Vikrant Singh

<>MSFT - VR NVL72 Microsoft Corporation (NASDAQ:MSFT) became the first cloud service provider to begin validating the Vera Rubin NVL72 system. Developed by Nvidia, the next-generation rack-scale AI supercomputer is designed for training and inference of trillion-parameter models >>

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🚀 GenAI for Collaborative AI 🚀 Our paper “Privacy-Aware RAG-Enabled LLMs for Collaborative AI in Organizations “ has been accepted to the IEEE CARS conference! It is a 1-year work that focuses on preserving privacy during automated information sharing with Microsoft‘s Autogen, comparing the effectiveness of Meta‘s Llama3.3 and OpenAI‘s GPT-4o in LLM-driven inter-organizational communication. This research was made possible through collaboration with Idaho National Laboratory! Stay tuned for more GenAI work!

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We are excited to share that the new OpenAI models -- gpt-oss-20b and gpt-oss-120b -- are now available as NVIDIA NIM microservices, making it easy to deploy them on any GPU-accelerated infrastructure with flexibility, data privacy, and enterprise-grade security. Developers – you can also try out a preview of the gpt-oss models for free using our preview API and the web playground environment.

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Excited to host Prof. Rose Yu (UC San Diego) at IEEE SPS San Diego for a session at DMEMS 2026 Topic: Agentic AI Systems for Real-Time Decision Making As AI systems evolve from predictive models to autonomous, agent-driven workflows, this session will explore how LLMs, reinforcement learning, and multi-agent systems are being applied in real-world environments across IoT, edge AI, and distributed systems. The session will include a technical talk followed by an industry discussion and Q&A. 📅 April 22, 4:00 PM 📍 Del Mar Fairgrounds (DMEMS 2026) Event details: https://lnkd.in/g9ab5AAY Looking forward to engaging discussions with the community. Rose Yu, IEEE Signal Processing Society #AI #AgenticAI #MachineLearning #IEEE #EdgeAI

