The Razr Fold Adds a Book-Style Foldable to Motorola's Lineup At CES 2026, the company also announced a new smartwatch, stylus, Bluetooth tracker, and even a weird AI pendant. Motorola has been honing its flip-style folding Razr smartphones for more than five years now, but it's finally time for a new of fold . At CES 2026, the company unveiled the Razr Fold, its first book-style folding phone akin to the Samsung Galaxy Z Fold series or Google's Pixel Fold, bringing more competition to the space in the US. If you've seen Google's or Samsung's options, the Razr Fold will look and feel familiar. It has a 6.6-inch display on the front screen, and when you open it up, you're treated to an 8.1-inch 2K resolution screen, around the same size as its competitors.

In this paper, we construct a class of stochastic interpolation neural network operators (SINNOs) with random coefficients activated by sigmoidal functions. We establish their boundedness, interpolation accuracy, and approximation capabilities in the mean square sense, in probability, as well as path-wise within the space of second-order stochastic (random) processes \( L^2(Ω, \mathcal{F},\mathbb{P}) \). Additionally, we provide quantitative error estimates using the modulus of continuity of the processes. These results highlight the effectiveness of SINNOs for approximating stochastic processes with potential applications in COVID-19 case prediction.

Having made colossal profits as well as losses on previous investments, founder Masayoshi Son has pivoted SoftBank toward artificial intelligence. Japanese tech investor SoftBank said Wednesday that its stake in OpenAI is now around 11% after completing the second stage of a $41-billion investment in the maker of ChatGPT. Having made colossal profits as well as losses on previous investments, flamboyant founder Masayoshi Son has pivoted SoftBank toward artificial intelligence. SoftBank had announced in April its planned investment of up to $40 billion in Open AI, and on Wednesday it said that the second tranche of $22.5 billion was completed. The final investment reached $41 billion and includes $30 billion from SoftBank's Vision Fund plus $11 billion from other third-party co-investors, it said.

Acquisition would further expand SoftBank's investments in artificial intelligence as it tries to center itself in the boom SoftBank Group will acquire digital infrastructure investor DigitalBridge Group in a deal valued at $4bn, the companies said on Monday, as the Japanese investment firm looks to deepen its AI-related portfolio. The acquisition would expand SoftBank's exposure to digital infrastructure as the Japanese conglomerate is positioning its portfolio to focus on artificial intelligence. SoftBank's billionaire founder Masayoshi Son is seeking to capitalize on surging demand for the computing capacity that underpins artificial intelligence applications. DigitalBridge invests in digital infrastructure sectors such as datacenters, cell towers, fiber networks, small-cell systems and edge infrastructure, with a portfolio including companies such as Vantage Data Centers, Zayo, Switch and AtlasEdge. Founded in 1991 as real estate-focused Colony Capital, the firm pivoted under CEO Marc Ganzi into digital infrastructure and rebranded as DigitalBridge in 2021 after shedding most of its legacy property assets.

There is a growing interest in device-control systems that can interpret human natural language instructions and execute them on a digital device by directly controlling its user interface. We present a dataset for device-control research, Android in the Wild (AitW), which is orders of magnitude larger than current datasets. The dataset contains human demonstrations of device interactions, including the screens and actions, and corresponding natural language instructions. It consists of 715k episodes spanning 30k unique instructions, four versions of Android (v10-13), and eight device types (Pixel 2 XL to Pixel 6) with varying screen resolutions. It contains multi-step tasks that require semantic understanding of language and visual context. This dataset poses a new challenge: actions available through the user interface must be inferred from their visual appearance, and, instead of simple UI element-based actions, the action space consists of precise gestures (e.g., horizontal scrolls to operate carousel widgets). We organize our dataset to encourage robustness analysis of device-control systems, i.e., how well a system performs in the presence of new task descriptions, new applications, or new platform versions. We develop two agents and report performance across the dataset.

Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the application does not learn anything about the text, and the author of the text does not learn anything about the text classification model used by the application beyond what is given by the classification result itself. We perform end-to-end experiments with an application for detecting hate speech against women and immigrants, demonstrating excellent runtime results without loss of accuracy.

