Earlier today, Meta announced that it was testing a new Meta AI chatbot for Threads that would function a lot like Grok on X. Even though the early beta isn't available to most people on the platform yet, a number of Threads users have discovered its not possible to opt out of the feature or block chatbot's the account. While most people aren't able to interact with bot yet -- the initial testing is limited to Malaysia, Saudi Arabia, Mexico, Argentina and Singapore -- the public-facing @ meta.ai account is viewable to everyone on the platform. The account's initial post has been met with a flood of angry replies from users demanding to know why, unlike any other Threads account, there's no option to block it entirely. Some users have even said that they have reported the account for spam, which typically ends with the option to block, only to find out that the block didn't actually go into effect.

Pairwise comparisons are widely used in decision analysis, preference modeling, and evaluation problems. In many practical situations, the observed comparison matrix is not reciprocal. This lack of reciprocity is often treated as a defect to be corrected immediately. In this article, we adopt a different point of view: part of the nonreciprocity may reflect a genuine variation in the evaluation scale, while another part is due to random perturbations. We introduce an additive model in which the unknown underlying comparison matrix is consistent but not necessarily reciprocal. The reciprocal component carries the global ranking information, whereas the symmetric component describes possible scale variation. Around this structured matrix, we add a random perturbation and show how to estimate the noise level, assess whether the scale variation remains moderate, and assign probabilities to admissible ranking regions in the sense of strict ranking by pairwise comparisons. We also compare this approach with the brutal projection onto reciprocal matrices, which suppresses all symmetric information at once. The Gaussian perturbation model is used here not because human decisions are exactly Gaussian, but because observed judgment errors often result from the accumulation of many small effects. In such a context, the central limit principle provides a natural heuristic justification for Gaussian noise. This makes it possible to derive explicit estimators and probability assessments while keeping the model interpretable for decision problems.

The natural gradient method is widely used in statistical optimization, but its standard formulation assumes a Euclidean parameter space. This paper proposes an inversion-free stochastic natural gradient method for probability distributions whose parameters lie on a Riemannian manifold. The manifold setting offers several advantages: one can implicitly enforce parameter constraints such as positive definiteness and orthogonality, ensure parameters are identifiable, or guarantee regularity properties of the objective like geodesic convexity. Building on an intrinsic formulation of the Fisher information matrix (FIM) on a manifold, our method maintains an online approximation of the inverse FIM, which is efficiently updated at quadratic cost using score vectors sampled at successive iterates. In the Riemannian setting, these score vectors belong to different tangent spaces and must be combined using transport operations. We prove almost-sure convergence rates of $O(\log{s}/s^α)$ for the squared distance to the minimizer when the step size exponent $α>2/3$. We also establish almost-sure rates for the approximate FIM, which now accumulates transport-based errors. A limited-memory variant of the algorithm with sub-quadratic storage complexity is proposed. Finally, we demonstrate the effectiveness of our method relative to its Euclidean counterparts on variational Bayes with Gaussian approximations and normalizing flows.

Transformer architectures have achieved remarkable empirical success in modeling contextual relationships in natural language, yet a precise mathematical characterization of their expressive power remains incomplete. In this work, we introduce a measure-theoretic framework for contextual representations in which texts are modeled as probability measures over a semantic embedding space, and contextual relations between words, are represented as coupling measures between them. Within this setting, we introduce Sinkhorn Transformer, a transformer-like architecture. Our main result is a universal approximation theorem: any continuous coupling function between probability measures, that encodes the semantic relation coupling measure, can be uniformly approximated by a Sinkhorn Transformer with appropriate parameters.

Panel data methods are widely used in empirical analysis to address unobserved heterogeneity, but causal inference remains challenging when treatments are endogenous and confounding variables high-dimensional and potentially nonlinear. Standard instrumental variables (IV) estimators, such as two-stage least squares (2SLS), become unreliable when instrument validity requires flexibly conditioning on many covariates with potentially non-linear effects. This paper develops a Double Machine Learning estimator for static panel models with endogenous treatments (panel IV DML), and introduces weak-identification diagnostics for it. We revisit three influential migration studies that use shift-share instruments. In these settings, instrument validity depends on a rich covariate adjustment. In one application, panel IV DML strengthens the predictive power of the instrument and broadly confirms 2SLS results. In the other cases, flexible adjustment makes the instruments weak, leading to substantially more cautious causal inference than conventional 2SLS. Monte Carlo evidence supports these findings, showing that panel IV DML improves estimation accuracy under strong instruments and delivers more reliable inference under weak identification.

