Ben (Xiaojun) Li

🌟 Exploring the Intersection of Business and Technology re AI! 🌟 Before my decade-long focus on AI, connecting technology and business in various domains has always been my passion; This is why I enjoyed this conversation with David Luan - No BS, No Twitter exaggerations, No clickbate - Just a super well informed, super smart David sharing his insights from years of hands-on experience: #Startegy, #Software, #Hardware, #Platforms, #ModelSpace and everything in between - LMK what you think of his projections. In This Episode: 1. The Biggest Lessons from OpenAI and Google Brain: What did OpenAI realize that no one else did that allowed them to steal the show with ChatGPT? Why did it take 6 years after the introduction of transformers for ChatGPT to be released? What are 1-2 of David’s biggest lessons from his time leading teams at OpenAI and Google Brain? 2. Foundation Models: The Hard Truths: Why does David strongly disagree that the performance of foundation models is at a stage of diminishing returns? Why does David believe there will only be 5-7 foundation model providers? What will separate those who win vs those who do not? Does David believe we are seeing the commoditization of foundation models? How and when will we solve core problems of both reasoning and memory for foundation models? 3. Bunding vs Unbundling: Why Chips Are Coming for Models: Why does David believe that Jensen and Nvidia have to move into the model layer to sustain their competitive advantage? Why does David believe that the largest model providers have to make their own chips to make their business model sustainable? What does David believe is the future of the chip and infrastructure layer? 4. The Application Layer: Why Everyone Will Have an Agent: What is the difference between traditional RPA vs agents? Why are agents a 1,000x larger business than RPA? In a world where everyone has an agent, what does the future of work look like? Why does David disagree with the notion of “selling the work” and not the tool? What is the business model for the next generation of application-layer AI companies? https://lnkd.in/dUpWWzvN

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Meet AGUVIS: A pure vision-based framework for autonomous Computer/Mobile-use GUI agents, operating seamlessly across web, desktop, and mobile platforms without UI code. Key Features & Contributions - Pure Vision Framework: First fully autonomous pure vision GUI agent capable of performing tasks independently without relying on closed-source models - Cross-Platform Unification: Unified action space and plugin system that works consistently across different GUI environments - Comprehensive Dataset: Large-scale dataset of GUI agent trajectories with multimodal grounding and reasoning - Two-Stage Training: Novel training pipeline focusing on GUI grounding followed by planning and reasoning - Inner Monologue: Explicit planning and reasoning capabilities integrated into the model training - Generalization to OOD (Out-of-Distribution): AGUVIS demonstrates impressive generalization on OSWorld benchmark even without desktop trajectory data during training. Project Page: https://lnkd.in/guzkwpbB Paper: https://lnkd.in/gs7wXyJS GitHub: https://lnkd.in/gWeSkFmK Collaboratiion among Yiheng Xu Zekun Wang Junli Wang Dunjie Lu Tianbao Xie Amrita Saha Doyen Sahoo Tao Yu Caiming Xiong

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NVIDIA has introduced something amazing—Project DIGITS, which could bring supercomputers into our homes. This means we might soon be able to use powerful AI tools, do advanced research, or create incredible things right from our own desks. It makes you wonder: what’s next? Could we soon see AI in every device we use, making our daily lives even easier and more connected? The possibilities are exciting, and the future looks full of opportunities.

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YES! Thanks, Dean Takahashi, VentureBeat, for covering NVIDIA's Inference Microservices (NIMs), including those powered by partners such as Domino Data Lab. Excited to innovate jointly with NVIDIA on an #AI form factor that makes it even easier to deploy, manage, and scale models built in #DominoDataLab on #NVIDIA's infrastructure Here's the article: https://gag.gl/fxt56p #GenerativeAI #GenAI #ResponsibleAI #datascience #MachineLearning #ml #mlops #Innovation #EnterpriseAI #AIatscale

