Category: Technology

Amazon has agreed to acquire Bee, a San Francisco-based startup that developed an AI-powered wearable device resembling a $50 wristband. This device continuously listens to the wearer’s conversations and surroundings, transcribing audio to provide personalized summaries, to-do lists, reminders, and suggestions through an associated app. Bee’s technology can integrate user data such as contacts, email, calendar, photos, and location to build a searchable log of daily interactions, enhancing its AI-driven insights. The acquisition, announced in July 2025 but not yet finalized, will see Bee’s team join Amazon to integrate this wearable AI technology with Amazon’s broader AI efforts, including personal assistant functionalities.

The AI wristband uses built-in microphones and AI models to automatically transcribe conversations unless manually muted. While the device’s accuracy can sometimes be affected by ambient sounds or media, Amazon emphasized its commitment to user privacy and control, intending to apply its established privacy standards to Bee’s technology. Bee claims it does not store raw audio recordings and uses high security standards, with ongoing tests of on-device AI models to enhance privacy.

This acquisition complements Amazon’s previous ventures into wearable tech, such as the discontinued Halo health band and its Echo smart glasses with Alexa integration. Bee represents a cost-accessible entry into AI wearables with continuous ambient intelligence, enabling Amazon to expand in this competitive market segment, which includes other companies like OpenAI and Meta developing AI assistants and wearables.

The financial terms of the deal have not been disclosed. Bee was founded in 2022, raised $7 million in funding, and is led by CEO Maria de Lourdes Zollo. Bee’s vision is to create personal AI that evolves with users to enrich their lives. Amazon plans to work with Bee’s team for future innovation in AI wearables post-acquisition.

Google Shopping introduced a new AI-powered shopping experience called “AI Mode,” featuring several advanced tools to enhance product discovery, try-ons, and price tracking.

Here is the Key updates include:

• Virtual Try-On: Shoppers in the U.S. can upload a full-length photo of themselves and virtually try on styles from billions of apparel items across Google Search, Google Shopping, and Google Images. This tool helps visualize how clothes might look without needing to physically try them on, making shopping more personalized and interactive.

• AI-Powered Shopping Panel: When searching for items, AI Mode runs simultaneous queries to deliver highly personalized and visually rich product recommendations and filters tailored to specific needs or preferences. For example, searching for travel bags can dynamically update to show waterproof options suitable for rainy weather.

• Price Alerts with Agentic Checkout: Users can now “track price” on product listings, specify preferred size, color, and target price. Google will notify shoppers when the price drops to their desired range, helping them buy at the right time.

• Personalized and Dynamic Filters: The system uses Google’s Gemini AI models paired with the Shopping Graph that contains over 50 billion fresh product listings, enabling precise filtering by attributes like size, color, availability, and price.

• Personalized Home Feed and Dedicated Deals Page: Google Shopping offers customized feeds and dedicated deals sections tailored to individual shopping habits and preferences.

These features are designed to make online shopping more intuitive, personalized, and efficient, leveraging AI to guide buyers from product discovery through to purchase.Google plans to roll out these features broadly in the U.S. in the coming months of 2025, enhancing the online shopping experience through AI-driven insights and assistance.

Oracle has launched the MCP Server for Oracle Database, a new technology aimed at powering context-aware AI agents for enterprise data interaction by leveraging the Model Context Protocol (MCP), an open protocol designed to enable secure, contextual communication between large language models (LLMs) and databases.

What MCP Server Does:

• Natural Language AI Interaction: It lets users and AI agents interact with Oracle Database using natural language commands, which are automatically translated into SQL queries. This simplifies querying, managing, and analyzing complex enterprise data without requiring deep SQL expertise.
• Agentic AI Workflows: Beyond generating SQL code, AI agents can now directly execute queries and perform read/write operations such as creating indexes or optimizing workloads, enabling more autonomous, actionable database workflows.
• Context Awareness & Security: The MCP Server operates within the permission boundaries of authenticated users, maintaining strict security by isolating AI interactions in a dedicated schema to ensure data privacy and access control. It uses existing credential management and logs AI activity for auditability.
• Seamless Integration: It is built into Oracle SQLcl, the modern command-line interface for Oracle Database, and accessible via extensions like Oracle SQL Developer for Visual Studio Code, facilitating easy adoption without complex middleware layers.
• Enterprise Productivity: The MCP Server enables AI copilots to retrieve metadata, analyze performance, generate compliance reports, and forecast trends directly from enterprise data, speeding up decision-making across industries like finance, retail, and healthcare.
• Built on Open Standards: MCP is considered a “USB-C port” for AI systems to interface with live data sources dynamically, making Oracle the first major database provider to implement this protocol for LLM-driven agents.

Benefits for Enterprises:

• Empowers developers and analysts with AI assistants that can interact directly with data in Oracle databases using plain English.
• Eliminates the need for manual query writing or custom integration layers.
• Supports secure, long-running AI agent sessions capable of complex and autonomous data tasks.
• Provides detailed monitoring, logging, and governance for AI interactions.
• Enhances user productivity by enabling AI to perform advanced data operations in real time.

