Mobile devices have become essential enablers for AI applications, particularly in scenarios that require real-time performance. Vision Transformer (ViT) has become a fundamental cornerstone in this regard due to its high accuracy. Recent efforts have been dedicated to developing various transformer architectures that offer im- proved accuracy while reducing the computational requirements. However, existing research primarily focuses on reducing the theoretical computational complexity through methods such as local attention and model pruning, rather than considering realistic performance on mobile hardware. Although these optimizations reduce computational demands, they either introduce additional overheads related to data transformation (e.g., Reshape and Transpose) or irregular computation/data-access patterns.

After just nine years, Google is moving Assistant to the graveyard. Last fall, Google announced it was officially replacing Assistant, the default Android assistant on your phone, with Gemini. You still had the option to use Assistant, but now that option is ending and users must move to Gemini. In a recent blog post, Google announced it's officially winding down Assistant. "Later this year," the post read, "the classic Google Assistant will no longer be accessible on most mobile devices or available for new downloads on mobile app stores."

Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their training data, lack the capability to function effectively as operation assistants. Instead, MLLM-based agents, which enhance capabilities through tool invocation, are gradually being applied to this scenario. However, the two major navigation challenges in mobile device operation tasks -- task progress navigation and focus content navigation -- are difficult to effectively solve under the single-agent architecture of existing work. This is due to the overly long token sequences and the interleaved text-image data format, which limit performance.

PopSign is a smartphone-based bubble-shooter game that helps hearing parentsof deaf infants learn sign language. To help parents practice their ability to sign,PopSign is integrating sign language recognition as part of its gameplay. Fortraining the recognizer, we introduce the PopSign ASL v1.0 dataset that collectsexamples of 250 isolated American Sign Language (ASL) signs using Pixel 4Asmartphone selfie cameras in a variety of environments. It is the largest publiclyavailable, isolated sign dataset by number of examples and is the first dataset tofocus on one-handed, smartphone signs. We collected over 210,000 examplesat 1944x2592 resolution made by 47 consenting Deaf adult signers for whomAmerican Sign Language is their primary language.

In a blog post announcement, Google wrote: "Over the coming months, we're upgrading more users on mobile devices from Google Assistant to Gemini; and later this year, the classic Google Assistant will no longer be accessible on most mobile devices or available for new downloads on mobile app stores." Assistant will remain only on phones running Android 9 or earlier that don't have at least 2 GB of RAM. Google has recently made a concerted effort to get people to use Gemini. Last month, it pulled the tool from its search app, for instance, and redirected users to the standalone Gemini app. And it's not just phones that'll be migrating to Gemini.

Google Assistant's days are numbered. Google announced Friday that all Android devices are switching to Gemini as their default assistant and "the classic Google Assistant will no longer be accessible on most mobile devices." The company says it's working to convert more mobile devices from Google Assistant to Gemini in 2025, and plans on "upgrading tablets, cars and devices that connect to your phone, such as headphones and watches" to the new AI assistant. That presumably includes other platforms like iOS, as well. While smart home devices don't seem to be a focus at Google as of late, the company also reaffirmed plans to use Gemini to power a new experience on speakers, displays, and streaming boxes.

At Tehran's Amirkabir University, authorities installed facial recognition software at its entrance gate to also find women not wearing the hijab, the report said. Surveillance cameras on Iran's major roads are also being used to search for uncovered women. Investigators also said they obtained the "Nazer" mobile phone app offered by Iranian police, which allows "vetted" members of the public and the police to report on uncovered women in vehicles, including ambulances, buses, metro cars and taxis. "Users may add the location, date, time and the licence plate number of the vehicle in which the alleged mandatory hijab infraction occurred, which then'flags' the vehicle online, alerting the police," the report said. According to the report, a text message is then sent to the registered owner of the vehicle, warning them they had been found in violation of the mandatory hijab laws.

As a fan of Star Trek: Deep Space Nine, I've always loved the episode Little Green Men, in which Quark, Rom, and Nog are accidentally transported back to 20th-century Earth. In one interesting scene, we learn that the Ferengi insert tiny universal translators into their large ears so they can converse with people in different languages. Though I'm not a Ferengi, I'm looking forward to a similar feature that reportedly will reach Apple AirPods later this year. Also: Apple reveals the secret to updating every AirPods model - and it's easier than you think In a Bloomberg story published Thursday, tech reporter Mark Gurman said that Apple plans to update its AirPods with a live translation feature that would let you hear other languages translated into your own. Citing information from "people with knowledge of the matter," Gurman said that the new feature will be tied to iOS 19 and arrive as part of an AirPods software upgrade due later this year.

In this episode, we dive into Devindra's review of the excellent M4-equipped MacBook Air (and briefly chat about the new Mac Studio). We also discuss Apple's surprise announcement that it's delaying its smarter, AI-infused Siri, which may not arrive until next year. Did Apple over-promise last year, or is it wise to hold off on advanced AI features until they're ready? After all, Apple doesn't want a fiasco like Microsoft's Recall announcement. Listen below or subscribe on your podcast app of choice.

This evolution indicates an expansion from industrial uses into diverse fields, including healthcare [61], [59]. The core functionalities of digital twins include an accurate mirroring of their physical counterparts, capturing all associated processes in a data-driven manner, maintaining a continuous connection that synchronizes with the real-time state of their physical twins, and simulating physical behavior for predictive analysis [85]. In the context of healthcare, a novel extension of this technology manifests in the form of Human Digital Twins (HDTs), designed to provide a comprehensive digital mirror of individual patients. HDTs not only represent physical attributes but also integrate dynamic changes across molecular, physiological, and behavioral dimensions. This advancement is aligned with a shift toward personalized healthcare (PH) paradigms, enabling tailored treatment strategies based on a patient's unique health profile, thereby enhancing preventive, diagnostic, and therapeutic processes in clinical settings [44], [50]. The personalization aspect of HDTs underscores their potential to revolutionize healthcare by facilitating precise and individualized treatment plans that optimize patient outcomes [72]. Although the potential of digital twins in healthcare has garnered much attention, practical applications remain newly developing, with critical literature highlighting that many implementations are still in exploratory stages [59]. Notably, institutions like the IEEE Computer Society and Gartner recognize this technology as a pivotal component in the ongoing evolution of healthcare systems that emphasize both precision and personalization [31], [89].