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.

Abby Johnson, former Planned Parenthood clinic director, releases documentary "Unthinkable" exposing what she calls abortion industry secrets after leaving in 2009.

The developers of a browser tool that changes AI-centric LinkedIn posts to Allen Iverson facts want to help "take back control of your experience of the internet." Give yourself a nice gift this holiday season. Download a free Chrome extension that replaces those incessant LinkedIn posts about artificial intelligence with facts about a very different kind of AI: Allen Iverson. Yes, the answer to your generative AI woes is "The Answer," the crossover king, the four-time NBA scoring champ. One of the defining traits of LinkedIn has always been unhinged posts from power users--the r/LinkedInLunatics subreddit exists for a reason--but the obsessive tenor of LinkedIn posting has become, somehow, more unbearable over the past few years as the generative AI hype cycle has grown.

House Speaker Mike Johnson, R-La., defends congressional investigations into the deadly Caribbean strike while comparing it to Obama-era drone operations.

Do you need more sleep in fall and winter? Less sunlight, colder weather, and diet changes make us sleepier--and that's OK. Winter mornings make staying under the covers feel impossible to resist. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a crisp, fall day in mid-November, and though your calendar is filled with evening get-togethers and morning runs, you're feeling sluggish.

Sea lampreys--invasive, leechlike creatures that once nearly destroyed the Great Lakes' fishing economy--are kept in check by a small U.S.-Canadian program. Will it survive Trump's slash-and-burn campaign? Ally Porter walked ahead of me as we sidestepped down a steep, loamy embankment. Our path lit only by headlamps, a waning sliver of moon, and what seemed to be thousands of stars, we made our way to a mucky riverbank about twenty feet below. At one point, I lost my footing and ended up wedged against a tree trunk. Porter, who had two tight braids that landed just below her shoulders, kept going. She moved with ease through several inches of sludge, toward a yellow glow stick tied to a tree at the water's edge.

Determining the appropriate number of clusters in unsupervised learning is a central problem in statistics and data science. Traditional validity indices such as Calinski-Harabasz, Silhouette, and Davies-Bouldin-depend on centroid-based distances and therefore degrade in high-dimensional or contaminated data. This paper proposes a new robust, nonparametric clustering validation framework, the High-Dimensional Between-Within Distance Median (HD-BWDM), which extends the recently introduced BWDM criterion to high-dimensional spaces. HD-BWDM integrates random projection and principal component analysis to mitigate the curse of dimensionality and applies trimmed clustering and medoid-based distances to ensure robustness against outliers. We derive theoretical results showing consistency and convergence under Johnson-Lindenstrauss embeddings. Extensive simulations demonstrate that HD-BWDM remains stable and interpretable under high-dimensional projections and contamination, providing a robust alternative to traditional centroid-based validation criteria. The proposed method provides a theoretically grounded, computationally efficient stopping rule for nonparametric clustering in modern high-dimensional applications.

U.S. District Judge Terry Moorer fined Alabama lawyer James Johnson $5,000 for using artificial intelligence to create fake case citations in court filings.

This work focuses on visualizing uncertainty of local divergence of two-dimensional vector fields. Divergence is one of the fundamental attributes of fluid flows, as it can help domain scientists analyze potential positions of sources (positive divergence) and sinks (negative divergence) in the flow. However, uncertainty inherent in vector field data can lead to erroneous divergence computations, adversely impacting downstream analysis. While Monte Carlo (MC) sampling is a classical approach for estimating divergence uncertainty, it suffers from slow convergence and poor scalability with increasing data size and sample counts. Thus, we present a two-fold contribution that tackles the challenges of slow convergence and limited scalability of the MC approach. (1) We derive a closed-form approach for highly efficient and accurate uncertainty visualization of local divergence, assuming independently Gaussian-distributed vector uncertainties. (2) We further integrate our approach into Viskores, a platform-portable parallel library, to accelerate uncertainty visualization. In our results, we demonstrate significantly enhanced efficiency and accuracy of our serial analytical (speed-up up to 1946X) and parallel Viskores (speed-up up to 19698X) algorithms over the classical serial MC approach. We also demonstrate qualitative improvements of our probabilistic divergence visualizations over traditional mean-field visualization, which disregards uncertainty. We validate the accuracy and efficiency of our methods on wind forecast and ocean simulation datasets.

'To them, ageing is a technical problem that can, and will, be fixed': how the rich and powerful plan to live for ever When Xi Jinping and Vladimir Putin were caught on mic talking about living for ever, it seemed straight out of a sci-fi fantasy. You have everything you could want at your disposal: power, influence, money. But, the problem is, your time at the top is fleeting. In early September, China's Xi Jinping and Russia's Vladimir Putin were caught on mic talking about strategies to stay young. "With the development of biotechnology, human organs can be continuously transplanted, and people can live younger and younger, and even achieve immortality," Putin said via an interpreter to Xi. "There's a chance," he continued, "of also living to 150 [years old]." But is this even possible, and what would it mean for the world if the people with power were able to live for ever? Over the centuries, we have used ever more sophisticated technology to heal ourselves into unprecedented longevity. In the 20th century, it was innovations in public health and medicine that effected this transformation, allowing today's children to live longer, healthier lives than at any time in history.