AUSTIN, Texas, March 18, 2025 (GLOBE NEWSWIRE) -- SoftServe, a premier IT consulting and digital services provider, today introduced the SoftServe QA Agent, an agentic AI solution aiming to accelerate quality and assurance (QA) processes with AI test automation. Launched during NVIDIA’s annual conference, GTC 2025, this new offering is SoftServe’s latest development on AI agents, preparing enterprises for a future where agentic AI and physical AI converge to redefine automation, robotics, and decision-makin ...

Core Insights - VAST Data has launched VAST InsightEngine, a secure full-stack system for real-time data inferencing and scalable AI, in collaboration with NVIDIA DGX systems [1][5][7] - The platform aims to simplify AI deployments for enterprises, providing fast, scalable, and secure data services [1][2][5] Product Features - VAST InsightEngine integrates automated data ingestion, exabyte-scale vector search, event-driven orchestration, and GPU-optimized inferencing into a single system [2][3] - The system is designed to eliminate data bottlenecks and latency issues, ensuring seamless data flow and scalable AI inferencing [3][4] Security and Compliance - The platform includes enterprise-grade unified security features such as built-in encryption, access controls, and real-time monitoring [7] - VAST InsightEngine safeguards AI pipelines from threats and compliance risks, ensuring trusted and resilient data processing [7] Market Positioning - VAST Data positions itself as a leader in AI infrastructure, aiming to empower enterprises to unlock the full potential of their data [9] - The company has rapidly grown since its launch in 2019, becoming the fastest-growing data infrastructure company in history [9]

Core Insights - NVIDIA has launched the Llama Nemotron family of models, which are designed to provide advanced AI reasoning capabilities for developers and enterprises [1][4] - The new models enhance multistep math, coding, reasoning, and complex decision-making through extensive post-training, improving accuracy by up to 20% and optimizing inference speed by 5x compared to other leading models [2][3] Model Features - The Llama Nemotron model family is available in three sizes: Nano, Super, and Ultra, each tailored for different deployment needs, with the Nano model optimized for PCs and edge devices, the Super model for single GPU throughput, and the Ultra model for multi-GPU servers [5] - The models are built on high-quality curated synthetic data and additional datasets co-created by NVIDIA, ensuring flexibility for enterprises to develop custom reasoning models [6] Industry Collaboration - Major industry players such as Microsoft, SAP, and Accenture are collaborating with NVIDIA to integrate Llama Nemotron models into their platforms, enhancing AI capabilities across various applications [4][7][8][10] - Microsoft is incorporating these models into Azure AI Foundry, while SAP is using them to improve its Business AI solutions and AI copilot, Joule [7][8] Deployment and Accessibility - The Llama Nemotron models and NIM microservices are available as hosted APIs, with free access for NVIDIA Developer Program members for development, testing, and research [12] - Enterprises can run these models in production using NVIDIA AI Enterprise on accelerated data center and cloud infrastructure, with additional tools and software to facilitate advanced reasoning in collaborative AI systems [16]

Core Insights - NVIDIA has introduced the Blackwell Ultra AI factory platform, enhancing AI reasoning capabilities and enabling organizations to accelerate applications in AI reasoning, agentic AI, and physical AI [1][15] - The Blackwell Ultra platform is built on the Blackwell architecture and includes the GB300 NVL72 and HGX B300 NVL16 systems, significantly increasing AI performance and revenue opportunities for AI factories [2][3] Product Features - The GB300 NVL72 system delivers 1.5 times more AI performance compared to the previous GB200 NVL72, and increases revenue opportunities by 50 times for AI factories compared to those built with NVIDIA Hopper [2] - The HGX B300 NVL16 offers 11 times faster inference on large language models, 7 times more compute, and 4 times larger memory compared to the Hopper generation [5] System Architecture - The GB300 NVL72 connects 72 Blackwell Ultra GPUs and 36 Arm Neoverse-based Grace CPUs, designed for test-time scaling and improved AI model performance [3] - Blackwell Ultra systems integrate with NVIDIA Spectrum-X Ethernet and Quantum-X800 InfiniBand platforms, providing 800 Gb/s data throughput for each GPU, enhancing AI factory and cloud data center capabilities [6] Networking and Security - NVIDIA BlueField-3 DPUs in Blackwell Ultra systems enable multi-tenant networking, GPU compute elasticity, and real-time cybersecurity threat detection [7] Market Adoption - Major technology partners including Cisco, Dell Technologies, and Hewlett Packard Enterprise are expected to deliver servers based on Blackwell Ultra products starting in the second half of 2025 [8] - Leading cloud service providers such as Amazon Web Services, Google Cloud, and Microsoft Azure will offer Blackwell Ultra-powered instances [9] Software Innovations - The NVIDIA Dynamo open-source inference framework aims to scale reasoning AI services, improving throughput and reducing response times [10][11] - Blackwell systems are optimized for running new NVIDIA Llama Nemotron Reason models and the NVIDIA AI-Q Blueprint, supported by the NVIDIA AI Enterprise software platform [12] Ecosystem and Development - The Blackwell platform is supported by NVIDIA's ecosystem of development tools, including CUDA-X libraries, with over 6 million developers and 4,000+ applications [13]

