When you purchase through links in our articles, we may earn a small commission. MSI reveals AI-ready mini PCs and pro displays at CES 2026, including Cubi NUC AI+ systems, Pro Max AI+ desktops, and new QD-OLED PRO MAX monitors. MSI showed off a bunch of intriguing hardware for professionals at this year's CES, with a wide range of miniature AI PCs, and an utterly gorgeous OLED monitor. Whether you're looking to get your boss to fund a new upgrade for work or it's time to overhaul the home office, the kit MSI has on show at CES 2026 could be what you're looking for. Mini PCs continue to trend upward in offices and creator setups.

Cloud cover in multispectral imagery (MSI) significantly hinders early-season crop mapping by corrupting spectral information. Existing Vision Transformer(ViT)-based time-series reconstruction methods, like SMTS-ViT, often employ coarse temporal embeddings that aggregate entire sequences, causing substantial information loss and reducing reconstruction accuracy. To address these limitations, a Video Vision Transformer (ViViT)-based framework with temporal-spatial fusion embedding for MSI reconstruction in cloud-covered regions is proposed in this study. Non-overlapping tubelets are extracted via 3D convolution with constrained temporal span $(t=2)$, ensuring local temporal coherence while reducing cross-day information degradation. Both MSI-only and SAR-MSI fusion scenarios are considered during the experiments. Comprehensive experiments on 2020 Traill County data demonstrate notable performance improvements: MTS-ViViT achieves a 2.23\% reduction in MSE compared to the MTS-ViT baseline, while SMTS-ViViT achieves a 10.33\% improvement with SAR integration over the SMTS-ViT baseline. The proposed framework effectively enhances spectral reconstruction quality for robust agricultural monitoring.

Feature-attribution methods (e.g., SHAP, LIME) explain individual predictions but often miss higher-order structure: sets of features that act in concert. We propose Modules of Influence (MoI), a framework that (i) constructs a model explanation graph from per-instance attributions, (ii) applies community detection to find feature modules that jointly affect predictions, and (iii) quantifies how these modules relate to bias, redundancy, and causality patterns. Across synthetic and real datasets, MoI uncovers correlated feature groups, improves model debugging via module-level ablations, and localizes bias exposure to specific modules. We release stability and synergy metrics, a reference implementation, and evaluation protocols to benchmark module discovery in XAI.

Retrieving relevant imagery from vast satellite archives is crucial for applications like disaster response and long-term climate monitoring. However, most text-to-image retrieval systems are limited to RGB data, failing to exploit the unique physical information captured by other sensors, such as the all-weather structural sensitivity of Synthetic Aperture Radar (SAR) or the spectral signatures in optical multispectral data. To bridge this gap, we introduce CrisisLandMark, a new large-scale corpus of over 647,000 Sentinel-1 SAR and Sentinel-2 multispectral images paired with structured textual annotations for land cover, land use, and crisis events harmonized from authoritative land cover systems (CORINE and Dynamic World) and crisis-specific sources. We then present CLOSP (Contrastive Language Optical SAR Pretraining), a novel framework that uses text as a bridge to align unpaired optical and SAR images into a unified embedding space. Our experiments show that CLOSP achieves a new state-of-the-art, improving retrieval nDGC@1000 by 54% over existing models. Additionally, we find that the unified training strategy overcomes the inherent difficulty of interpreting SAR imagery by transferring rich semantic knowledge from the optical domain with indirect interaction. Furthermore, GeoCLOSP, which integrates geographic coordinates into our framework, creates a powerful trade-off between generality and specificity: while the CLOSP excels at general semantic tasks, the GeoCLOSP becomes a specialized expert for retrieving location-dependent crisis events and rare geographic features. This work highlights that the integration of diverse sensor data and geographic context is essential for unlocking the full potential of remote sensing archives.

