Category: AI Related

  • Oracle launches MCP Server for Oracle Database to power context-aware AI agents for enterprise data

    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.

  • Amazon Bedrock AgentCore;Deploy and operate AI agents securely at scale – using any framework and model

    Amazon Bedrock AgentCore, launched in preview in July 2025, is a fully managed, modular platform designed to deploy, operate, and scale secure, enterprise-grade AI agents using any open-source framework and foundation models inside or outside of Amazon Bedrock. It provides purpose-built infrastructure for dynamic, long-running, multi-step agent workloads with strong security, flexibility, and observability.

    Key Capabilities of Amazon Bedrock AgentCore:

    • Secure, scalable deployment: Supports long-running agent processes (up to 8 hours) with complete session isolation and native integration for identity and access management, allowing seamless agent authentication and permission delegation across services.
    • Agent enhancement tools:
      • Persistent memory for maintaining agent knowledge across interactions with fine-grained developer control over short-term and long-term memory.
      • Built-in tools including a secure browser runtime to enable agents to perform complex web-based workflows.
      • A secure code interpreter for safe execution of code needed for tasks like data visualization.
    • Operational monitoring: Offers real-time dashboards via Amazon CloudWatch to track token usage, latency, session duration, error rates, and full workflow auditability to aid debugging, compliance, and operational insights. Integrates with existing monitoring systems through OpenTelemetry.
    • Flexible integration: Works with any AI agent framework such as CrewAI, LangGraph, LlamaIndex, and Strands Agents. Supports any foundation model inside or outside Amazon Bedrock, letting developers build agents “their way” with full control over integration and operation.
    • Enterprise-grade security and trust: Provides session isolation, password and token vaults, secure authorization protocols, and tools to enforce just-enough access principles ensuring agents operate safely at scale.

    Modular Services:

    • AgentCore Runtime: Serverless, secure runtime for deploying and scaling AI agents with fast cold starts and payload support for multi-modal data types.
    • AgentCore Identity: Seamless, OAuth-compatible identity and access management that integrates with existing identity providers, simplifying authentication and consent management.
    • AgentCore Memory: Manages agent memory infrastructure with features for sharing knowledge across sessions and agents, improving personalization and contextual awareness.

    Use Cases & Customers:

    • Financial services leader Itaú Unibanco uses AgentCore for hyper-personalized, secure, scalable banking AI agents.
    • Innovaccer builds healthcare AI agents that safely interface with sensitive data via Bedrock Gateway.
    • Epsilon accelerates personalized marketing campaigns by reducing build times and boosting engagement.
    • Box experiments with Bedrock AgentCore runtime for enterprise content management enhanced by agentic AI.

    Benefits:

    • Accelerates AI agent development from prototype to production by offloading infrastructure complexity.
    • Enables enterprises to deploy sophisticated, tool-augmented AI agents with persistent memory and web/code interaction capabilities securely and at scale.
    • Helps ensure operational reliability, security, and compliance with end-to-end observability and controls.

    In summary, Amazon Bedrock AgentCore is a comprehensive, secure, and flexible platform for enterprises to rapidly build, deploy, and scale intelligent agentic AI across various domains with full control over tooling, identity, memory, and observability. It supports any framework or foundation model and is designed to meet demanding enterprise requirements for security, scalability, and compliance.

  • OpenAI Introducing ChatGPT agent: bridging research and action

    The ChatGPT agent, introduced by OpenAI in July 2025, is a new unified agentic system that enables ChatGPT to think and act autonomously by proactively choosing from a toolbox of agentic skills to execute complex, multi-step tasks on your behalf using its own virtual computer.

    Core Capabilities:

    • Autonomous task execution: ChatGPT can navigate websites, interact with web pages (click, scroll, type), log in securely when needed, run code, conduct complex analysis, and produce editable outputs such as slideshows and spreadsheets.
    • Unified system integrating previous tools: It combines the web interaction strength of Operator, deep synthesis skills of deep research, and ChatGPT’s intelligence, offering seamless transitions within a single conversation from casual inquiry to detailed task automation.
    • Multitool environment: Equipped with multiple tools including:
      • Visual browser for graphical browsing,
      • Text-based browser for data-heavy queries,
      • A terminal for code execution,
      • Direct API access,
      • Connectors for apps like Gmail and GitHub to access contextual user data securely.

