Lu Lu

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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Waymo grew from 100K rides per week to 150K rides in two months. At 7.8M rides per year this is nothing compared to the 10B rides Uber provides. But here is the catch, Uber's costs are more or less fixed. Google's are going to come down exponentially. The cost for an AI team will go down very fast if expressed per ride. Given no drivers are needed and vehicle costs are coming down as well, it is just a matter of scaling up for Google to steal away clients from first Uber but afterwards also public transport, second car ownership and finally first car ownership. In 2025, Waymo can easily go to 1 or multiple millions of rides per month. 2026, 10-50M and in 2027, 1B per month. Giving that Tesla will want to launch in 2025 and scale in 2026, consumers will be the big winners. https://lnkd.in/e4rcAH6D

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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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From my conversations, I'm noticing different trends between how AI engineers deploy evaluation models (model as a judge) in production monitoring vs application testing. 💡 Value - Monitoring: provides real time alerts of performance or used to preemptively block problematic outputs from surfacing to the user -Testing: regression testing applications to establish baseline performance before pushing to deployment ⚡ Speed - Monitoring: near real time, requires extremely fast token processing - Testing: batch, async, often processing large test sets, lower token processing speed required 🎯 Accuracy - Monitoring: overindex on false positives, better to flag something as problematic and be wrong than to let something slip between the cracks - Testing: high correlation with human graders, be as accurate and robust as possible 💰 Cost - Monitoring: prefer cheaper models, otherwise the additional inference cost per generation will add up exponentially - Testing: willing to take on higher costs, calculation is accurate testing leads to better performing outputs 🛑 Risk - Monitoring: if your monitoring judge gets something wrong, there is a high consequence because it directly impacts the end user - Testing: if your testing judge gets something wrong, async review gives some potential to remediate the issue At the moment, it seems using model as a judge for testing is a little more popular due to the high cost constraints of constant monitoring, but that is subject to change as more specialized models come out. As the industry matures, I strongly believe we're gonna see specialization between models for monitoring and models for testing. If you're currently using model as a judge techniques, would love to chat more! Let me know what you think. Twitter Handle: https://lnkd.in/eAsQvtiu

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Huge congratulations to the LanceDB team, Chang She and Lei Xu, on securing an $8M funding round from CRV! Their mission to build The Database for Multi-modal AI is an exciting one. It's amazing to see how far they've come since Chang's days as a star VP Eng at Tubi. Check out the TechCrunch article for more details on their journey. #AI #technews #fundinground

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$2 H100s: How the GPU Bubble Burst Last year, H100s were $8/hr if you could get them. Today, there's 7 different resale markets selling them under $2. What happened? The market has flipped from shortage ($8/hr) to oversupplied ($2/hr), because of reserved compute resales, open model finetuning, and decline in new foundation model co’s. Rent instead. https://lnkd.in/gpQEidmC

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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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Today, I found a fascinating way to think about “Tokens” in LLMs. It came from a conversation between Jensen Huang and Patrick Collison at a recent Stripe event. When asked about the future of compute capacity in five years, Jensen smartly dodged that question a bit, but then gave an excellent explanation and analogy about why tokens are the new forces that will power the next decades of humanity. He explained that we're now producing something unprecedented (and at scale): floating point numbers that possess “value”, which we now call tokens. These tokens are valuable because they encapsulate “intelligence”. People are now taking these tokens and transforming them into English, French, images, videos, chemicals, proteins, robotic movements, etc. And many are working hard to expand the range of concepts and ideas we can create with these tokens. He then goes on to made a compelling comparison between tokens and electricity. In the past industrial revolution, we successfully found a way to convert “atoms” into “electrons” (by boiling water to power electricity turbines). And now, we've discovered a way to convert "electrons" into "tokens" (by using energy to power data centers that train and run LLMs). When electricity was first introduced, few people understood its value. Today, paying for kilowatts is routine. The same will happen with tokens. Right now, only early adopters and builders are paying tokens. Soon, everyone will be paying for tokens on a daily basis to supercharge productivity and power new products and services. Many new industries will be born from and built on top of tokens. When I first heard about this way of thinking, I got goosebumps. Perhaps it’s because Jensen has a good storytelling skill that helps him sell what he builds, but I have to admit this way of thinking gave me a brief surge of pride. I'm proud of humanity's collective effort and creativity, which have taken us from living under rocks to building machines that can "think." That's nothing short but a miracle.

