Zewei Liu

Interesting comparison of human-based ride hailing services (UberX, Lyft) vs autonomous-driving one from Waymo. TLDR: Waymo is more expensive and a lot slower to get you to your destination. And this is in the city where Waymo vehicles and software are probably most optimized for. This isn't to bash on Waymo and other self-driving car platforms, which have made incredible progress. That being said, the problem is not solved and it will take many more billions and many more years to get autonomous driving services like this to be price and performance competitive to current ride hailing services. https://lnkd.in/eb4uZ2Um #autonomouscars #AI

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Today I'm sharing this 🔥 video from our recent Engineering onsite at Demandbase HQ in SF! https://lnkd.in/eFAFR6QG We were so fortunate to host Zhengxuan Wu and Aryaman Arora, two upcoming AI researchers from Stanford University Department of Computer Science. Their approach to fine tuning, called ReFT, targets the representations emitted by neural net layers rather than the weights of the layers themselves. This makes it very flexible when using the same base model for swapping multiple fine tunes. The memory overhead is also very low for their intervention weights. Another highlight is how powerful it can be. One POC they showed forced the LLM to speak in only emojis exhibiting ReFT's power to control output. Control is crucial when, for example, you want to prevent your bot from offering customers unauthorized refunds!! If you're already familiar with LoRA, it should be pretty simple to use with only a few extra hyperparameters -- details in the video. Here's a link to the repo. Give them a star! https://lnkd.in/eJScNbzK Thanks again for letting us host your first external talk Aryaman and Zen. It's very exciting tech!!

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I've mentioned this to #investors outside of sports and entertainment. The adoption curve for technology in sports is a hockey stick. Years of pilots in fast paced, high stress environments, but if the application is scoped out properly with the right forces behind it can lead to BIG wins in terms of adoption. I don't know much about AiFi Inc. but this looks to be an awesome example on the adoption for checkout free applications. Well done. For #roboticsINsports check out Hop Robotics

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Check out the latest in our "Debuggability" blog series! This blog is authored by Kevin Downey Jason Johl and me and talks about how we built a Debuggability Paved Road at Intuit using Ephemeral Containers and Argo Workflows. We'd love to hear your thoughts on the topic. Also, what tools do you build as-a-platform to help debug service, compute and network issues in Kubernetes at scale? #intuit #kubernetes #debug

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Three insights from the TEDAI San Francisco X Factory X AI at Meta CodeGen Hackathon. Had the pleasure of judging a fantastic hackathon in SF yesterday. Here's the biggest things I took away from iteration on the cutting edge of GenAI for code: 1) 𝙂𝙚𝙣𝘼𝙄 𝙘𝙤𝙙𝙞𝙣𝙜 𝙨𝙩𝙖𝙧𝙩𝙚𝙙 𝙬𝙞𝙩𝙝 𝙖𝙘𝙩𝙞𝙫𝙚𝙡𝙮 𝙖𝙨𝙨𝙞𝙨𝙩𝙞𝙣𝙜 𝙙𝙚𝙫𝙨 𝙖𝙩 𝚠̲𝚛̲𝚒̲𝚝̲𝚎̲ 𝙩𝙞𝙢𝙚, 𝙗𝙪𝙩 𝙂𝙚𝙣𝘼𝙄 𝙖𝙜𝙚𝙣𝙩𝙨 𝙣𝙖𝙩𝙪𝙧𝙖𝙡𝙡𝙮 𝙛𝙞𝙩 𝙖𝙩 𝙙𝙞𝙛𝙛 𝚛̲𝚎̲𝚟̲𝚒̲𝚎̲𝚠̲ 𝙩𝙞𝙢𝙚. Iterative agenic workflows can run at review with automated vetting of generations, and only serve edit suggestions shown to improve a tested outcome (i.e. memory efficiency). Bonus is that this can scale with inference time compute to try more or less variations or revision cycles. 2) '𝘾𝙤𝙙𝙚' 𝙖𝙨 𝙖𝙣 𝙖𝙗𝙨𝙩𝙧𝙖𝙘𝙩 𝙘𝙤𝙣𝙘𝙚𝙥𝙩 𝙤𝙥𝙚𝙣𝙨 𝙖 𝙬𝙤𝙧𝙡𝙙 𝙤𝙛 𝙥𝙤𝙨𝙨𝙞𝙗𝙞𝙡𝙞𝙩𝙞𝙚𝙨 𝙛𝙤𝙧 𝙣𝙤𝙣-𝙨𝙤𝙛𝙩𝙬𝙖𝙧𝙚-𝙙𝙚𝙫 𝙚𝙣𝙜𝙞𝙣𝙚𝙚𝙧𝙞𝙣𝙜 𝙙𝙞𝙨𝙘𝙞𝙥𝙡𝙞𝙣𝙚𝙨. Many tools in these areas involve cumbersome UI's that ultimately translate points and clicks into complex config files, and LLM's are pretty good at editing these underlying files directly. 3) 𝘾𝙤𝙙𝙞𝙣𝙜 𝙖𝙗𝙞𝙡𝙞𝙩𝙞𝙚𝙨 𝙞𝙣 𝙢𝙤𝙙𝙚𝙡𝙨 𝙖𝙧𝙚 𝙜𝙚𝙩𝙩𝙞𝙣𝙜 𝙢𝙤𝙧𝙚 𝙘𝙤𝙢𝙥𝙖𝙘𝙩 𝙩𝙝𝙖𝙣 𝙚𝙫𝙚𝙧. One hackathon team was able to use a combination of synthetic data creation/refinement and model distillation (in this case from my near and dear Llama 405B) to fit highly advanced code translation ability in a 14B model. I have a feeling this trend will continue, which will open up more inference compute heavy coding opportunities like broad repo summarization. Bonus here - the resulting model was open sourced by Jason Lu: https://lnkd.in/gswMbCR3 Shout out to SHACK15 and Cerebral Valley for hosting. Incredible views and vibes.