    User Control & Safety:

    • Users retain full control over the agent:
      • ChatGPT requests permission before performing any consequential action.
      • Users may interrupt, take over the browser, pause, or stop tasks at any time.
    • Strong risk mitigation against prompt injection and other adversarial attacks has been implemented.
    • Privacy controls allow users to delete browsing data and log out of sessions; credentials and sensitive data entered during browser takeover sessions are never stored by the model.

    Practical Applications:

    • Automates everyday and professional workflows such as:
      • Calendar briefing based on news,
      • Planning and purchasing groceries,
      • Competitor analysis with slide deck creation,
      • Automating financial modeling,
      • Converting screenshots to presentations,
      • Booking travel and appointments,
      • Editing complex spreadsheets, where it significantly outperforms other models.

    Performance and Benchmarks:

    • Achieves state-of-the-art results across benchmarks measuring web browsing, economic knowledge work, data science, spreadsheet editing, and complex mathematical problem solving.
    • Outperforms prior models and often matches or surpasses human performance in professional tasks.

    Availability:

    • Available to Pro, Plus, and Team users, activated via the tools dropdown in ChatGPT by selecting “agent mode” at any point during a conversation.

    Safety and Ethical Considerations:

    • Classified as having high biological and chemical capability risk; enhanced safeguards include threat modeling, refusal training, and expert review.
    • Collaboration with biosecurity experts ensures robust safety and compliance.

    In essence, ChatGPT agent represents a significant advancement toward truly autonomous AI assistants capable of complex, real-world task execution with user-controlled, transparent, and secure workflows.

  • NVIDIA Nemotron – Foundation Models for Agentic AI

    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.

  • Meta Strengthens AI Capabilities with Acquisition of Voice Technology Startup Play AI

    Meta has acquired Play AI, a California-based startup specializing in AI-generated human-sounding voices, marking a strategic expansion of Meta’s AI capabilities in voice synthesis and conversational technology. The entire Play AI team is set to join Meta and report to Johan Schalkwyk, who recently joined Meta from another voice AI startup, positioning them within Meta’s AI research efforts focused on natural language interaction, AI characters, wearables, and audio content creation.

    Let’s have a look at the strategic significance:

    • Voice AI Enhancement: Play AI’s technology enables cloning of human-like voices and generation of speech with “hyper-realism” across languages, accents, and dialects, which aligns with Meta’s push to improve voice-driven digital interactions across platforms such as WhatsApp, Instagram, and the Meta Quest ecosystem.

    • Integration Across Meta’s AI Roadmap: Play AI’s expertise complements Meta’s initiatives in AI characters, wearable technology, and audio content production, supporting future immersive and conversational AI experiences.

    • Talent Acquisition: The Play AI team’s integration adds specialized talent to Meta’s growing AI division, augmenting a period of aggressive recruitment from OpenAI, Google, and Apple, and builds upon Meta’s broader AI investments including the Scale AI acquisition and formation of a superintelligence lab led by Alexandr Wang.

    • Ethical AI Focus: Play AI has partnered with firms like Reality Defender to combat AI voice deepfakes, emphasizing responsible AI development—an aspect that may influence Meta’s approach to synthetic voice technology

    Financial terms of the acquisition remain undisclosed. However, the deal was finalized in July 2025 after extensive discussions.Meta’s acquisition of Play AI accelerates its capacity in voice synthesis and conversational AI, signifying its ambition to lead in immersive, voice-enabled AI experiences across its expansive ecosystem.

  • GPUHammer: New RowHammer Attack Variant Degrades AI Models on NVIDIA GPUs

    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

  • Scientists create biological ‘artificial intelligence’ system,PROTEUS

    Australian scientists, primarily at the University of Sydney’s Charles Perkins Centre, have developed a groundbreaking biological artificial intelligence system named PROTEUS (PROTein Evolution Using Selection) that can design and evolve molecules with new or improved functions directly inside mammalian cells.