Oracle’s MCP Server is a pivotal advancement that brings agentic, context-aware AI capabilities directly into enterprise database environments, enabling secure, intelligent, and autonomous data interaction at scale for business-critical applications.

NVIDIA Nemotron is a family of multimodal foundation models designed specifically for building enterprise-grade agentic AI with advanced reasoning capabilities. These models enable AI agents that can perform complex tasks such as graduate-level scientific reasoning, advanced math, coding, instruction following, tool calling, and visual reasoning.

Let’s have a look at the key Features of NVIDIA Nemotron:

• Agentic Reasoning: Nemotron models excel in reasoning tasks, enabling AI systems to understand, plan, and act autonomously with a level of cognitive reasoning close to human logic. They combine structured thinking with contextual awareness for dynamic and adaptable AI behaviors.

• Multimodal Capabilities: These models handle both text and vision tasks, such as enterprise optical character recognition (OCR) and complex instruction or tool use.

• Model Variants Optimized for Different Environments:

  • Nano: Optimized for cost-efficiency and edge deployment, suitable for RTX AI PCs and workstations.

  • Super: Balanced for high accuracy and compute efficiency on a single GPU.

  • Ultra: Designed for maximum accuracy and throughput in multi-GPU data center environments.

• Open and Customizable: Built on popular open-source reasoning models (notably Llama), Nemotron models are post-trained with high-quality datasets to align with human-like reasoning. They are available under an open license for enterprises to customize and control data, with models and training data openly published on platforms like Hugging Face.

• Compute Efficiency: Using techniques such as pruning of larger models and NVIDIA’s TensorRT-LLM optimization, Nemotron achieves top compute efficiency, delivering high throughput and low latency across devices from edge to data center.

• Integration and Deployment: Nemotron models are available as optimized NVIDIA NIM microservices, facilitating peak inference performance, flexible deployment, security, privacy, and portability. They are integrated with tools like NVIDIA NeMo for customizing agentic AI, NVIDIA Blueprints for accelerating development, and NVIDIA AI Enterprise for enterprise-grade production readiness.

• Industry Adoption: NVIDIA collaborates with leading AI agent platform providers like SAP and ServiceNow to adopt Nemotron models for practical enterprise deployment.

• Foundation for LLM-based AI Agents: An example in the Nemotron family is the “llama-3.1-nemotron-70b-instruct” large language model, which enhances LLM helpfulness and agentic task performance through specialization.

NVIDIA Nemotron models provide a commercially viable, highly optimized, and open foundation modeling solution tailored for creating advanced agentic AI systems capable of reasoning, acting, and interacting with complex environments with human-like intelligence and scalability across hardware platforms.

The GPUHammer attack is a newly demonstrated hardware-level exploit targeting NVIDIA GPUs, specifically those using GDDR6 memory like the NVIDIA A6000. It is an adaptation of the well-known RowHammer attack technique, which traditionally affected CPU DRAM, but now for the first time has been successfully applied to GPU memory.

What is GPUHammer?

• GPUHammer exploits physical vulnerabilities in GPU DRAM by repeatedly accessing (“hammering”) specific memory rows, causing electrical interference that flips bits in adjacent rows.

• These bit flips can silently corrupt data in GPU memory without direct access, potentially altering critical information used by AI models or other computations running on the GPU.

• The attack can degrade the accuracy of AI models drastically. For instance, an ImageNet-trained AI model’s accuracy was shown to drop from around 80% to under 1% after the attack corrupted its parameters.

Technical Challenges Overcome

• GPU memory architectures differ significantly from CPU DRAM with higher refresh rates and latency, making traditional RowHammer attacks ineffective.

• The researchers reverse-engineered memory mappings and developed GPU-specific hammering techniques to bypass existing memory protections such as Target Row Refresh (TRR).

Impact on AI and Data Integrity

• A single bit flip caused by GPUHammer can poison training data or internal AI model weights, leading to catastrophic failures in model predictions.

• The attack poses a specific risk in shared computing environments, such as cloud platforms or virtualized desktops, where multiple tenants share GPU resources, potentially enabling one user to corrupt another’s computations or data.

• Unlike CPUs, GPUs often lack certain hardware security features like instruction-level access control or parity checking, increasing their vulnerability.

NVIDIA’s Response and Mitigations

NVIDIA has issued an advisory urging customers to enable system-level Error Correction Codes (ECC), which can help detect and correct some memory errors caused by bit flips, reducing the risk of exploitation. Users of affected GPUs, such as A6000, may experience a performance penalty (up to ~10%) when enabling ECC or other mitigations.Newer NVIDIA GPUs like the H100 and RTX 5090 currently do not appear susceptible to this variant of the attack.

The GPUHammer attack reveals a serious new hardware security threat to AI infrastructure and GPU-driven computing, highlighting the need for stronger hardware protections as GPUs become central to critical AI workloads