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Waymo is probably both more expensive and longer waits on average than Uber or Lyft today, but they are close enough, and rapidly getting better. These markets will flip to fully autonomous when the tech is ready.

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Want a preview of what I'm going to be talking about in my keynote for RubyConf? You can catch a glimpse of it here!

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Discover our global ecosystem of #NVIDIAInception startups. We support startups at every stage to innovate, build, and grow with special access to: 💡 Technical expertise ✨ Preferred pricing on hardware and software 🚀 Go-to-market support 🤝 Introductions to VCs and customers Take a closer look at how we support startups: NVIDIA for Startups

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🎉 Big congratulations to our incredible team (Haoyu Chen, Nick Greenquist,Yuxing Wang, Sridevi Maharaj, Ruiyang Wu and many more) for delivering several quarters-long impactful features on the Play Store! 🚀 We’re thrilled to have launched key updates like Search Home, Sports Moment, and WearOS recommendations, all designed to enhance personalization and help billions of users worldwide search and discover content they love. 🌍✨ Check out the Play Store Q3 updates: https://lnkd.in/dn5wx8Mi #GooglePlay #Personalization #Recommendation

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In this episode, we discuss Learning Task Decomposition to Assist Humans in Competitive Programming by Jiaxin Wen, Ruiqi Zhong, Pei Ke, Zhihong Shao, Hongning Wang, Minlie Huang. The paper presents a method to enhance human understanding and repair of language model (LM)-generated solutions by automatically breaking down complex solutions into simpler subtasks. They introduce a novel objective called assistive value (AssistV) to measure how easily humans can repair these subtasks and validate their method through a dataset of human repair experiences. The approach significantly improves the problem-solving ability and speed of non-experts in competitive programming, allowing them to solve more problems and match the performance of unassisted experts.

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Xian Ke, this is incredible! The speed at which you and Vishal Punjabi leveraged crewAI, Streamlit, and various LLM models to create Finbot, a powerful company analysis tool for investors, was mind-blowing. ICYMI, Finbot is here: https://lnkd.in/eSQD69qA I couldn't help but chuckle when I saw the Finbot name - it reminded me of the Wall Street Bets crowd. Maybe you can get them to yolo their life savings based on Finbot's analysis? (Kidding, of course. Or am I? 💎🙌) But in all seriousness, your enthusiasm and passion for AI and LLMs is contagious. It's engineers like you who are pushing the boundaries of what's possible with AI and paving the way for a brighter future.🏆 I'd love to extend an invitation to any other software engineers from the bootcamp to join our Devs Do AI Discord server. It's a great place for engineers without an ML / AI background to connect with like-minded devs, share knowledge, and collaborate on LLM projects. DM me for the invite link. 🤝

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Nice example of model checking reproducing a tricky real-world bug. The TLA⁺ source code is also very short at just ~150 lines.

