “The Terminator,” it didn’t bother him that Skynet, the movie’s world-destroying A.I., was a neural net; he was pleased to see the technology portrayed as promising.

Read the rest of this article at: The New Yorker