The updated Essential Space is available on all 2025 and 2026 Nothing phones with an Essential Key. In the mad dash many companies have made to incorporate AI features into their phones, Nothing arrived at one of the better ideas with Essential Space on the Nothing Phone 3a in 2025. The AI-powered app turns screenshots and voice recordings into actionable to-do lists and transcriptions, and now Nothing is rolling out an update to make the app easier to search and capable of recognizing new kinds of content. As part of the update, Essential Space now recognizes Events, displaying them in their own card with fields for the date, time and location. That means, for example, if you add a photo of a flyer for pottery class to the app, Essential Space will be able to pull the details of when and where it's happening, and track it in much the same way it does tasks or to-dos.

A London doctor controlled robotic surgical arms in Gibraltar to perform prostate cancer telesurgery in near real time from 1,500 miles away.

Continuous word representation (aka word embedding) is a basic building block in many neural network-based models used in natural language processing tasks. Although it is widely accepted that words with similar semantics should be close to each other in the embedding space, we find that word embeddings learned in several tasks are biased towards word frequency: the embeddings of high-frequency and low-frequency words lie in different subregions of the embedding space, and the embedding of a rare word and a popular word can be far from each other even if they are semantically similar. This makes learned word embeddings ineffective, especially for rare words, and consequently limits the performance of these neural network models. In order to mitigate the issue, in this paper, we propose a neat, simple yet effective adversarial training method to blur the boundary between the embeddings of high-frequency words and low-frequency words. We conducted comprehensive studies on ten datasets across four natural language processing tasks, including word similarity, language modeling, machine translation and text classification. Results show that we achieve higher performance than the baselines in all tasks.

PCWorld reports AMD's new "agent PC" concept featuring Ryzen AI Max+ processors designed to run AI agents continuously as dedicated secondary machines. These $2,000+ systems offer 128GB memory capacity and local AI processing through OpenClaw platform, providing privacy advantages over cloud solutions. High component costs and complex installation processes currently limit consumer adoption, with alternatives like Raspberry Pi potentially more practical. You already have a laptop or desktop PC, but now AMD thinks you need another one--an "agent PC" to support your main machine. AMD has responded to the growing success of OpenClaw's AI agents with a new suggestion: customers should buy "agent PCs," which would take the power of the Ryzen AI Max+ processor (surprise!) and repurpose it to run an agent swarm.

Iranian forces deploy explosive drone boats disguised as fishing vessels in Strait of Hormuz, defense expert Cameron Chell warns, marking new phase of hybrid warfare.

Technology Robots'Pokémon Go' players have been unknowingly training delivery robots The massive crowdsourcing effort could use real-world to help robots deliver pizza. A woman holds up her cell phone as she plays the Pokémon Go game in Lafayette Park in front of the White House in Washington, DC on July 12, 2016. Breakthroughs, discoveries, and DIY tips sent six days a week. Nearly a decade ago, turned the real world into a digital scavenger hunt, with virtual creatures hiding in plain sight. The early augmented reality smartphone app prompted hundreds of millions of players to wander into parks, parking lots, and even dimly lit alleyways, peering through their phone cameras in search of Pikachus and Charizards that the app superimposed onto their surroundings.

An important class of distance metrics proposed for training generative adversarial networks (GANs) is the integral probability metric (IPM), in which the neural net distance captures the practical GAN training via two neural networks. This paper investigates the minimax estimation problem of the neural net distance based on samples drawn from the distributions. We develop the first known minimax lower bound on the estimation error of the neural net distance, and an upper bound tighter than an existing bound on the estimator error for the empirical neural net distance. Our lower and upper bounds match not only in the order of the sample size but also in terms of the norm of the parameter matrices of neural networks, which justifies the empirical neural net distance as a good approximation of the true neural net distance for training GANs in practice.

Many state-of-the-art algorithms for solving Partially Observable Markov Decision Processes (POMDPs) rely on turning the problem into a "fully observable" problem--a belief MDP--and exploiting the piece-wise linearity and convexity (PWLC) of the optimal value function in this new state space (the belief simplex). This approach has been extended to solving ρ-POMDPs--i.e., for information-oriented criteria--when the reward ρ is convex in . General ρ-POMDPs can also be turned into "fully observable" problems, but with no means to exploit the PWLC property. In this paper, we focus on POMDPs and ρ-POMDPs with λ ρ -Lipschitz reward function, and demonstrate that, for finite horizons, the optimal value function is Lipschitz-continuous. Then, value function approximators are proposed for both upper-and lower-bounding the optimal value function, which are shown to provide uniformly improvable bounds. This allows proposing two algorithms derived from HSVI which are empirically evaluated on various benchmark problems.

Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods. Among those variational regularization models are one of the most popular approaches. We propose a new framework for applying data-driven approaches to inverse problems, using a neural network as a regularization functional. The network learns to discriminate between the distribution of ground truth images and the distribution of unregularized reconstructions. Once trained, the network is applied to the inverse problem by solving the corresponding variational problem. Unlike other data-based approaches for inverse problems, the algorithm can be applied even if only unsupervised training data is available. Experiments demonstrate the potential of the framework for denoising on the BSDS dataset and for computer tomography reconstruction on the LIDC dataset.