When you purchase through links in our articles, we may earn a small commission. At CES 2026, UGREEN signaled a decisive shift in the evolution of network-attached storage with the launch of two new flagship systems: the NASync iDX6011 and NASync iDX6011 Pro . Positioned as "AI-native" private cloud solutions, these devices go far beyond traditional NAS roles of backup and file sharing. Instead, they reimagine local storage as an intelligent, high-performance data partner--one that understands, organizes, and protects your data entirely on-device, without dependence on the cloud. As personal and professional data volumes continue to grow, many NAS systems struggle to offer anything more than raw capacity.

Digital scammers have never been so successful. Last year Americans lost 16.6 billion to online crimes, with almost 200,000 people reporting scams like phishing and spoofing to the FBI. More than 470 million was stolen in scams that started with a text message last year, according to the Federal Trade Commission. And as the biggest mobile operating system maker in the world, Google has been scrambling to do something, building out tools to warn consumers about potential scams. Ahead of Google's Android 16 launch next week, the company said on Tuesday that it is expanding its recently launched AI flagging feature for the Google Messages app, known as Scam Detection, to provide alerts on potentially nefarious messages like possible crypto scams, financial impersonation, gift card and prize scams, technical support scams, and more.

Both Microsoft and Apple took swings with their respective operating systems, with Microsoft debuting its "Copilot PC" branding for AI-capable laptops and Apple releasing Apple Intelligence. These early examples offered mixed results. Some features, like real-time translations and on-device speech-to-text, can be useful. Others, like Microsoft's Windows Recall, have yet to prove themselves. All of this hype for AI has important implications for the new year.

Artificial Intelligence (AI) models for time-series in pervasive computing keep getting larger and more complicated. The Transformer model is by far the most compelling of these AI models. However, it is difficult to obtain the desired performance when deploying such a massive model on a sensor device with limited resources. My research focuses on optimizing the Transformer model for time-series forecasting tasks. The optimized model will be deployed as hardware accelerators on embedded Field Programmable Gate Arrays (FPGAs). I will investigate the impact of applying Quantization-aware Training to the Transformer model to reduce its size and runtime memory footprint while maximizing the advantages of FPGAs.

Ahead of Global Accessibility Awareness Day this week, Apple is issuing its typical annual set of announcements around its assistive features. Many of these are useful for people with disabilities, but also have broader applications as well. For instance, Personal Voice, which was released last year, helps preserve someone's speaking voice. It can be helpful to those who are at risk of losing their voice or have other reasons for wanting to retain their own vocal signature for loved ones in their absence. Today, Apple is bringing eye-tracking support to recent models of iPhones and iPads, as well as customizable vocal shortcuts, music haptics, vehicle motion cues and more.

There is no shortage of articles discussing which deep learning framework is the best. In this article, we want to focus on a niche. Which framework can make your life easier if your goal is On-Device Deployment? We also explore the controversial topic of building your in-house on-device Inference Engine. TensorFlow comes with TensorFlow Lite for Android, iOS, and single-board computers (e.g.

Quadric introduced a unified silicon and software platform that is designed to provide on-device AI. Built to accelerate computation speeds while reducing power consumption, Quadric's new general-purpose processor platform was created to meet the computing needs of autonomous smart sensors, IoT devices, factory automation, robots, 5G infrastructure, and medical imaging. The platform is designed to handle any AI algorithm, as well as classic algorithms used for tasks such as digital signal processing, high-performance computing, and image processing. The Quadric processor architecture is based on a hybrid data-flow and Von Neumann machine that enables high-performance on-device computing for demanding workloads including neural networks, machine learning, computer vision, and basic linear algebra subprograms (BLAS). The instruction-driven architecture enables software manageability of hardware to keep pace with on-device computing.

Over the years, 5G and Artificial Intelligence (AI) have both proven to be transformative technologies in their own ways. Both these technologies have one thing in common – both need to deal with massive amounts of data. We are on the cusp of an era where they will complement and enhance each other and bring the power of Machine Learning to our -devices, enabling richer, smoother, and more personalized experiences than ever before. Most of the initial implementations of AI/ML applications involved offline machine learning with large amounts of historical data and the inferencing engine resided in the cloud. As end devices became more powerful, some of the inferencing schemas moved to end devices.

Google today announced TensorFlow Lite Model Maker, a tool that adapts state-of-the-art machine learning models to custom data sets using a technique known as transfer learning. It wraps machine learning concepts with an API that enables developers to train models in Google's TensorFlow AI framework with only a few lines of code, and to deploy those models for on-device AI applications. Tools like Model Maker could help companies incorporate AI into their workflows faster than before. According to a study conducted by Algorithmia, 50% of organizations spend between 8 and 90 days deploying a single machine learning model, with most blaming the duration on a failure to scale. Model Maker, which currently only supports image and text classification use cases, works with many of the models in TensorFlow Hub, Google's library for reusable machine learning modules.

Equipped with best-in-class AI solutions, Samsung Exynos processors enable users to enjoy the next-generation mobile experiences. Launched in 2018, Exynos 9810 was the first processor in the series with deep learning software. With the addition of neural processing unit integration, the Exynos series delivers unmatched performance for mobile AI operations. The newly introduced Exynos 990 processor, featuring dual-core neural processing unit (NPU) and improved digital signal processor (DSP), makes on-device AI practical through faster AI processing capabilities up to approx. By developing algorithms that are four times lighter and eight times faster than existing solutions, Samsung Exynos will continuously set a new standard for AI processing to push the boundaries of the next generation mobile experience.