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‘I Hope Suffering Happens To You’: Nvidia CEO’s Case For Hiring For Grit Over Pedigree by Jack Kelly Forbes “I wish you ample doses of pain and suffering.” That is the unconventional message NVIDIA CEO and founder Jensen Huang had for students at Stanford University, his alma mater, where he spoke last month at the Stanford Institute for Economic Policy Research. According to Huang, elite educational pedigrees alone are not necessarily the best predictor of success. “Greatness is not intelligence. Greatness comes from character. And character isn’t formed out of smart people, it’s formed out of people who suffered,” said the Stanford alum. Instead, the man who presides over one the world’s most valuable companies in terms of stock market valuation, now looks to find certain traits in job applicants that include resilience, grit and determination, which he contends lead to innovation and success in the workplace. Counterintuitively, Huang believes that having very high expectations coming out of an Ivy League education can potentially set people up for less resilience when faced with failure or adversity, which are inevitable in any career path. “Unfortunately, resilience matters in success,” he said to the students. “I don’t know how to teach it to you except for I hope suffering happens to you.” His comments indicate a view that real-world experience, involving struggles, obstacles and suffering, can instill the resilience needed for long-term success. Therefore, he believes that employers should look beyond just the prestige factor of a university and more closely at the inner drive, mindset and resolution of potential hires. “To this day I use the phrase ‘pain and suffering’ inside our company with great glee,” said the chief executive. “I mean that in a happy way, because you want to refine the character of your company. You want greatness out of them.” Hiring PHDs The Nvidia founder’s statements about valuing resilience over educational pedigrees align with the old Wall Street adage about hiring what they call “PHDs”—the poor, hungry and driven. The core parallels are prioritizing hunger, grit and motivation over academic credentials alone, believing that candidates who have had to overcome adversity, financial hardships or personal trauma offer invaluable drive and tenacity. The PHD concept essentially argues that struggle itself, if handled constructively, can be great preparation for thriving in ultra-competitive business environments where stamina and an insatiable work ethic are keys to success. A person who had to fight for opportunities may have a stronger inner drive and motivation to succeed compared to ... https://lnkd.in/e5XZitzS

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Effortless Integration for Smarter Insights: CloudEagle.ai Meets Bitbucket Integrating CloudEagle.ai with Bitbucket takes just minutes and gives you: ✔ Simplified App Tracking: No more manual reports, get the app usage data you need, when you need it. ✔ Centralized Control: Manage your Bitbucket security from the CloudEagle dashboard. ✔ Proactive Monitoring: Catch unusual login activity before it becomes a problem. Ready for a smarter, more secure development workflow? Book a demo today - https://lnkd.in/gXAhChrt #saas #saasmanagement #toolstuesday Atlassian #jira #toolstuesday

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NVIDIA's CEO, Jensen Huang, says the next wave of AI is physical #AI. The future factories will be robotic, which will orchestrate robots that are building products that are #robotic. So basically Skynet. Fantastic. 🤔 Jensen Huang also announced the Rubin AI chip platform, set to launch in 2026. The GPUs will support next-gen AI applications with enhanced parallel processing power. Rubin is a follow-on to NVIDIA's Blackwell platform, continuing the tradition of innovation. Nvidia is making an insane amount of money with the H100. 🟢 Production ramp-up for the H200 in Q2 2024. 🟢 Blackwell will start ramping in Q3 2024, with Blackwell Ultra following in 2025. 🟢 And now, Jensen is introducing Rubin for 2026. I haven't seen another company innovating at this pace and scale.

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CEO of NVIDIA Jensen Huang: “We never talk about market share in our company… Why are you fighting people for market share? The whole concept of market share says there’s a whole bunch of other people doing the same thing. And if they’re doing the same thing, why are we doing it? Why am I squandering the lives of these incredibly talented people to do something that has already been done? --- Found this helpful? Follow for more content like this. --- #startups #business #startup #entrepreneurship

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Ever heard the one about the company that built a trillion-dollar business from "zero billion" products? 🤯 Jensen Huang shared mind-blowing insights on the Acquired episode! 🎧 Key Takeaways: - Spotting the "zero billion" opportunities: Recognize hidden gems with massive potential. Intuition is key to seize the opportunity. - Leading is hard: In traditional enterprises, information flows top to bottom, causing delays. Nvidia everyone gets the information at the same time so leading is hard. - Building a company like a computer: Organize teams like a system, with each layer contributing to the whole. This episode is a goldmine for leadership, tech, and business brilliance enthusiasts! #NVIDIA #leadership #innovation #AcquiredFM Listen here: https://lnkd.in/gWcyYexE

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Recently I came across some great insights from Nvidia's CEO, Jensen Huang. He emphasized the power of asking the right questions to drive innovation. With AI transforming our world, it's not just about having answers anymore, but instead about crafting smart prompts. We don't need to have all the answers; we just need to guide our teams to ask the right questions. In my own experience, I've found that asking the right questions can lead to amazing breakthroughs. Let's embrace curiosity and strategic questioning to navigate our complex business world more effectively.

