Researchers at the company are trying to understand their A.I. system's mind--examining its neurons, running it through psychology experiments, and putting it on the therapy couch. It has become increasingly clear that Claude's selfhood, much like our own, is a matter of both neurons and narratives. A large language model is nothing more than a monumental pile of small numbers. It converts words into numbers, runs those numbers through a numerical pinball game, and turns the resulting numbers back into words. Similar piles are part of the furniture of everyday life. Meteorologists use them to predict the weather. Epidemiologists use them to predict the paths of diseases. Among regular people, they do not usually inspire intense feelings. But when these A.I. systems began to predict the path of a sentence--that is, to talk--the reaction was widespread delirium. As a cognitive scientist wrote recently, "For hurricanes or pandemics, this is as rigorous as science gets; for sequences of words, everyone seems to lose their mind." It's hard to blame them. Language is, or rather was, our special thing. We weren't prepared for the arrival of talking machines. Ellie Pavlick, a computer scientist at Brown, has drawn up a taxonomy of our most common responses. There are the "fanboys," who man the hype wires. They believe that large language models are intelligent, maybe even conscious, and prophesy that, before long, they will become superintelligent. The venture capitalist Marc Andreessen has described A.I. as "our alchemy, our Philosopher's Stone--we are literally making sand think." The fanboys' deflationary counterparts are the "curmudgeons," who claim that there's no there, and that only a blockhead would mistake a parlor trick for the soul of the new machine. In the recent book " The AI Con," the linguist Emily Bender and the sociologist Alex Hanna belittle L.L.M.s as "mathy maths," "stochastic parrots," and "a racist pile of linear algebra." But, Pavlick writes, "there is another way to react." It is O.K., she offers, "to not know." What Pavlick means, on the most basic level, is that large language models are black boxes. We don't really understand how they work. We don't know if it makes sense to call them intelligent, or if it will ever make sense to call them conscious. The existence of talking machines--entities that can do many of the things that only we have ever been able to do--throws a lot of other things into question. We refer to our own minds as if they weren't also black boxes.

How a gift for shooting the shit turned into an online empire--and a political force. Trust in American mass media has plummeted; more than three thousand newspapers have disappeared in the past two decades, and many people get their news from social platforms. In this chaotic media multiverse, Rogan has emerged as a figure of singular influence. For a long time, I stayed up through the night listening to tall-tale tellers, U.F.O. I could not get enough of it. I was a fairly ordinary kid, Jersey-born, but the house I lived in was shadowed by illness. My mother had been diagnosed with a debilitating neurological disease when she was in her early thirties. Every year, she got worse. During the day, I wanted nothing more than to please my mother, do well in school, lighten her load. At night, I wanted only to climb into the shelter of my bed and turn on the radio. I was hungry for elsewhere, for other lives--for what was being said down the street, over the bridge, beyond the horizon. On clear nights, the signal was strong. You could hear the country expressing itself incessantly: everyone was phoning in, suggesting three-way trades, bitching about the mayor, speaking in tongues, raging, joking, climbing out on a ledge and threatening to jump. When I wanted a few hours of sleep before school, I tuned in to a ballgame on the West Coast. The staticky murmur of the crowd in Anaheim or Chavez Ravine was a sure slide to oblivion. Mostly, though, I wanted nothing to do with sleep. Mostly, I was tuned in, midnight to five-thirty, to "The Long John Nebel Show."

Pierre Huyghe's A.I. Art Monster Takes Over a Night Club in Berlin In "Liminals," a terrifying, overwhelming new installation, the artist erases the boundary between humans and the void. At the heart of the new piece is a fifty-five-minute film looped on an enormous screen. My preparation for "Liminals," an art work by Pierre Huyghe showing in Berlin, at Halle am Berghain, involved a small suitcase of books and articles about quantum physics, the science of sound, post-1968 France, relational aesthetics, and the sociology of techno. In the end, none of them proved useful. Among the heady possibilities dangled by the press release was an environment that would feature video, sound, light, and dust; exist outside of space and time; and operate in a state of quantum flux where "every moment is a maybe."

Clockwise from top left: Rachel Johnson, Polly Toynbee, Jeanette Winterson, Cathy Newman, Ann Widdecombe, Fiona Marr. Clockwise from top left: Rachel Johnson, Polly Toynbee, Jeanette Winterson, Cathy Newman, Ann Widdecombe, Fiona Marr. I asked AI to name my wife. Authors, a newsreader, a lawyer and an esteemed colleague: they're all great - but I'm not married to any of them. Can we really depend on this technology?

Australia's AI boom may revive productivity, CBA says Cabinets housing servers inside a data hall at a NextDC data center in Sydney. The company partnered with OpenAI last December to build a large-scale computing cluster in Sydney. Australia has become the world's third-largest artificial intelligence investment destination behind the U.S. and China, a result that's set to spur productivity in an economy currently struggling with a low potential growth rate and high inflation, Commonwealth Bank of Australia says. CBA's updated estimates suggest that Australia's data center pipeline is closer to 6 gigawatt, or 150 billion Australian dollars ($105 billion), implying installed capacity could more than triple over the period to 2030, according to a research note released Monday by economists led by Luke Yeaman. Shares in Australian data centers jumped following the report, with Goodman Group rising as much as 6.9%, the most since Dec. 23.

Predominately in explainable artificial intelligence (XAI) research, the Shapley value (SV) is applied to determine feature attributions for any black box model. Shapley interaction indices extend the SV to define any-order feature interactions. Defining a unique Shapley interaction index is an open research question and, so far, three definitions have been proposed, which differ by their choice of axioms. Moreover, each definition requires a specific approximation technique. Here, we propose SHAPley Interaction Quantification (SHAP-IQ), an efficient sampling-based approximator to compute Shapley interactions for arbitrary cardinal interaction indices (CII), i.e. interaction indices that satisfy the linearity, symmetry and dummy axiom.