PCWorld reports that Google's new Docs Live feature uses Gemini AI to transform verbal "brain dumps" into formatted documents, pulling information from Drive, Gmail, and other Google services. This productivity enhancement aims to reduce cognitive load for mentally fatigued users by enabling conversational document creation and automatic task generation in Google Keep. The advanced AI features, including Gmail's new AI Inbox for enhanced search and email drafting, will require Google AI Pro or Ultra subscriptions starting this summer. We've all been in this situation: You know what you want to say, but you're too mentally exhausted, distracted, or confused to actually say it. Google's new conversational AI, Docs Live, wants to help. The metaphor Google is using here is a "brain dump," and Google is applying this technique to Docs, Gmail, and Google Keep. Google's trying to offload more of the "thinking" away from you and on to Google apps, using what it knows about you -- naturally!

Google is rolling out updates to Gmail's'Help me write' AI feature that personalizes email drafts by analyzing your previous writing style and mining your inbox for context. PCWorld reports the enhanced tool can now access information from Google Drive and Gmail to create more natural-sounding, contextually relevant email responses. Available exclusively to Google AI Plus, Pro, Ultra, or business subscribers, the rollout began May 5th and may take up to 15 days to reach all users. In a recent Google Workspace Updates blog post, the company announced that it has begun rolling out two updates to the "Help me write" feature in Gmail, which are designed to do two things: make AI-generated emails sound more like you wrote them yourself, and enable the ability to gather more context for generated emails.

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.

Google Is Adding an'AI Inbox' to Gmail That Summarizes Emails New Gmail features, powered by the Gemini model, are part of Google's continued push for users to incorporate AI into their daily life and conversations. Google is putting even more generative AI tools into Gmail as part of its goal to further personalize user inboxes and streamline searches. On Thursday, the company announced a new "AI Inbox" tab, currently in a beta testing phase, that reads every message in a user's Gmail and suggests a list of to-dos and key topics, based on what it summarizes . In Google's example of what this AI Inbox could look like in Gmail, the new tab takes context from a user's messages and suggests they reschedule their dentist appointment, reply to a request from their child's sports coach, and pay an upcoming fee before the deadline. Also under the AI Inbox tab is a list of important topics worth browsing, nestled beneath the action items at the top.

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.