Siri AI, Apple's long-anticipated redesign of its digital assistant, will be available in five additional languages come October, the company announced today during its fall iPhone event. Siri AI is already available in English through the iOS 27 and iPadOS 27 betas, and will roll out more broadly to English-speaking users once Apple makes those updates available to the public on Monday, September 14. During today's iPhone event, the company said it plans to bring Siri AI to French, Japanese, Korean, Portuguese and Spanish speaking users sometime next month. Apple also confirmed that Siri AI would feature daily usage for prompts that require the assistant to make use of the company's cloud compute AI models. We're waiting on the company to share more details, but it said that its iCloud plans would offer expanded usage limits.

Many classic tasks in vision -- such as the estimation of optical flow or stereo disparities -- can be cast as dense correspondence matching. Well-known techniques for doing so make use of a cost volume, typically a 4D tensor of match costs between all pixels in a 2D image and their potential matches in a 2D search window.

There has been enormous progress in the last few years in designing neural networks that respect the fundamental symmetries and coordinate freedoms of physical law. Some of these frameworks make use of irreducible representations, some make use of high-order tensor objects, and some apply symmetry-enforcing constraints. Different physical laws obey different combinations of fundamental symmetries, but a large fraction (possibly all) of classical physics is equivariant to translation, rotation, reflection (parity), boost (relativity), and permutations. Here we show that it is simple to parameterize universally approximating polynomial functions that are equivariant under these symmetries, or under the Euclidean, Lorentz, and Poincaré groups, at any dimensionality $d$. The key observation is that nonlinear O($d$)-equivariant (and related-group-equivariant) functions can be universally expressed in terms of a lightweight collection of scalars---scalar products and scalar contractions of the scalar, vector, and tensor inputs. We complement our theory with numerical examples that show that the scalar-based method is simple, efficient, and scalable.

We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input. To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time. Our approach uses self-attention to reason about relationships between datapoints explicitly, which can be seen as realizing non-parametric models using parametric attention mechanisms. However, unlike conventional non-parametric models, we let the model learn end-to-end from the data how to make use of other datapoints for prediction. Empirically, our models solve cross-datapoint lookup and complex reasoning tasks unsolvable by traditional deep learning models. We show highly competitive results on tabular data, early results on CIFAR-10, and give insight into how the model makes use of the interactions between points.

This means that they can understand and generate more complex and nuanced language, which can be useful for a variety of tasks such as language translation, question answering, and sentiment analysis.

We appreciate the reviewer's valuable comments, and we were glad to read the positive comments regarding the We also appreciate the thorough feedback for further improvements. What is trained in the PRE-approach? Is there benefit in using the differentiable PDE solver? Do steps of a differentiable simulator correspond to time steps? Y es, in our text "step" typically means time step.