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They don't cope well in certain situations and the'transparent' mode lets in unwanted noise

Timothee Chalamet, Oscars laughing stock: All the brutal digs aimed at star after he missed out on Best Actor and'looked like he wanted to cry' A-list stars ditch formal Oscars red carpet dresses for sexy party looks - with Jeff Goldblum's wife Emilie Livingston, Heidi Klum, Amelia Gray Hamlin and Kate Hudson turning up the heat at Vanity Fair bash Teyana Taylor erupts backstage at Oscars after being'shoved' Chilling new details of dismembered Emily Pike's final hours after she was snatched in Arizona desert and man detectives now believe murdered her Dark truth about secret new filler treatment that uses tissue from DEAD PEOPLE... as doctors issue urgent warning Awful Timothee Chalamet's ego is bigger than Kylie's inflated butt... but it's so clear what's really going on here. Israel blows up Ayatollah Khamenei's personal jet amid claims his injured heir Mojtaba'has been flown to Moscow for treatment' Kate lets Diana take the spotlight: Princess skips Mother's Day post after emotional cancer message and Photoshop furore Baseball fans fume after'terrible' umpire error ends USA's controversial showdown with Dominican Republic in WBC semifinal How Oscars 2026 proved Hollywood has overdosed on Ozempic: Leading doctors name stars now at'extreme' risk... and reveal terrifying new side effects Trump warns of'very bad future' for Nato if his call for warships to police Strait of Hormuz is refused - hinting he could punish Ukraine Kim Kardashian struggles to WALK in skintight golden gown and towering'stripper heels' as she attends the Vanity Fair Oscars party Oscars presenter Kumail Nanjiani blasted for horrific Holocaust joke: 'Do not invite him back' Real reason Sean Penn skipped Oscars 2026... as disappointed fans blast his boycott'It's like he was possessed': Terrifying moment Alexander brother turned into a'monster' and raped me... and the four chilling words he said after horror attack - alleged victim claims Dubai'arrests foreign survivors of Iranian drone strike after they sent images of explosion aftermath to loved ones to prove they were safe' The Mediterranean is overdue a TSUNAMI: Scientists warn there's a 100% chance an enormous wave will hit the French Riviera in the next 30 years - as they call for urgent evacuation drills The French Riviera is famed for its sunny, year-round climate, azure waters and luxury resorts - but it'll be hit by a tsunami in the next 30 years, scientists predict. Experts say there is a '100 per cent' chance a great wave will form in the Mediterranean Sea in the next few decades. The tsunami could hit France's southern coastline in as little as 10 minutes from the trigger, causing chaos for tens of thousands of people who flock there during the summer months. While the country does have a national tsunami alert system, this only covers waves caused by distant earthquakes .

Timothee Chalamet, Oscars laughing stock: All the brutal digs aimed at star after he missed out on Best Actor and'looked like he wanted to cry' A-list stars ditch formal Oscars red carpet dresses for sexy party looks - with Jeff Goldblum's wife Emilie Livingston, Heidi Klum, Amelia Gray Hamlin and Kate Hudson turning up the heat at Vanity Fair bash Teyana Taylor erupts backstage at Oscars after being'shoved' Chilling new details of dismembered Emily Pike's final hours after she was snatched in Arizona desert and man detectives now believe murdered her Dark truth about secret new filler treatment that uses tissue from DEAD PEOPLE... as doctors issue urgent warning Awful Timothee Chalamet's ego is bigger than Kylie's inflated butt... but it's so clear what's really going on here. Israel blows up Ayatollah Khamenei's personal jet amid claims his injured heir Mojtaba'has been flown to Moscow for treatment' Kate lets Diana take the spotlight: Princess skips Mother's Day post after emotional cancer message and Photoshop furore Baseball fans fume after'terrible' umpire error ends USA's controversial showdown with Dominican Republic in WBC semifinal How Oscars 2026 proved Hollywood has overdosed on Ozempic: Leading doctors name stars now at'extreme' risk... and reveal terrifying new side effects Trump warns of'very bad future' for Nato if his call for warships to police Strait of Hormuz is refused - hinting he could punish Ukraine Kim Kardashian struggles to WALK in skintight golden gown and towering'stripper heels' as she attends the Vanity Fair Oscars party Oscars presenter Kumail Nanjiani blasted for horrific Holocaust joke: 'Do not invite him back' Real reason Sean Penn skipped Oscars 2026... as disappointed fans blast his boycott'It's like he was possessed': Terrifying moment Alexander brother turned into a'monster' and raped me... and the four chilling words he said after horror attack - alleged victim claims Dubai'arrests foreign survivors of Iranian drone strike after they sent images of explosion aftermath to loved ones to prove they were safe' Is Antarctica's Doomsday Glacier about to COLLAPSE? Antarctica's Doomsday Glacier could'snowball' towards collapse, as a study shows the ice is melting faster than expected. Scientists from the University of Edinburgh predict that the glacier - whose official name is Thwaites - could shed 200 gigatonnes of ice every single year by 2067. That is more than the current ice loss of the entire Antarctic Ice Sheet, which has been losing 150 gigatonnes of ice per year for the last two decades.