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As an AI/ML researcher and technology leader, I notice the world of technology living with a certain sense of angst in the era of AI. Unless we are living under a rock, AI tools, coding agents, and LLMs are no longer technologies of the future. They are already part of the day-to-day workflow of many software engineers. 𝗬𝗲𝘁 𝗮𝘀 𝗹𝗲𝗮𝗱𝗲𝗿𝘀, 𝘄𝗵𝗮𝘁 𝗹𝗲𝗻𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝘂𝘀𝗲 𝘁𝗼 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀? If our investments must move the bottom line of the business, what guardrails should guide how we employ AI? In my view, every strategic conversation about AI should answer three questions: 𝟭. 𝗗𝗼𝗲𝘀 𝗺𝘆 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗮𝗹𝗶𝗴𝗻 𝘄𝗶𝘁𝗵 𝗺𝘆 𝗰𝗼𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀? Not every business problem requires next-generation technology. While there is pressure, even with a fear of missing out to adopt AI quickly, 𝙡𝙚𝙖𝙙𝙚𝙧𝙨 𝙢𝙪𝙨𝙩 𝙚𝙣𝙨𝙪𝙧𝙚 𝙞𝙩 𝙩𝙧𝙪𝙡𝙮 𝙖𝙡𝙞𝙜𝙣𝙨 𝙬𝙞𝙩𝙝 𝙩𝙝𝙚 𝙫𝙖𝙡𝙪𝙚 𝙩𝙝𝙚 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙙𝙚𝙡𝙞𝙫𝙚𝙧𝙨. Building capability purely because it is the buzz of the moment can be worse than being slightly late on the adoption curve. 𝟮. 𝗗𝗼𝗲𝘀 𝘁𝗵𝗲 𝗰𝗼𝘀𝘁-𝗯𝗲𝗻𝗲𝗳𝗶𝘁 𝗼𝗳 𝗔𝗜 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗮𝗱𝗱 𝘃𝗮𝗹𝘂𝗲? Technology can be exciting, but leaders must ask: 𝙝𝙤𝙬 𝙬𝙞𝙡𝙡 𝙩𝙝𝙞𝙨 𝙧𝙚𝙛𝙡𝙚𝙘𝙩 𝙞𝙣 𝙩𝙝𝙚 𝙋&𝙇 𝙞𝙣 𝙩𝙝𝙚 𝙣𝙚𝙖𝙧 𝙖𝙣𝙙 𝙡𝙤𝙣𝙜 𝙩𝙚𝙧𝙢? 𝙒𝙞𝙡𝙡 𝙩𝙝𝙚 𝙞𝙣𝙫𝙚𝙨𝙩𝙢𝙚𝙣𝙩 𝙨𝙩𝙖𝙣𝙙 𝙩𝙝𝙚 𝙩𝙚𝙨𝙩 𝙤𝙛 𝙩𝙞𝙢𝙚 𝙖𝙣𝙙 𝙙𝙚𝙡𝙞𝙫𝙚𝙧 𝙙𝙪𝙧𝙖𝙗𝙡𝙚 𝙫𝙖𝙡𝙪𝙚? 𝟯. 𝗗𝗼 𝘄𝗲 𝗿𝗲𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗼𝗿 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝘆𝘀𝘁𝗲𝗺 𝗵𝗲𝗿𝗲, 𝗼𝗿 𝗰𝗮𝗻 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗱𝗼 𝘁𝗵𝗲 𝗷𝗼𝗯? Today, everyone wants to label their solution with GenAI, agents, or agentic systems to be noticed. That is understandable. 𝘽𝙪𝙩 𝙖𝙨 𝙡𝙚𝙖𝙙𝙚𝙧𝙨, 𝙬𝙚 𝙢𝙪𝙨𝙩 𝙣𝙤𝙩 𝙧𝙚𝙬𝙖𝙧𝙙 𝙩𝙝𝙚 𝙢𝙚𝙧𝙚 𝙥𝙧𝙚𝙨𝙚𝙣𝙘𝙚 𝙤𝙛 𝗚𝗲𝗻𝘼𝙄 𝙞𝙣 𝙖 𝙨𝙤𝙡𝙪𝙩𝙞𝙤𝙣. 𝙄𝙣𝙨𝙩𝙚𝙖𝙙, 𝙬𝙚 𝙨𝙝𝙤𝙪𝙡𝙙 𝙧𝙚𝙬𝙖𝙧𝙙 𝙨𝙤𝙪𝙣𝙙 𝙩𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡 𝙟𝙪𝙙𝙜𝙢𝙚𝙣𝙩. Sometimes the right answer is: • Traditional ML is sufficient. • A deterministic system is better. • A rule-based SaaS solution is the right tool. And that is good engineering leadership. For many businesses, AI is ultimately an enabler, just as technology and data have always been. 𝗕𝘂𝘁 𝗶𝗳 𝗳𝗲𝗮𝗿 (𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁) 𝗱𝗿𝗶𝘃𝗲𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝘄𝗲 𝗺𝗮𝘆 𝗲𝗻𝗱 𝘂𝗽 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘀𝗮𝗸𝗲 𝗼𝗳 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 𝙎𝙤 𝙗𝙮 𝙖𝙡𝙡 𝙢𝙚𝙖𝙣𝙨, 𝙜𝙤 𝙖𝙡𝙡 𝙞𝙣 𝙤𝙣 𝘼𝙄, 𝙗𝙪𝙩 𝙢𝙖𝙠𝙚 𝙨𝙪𝙧𝙚 𝙞𝙩 𝙥𝙖𝙨𝙨𝙚𝙨 𝙩𝙝𝙧𝙤𝙪𝙜𝙝 𝙩𝙝𝙚 𝙧𝙞𝙜𝙝𝙩 𝙡𝙚𝙣𝙨 𝙛𝙞𝙧𝙨𝙩. Agree or disagree? I’d love to hear how other leaders are thinking about this. #ArtificialIntelligence #GenAI #TechnologyLeadership #EngineeringLeadership #TechStrategy #AIAdoption #SoftwareEngineering #ProductStrategy #LeadershipPerspective #InnovationLeadership #AIinBusiness

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