Core Insights - Cisco is introducing new AI-powered collaboration solutions aimed at enhancing customer and employee experiences, with a focus on predictive and automated interactions [2][6] - The company is transitioning traditional contact centers into customer experience centers, utilizing AI to improve service efficiency and customer satisfaction [4][6] AI Innovations - The Webex AI Agent will be generally available on March 31, 2025, providing a 24/7 self-service solution that interacts with customers in a natural manner, reducing wait times and improving service [4][6] - The Cisco AI Assistant for Webex Contact Center will receive updates in Q2 2025, including features like suggested responses and real-time transcription to enhance agent performance [7] Employee Experience Enhancements - New tools for employees include workflow automation capabilities that streamline routine tasks and improve productivity across various enterprise applications like Salesforce and ServiceNow [8][12] - The Webex Calling Customer Assist solution empowers employees to assist customers effectively, integrating AI features for better call routing and analytics [9] Integration and Collaboration - Cisco is enhancing its collaboration portfolio with features that allow seamless integration of AI-driven innovations across its platforms, improving user experiences in various workspaces [9][12] - The introduction of Apple AirPlay on Cisco devices for Microsoft Teams Rooms facilitates instant wireless content sharing, enhancing collaboration capabilities [13]

Core Viewpoint - The recent market sell-off, with the Nasdaq Composite down over 13% from its all-time highs, presents potential long-term buying opportunities in the technology sector. Group 1: Nvidia - Nvidia is the leader in AI infrastructure, with its GPUs providing essential processing power for AI model training and inference [2][3] - The company's revenue has more than doubled in both fiscal years 2024 and 2025 [2] - Nvidia holds approximately 90% market share in the GPU space, supported by its CUDA software platform, and is currently down nearly 22% from its all-time highs [4] Group 2: Broadcom - Broadcom is focusing on custom AI chips, providing an alternative to Nvidia's high-priced offerings [5] - The company has three main AI chip customers with a combined serviceable addressable market of $60 billion to $90 billion for fiscal 2027 [6] - Broadcom's stock is down about 23% from its all-time highs set in December 2024, presenting a buying opportunity [7] Group 3: Alphabet - Alphabet is a leader in digital advertising and cloud computing, with significant growth in its cloud unit, which saw a 30% revenue increase last quarter [8][10] - The company is well-positioned to leverage AI for new ad formats, potentially tapping into a large new market [9] - Alphabet's stock is down about 21% from highs set early last month, making it an attractive long-term investment [10] Group 4: Salesforce - Salesforce aims to lead in agentic AI, which automates tasks with minimal human supervision, offering significant business applications [11] - The launch of Agentforce has attracted 5,000 customers, including 3,000 paying customers, since its introduction [12][13] - The stock is down nearly 26% since December 2024, providing a good entry point for investors [13] Group 5: GitLab - GitLab is a fast-growing DevSecOps platform, with a high-margin subscription model benefiting from AI integration [14] - The company has seen a 29% increase in revenue last quarter, marking its sixth consecutive quarter of growth between 29% to 33% [16] - GitLab's stock is down about 31% from early February highs, presenting a strong buying opportunity [14][17]

Core Viewpoint - The article emphasizes the importance of legal preparation and governance for early-stage entrepreneurs, highlighting the need for a solid understanding of legal frameworks to avoid potential pitfalls in business development [1][2][3]. Summary by Sections Legal Preparation - Early-stage entrepreneurs must understand the legal preparations necessary for starting a business, including issues related to non-compete agreements and intellectual property rights [3][4]. Company Structure - The article discusses the importance of selecting an appropriate company structure, detailing the advantages and disadvantages of various structures and how they relate to future financing and listing needs [3][4]. Equity Distribution - It outlines the principles of equity distribution among founding teams, emphasizing the need for a healthy equity split to foster a supportive entrepreneurial environment [4]. Governance Structure - The governance structure is crucial for decision-making efficiency, with insights drawn from recent corporate governance challenges faced by companies like OpenAI [4]. Employee Incentives - The article addresses the significance of employee equity incentives, providing a framework for founders to establish effective incentive plans that align with company goals [4]. Upcoming Course Information - The YUE 06 program is set to begin soon, focusing on various modules including AI, recruitment, product development, commercialization, and financing, aimed at equipping early-stage entrepreneurs with essential skills and knowledge [5][6][8].

