Gear Wearables Google's Android XR smart glasses hope to succeed where AI-first wearables have failed The audio-only frames pair with Android and iOS so a Gemini agent can run errands on your phone while you stay heads-up. 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. We may earn revenue from the products available on this page and participate in affiliate programs. Google put AI on people's faces more than a decade ago with its Google Glass wearable. It was designed to put a computer directly on your face, but the world (and to some extent, the hardware) wasn't quite ready for that yet.

Your cell phone can handle basic language translation, but bespoke tools can offer a much more immersive experience. Hans Christian Andersen once said, "To travel is to live," and while that's a romantic notion, he probably wasn't careening through Gyeongju, South Korea, at midnight in the back of a taxi with a driver who didn't speak a lick of English. Today's world traveler has it awfully easy when it comes to understanding the local lingo, as even a basic modern cell phone app can offer a pretty good translation of common phrases delivered in everything from Abkhaz to Zulu. Type or speak a sentence or two into the app, tap a button, and out it returns in the language of your choice. Tap another button, and your phone can even speak those sentences aloud.

OpenAI's chatbot has some weird linguistic tics in Chinese that are driving users crazy. Are you even online in 2026 if you haven't experienced the verbal tics of ChatGPT? It loves goblins, em dashes, and "it's not A; it's B" sentence constructions. But what you might not know is that the chatbot also has plenty of strange phrases it loves to say in Chinese, and they are driving Chinese users crazy. ChatGPT does a decent job answering questions in Chinese, which is why it's widely used in China despite being blocked by the government.

Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to test specific properties and failure modes of learning algorithms is thus a problem of high interest, as it has a direct impact on innovation in the field. In this sense, we introduce Synbols -- Synthetic Symbols -- a tool for rapidly generating new datasets with a rich composition of latent features rendered in low resolution images. Synbols leverages the large amount of symbols available in the Unicode standard and the wide range of artistic font provided by the open font community. Our tool's high-level interface provides a language for rapidly generating new distributions on the latent features, including various types of textures and occlusions. To showcase the versatility of Synbols, we use it to dissect the limitations and flaws in standard learning algorithms in various learning setups including supervised learning, active learning, out of distribution generalization, unsupervised representation learning, and object counting.

Current image steganography techniques are mainly focused on cover-based methods, which commonly have the risk of leaking secret images and poor robustness against degraded container images. Inspired by recent developments in diffusion models, we discovered that two properties of diffusion models, the ability to achieve translation between two images without training, and robustness to noisy data, can be used to improve security and natural robustness in image steganography tasks. For the choice of diffusion model, we selected Stable Diffusion, a type of conditional diffusion model, and fully utilized the latest tools from open-source communities, such as LoRAs and ControlNets, to improve the controllability and diversity of container images. In summary, we propose a novel image steganography framework, named Controllable, Robust and Secure Image Steganography (CRoSS), which has significant advantages in controllability, robustness, and security compared to cover-based image steganography methods. These benefits are obtained without additional training. To our knowledge, this is the first work to introduce diffusion models to the field of image steganography. In the experimental section, we conducted detailed experiments to demonstrate the advantages of our proposed CRoSS framework in controllability, robustness, and security.

These appendices contain demonstrations of the results in the main text as well as additional technical notes.

Motion Compensation We compare our method to the traditional motion-compensated coding378 approach that forms the core of inter-picture coding in well established compression standards such379 as MPEG. Block matching is an essential component of these standards, allowing the compression of380 video content by up to three orders of magnitude with moderate loss of information. For each block381 in a frame, typical coders search for the most similar spatially displaced block in the previous frame382 (typically measured with MSE), and communicate the displacement coordinates to allow prediction383 of frame content by translating blocks of the (already transmitted) previous frame. We implemented384 a "diamond search" algorithm [29] operating on blocks of 8 8 pixels, with a maximal search385 distance of 8 pixels which balances accuracy of motion estimates and speed of estimation (the search386 step is computationally intensive). We use the estimated displacements to perform causal motion387 compensation (cMC), using displacement vectors estimated from the previous two observed frames388 (xt 1 and xt) to predict the next frame (xt+1) rather than the current one (as in MPEG).389