    How PROTEUS Works

    • Biological AI via Directed Evolution: PROTEUS harnesses the technique of directed evolution, which mimics natural evolution by iteratively selecting molecules with desired traits. Unlike traditional directed evolution that operates mainly in bacterial cells and takes years, PROTEUS accelerates this process drastically—from years to just weeks—directly within mammalian cells.

    • Problem-Solving Mode: Similar to how users input prompts to AI platforms, PROTEUS can be tasked with complex biological problems with uncertain solutions, for example, how to efficiently switch off a human disease gene in the body. It then explores millions of molecular sequences to find molecules highly adapted to solve that problem.

    • Mammalian Cell Environment: The ability to evolve molecules inside mammalian cells is unique and significant because it allows developing molecules that function well in the human body’s physiological context, improving therapeutic relevance.

    Applications and Implications

    • Drug Development and Gene Therapies: PROTEUS can create highly specific research tools and gene therapies, including improving gene editing technologies like CRISPR by enhancing their effectiveness and precision.

    • Molecule Enhancement: Researchers have already used PROTEUS to develop better-regulated proteins and nanobodies (small antibody fragments) that detect DNA damage, which is critical in cancer.

    • Broad Potential: The technology is not limited to these examples and holds promise for designing virtually any protein or molecule with enhanced or new functions to solve biotech and medical challenges

    This fusion of biological systems and AI represents a shift in bioengineering, enabling rapid, in vivo molecular evolution that was previously impossible. PROTEUS dramatically shortens development timelines for novel medicines and biological tools, potentially revolutionizing precision medicine and biotechnology.PROTEUS is a revolutionary AI-driven biological system that uses directed evolution inside mammalian cells to quickly discover and engineer molecules optimized for medical and biotech solutions. By combining AI-style problem-solving with accelerated biological evolution, this technology opens new frontiers in drug design, gene therapy, and molecular biology tailored to function effectively within the human body.

  • Claude AI chatbot directly creates and edits Canva designs via conversational commands

    Anthropic has announced a new integration that enables its Claude AI chatbot to directly create and edit Canva designs via conversational commands. This feature is part of a broader expansion of Claude’s automation capabilities, enhancing user productivity by combining advanced AI language understanding with creative design tools.

    Here is the Key Details:

    • Canva Integration: Users can instruct Claude to generate or modify Canva graphics, presentations, social media posts, and other visual materials through natural language prompts.

    • Seamless Workflow: By bridging conversational AI with Canva’s design platform, Claude simplifies design creation without requiring users to manually interact with Canva’s interface.

    • Automation Expansion: This update is part of Claude’s growing set of automation features that help execute complex, multi-step tasks by understanding nuanced human instructions.

    • Use Cases: Examples include:

      • Creating new presentation slides based on text prompts.

      • Editing existing designs by changing colors, layouts, or adding/removing elements.

      • Generating branded marketing materials styled per company guidelines.

    • Benefit: Streamlines the creative process for marketers, content creators, and teams by reducing time spent on repetitive or technical design tasks.

    So What It Means:

    This integration reflects a trend where AI agents are increasingly augmenting or automating creative workflows. By embedding AI directly into popular design platforms like Canva, users can focus more on strategic content and messaging while AI handles detailed execution.

    How to Use:

    To use this feature, users typically:

    1. Connect their Claude AI chatbot with their Canva account through a permissions link.

    2. Engage Claude via chat, providing clear instructions like “Create a Canva slide for our Q3 sales report with graphs and bullet points.”

    3. Claude then generates or edits the design accordingly, delivering the result within Canva for review or final tweaks.

  • Elon Musk’s AI bot introduces anime companion

    Elon Musk’s AI company xAI has launched a new feature for its chatbot, Grok, introducing interactive anime-inspired companions. The rollout is seen as a significant step towards personalized AI companionship, offering playful, animated avatars within the app. This latest move combines Musk’s signature flair for spectacle with the rising trend of emotional AI companions.

