Jason M Pai

Artificial intelligence is getting bigger and smarter – but it’s also racking up a massive energy bill. As researchers are racing to find greener ways to power AI, a new study led by associate professor Tianyi Chen, with collaborators from IBM and Rensselaer Polytechnic Institute, offers a promising solution with a breakthrough in analog training. “This opens the door to models that are inherently faster, smaller, and far more energy-efficient than anything we have today,” said Chen. “Our research could trigger a genuine step-change in how we build and use AI.” Learn more: https://lnkd.in/eEtC9-xq Cornell Engineering

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Nvidia CEO Jensen Huang and Palantir CEO Alex Karp discussed AI's role in the job market with CNBC on Tuesday, saying new technologies will compliment and assist job growth, rather than completely take over the labor market. Find more from GTC: cnb.cx/4omU2Z1

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Agility Robotics, a TDK Ventures portfolio company, is expanding its collaboration with NVIDIA to accelerate the development of Digit, the world’s leading humanoid robot built for real work in real environments. By integrating NVIDIA Isaac Sim and Isaac Lab, Agility is training Digit to handle complex tasks using reinforcement learning and large-scale simulation, advancing what’s possible with physical AI. This partnership enables digital twin testing at scale and supports real-world deployments like Schaeffler, where Digit is already solving hard-to-automate workflows. This is the next frontier of robotics—built to scale, and already on the job. Learn more: https://lnkd.in/dH_qVaa4

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Tareq Amin and Jensen Huang highlight how the Humain–NVIDIA partnership is enabling compute at scale, powered by advanced AI infrastructure, AI factories, and frontier models. #SaudiUSForum2025

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❓ Do you believe Materials Science is everywhere? 10 years ago, a UC Berkeley materials science undergrad joined our lab. After a month reviewing corrosion papers, he quit — and went to a coding bootcamp. That moment stayed with me. Because hard science takes tremendous efforts and patience, progress rarely happens overnight. But when it does, it changes the world. Take Materials Science as an example, I would believe it is, quite literally, everywhere. From quantum materials to polymers, semiconductors, and beyond — Materials Science shapes the world we live in. And yet, its quiet brilliance often goes unseen. Let’s make this a live discussion, instead of chatting with ChatGPT... 👇 To all brilliant experts, please share one material and one application you know well or you are creating — in one sentence. I will gather them, to learn and for fun! A possible format: Application - Material + Core Function (highlight your company if you like). ➔ F-35 Fighter Jet – Radar-absorbent carbon/polymer composites that hide jet from radar (Lockheed Martin) ➔ Solar Cell – Perovskite film converting sunlight into electricity. ➔ Golden Gate Bridge – Zinc-rich primer shielding steel from salty fog. ➔ Sunscreen – Titanium oxide nanoparticles reflecting UV rays. ♻️ Friends, please repost to help gather more inputs and insights! #MaterialsScience #AdvancedMaterials #HardTech #Research P.S. Why this post? To remind us all that, in the era of AI, more people start replying on AI, yes, AI can certainly help, but human still need think and create harder (even slowly), which is vital for humankind evolution. Respect to Dr. Hitoshi Nakamura, check and learn about his battery experiments, you will know!

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Kai Wang, Director, Autonomy Behaviors, will be speaking at NVIDIA GTC next week on how generative AI is transforming the autonomous vehicle industry by scaling into new markets, enhancing safety, and building consumer trust. 🔗 https://lnkd.in/gH_S_AFK #GTC25 #AI

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NVIDIA CEO Jensen Huang just spotlighted Qodo’s context-aware engine — built with NVIDIA’s software stack and designed to deliver high-precision code retrieval at enterprise scale 💥 Live on stage, Jensen showcased how Qodo empowers both human and AI developers to retrieve precise context for planning, writing, learning, testing, and reviewing code — even across large, complex codebases. Our modular generation stack tackles real-world software challenges with: ✅ Deep reasoning agents for code search and retrieval ✅ High-precision code question and answering  ✅ Full ingest pipeline for large, complex codebases ✅ Real-time graph-based retrieval from structured context pools ✅ Flexible deployment options — including on-prem setups aligned with NVIDIA infrastructure

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💡Let’s talk about how open source is shaping the future of AI infrastructure Last week, 𝗥𝗮𝘆 joined the 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 under the 𝗟𝗶𝗻𝘂𝘅 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻, marking an important step toward a unified, community-driven AI compute stack Linda Haviv sits down with Robert Nishihara (co-creator of Ray and co-founder of Anyscale) to chat about Ray joining the PyTorch Foundation, the future of AI infrastructure, & more. 🎧 Full conversation below: 𝟬𝟬:𝟬𝟬 Big News: Ray Joins the Linux Foundation under the PyTorch Foundation 𝟬𝟬:𝟬𝟳 Why was now the right time? 𝟬𝟭:𝟭𝟯 How Ray integrates with other Open Source projects 𝟬𝟭:𝟰𝟮 How Companies Are Scaling AI Workloads in Production 𝟬𝟮:𝟱𝟲 Ray's Vision for the Next Five Years 𝟬𝟯:𝟰𝟭 Key Design Principles Behind Ray's Success 𝟬𝟲:𝟬𝟱 The Rise of Multimodal and GPU-Driven Data Processing 𝟬𝟳:𝟰𝟯 The Emerging AI Compute Stack 𝟬𝟵:𝟮𝟴 Why Open Source is shaping the Future of AI Infrastructure 𝟭𝟬:𝟮𝟭 Getting Involved with the Ray Community

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Dave Patterson, UC Berkeley professor and a legend in the chip industry, predicted that processing-near-memory would beat processor-in-memory back in 2002. Research today with CXL prove the point. Thank you Won Woo Ro of Yonsei University for the post on Patterson's lecture at the SK AI Summit 2025. And to point out one detail from one of Professor Won's photos from the event: the winning processor-near-memory solution in Patterson's lecture relied on a Marvell Structera CXL accelerator. (Arrow added by me.) and a link to the original post: https://lnkd.in/gHe84EMT

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