MSI's latest Cubi NUC AI 2MG mini PC is as much smart speaker or laptop as it is a small, compact, desktop NUC: It boasts a dedicated hardware Copilot button as well as a dedicated fingerprint reader, and you can talk to it, too. Starting at 899, MSI's little mini PC is also Copilot qualified, with either a Core Ultra 9 288V or Core Ultra 7 258V chip inside. Both are Core Ultra Series 2 "Lunar Lake" chips. The hardware design, however, is something special. This isn't the first mini PC with a dedicated Copilot button -- that was the Asus NUC 14 Pro AI, launched at the 2025 edition of CES. However, this is the first mini PC that I can recall with a dedicated fingerprint reader underneath the power button, a feature normally associated with laptops like the Samsung Galaxy series.

MSI has a new OLED gaming monitor. It also has a built-in neural processing unit (or NPU). If you're familiar with that term, you know what comes next: this OLED monitor has "AI" built into it. After reading the official promo for the MSI MPG 271QR QD-OLED X50 and an extended session of bouncing various four-letter words off the walls of my office, I have to admit that this isn't the worst way to jump on the "AI" bandwagon. The NPU is tied into a CMOS sensor (a very basic camera) and a presence detection system, which detects whether a real human is sitting in front of it.

Cloud cover in multispectral imagery (MSI) poses significant challenges for early season crop mapping, as it leads to missing or corrupted spectral information. Synthetic aperture radar (SAR) data, which is not affected by cloud interference, offers a complementary solution, but lack sufficient spectral detail for precise crop mapping. To address this, we propose a novel framework, Time-series MSI Image Reconstruction using Vision Transformer (ViT), to reconstruct MSI data in cloud-covered regions by leveraging the temporal coherence of MSI and the complementary information from SAR from the attention mechanism. Comprehensive experiments, using rigorous reconstruction evaluation metrics, demonstrate that Time-series ViT framework significantly outperforms baselines that use non-time-series MSI and SAR or time-series MSI without SAR, effectively enhancing MSI image reconstruction in cloud-covered regions.

--Multispectral satellite images play a vital role in agriculture, fisheries, and environmental monitoring. However, their high dimensionality, large data volumes, and diverse spatial resolutions across multiple channels pose significant challenges for data compression and analysis. This paper presents ImpliSat, a unified framework specifically designed to address these challenges through efficient compression and reconstruction of multispectral satellite data. ImpliSat leverages Implicit Neural Representations (INR) to model satellite images as continuous functions over coordinate space, capturing fine spatial details across varying spatial resolutions. Furthermore, we introduce a Fourier modulation algorithm that dynamically adjusts to the spectral and spatial characteristics of each band, ensuring optimal compression while preserving critical image details.

Registration of histological and mass spectrometry imaging (MSI) allows for more precise identification of structural changes and chemical interactions in tissue. With histology and MSI having entirely different image formation processes and dimensionalities, registration of the two modalities remains an ongoing challenge. This work proposes a solution that synthesises histological images from MSI, using a pix2pix model, to effectively enable unimodal registration. Preliminary results show promising synthetic histology images with limited artifacts, achieving increases in mutual information (MI) and structural similarity index measures (SSIM) of +0.924 and +0.419, respectively, compared to a baseline U-Net model. Our source code is available on GitHub: https://github.com/kimberley/MIUA2025.

The first time you make anything, it probably won't come out perfect, so it wasn't a huge surprise when MSI's debut gaming handheld struggled out of the gate. And that's before you consider the unorthodox choice to go with an Intel chip instead of one from AMD like practically all of its rivals. However, MSI didn't give up, and now it's back with not one but two versions of its second-gen handheld, headlined by the Claw 8 AI . Not only is it bigger than before, it has twice as many Thunderbolt 4 ports, a way bigger battery and some of the best performance we've seen from any device in this category. But more importantly, as the follow-up to a device plagued by lackluster software and unfinished drivers, it feels like the Claw got its bite back. With its 8-inch screen, the Claw 8 AI is bigger than its predecessor and a number of its rivals like the ROG Ally X, though it's still smaller than Lenovo's chunky 8.8-inch Legion Go.