Gmail Is Killing POP and Gmailify Access. If you have multiple email accounts, your Gmail setup may soon need some reorganizing. Google giveth, and Google taketh away. Two long-standing features are being removed from Gmail, and they both relate to how you access messages from other, non-Google email accounts through the Gmail interface. The features we're talking about are Gmailify and POP access, and if you rely on them to consolidate multiple email accounts into your Gmail inbox, you're going to have to find a different approach.

Each module could be either parallel module or fusion module, which is determined by optimizing the architecture parametersαp and αf. Specifically,the learned twoarchitectures both contain eight fusion modules and four parallel modules, and the only one difference between them is the position ofthefusion andtheparallel modules. From theobservations, wecould conclude that: 1) themulti-scale information isremarkably important toimage restoration. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. From the top to the bottom for each image, the noise levels areσ = 30,50,70. From the left to the right are Input, BM3D[1],RED[9],WNNM[3],NLRN[6],DuRN-P [7],N3Net[10],CLEARER, andGround truth.

Make full use of the 15GB you get in Gmail, Photos, and Drive for free. Breakthroughs, discoveries, and DIY tips sent six days a week. If Google keeps bothering you to pay for cloud storage, it's not just you. You only get a relatively measly 15GB of storage free of charge with a Google account, and you have to split that across Gmail, Google Photos, and Google Drive. That 15GB can fill up quickly, but it doesn't have to.

The modern web stack, which is dominated by browser-based applications and API-first backends, now operates under an adversarial equilibrium where automated, AI-assisted attacks evolve continuously. Content Delivery Networks (CDNs) and edge computing place programmable defenses closest to users and bots, making them natural enforcement points for machine-learning (ML) driven inspection, throttling, and isolation. This survey synthesizes the landscape of AI-enhanced defenses deployed at the edge: (i) anomaly- and behavior-based Web Application Firewalls (WAFs) within broader Web Application and API Protection (WAAP), (ii) adaptive DDoS detection and mitigation, (iii) bot management that resists human-mimicry, and (iv) API discovery, positive security modeling, and encrypted-traffic anomaly analysis. We add a systematic survey method, a threat taxonomy mapped to edge-observable signals, evaluation metrics, deployment playbooks, and governance guidance. We conclude with a research agenda spanning XAI, adversarial robustness, and autonomous multi-agent defense. Our findings indicate that edge-centric AI measurably improves time-to-detect and time-to-mitigate while reducing data movement and enhancing compliance, yet introduces new risks around model abuse, poisoning, and governance.

Accurate and interpretable gait analysis plays a crucial role in the early detection of Parkinsons disease (PD),yet most existing approaches remain limited by single-modality inputs, low robustness, and a lack of clinical transparency. This paper presents an explainable multimodal framework that integrates RGB and Depth (RGB-D) data to recognize Parkinsonian gait patterns under realistic conditions. The proposed system employs dual YOLOv11-based encoders for modality-specific feature extraction, followed by a Multi-Scale Local-Global Extraction (MLGE) module and a Cross-Spatial Neck Fusion mechanism to enhance spatial-temporal representation. This design captures both fine-grained limb motion (e.g., reduced arm swing) and overall gait dynamics (e.g., short stride or turning difficulty), even in challenging scenarios such as low lighting or occlusion caused by clothing. To ensure interpretability, a frozen Large Language Model (LLM) is incorporated to translate fused visual embeddings and structured metadata into clinically meaningful textual explanations. Experimental evaluations on multimodal gait datasets demonstrate that the proposed RGB-D fusion framework achieves higher recognition accuracy, improved robustness to environmental variations, and clear visual-linguistic reasoning compared with single-input baselines. By combining multimodal feature learning with language-based interpretability, this study bridges the gap between visual recognition and clinical understanding, offering a novel vision-language paradigm for reliable and explainable Parkinsons disease gait analysis. Code:https://github.com/manaralnaasan/RGB-D_parkinson-LLM

