These second-generation smart glasses give you superpowers--if the software behaves. Optional R1 smart ring makes control simpler. Software stability remains an issue. Smart ring is an extra $249. Navigation needs a little finessing.

A growing collection of pocket-sized gadgets lets you record your meetings and extract value from them. Whether sitting in class, a meeting, or an interview, I've never been fond of taking notes, and I'm far from alone. Not only does the process of scribbling something down cause me to miss what was said immediately after, but I also suffer from awful handwriting, meaning that I can rarely read the notes anyway. Recording interviews has long been a solution, but transcribing interviews is another step (with extra cost) that can leave you with thousands of words of material to sift through, much of it irrelevant. AI notetakers--massively popular at CES 2026 --have emerged to offer a new way of making IRL notetaking easier and faster, putting the power of AI into (or at least adjacent to) a portable device that evokes the microcassette recorder of yesteryear.

In the future, tiny flying robots could be deployed to aid in the search for survivors trapped beneath the rubble after a devastating earthquake. So far, aerial microrobots have only been able to fly slowly along smooth trajectories, far from the swift, agile flight of real insects -- until now. MIT researchers have demonstrated aerial microrobots that can fly with speed and agility that is comparable to their biological counterparts. A collaborative team designed a new AI-based controller for the robotic bug that enabled it to follow gymnastic flight paths, such as executing continuous body flips. With a two-part control scheme that combines high performance with computational efficiency, the robot's speed and acceleration increased by about 450 percent and 250 percent, respectively, compared to the researchers' best previous demonstrations.

It's a seductive distraction from the advances in AI that are most likely to improve or even save your life On April 28, 2022, at a highly anticipated concert in Spokane, Washington, the musician Paul McCartney astonished his audience with a groundbreaking application of AI: He began to perform with a lifelike depiction of his long-deceased musical partner, John Lennon. Using recent advances in audio and video processing, engineers had taken the pair's final performance (London, 1969), separated Lennon's voice and image from the original mix and restored them with lifelike clarity. For years, researchers like me had taught machines to "see" and "hear" in order to make such a moment possible. As McCartney and Lennon appeared to reunite across time and space, the arena fell silent; many in the crowd began to cry. As an AI scientist and lifelong Beatles fan, I felt profound gratitude that we could experience this truly life-changing moment. Later that year, the world was captivated by another major breakthrough: AI conversation.

We are glad that our reviewers agree on the merits and relevance of our work. R3/R4: Applying Freeze-Thaw BO in the settings considered. See Figure 1 for further illustration of why FT struggles in DRL settings. Fabolas uses a different way of obtaining low-fidelity information. R3: Sec 3.2 and 3.3 should be reversed as Sec 3.2 makes reference to Eq (7).

Microscale robots have a variety of potential applications in medicine, environmental monitoring, and tissue engineering, due to their small size and capabilities of sensing and manipulation at the small scale [1]. Recent research has demonstrated their potential in applications ranging from ocular drug delivery and in vitro fertilization to root canal prevention and tumor treatment [2, 3]. The most common actuation methods for microscale robots are acoustic and electromagnetic actuation [4]. Acoustic microrobots, for instance, can be manipulated using sound waves to achieve precise movements, while electromagnetic microrobots rely on magnetic fields for their actuation and control. Traditional open-loop control systems for acoustic and magnetic microrobots often fail to provide the necessary accuracy and reliability required for the above applications [5].

As industrial robots move into shared human spaces, their opaque decision making threatens safety, trust, and public oversight. This artwork, Airy, asks whether complex multi agent AI can become intuitively understandable by staging a competition between two reinforcement trained robot arms that snap a bedsheet skyward. Building on three design principles, competition as a clear metric (who lifts higher), embodied familiarity (audiences recognize fabric snapping), and sensor to sense mapping (robot cooperation or rivalry shown through forest and weather projections), the installation gives viewers a visceral way to read machine intent. Observations from five international exhibitions indicate that audiences consistently read the robots' strategies, conflict, and cooperation in real time, with emotional reactions that mirror the system's internal state. The project shows how sensory metaphors can turn a black box into a public interface.

We are glad that our reviewers agree on the merits and relevance of our work. R3/R4: Applying Freeze-Thaw BO in the settings considered. See Figure 1 for further illustration of why FT struggles in DRL settings. Fabolas uses a different way of obtaining low-fidelity information. R3: Sec 3.2 and 3.3 should be reversed as Sec 3.2 makes reference to Eq (7).

When you purchase through links in our articles, we may earn a small commission. More and more applications support the use of NPUs in modern AI notebooks. Since Intel integrated a dedicated Neural Processing Unit (NPU) into modern notebooks with the Core Ultra processors and AMD with the Ryzen AI series, the software landscape has visibly changed. Applications from various fields, such as image processing, video production, communication and document processing, are increasingly using this specialized hardware to execute AI functions locally, becoming faster and more energy-efficient. Frameworks help developers to program applications that also offer NPU support.

Despite advances in AI for contact centers, customer experience (CX) continues to suffer from high average handling time (AHT), low first-call resolution, and poor customer satisfaction (CSAT). A key driver is the cognitive load on agents, who must navigate fragmented systems, troubleshoot manually, and frequently place customers on hold. Existing AI-powered agent-assist tools are often reactive driven by static rules, simple prompting, or retrieval-augmented generation (RAG) without deeper contextual reasoning. We introduce Agentic AI goal-driven, autonomous, tool-using systems that proactively support agents in real time. Unlike conventional approaches, Agentic AI identifies customer intent, triggers modular workflows, maintains evolving context, and adapts dynamically to conversation state. This paper presents a case study of Minerva CQ, a real-time Agent Assist product deployed in voice-based customer support. Minerva CQ integrates real-time transcription, intent and sentiment detection, entity recognition, contextual retrieval, dynamic customer profiling, and partial conversational summaries enabling proactive workflows and continuous context-building. Deployed in live production, Minerva CQ acts as an AI co-pilot, delivering measurable improvements in agent efficiency and customer experience across multiple deployments.