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NVIDIA's meteoric rise to become a global leader in #AI computing may seem like an overnight success story. However, as the company's founder and CEO, Jensen Huang, shares, it was actually a 30-year journey filled with crucible moments that shaped the company's trajectory. In a fascinating podcast episode from Sequoia Capital's "Crucible Moments" series, we get an inside look at the pivotal decisions and challenges that defined NVIDIA's path: 🔹 Founding in 1993: Jensen, Chris, and Curtis bet on the potential of 3D graphics for PCs, an unproven market then. They raised money from Sequoia and set out to build a chip that would enable gaming on PCs. 🔹 Failing with the NV1: Days from bankruptcy, NVIDIA scrapped their first chip architecture and raced against the clock to develop the Riva 128, which became a hit. 🔹 Dominating #PCGaming: NVIDIA rode the wave of the PC gaming revolution, becoming the world's leading provider of 3D graphics chips. But they knew they couldn't rest on their laurels. 🔹 Pivoting to AI: In 2012, NVIDIA made a bold bet on AI computing, a market many doubted would ever take off. Jensen committed the company to this new direction, allocating resources and evangelizing the opportunity. 🔹 Becoming the AI Leader: NVIDIA is now powering the AI revolution with its chips and software by executing its AI vision. It's become the go-to partner for AI researchers and tech giants alike. As Jensen says, "It feels like this overnight success was 30 years in the making." NVIDIA's story is a testament to the power of long-term thinking, perseverance, and bold bets. It's a must-listen for any founder or tech enthusiast. NVIDIA: An Overnight Success 30 Years in the Making #NVIDIA #AI #Entrepreneurship #TechHistory https://lnkd.in/dimgnbHe Peter Ndiang'ui, Faraj Mansur, Jack Ngare, Harry Hare

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Smaller #genAI models are changing the game! Introducing #mistral-NeMo by #nvidia and #mistralAI with 128k token context window - a tool for businesses that can run on RTX GPUs which you may have at your home. Super inexpensive with no high cloud costs, keeps data private on your private computer! #nvidia #mistralai #genaiforenterprise #responsibleai

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Henjin Dex Testnet Tutorials Henjin.xyz | Native Taiko DEX is an ecosystem-focused, community-driven DEX and Launchpad built on Taiko Labs A few days ago Henjin announced the announcement that the testnet is live, on his twitter post April 26, 2024, and now they are issuing a campaign by giving a Role on discord as "Testnet Freaks" you can get it by completing the tasks in Galxe, here is the tutorial : https://lnkd.in/gEQf5g9R #Defi #Taiko #DEX #Swap #Blockchain

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Check out the latest episode of our Tech Teams Today podcast with Yongsheng Wu, VP of Engineering at Granica - now available on all major podcast platforms. #AI #EngineeringLeadership #DataScience #MachineLearning #TechTeamsToday #Revelo