As AI models continue to grow in complexity and size, so does the demand for vast computational resources and access to large-scale distributed datasets. At the same time, growing concerns about data privacy, ownership, and regulatory compliance make it increasingly difficult to centralize data for training. FL has emerged as a promising paradigm for addressing these challenges, enabling the training of collaborative models across multiple data silos without requiring the raw data to leave its source. While FL has gained traction in mobile and edge environments, such as smart-phones and IoT devices, its application in large-scale computing platforms like HPC clusters and cloud infrastructure remains underexplored. Meanwhile, the convergence of HPC and cloud computing is reshaping the landscape of modern data-intensive applications. These hybrid environments combine the raw power and efficiency of HPC with the scalability and flexibility of the cloud, making them well-suited for training large AI models. However, this integration brings new challenges: heterogeneous hardware (e.g., Central Processing Units (CPUs), Graphics Processing Units (GPUs), Tensor Processing Units (TPUs)), inconsistent network performance, dynamic resource availability, and non-uniform data distributions across clients. In this context, the deployment of federated learning across such mixed infrastructure is both a timely opportunity and a technical challenge. This paper explores how FL can be adapted and optimized to run efficiently across heterogeneous HPC and cloud environments, with a focus on scalability, system resilience, and performance under non-IID data conditions.

Google's Gemini Deep Research now scans Gmail, Drive and Chat content to support AI research, but users can disable these privacy features through Gmail settings.

Are tech companies using your private data to train AI models? Leading tech companies are in a race to release and improve artificial intelligence (AI) products, leaving users in the United States to puzzle out how much of their personal data could be extracted to train AI tools. Meta (which owns Facebook, Instagram, Threads and WhatsApp), Google and LinkedIn have all rolled out AI app features that have the capacity to draw on users' public profiles or emails. Google and LinkedIn offer users ways to opt out of the AI features, while Meta's AI tool provides no means for its users to say "no, thanks." Anthropic's AI hacking claims divide experts Posts warned that the platforms' AI tool rollouts make most private information available for tech company harvesting .

Boost your productivity and save with exclusive Google Workspace coupons from WIRED. Get up to 14% off plans for three months, including Starter, Standard, and Plus tiers. Google Workspace is the modern business world's de facto productivity suite, and it's only gotten better over the years. There's the centralization of Google Docs, Drive, and Gmail, of course, but Google has bolstered its productivity suite with an AI infusion via Gemini, as well as simplified its offerings to work for massive corporations all the way down to individual users . If you want to get the best price, you need a Google Workspace promo code.

The recently published "certainty - scope" conjecture offers a compelling insight into the inherent trade - off present within artificial intelligence (AI) systems. As general research, this investigation remains vital as a philosophical undertaking and a potential guide for directing AI investments, design, and deployment, especially in safety - critical and mission - critical domains where risk levels are substantially elevated. W hile maintaining intellectual coherence, its formalization ultimately consolidates this insight into a suspended epistemic truth, which resists operational implementation within practical systems. This paper argues that the conjecture's objective to furnish insights for engineering de sign and regulatory decision - making is limited by two fundamental factors: first, its dependence on incomputable constructs and its failure to capture the generality factors of AI, rendering it practically unimplementable and unverifiable; second, its foundational ontological assumption of AI systems as self - contained epistemic entities, distancing it from the complex and dynamic socio - technical environments where knowledge is co - constructed. We conclude that this dual breakdown -- an epistemic closure deficit and an embeddedness bypass -- hinders the conjecture's transition to a practical and actionable framework suitable for informing and guiding AI deployments . In response, we point towards a possible framing of the epistemic challenge, emphasizing the inherent epistemic burdens of AI within complex human - centric domains. Keywords: artificial intelligence (AI), AI governance, algorithmic information theory (AIT), certainty - scope trade - off, complex systems, computability & operationalization, epistemic entanglement, epistemic certainty, hybrid AI systems, information theory, Kolmogorov complexity, risk - based assurance, safety - critical AI, socio - technical systems, verification and validation (V&V).