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I'm happy to share a blog post on our newest Amazon Just Walk Out Technology, which offers a frictionless, checkpoint-free shopping experience. This new system is powered by a store domain-focused foundation model. It is end-to-end learned, transforming raw sensing data directly into receipts. One of our foundation model's key strengths is its ability to perform a diverse range of tasks across multiple modalities (e.g. videos, images, sensor timeseries, 3D), all within a single neural network. This enables knowledge transfer, allowing the tasks and modalities to mutually enhance each other. Read this blog for more details: https://lnkd.in/gpC9um5P

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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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My cofounder Saumil goes on the Stack Overflow Podcast to deep dive on AI Agents! He goes into detail how we leverage AI agents to power automated workflows to accomplish the most complex tasks. Including agents firing off tasks to other agents to tackle the details. Need an agent to verify your work? No problem. Give it a listen and let us know what you think!

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OpenAI's preview release of the o1 chain-of-thought models definately represents a significant advancement in LLM capabilities. Trained using reinforcement learning to "think" before responding, these models are showing remarkable improvements in complex tasks. 🔗 Chain-of-Thought Training: The models can handle complicated prompts requiring backtracking and deep reasoning, moving beyond simple next-token prediction. In short: o1 can break down problems, correct mistakes, and try different approaches. 👻 Hidden Reasoning Tokens: These tokens allow the model to perform extensive "reasoning" internally but are not visible in the API response, raising questions about transparency. 📊 Impressive results: According to https://lnkd.in/eNkxTEbT it achieved high scores in competitive programming (Codeforces) and math competitions (AIME), surpassing some human experts. ⏱️ Trade-Offs: Longer response times (from seconds to several minutes) and lack of system prompt support make them less practical for real-time applications. ⚙️ Integration: Features like no system prompt support and hidden reasoning tokens can limit flexibility and transparency. At LEAGUES, although we can't utilize these models yet due to response times and our app's current focus, we are excited about the potential applications in the future. The ability for AI to engage in sophisticated "reasoning" could revolutionize user experiences across industries. However, the introduction of hidden reasoning tokens also raises some questions about transparency and trust, at least for me. As AI developers and leaders, we need to consider how to balance these capabilities with the need for interpretability and user confidence.

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Notion vs Glean reminds me a lot of Tesla vs Waymo... Just like I think Tesla is going to solve the 'software' problem of autonomous vehicles before Waywmo solves the 'hardware' problem... I think that Notion is going to solve 'enterprise knowledge search' before Glean solves the problem of document and knowledge creation... Why? A couple reasons: 1) Just like Tesla, Notion has a huge advantage when it comes to the amount of underlying data and documents to train on... They already have 100M+ users, and likely 10x that in terms of the number of documents, files, etc. in team's workspaces. 2) It's very hard to change people's behaviour re: where/how they create 'work'. It's why a lot of companies have come and gone in that category... As exciting as Glean's AI writing assistants might seem, most people are just fine (or even prefer) a good old fashioned editor/writer. I could be way off here, but this will be an intersting one to follow. Thoughts? Where am I getting it wrong? #ai #enterprise #knowledge #management #notion #glean

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Daniel Wilson and Reza Rassool Thanks for the opportunity to join the conversation, and more importantly to contribute to the critical work of Kwaai Personal AI -- turning AI into a meaningful benefit versus a threat in people's lives. (My comments start about 30 min in, after Reza's introduction to Kwaai))

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I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

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So excited to see our team's work featured here! These are fundamentals directions for recommender systems, essential for advancing the field. Excited to see how others will build on this and drive deeper insights. https://lnkd.in/gkqpq7HS

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