OpenAI has launched a plugin for the Apple Messages app, and as Bloomberg notes, it could raise privacy and security concerns. At the moment, it's only available to ChatGPT Work and Codex users on Mac, allowing them to search messages and even draft and send replies through the chatbot on their desktop. In the example OpenAI posted, the user asked ChatGPT to look for conversations they'd missed the day before in their Messages app. They then used the chatbot to write and send their reply. The feature is, at least, opt-in.

Charles Barkley dunks all over pickleball: 'All these old people are killing themselves' Bill Maher calls out the lunacy over Sophie Cunningham's trans comments being twisted The WNBA has limited options after two men declared for the draft, and those options aren't great Ex-MLB pitcher gives his take on Mets reliever Jefry Yan's wild strikeout split celebration President Trump pumps up the'Protect College Sports Act' as the Senate works to pass it Carrie Underwood still has her Sunday Night Football fastball, awkward Jim Harbaugh & Baywatch Livvy Dunne! Former Wisconsin governor calls for'common sense' amid far-left surge Rep. Ralph Norman details bid for South Carolina Senate seat JD Vance warns Iran is'hurting in a big way' as negotiations continue Mike Pompeo says there's'no doubt' Iran is weaker Byron Donalds slams likely Dem opponent as'Trojan horse' for radical left Brian Urlacher backs WNBA's Sophie Cunningham over biological men in women's sports Byron Donalds slams likely Dem opponent as'Trojan horse' for radical left Trump says Iran deal is'coming soon', military expert weighs in The Red Sox set a record as the fastest team in MLB history to go from 14 games under .500 The Boston Red Sox are one win away from tying the longest winning streak in franchise history at 15 games, a record set in 1946. Everything I've written about the 2026 Boston Red Sox has been wrong. It was, and is, fake news.

Beijing is competing with the U.S. for tech supremacy. Who wins will have huge political implications. As Donald Trump has cut research funds, China has pursued what one investor calls an "all-hands-on-deck approach to national innovation." Kai-Fu Lee, the C.E.O. of the artificial-intelligence company 01.AI, lives in a mirrored high-rise in Beijing, near a procession of landmarks that emperors considered the center of the cosmos. Lee takes early meetings at a nearby hotel that could be in any country, except that the room service is delivered by robots painted in the livery of butlers and maids. State propaganda hails these robots as emblems of China's ascent; in the hallway, most people sidestep them with the kind of indifference usually reserved for a Roomba. Over breakfast one recent morning, Lee, wearing a white polo shirt and rimless glasses, spoke about the future with an air of sanguine curiosity. He was born in Taiwan in 1961 and built his career by following the moving ...

New research suggests that an enormous amount of chivalric manuscripts disappeared. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Researchers have recreated the evolutionary trees of medieval texts. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Get your news from a source that's not owned and controlled by oligarchs. The trees are now considered invasive, and their bark contributes to wildfire risk. In 2023, author and poet Victoria Chang watched as the massive eucalyptus tree across the street from her home in Los Angeles was cut down. As the men lopped off the tree's limbs, Chang realized she hadn't spent much time really looking at it. She reflected that the tree had probably taken years to grow and was so easily cut down in just a few days. Chang felt compelled to write poems about this feeling that would later evolve into her latest poetry collection, which asks what it means to be human in the face of nature.

We study the Bayesian fixed-budget best-arm identification problem in which a learner can abstain from making a terminal recommendation. Subject to an abstention budget $α$, we analyze the probability of undetected error--the risk of recommending a suboptimal arm without abstaining. Our central finding is that abstention induces a phase transition: without abstention, the error probability decays polynomially in the sampling budget $T$; in contrast, introducing any small positive abstention budget shifts this to an exponential decay. For Gaussian priors and rewards, in the regime $T\to\infty$ followed by $α\downarrow0$, we establish exact matching information-theoretic lower bounds and algorithmic upper bounds on the optimal error exponent, which takes the form $\exp(-\frac{α^{2}T}{8κ_ν^{2}})$. The hardness parameter $κ_ν$ represents the prior density of the top-two gap at zero, highlighting that nearly tied instances drive the fundamental error. We introduce an adaptive algorithm, PGWS, that successfully achieves this optimal exponent by expending its abstention budget on statistically ambiguous instances. We further demonstrate that this polynomial-to-exponential improvement is exclusively a Bayesian phenomenon--in the frequentist setting, abstention only affects lower-order exponent terms. We also extend our results beyond the Gaussian model.

Lost books by ancient philosophers recovered from'unreadable' scrolls Long-lost works of ancient philosophy have been recovered from papyrus scrolls that were scorched by the AD 79 eruption of Mount Vesuvius and thought to be impossible to read. For the first time, researchers have used AI to extract the entire surviving text from super-high-resolution 3D scans of a scroll without unrolling it. The scrolls come from the library of Herculaneum, which was buried along with Pompeii nearly 2000 years ago. Scholars have been trying to read the carbonised scrolls, which resemble lumps of charcoal, since the library was discovered in 1752. Physically unwrapping them risks their destruction and the ink they are written in is mostly indistinguishable from the charred papyri - at least to human eyes.

Google's first new smart speaker in six years is here and once again leads its competitors--now with paywalled features. Sounds a little more human than competitors. Gemini is helpful and smart. Some assistant features are hidden behind paywalls. Works best if you buy or have bought several Google devices for your home.

In many domains, practitioners seek models that produce accurate forecasts while faithfully capturing latent system dynamics. Existing approaches typically sacrifice one of these goals: deep state space models often assume Gaussian latent transitions, limiting fit and forecasting, while diffusion models are highly expressive but lack principled inference for the underlying dynamics. To combine the strengths of both, we introduce the Diffusion-Driven State Space Model (DDSSM), which replaces the conventional Gaussian transition distribution with a diffusion model. Our DDSSM resolves the open problem of how to jointly train an autoencoder and a diffusion model on sequential data, thereby extending the literature on latent diffusion models for time series. Moreover, we find that the DDSSM empirically outperforms a state-of-the-art deep SSM at fitting and forecasting a simulated time series with multimodal transitions.

Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfall of this approach is that it leads to doubly intractable objectives that are difficult to optimise, while customising them to particular downstream tasks of interest can also be difficult. Following first principles decision theory, we demonstrate that BED can alternatively be formulated in terms of an expected future loss (EFL) on downstream actions, providing a simple and naturally task-driven framework. Critically, we then show that all such EFLs can be rearranged into singly intractable objectives that can be jointly optimised with respect to both the design policy and a downstream action policy using stochastic gradients, an approach we refer to as ACTION-BED. This formulation further sidesteps the need for any explicit posterior or marginal likelihood estimation and is naturally implicit, requiring only the ability to sample from the joint model over model parameters and data, and evaluate the downstream loss function. It thus allows design policies to be learned more effectively, efficiently, and simply than existing methods, while providing easy customisation to different downstream tasks and losses.