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Empowering AI with Real-World Knowledge: The Future of Generative AI Recently, NVIDIA CEO Jensen Huang (黃仁勳) returned to his hometown of Taiwan, creating quite a stir. His keynote speech at COMPUTEX TAIPEI 2024 garnered significant attention. One part that particularly resonated with me was when he discussed the challenges of future generative AI in teaching machines the laws of physics in the real world. His original words were: "The next generation of AI needs to be physically based. Most of the AIs today don't understand the laws of physics; they're not grounded in the physical world. In order for us to generate images and videos and 3D graphics and many physics phenomena, we need AIs that are physically based and understand the laws of physics. The way you could do that is, of course, learning from video is one source, another way is synthetic data, simulation data, and another way is using computers to learn with each other." Not just the laws of physics; AI's performance is always limited by the data we can provide. No matter how complex the model gets, if the data doesn't contain specific information, the accuracy improvement is limited. I believe that future challenges will not only be about the models but also about how data scientists can re-evaluate the data to ensure that machines can obtain as much information as human experts. Last week, a professor asked me if AI could currently surpass clinical judgment. I said that it depends on how we feed the data in the future. If we still rely on just inputting some questionnaire responses or physiological indicators, it's unlikely. Humans can make more accurate judgments than machines because they base their decisions not only on clinical diagnostic checklists but also on short-term behavioral observations and prior knowledge about the individual. However, machines' prediction capabilities are limited by the amount of information they can access. In many fields, it has been found that, given enough information, machine learning can be more accurate than human experts. For example, in 2018, Lawgeex tested 30 legal documents, comparing interpretations by law professors from Stanford, Duke, USC, etc., with AI. The scholars' accuracy rate was 85%, while LawGeex's accuracy rate was 95%. This viewpoint may be gradually emerging in the industry as well. I input these paragraphs from Jensen Huang's speech into the next-generation patent search system we are developing to test its effectiveness in finding patents directly from the speech. Indeed, I found that there are already many machine learning patents aimed at enhancing the simulation of real-world parameters. I believe that the future trend will not only focus on more complex models. Data scientists will likely invest more research resources into integrating heterogeneous data to provide models with more comprehensive information. #AI #GenerativeAI #PatentSearch #Patent #InQuartik

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🚀 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗔𝗜: 𝗔𝗳𝗳𝗼𝗿𝗱𝗮𝗯𝗹𝗲 𝗚𝗣𝗨 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗶𝘀 𝗛𝗲𝗿𝗲! 🖥️👁️ 🌟 The future of cost-effective GPU cloud computing has arrived, transforming how we train and deploy ML and computer vision models. Check out these game-changing startups offering incredibly affordable GPU solutions: 1️⃣ RunPod 🏃‍♂️💨 Empowering developers with on-demand GPU instances. RunPod's flexible pricing and user-friendly interface make high-performance computing accessible to all. 2️⃣ Vast.ai 🌊🧠 Pioneering a unique marketplace for GPU rentals. Vast.ai connects GPU owners with AI researchers, creating a win-win ecosystem for affordable compute power. 3️⃣ Lambda ☁️🔬 Offering turnkey GPU cloud solutions tailored for AI workloads. Lambda Labs' pre-configured environments streamline the development process for data scientists. 4️⃣ Paperspace 📄💻 Providing a seamless platform for GPU-accelerated applications. Paperspace's intuitive tools and collaborative features make it a favorite among AI teams. Paperspace is now part of DigitalOcean 5️⃣ CoreWeave 🔧☁️ Specializing in large-scale GPU deployments for enterprise clients. Coreweave's cutting-edge infrastructure supports the most demanding AI and rendering tasks. 6️⃣ Genesis Cloud ☁️🌱 Focusing on sustainability in GPU cloud computing. Genesis Cloud combines eco-friendly practices with high-performance GPU instances for guilt-free AI development. 7️⃣ FluidStack💧🥞 Harnessing idle GPUs worldwide to create a distributed supercomputer. Fluidstack's innovative approach offers cost-effective solutions for resource-intensive tasks. 8️⃣ Vultr 🚀💪 Delivering GPU-powered virtual machines with global reach. Vultr's extensive network of data centers ensures low-latency access to GPU resources worldwide. 9️⃣ JarvisLabs.ai 🤖🧪 Crafting AI-optimized cloud environments for researchers. Jarvis Labs' specialized tools and support accelerate breakthroughs in machine learning. 🔟 Banana 🍌💻 Simplifying ML model deployment with serverless GPU infrastructure. Banana.dev's user-friendly platform makes AI accessible to developers of all skill levels. These innovative companies are democratizing high-performance GPU computing, making it available to everyone from solo developers to large enterprises! 💪💼With prices that'll make your wallet smile 😄💰 and performance that'll blow your mind 🤯 #AIML #ComputerVision #AIStartups #CloudComputing #gpu #GPUPower Image credit : nvidia.com

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Using generative AI, NVIDIA operations built an AI Planner agent, developed on NVIDIA Inference Microservices (NIM). The agent leverages LLM, NeMo Retriever and CuOpt NIM to reduce re-planning time from hours to just seconds. https://nvda.ws/4bIfS2T

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