In this exclusive Motley Fool interview, Salesforce (CRM -4.96%) CEO Marc Benioff shares his insights on the rise of agentic AI and its transformative impact on the company. He discusses how AI-powered agents are reshaping customer relationships, streamlining workflows, and driving innovation at Salesforce. Tune in to learn how this cutting-edge technology is shaping the future of enterprise software.*Stock prices used were the prices of March 12, 2025. The video was published on March 12, 2025. ...

Core Viewpoint - The article emphasizes the importance of integrating business logic with technology in the retail industry, particularly through the example of Belle Fashion Group's approach to AI implementation, which focuses on making business the "navigator" of technology rather than the other way around [5][8][20]. Group 1: Digital Transformation Challenges - The retail industry faces a paradox in intelligent transformation, where advanced models and algorithms often fail to align with business needs, leading to dissatisfaction among business departments [2][3]. - Since 2023, the digitalization of enterprises has been chasing the trend of large models, but there is a disconnect between business and cutting-edge technology, resulting in increased data governance costs and limitations of SaaS systems [4][9]. Group 2: Belle Fashion's AI Implementation - Belle Fashion has developed a methodology for AI implementation in collaboration with its long-term partner, Deepu Technology, focusing on transforming business rules into the "mother tongue" of AI [7][12]. - The company recognizes the illusion of large models in industrial scenarios and emphasizes the need for AI capabilities to be anchored in business rules and data quality [9][11]. Group 3: Data Governance and Management - Data governance is seen as the foundational issue for AI industrialization, requiring a shift from isolated technical perspectives to a strategic framework [13]. - Belle Fashion has moved from a label-based data processing approach to a dynamic context that allows AI to understand and reason with data, thus enhancing data governance [13][14]. Group 4: Intelligent Data Warehouse - The traditional data warehouse model is static, while Belle Fashion's intelligent data warehouse aims to create dynamic rules and insights from real-time business analysis [16][17]. - The shift from pre-defined static rules to a model that generates rules dynamically is crucial for enhancing business insights and decision-making [17][18]. Group 5: Agentic AI and Operational Efficiency - Agentic AI is highlighted as a key component in the final mile of AI industrialization, enabling real-time efficiency and seamless integration of business processes [21][22]. - By utilizing Agentic AI, Belle Fashion has transformed its management processes into traceable digital tracks, providing valuable data for model training [23]. Group 6: Future of AI in Retail - The article concludes that the best approach to intelligent transformation is to first reconstruct the understanding of business and human interactions before moving on to creation [26][27]. - The role of technology suppliers is evolving from traditional SaaS sales to a more collaborative approach that listens to business needs [25].

Industry Overview - The medical device industry is experiencing significant transformation in 2025, primarily driven by advancements in generative AI and agentic AI [1] - AI's role in optimizing workflows and improving patient care is becoming increasingly crucial due to a projected shortfall of 11 million health workers by 2030 [4] - Regulatory bodies are evolving to accommodate AI-enabled devices, with frameworks being developed to approve autonomous systems in healthcare, allowing for faster market entry of AI-powered medical devices while maintaining safety standards [5] Company Highlights - **NVIDIA**: The Clara platform enhances real-time medical imaging and predictive diagnostics, while BioNeMo advances drug discovery and biomarker identification [2] - **Resmed**: The company has a market cap of $34.8 billion and aims to improve 500 million lives through better residential healthcare by 2030, with a projected earnings growth rate of 22.7% in 2025 [9][8] - **GE HealthCare**: With a market cap of $39.03 billion, the company is focusing on AI and machine learning for clinical decision support and personalized therapies, expecting earnings growth of 4.7% in 2025 [11][10] - **Medtronic**: The company integrates AI across its portfolio, including systems for detecting colorectal polyps and adaptive deep brain stimulation, with a market cap of $119.6 billion and expected earnings growth of 5% in fiscal 2025 [14][13] Collaboration and Partnerships - MedTech companies are collaborating with AI leaders like Google Health and Microsoft to co-develop advanced solutions, accelerating AI integration across various medical applications [6] Market Dynamics - The shift towards outpatient procedures in areas like orthopedics and cardiology is driving demand for advanced imaging systems and interventional solutions [10] - AI-driven medical devices are becoming more adaptive, allowing for autonomous analysis of patient data and improved decision-making [3]