Rukmini Iyer

The 7th workshop on Neural Scaling Laws is about to start: https://lnkd.in/gQSB5k8v Scaling laws, emergency, alignment, transfer learning, with a focused track on multilingual models. Looking forward to kick it off with Irina Rish and discuss all these topics with Yuhai Tu, Paul Bogdan, Hailey Schoelkopf, Darshil Doshi, Rio Yokota, Neha Sengupta, Preslav Nakov, Tatiana Shavrina, PhD, Yishi Xu. Join in person or virtually! #icml2024 #ai

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Exciting and pleasantly surprising news in the AI world: US & China join forces to develop first international AI standard. A promising step towards rapid, scalable, and safer AI development. Collaboration over confrontation paves the way forward! #airevolution #artificialintelligence #largelanguagemodels #AICollaboration

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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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In Retrieval-Augmented Generation (RAG) and other tasks like Knowledge Base Gap Analysis & Discovery, the vector embedding of article chunks is crucial within the landscape of LLMs. Implementing better chunking strategies (e.g., dynamic chunking) and retrieval strategies (e.g., reranking models) significantly boosts RAG performance. To further improve RAG systems, Raghav Garg discusses how to generate effective vector embeddings for real-world, noisy Knowledge Base articles in this insightful blog post: https://lnkd.in/dyGQgi25 How do you optimize your RAG systems?

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Something that stuck with me during my interview with Naveen Rao, VP of Generative AI at Databricks: "I take the hard path." He built an AI-focused chip company Nervana in 2014 when machine learning was still academic theory and the chip industry was deeply out of favor. Naveen saw computing architecture would need to fundamentally change for ML workloads. And then in 2019, observing the power of LLMs like BERT, Naveen foresaw the need for enterprise-grade services to manage their training, deployment, and customization. That vision led to a $1.3B acquisition by Databricks in 2023. What I love about Naveen's approach is his willingness to build for markets that don't exist yet. "There isn't a market, employees don't even know about it. So how the hell do you build a company?" You educate. You convince. You get people to see what you see. It's brutal work, but it's important work. You're reshaping how people think about problems.

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AI Agent Systems! As we discussed with the Compound AI Systems blog in Feb, we're seeing this in practice. Moving from Monolithic to Modular design is a natural engineering progression..it means we can control quality, latency, etc for each app in Databricks! https://lnkd.in/gFacmiwR

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A number of folks asked me about getting the slides that John Fragalla, M.E. presented at the SC24 DDN Data Summit two weeks ago on "NCP Solutions: An Optimized Cloud Data Architecture with DDN" So I'll overdeliver - here's the FULL video of John's presentation! The whole video is worth watching - but I'll call out a few moments you should review, even if you're short on time: - 8:32 - The I/O story - which is CRITICAL to how your AI will operate - 11:26 - Checkpointing and Model Load - also important with keeping AI running - 16:34 - How architecting for peak performance saves MILLIONS John is a great friend, and if you'd like to dig deeper into this, drop me a line at [email protected] - I'll bring John along. :) #ArtificialIntelligence #AI #NCP #Cloud DDN NVIDIA

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It was great to exchange insights on AI with industry and non profit leaders at the private event before Axios AI+ SF Summit. Thanks Megan Morrone and Sam Sabin for hosting us! #AIrevolution #ArtificialIntelligence #LargeLanguageModels #GenerativeAI

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Looking forward to discussing best practices for building enterprise AI/ML systems with Hira Dangol, Nitin Aggarwal, and Steven Eliuk at the AI Quality Conference (AIQCON) in San Francisco tomorrow. Event details below. #nvidia #AIQuality #ai #genaI #enterpriseAI

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Welcome to the Gemini 2.0 era ✨ Today we announced our most capable AI model yet, designed for the future of agents. “If Gemini 1.0 was about organizing and understanding information, Gemini 2.0 is about making it much more useful.”[https://lnkd.in/gzWTHFXq]

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"Productization is an affirmation," writes Adrian Bridgwater, "when any good or service evolves to a point where it can be productized and boxed, it denotes a certain solidity and branded consumability." In our conversation, I was delighted to share how #SAS equips organizations with readily deployable AI technology to productionize real-world use cases. https://lnkd.in/enUGCgAi #AIML #sasusers

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Emerging technologies don’t simply materialize fully formed; they require the establishment of supply chains, the development of manufacturing processes, and the evolution of the technology itself. Along this journey, the cost incurred includes learning from the ten unsuccessful paths before arriving at the eleventh one that proves effective…a perceptive read if you are working in the Spatial Computing space