    Here is the Key Features:

    • Companions Launch: Announced on July 14, 2025, Grok’s “Companions” are animated, interactive characters now available to SuperGrok (premium) subscribers.
    • Anime Companion “Ani”: The standout is “Ani”—a blonde, gothic anime girl styled with pigtails, a black corset, and thigh-high fishnets. Her style is reminiscent of well-known anime tropes, and she’s designed as a customizable digital companion.
    • Other Characters: Alongside Ani, users can interact with “Rudy,” a sarcastic, animated red panda. There are indications more companions, including male characters, are being developed.
    • Interaction Modes: Users can chat with these avatars via text or voice; characters feature expressive head and body movements for a more dynamic AI experience.
    • NSFW Mode: Ani offers a “Not Safe For Work” setting, reportedly allowing the avatar to appear in lingerie after engaging with users, which sparked debate online. This mode is toggleable via settings and has led to a viral response.
    • Availability: The feature is initially accessible only to iOS users with Premium+ and SuperGrok subscriptions (costing up to $300/month). Android and desktop access are expected in the future.

    How to Access:

    • Open the Grok app on iOS.
    • Navigate to settings and enable the Companions feature.
    • Select your AI companion to begin interacting, either through chat or voice.

    Industry and Cultural Impact:

    The launch mirrors other successful virtual companion apps (such as Character.ai) and aims to drive engagement and personalization for paying users. The move follows controversy over Grok’s responses to sensitive topics and reflects a rapid pivot to lighthearted, character-driven AI for entertainment. Ani’s design, skirting copyright issues by resembling but not copying famous anime characters, has sparked conversation and meme-making among anime fans and tech watchers.

    Elon Musk’s xAI has added Companions to Grok, enabling users to personalize their interactions with AI through anime-style and cartoon avatars featuring playful, flirtatious, and sometimes adult-oriented personalities. As AI bots meet anime culture, the line between technology and digital companionship continues to blur

  • Kimi AI, developed by the Chinese startup Moonshot AI

    Kimi AI, developed by the Chinese startup Moonshot AI, highlight significant advancements and growing influence in the AI sector as of mid-2025:

    • Kimi K2 Release (July 2025): Moonshot AI launched an advanced open-source AI model called Kimi K2, featuring a mixture-of-experts (MoE) architecture with 1 trillion parameters and 32 billion activated parameters. This design reduces computation costs and speeds up performance. Kimi K2 excels in frontier knowledge, mathematics, coding, and general agentic tasks. It is available in two versions:

      • Kimi-K2-Base for researchers and developers seeking full control for fine-tuning.

      • Kimi-K2-Instruct for general-purpose chat and agentic AI experiences.

      Kimi K2 is freely accessible via web and mobile apps, reflecting a broader industry trend toward open-source AI to boost efficiency and adoption.

    • Kimi k1.5 Model (Early 2025): Prior to K2, Moonshot AI released Kimi k1.5, a multimodal AI model capable of processing text, images, and code, designed for complex problem-solving. It supports a massive 128k-token context window, enabling a “photographic memory” for text and enhanced reasoning. Kimi k1.5 reportedly outperforms GPT-4 and Claude 3.5 by up to 550% in certain logical reasoning tasks. It offers two reasoning modes (long and short chain-of-thought) and real-time web search across 100+ sites, with the ability to analyze up to 50 files simultaneously. English language support is included but still being optimized. The model is free and unlimited on the web, with a mobile app in development.

    • Capabilities and Competition: Moonshot AI positions Kimi as a strong competitor to leading US models like OpenAI’s GPT-4 and o1, with comparable or superior abilities in coding, math, multi-step reasoning, and multimodal input. The company emphasizes cost-effective development (approximately one-sixth the cost of comparable US models) and open-source accessibility to challenge global AI dominance.

    • Industry Impact: Kimi AI’s open-source approach and cutting-edge features contribute to China’s growing footprint in the AI market, intensifying the global AI arms race alongside other Chinese models like DeepSeek-R1 and international rivals such as Google Gemini.

    Kimi AI is currently at the forefront of AI innovation with its latest K2 model emphasizing open-source collaboration and its earlier k1.5 model demonstrating strong multimodal reasoning and competitive performance against top global AI systems. Moonshot AI continues to expand Kimi’s accessibility and capabilities, marking it as a significant player in the evolving AI landscape.