SoftBank races to fulfill $22.5 billion funding pledge to OpenAI by year-end SoftBank CEO Masayoshi Son attends an event to pitch AI for businesses in Tokyo in February. NEW YORK/TOKYO/SAN FRANCISCO - SoftBank Group is racing to close a $22.5 billion funding commitment to OpenAI by year-end through an array of cash-raising plans, including a sale of some investments, and could tap its undrawn margin loans borrowed against its valuable ownership in chip firm Arm Holdings, sources said. The all-in bet on OpenAI is among the biggest yet by SoftBank CEO Masayoshi Son, as the Japanese billionaire seeks to improve his firm's position in the race for artificial intelligence. To come up with the money, Son has already sold SoftBank's entire $5.8 billion stake in AI chip leader Nvidia, offloaded $4.8 billion of its T-Mobile U.S. stake and slashed staff. Son has slowed most other dealmaking at SoftBank's Vision Fund to a crawl, and any deal above $50 million now requires his explicit approval, two of the sources said.

Topology identification and inference of processes evolving over graphs arise in timely applications involving brain, transportation, financial, power, as well as social and information networks. This chapter provides an overview of graph topology identification and statistical inference methods for multidimensional relational data. Approaches for undirected links connecting graph nodes are outlined, going all the way from correlation metrics to covariance selection, and revealing ties with smooth signal priors. To account for directional (possibly causal) relations among nodal variables and address the limitations of linear time-invariant models in handling dynamic as well as nonlinear dependencies, a principled framework is surveyed to capture these complexities through judiciously selected kernels from a prescribed dictionary. Generalizations are also described via structural equations and vector autoregressions that can exploit attributes such as low rank, sparsity, acyclicity, and smoothness to model dynamic processes over possibly time-evolving topologies. It is argued that this approach supports both batch and online learning algorithms with convergence rate guarantees, is amenable to tensor (that is, multi-way array) formulations as well as decompositions that are well-suited for multidimensional network data, and can seamlessly leverage high-order statistical information.

Abstract--The rise of 5G/6G network technologies promises to enable applications like autonomous vehicles and virtual reality, resulting in a significant increase in connected devices and necessarily complicating network management. Even worse, these applications often have strict, yet heterogeneous, performance requirements across metrics like latency and reliability. Much recent work has thus focused on developing the ability to predict network performance. However, traditional methods for network modeling, like discrete event simulators and emulation, often fail to balance accuracy and scalability. Network Digital Twins (NDTs), augmented by machine learning, present a viable solution by creating virtual replicas of physical networks for real-time simulation and analysis. State-of-the-art models, however, fall short of full-fledged NDTs, as they often focus only on a single performance metric or simulated network data. We introduce M3Net, a Multi-Metric Mixture-of-experts (MoE) NDT that uses a graph neural network architecture to estimate multiple performance metrics from an expanded set of network state data in a range of scenarios. We show that M3Net significantly enhances the accuracy of flow delay predictions by reducing the MAPE (Mean Absolute Percentage Error) from 20.06% to 17.39%, while also achieving 66.47% and 78.7% accuracy on jitter and packets dropped for each flow. Emerging 5G and 6G mobile network architectures aim to support new applications like autonomous vehicles and mixed reality [1], [2], both of which require significantly expanded network capabilities. These and other new applications envisioned as part of the 5G and 6G network ecosystem will lead to massive numbers of connected devices with heterogeneous performance expectations, which increases the complexity and cost of managing communication networks [2]. For example, interactive applications like augmented reality generally require response latencies under 200ms [3], while safety-critical applications like autonomous vehicles might require highly reliable delivery of high-priority packets [4].

Network-on-Chip (NoC) design requires exploring a high-dimensional configuration space to satisfy stringent throughput requirements and latency constraints. Traditional design space exploration techniques are often slow and struggle to handle complex, non-linear parameter interactions. This work presents a machine learning-driven framework that automates NoC design space exploration using BookSim simulations and reverse neural network models. Specifically, we compare three architectures - a Multi-Layer Perceptron (MLP),a Conditional Diffusion Model, and a Conditional Variational Autoencoder (CVAE) to predict optimal NoC parameters given target performance metrics. Our pipeline generates over 150,000 simulation data points across varied mesh topologies. The Conditional Diffusion Model achieved the highest predictive accuracy, attaining a mean squared error (MSE) of 0.463 on unseen data. Furthermore, the proposed framework reduces design exploration time by several orders of magnitude, making it a practical solution for rapid and scalable NoC co-design.