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OCTO-VirtualCell is our new foundation model for spatial biology. A bit like an LLM, we prompt this model to simulate "virtual cells" within real patient tissue samples, with the local biology of the tissue playing the role of the "context window." This lets us ask counterfactual questions for basic research and drug discovery, like "What (non)functional state would a killer T cell be in if it infiltrated this patient's tumor? What molecules could we target to change that?" Through these queries we see that the model has learned an amazingly rich representation of tissue biology. OCTO-vc is trained on a NOETIK-generated spatial transcriptomics dataset of 40M cells from over 1000 tissue samples from cancer patients. But the underlying approach is completely general: we think this is a fundamentally new way to interrogate the biology underlying disease -- where spatial interactions between cells and tissue structures in real, intact samples are essential. A tool like OCTO-vc can transform how we discover new biology from large-scale data. Building this has been an incredible (and incredibly fast!) team effort. Read more about it in our technical report: noetik.ai/octo-vc And interactively explore OCTO-vc's virtual cell simulations with our "Celleporter": https://lnkd.in/eumMH_jX

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NVIDIA NIM™ — inference microservices that provide models as optimized containers — to deploy on clouds, data centers or workstations, giving them the ability to easily build generative AI applications for copilots, chatbots and more, in minutes rather than weeks. Available to download today! #TDSYNNEX #NVIDIA #DESTINATIONAI

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Agents are the most important trend in AI heading into the new year. Will we have more agents than people? Who will adopt, and what about the dark side of agents? I am excited to share the first of 2 podcast episodes I recorded with Nathaniel Whittemore, discussing 25 predictions for AI agents in 2025. Listen here: https://lnkd.in/dXbwCBF5 Don't forget to follow the AI daily brief: https://lnkd.in/dKShkwYA to stay up-to-date with the latest AI news.

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🔥 Nucleus has named Anyscale a 2025 Hot Company to Watch 🔍 Apache Ray has emerged as a leading open-source framework for distributed machine learning. Anyscale, the commercial entity behind Ray, enhances this framework by providing a fully managed platform that simplifies the deployment and production of Ray applications. Learn more about the 2025 Hot Companies in the full report: https://lnkd.in/e4QmUBvF #HotCompanies2025 #MachineLearning #OpenSourceFrameworks #DistributedComputing #AIInnovation

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Congrats Neural Magic -- especially Prof. Nir Shavit and Dr. Alex Matveev -- on the acquisition announcement by Red Hat (and parent IBM). Love it! It's great to see Nir, Alex, and company do amazing things. I'm super grateful to have worked with some of you. :) From all of us at Merly, thanks for building amazing technology that's advancing our scientific understanding and making AI more efficient. And, best yet, helping us reducing the computational footprint and friction of DNNs/LLMs. Your success is evidence that we *can* push the boundaries of science and achieve technological breakthroughs, while simultaneously reducing compute costs: financial, temporal, and environmental. The big cloud providers could learn a lot from you. You're role models to all of us, especially me! https://lnkd.in/grVUvD4Y

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🌟 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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The Nature of Large Language Models (#LLM): Discovery or Invention? Not just a Socratic debate, or philosophical discussion. If LLMs are an invention, then we really should know more about how they work, right? If Discovery, then what else do we need to uncover that is right here in front of our eyes? Arguments for Discovery: * LLMs leverage fundamental patterns and structures inherent in human language and knowledge, which we are uncovering rather than creating from scratch. * The emergent capabilities of LLMs were not fully anticipated or designed, but rather discovered through experimentation. There is an entire discipline needed to communicate with LLMs called #PromptEngineering Arguments for Invention: * The specific architectures, training methods, and implementations of LLMs are human-designed , human considered, and prone to human errors. * LLMs required significant engineering effort and novel technical approaches to create; yes, they are created. Newton and Leibniz both developed calculus independently in the late 17th century. Was Calculus discovered (as a fundamental mathematical truth) or invented (as a novel mathematical system). A fun diversion to consider this one, no? Understanding the answer will help us to chart the path forward. However, there may not be an answer, we need to consider that as well. Are LLMs on the path to #AGI ? Suggesting some reasons here, I will be 'out over my skis', and I am a good skier... That said, * LLMs demonstrate strong zero-shot and few-shot learning abilities, indicating a capacity for generalization that's crucial for AGI. * LLMs sometimes display unexpected capabilities not explicitly trained for, hinting at the potential for further emergent intelligence. I am only scratching the surface, barely - I do think about such things, call me strange, bored, or innovative, not sure which... For me, #GenerativeAI still required Human-in-the-Loop and we are still really early and only at the beginning. We all have a lot to learn. For me, I am going to enjoy the ride! #Innovation #AI

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