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You want to do Machine Learning on large scale Marketing solutions ? Check below 🚀

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Check out our latest blog to see how our Notification Team harnesses the power of Graph Neural Network (GNN) models to create personalized restaurant recommendations for user notifications.

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𝐅𝐚𝐬𝐭𝐢𝐧𝐨 𝐞𝐱𝐢𝐭𝐬 𝐬𝐭𝐞𝐚𝐥𝐭𝐡 𝐰𝐢𝐭𝐡 $7𝐌 𝐩𝐫𝐞-𝐬𝐞𝐞𝐝 𝐛𝐚𝐜𝐤𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐚𝐧𝐝 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐏𝐚𝐫𝐭𝐧𝐞𝐫𝐬 Latest blog post at Fastino dives deep into how AI is transforming the way we work and make decisions. Here are some key takeaways: 1️⃣ AI-Driven Efficiency: Discover how AI can streamline your operations, reducing costs and boosting productivity across your organization. 2️⃣ Enhanced Decision-Making: Learn how AI-powered analytics can provide deeper insights, enabling more informed and strategic business decisions. 3️⃣ Improved Customer Experiences: Explore the ways AI is revolutionizing customer interactions, from personalized recommendations to intelligent chatbots. 4️⃣ Future-Proofing Your Business: Understand why embracing AI now is crucial for staying competitive in an increasingly digital marketplace. 5️⃣ Ethical Considerations: We also discuss the importance of responsible AI implementation and its impact on your business reputation. #ArtificialIntelligence #BusinessEfficiency #AIInnovation #FutureOfWork #DataDrivenDecisions #CustomerExperience #AIEthics #BusinessAutomation #Fastino #TechTrends

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LLMs in 2024 = endless prompting, inconsistent results, opaque APIs, runaway costs, and a GPU arms race 🤬 I’m excited to announce I've joined Fastino to help rewrite this equation 👆 Here's to a future with enterprise LLMs that get it right 🥂 (If you're building enterprise-scale AI systems and the above equation sounds familiar, drop me a note.)

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I recently had the opportunity to attend 3 major AI events in the last 30 days, where I gained valuable insights into the future of artificial intelligence. Here are some key takeaways: Amazon AI Day: - Amazon CTO (Werner Vogels) highlighted the potential of generative AI and large language models in driving innovation and societal impact, particularly in healthcare and sustainability. - Explored how generative AI is revolutionizing development processes and transforming businesses across industries. MagicBall - AI Week: - Showcased advancements in AI infrastructure by over 24 startups from Bengaluru, emphasizing the city's significance in AI development. - Engaged in discussions on responsible AI development and ethical considerations for the future. Microsoft AI Day: - Microsoft's Innovation Hub India CTO (Sandeep Alur) discussed AI's critical role in digital transformation and the company's commitment to ethical AI development. - Showcased the revolutionary potential of Generative AI and its integration into Microsoft Azure. These events, combined with insights from the ISB CTO program, emphasize that AI is here to create new opportunities and possibilities, not to take away jobs. Embracing a growth mindset is crucial to thrive in the evolving AI landscape. The AI revolution has just begun, and it's essential to adapt and innovate for a sustainable future. #GenAI #AI #BrightFuture

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Sharing my thoughts on How Waymo (formerly known as the Google Self-Driving Car Project) is Pioneering Autonomous Driving with AI. Waymo has become a trailblazer in self-driving technology, and at the core of it all is AI. Leveraging advanced machine learning and deep neural networks, Waymo’s AI-powered system continuously improves through real-world data, making autonomous vehicles smarter, safer, and more reliable. Perception: AI allows Waymo’s cars to "see" and understand their surroundings by processing data from sensors like cameras, LIDAR, and radar. This helps detect pedestrians, other vehicles, and objects in real time. Prediction: The AI system predicts the behavior of everyone around the vehicle—whether it’s cars changing lanes or pedestrians crossing the street—ensuring smooth and safe driving decisions. Planning & Control: AI makes split-second decisions about accelerating, braking, and steering based on the analysis of complex traffic patterns, enabling Waymo vehicles to safely navigate roads. With millions of autonomous miles driven, Waymo’s AI is constantly learning and improving, helping create the future of safer and smarter transportation. The road ahead is AI-driven! www.rxsmeta.com #AI #AutonomousVehicles #Waymo #SelfDrivingCars #MachineLearning #SmartMobility #TransportationTech #FutureOfTransport #AIinAction #Innovation

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