The Morning After: Apple's dramatic Siri overhaul is coming and it might look like this The Morning After: Apple's dramatic Siri overhaul is coming and it might look like this WWDC is right around the corner. Apple is preparing to reintroduce the new Siri at WWDC 2026 -- and that's happening very soon. A report from offers an early preview of the update, with illustrations of what Apple's Gemini-powered AI agent will look like when it finally lands. The final version set to be introduced to the public in June could differ, s Mark Gurman added. Siri will soon live inside the iPhone's Dynamic Islandand, as before, you'll be able to wake the assistant by saying Siri or holding down your phone's power button.

Here's what Apple's Siri overhaul for iOS 27 could look like Here's what Apple's Siri overhaul for iOS 27 could look like Apple is reportedly redesigning the iPhone's interface around the new Siri. After nearly two years of delays and a $250 million settlement along the way, Apple is preparing to reintroduce the new Siri at WWDC 2026 . Mark Gurman has published an early preview of the update with illustrations that provide a glimpse of how Apple has redesigned the iPhone's interface to put the Gemini-powered AI agent front and center. The company often tests multiple designs of features internally, and the final version set to be introduced to the public in June could differ, Gurman warns, before stating Apple could release the new Siri as early as this September. As you can see from the illustrations shared, Siri will now live inside the iPhone's dynamic island.

The measure is part of a European Union-led strategy to eliminate all drunk-driving-related deaths and injuries by 2050. As of July 1, all vehicles sold within the European Union must include a standard, preinstalled interface that allows a breathalyzer lock to be added to the ignition system. This measure is part of a larger strategy promoted by the EU to reduce drunk-driving-related deaths and injuries by at least 50 percent by 2030. The requirement falls under the Vision Zero program, launched by European authorities more than five years ago, which aims to eliminate alcohol-related traffic fatalities entirely--or get as close to zero as possible--by 2050. The measure also aligns with the timetable established in the EU's General Safety Regulation, which sets specific deadlines for manufacturers to incorporate various safety features into vehicle designs, starting at the factory.

Differential privacy changes the effective sample size governing CVaR learning. For tail mass $τ$, the privacy-relevant sample size is not $n$, but $nτ$; equivalently, the effective private tail sample size is $εnτ$. Private CVaR excess risk decomposes into ordinary tail-risk statistical error and a privacy price. This decomposition is complete for scalar estimation and finite classes: scalar estimation has rate $Θ(B \min\{1,(nτ)^{-1/2}+(εnτ)^{-1}\})$, and finite classes of size $M$ have rate $Θ(B \min\{1,\sqrt{\log(2M)/(nτ)}+\log(2M)/(εnτ)\})$. These complete rates hold under pure DP, and their lower bounds extend to approximate DP in the stated small-$δ$ regimes. For convex Lipschitz learning, modular upper and lower reductions show that the CVaR-specific privacy term necessarily scales as $1/(εnτ)$, with dimension dependence inherited from private stochastic convex optimization. Together, these results identify ordinary private learning on $Θ(nτ)$ informative tail records as the canonical hard subproblem inside private CVaR learning.

Long-context inference is increasingly a memory-traffic problem. The culprit is the key--value (KV) cache: it grows with context length, batch size, layers, and heads, and it is read at every decoding step. Rotation-based scalar codecs meet this systems constraint by storing a norm, applying a shared random rotation, and quantizing one coordinate at a time. They are universal and random-access, but they discard the geometry created by the normalization step. After a Haar rotation, a block of $k$ consecutive coordinates is not a product source; it is a spherical-Beta source on the unit ball. We introduce \textsc{FibQuant}, a universal fixed-rate vector quantizer that keeps the same normalize--rotate--store interface while replacing scalar tables by a shared radial--angular codebook matched to this canonical source. The codebook combines Beta-quantile radii, Fibonacci\,/\,Roberts--Kronecker quasi-uniform directions, and multi-restart Lloyd--Max refinement. We prove that the resulting vector code strictly improves on its scalar product specialization at matched rate, with a high-rate gain that separates into a cell-shaping factor and a density-matching factor. The same construction gives a dense rate axis, including fractional-bit and sub-one-bit operating points, without calibration or variable-length addresses. On GPT-2 small KV caches, \textsc{FibQuant} traces a memory--fidelity frontier from $5\times$ compression at $0.99$ attention cosine similarity to $34\times$ at $0.95$. End-to-end on TinyLlama-1.1B, it is within $0.10$ perplexity of fp16 at $4\times$ compression and has $3.6\times$ lower perplexity than scalar \textsc{TurboQuant} at $b = 2$ ($8\times$ compression), where scalar random-access quantization begins to fail.

The new camera options will join the other features Apple will reportedly highlight at WWDC 2026: performance improvements and AI . The biggest change Apple is making to the Camera app is to make it more customizable. Rather than being stuck with the company's predetermined interface for shooting photos and capturing videos, you'll reportedly be able to tweak it to your liking. The app will reportedly also include more advanced options like controls for depth-of-field, exposure and the company's photo styles feature. Apple offers a theoretically easy way to tweak these settings on the iPhone by using the Camera Control button, but changing things from the touchscreen should be even easier.

PCWorld reports that Windows 11's new Xbox mode brings console-like gaming performance by disabling unnecessary processes and optimizing for controller navigation. The feature rollout is gradual across North America and Europe, but users can manually activate it using the free ViVeTool command-line program. This Xbox mode allows seamless switching between work and gaming while providing full-screen optimization and improved system resource management. One of the new features in the optional April 2026 update for Windows 11 ( KB5083631) is the long-awaited Xbox mode, which boosts gaming performance by disabling unnecessary processes . It's already been a few days, though, and many who have installed the update still aren't seeing Xbox mode yet due to the gradual rollout. As of now, users in North America have top priority for the Xbox mode rollout, followed by users in Europe. Even so, many in North America are still left hanging.

The neural population spiking activity recorded by intracortical brain-computer interfaces (iBCIs) contain rich structure. Current models of such spiking activity are largely prepared for individual experimental contexts, restricting data volume to that collectable within a single session and limiting the effectiveness of deep neural networks (DNNs). The purported challenge in aggregating neural spiking data is the pervasiveness of context-dependent shifts in the neural data distributions. However, large scale unsupervised pretraining by nature spans heterogeneous data, and has proven to be a fundamental recipe for successful representation learning across deep learning. We thus develop Neural Data Transformer 2 (NDT2), a spatiotemporal Transformer for neural spiking activity, and demonstrate that pretraining can leverage motor BCI datasets that span sessions, subjects, and experimental tasks. NDT2 enables rapid adaptation to novel contexts in downstream decoding tasks and opens the path to deployment of pretrained DNNs for iBCI control.

We follow the guidelines of the datasheets for datasets [1] to explain the composition, collection, recommended use case, and other details of the CityRefer dataset. For what purpose was the dataset created? We created this CityRefer dataset to facilitate research toward city-scale 3D visual grounding. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? Who funded the creation of the dataset? What do the instances that comprise the dataset represent?