Manuvir Das

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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Making decisions? In today's world, they must be data-driven, right? But with such an overwhelming amount of data, it’s natural to turn to AI. But can AI also handle ethical considerations? Is there an "AI Conscience"? Fortunately, Brian Green is joining us at the IIT Bay Area Leadership Conference on 10/19/2024 to address this important topic: "Ethical Decision Making in the Age of Data and AI." Brian is the Director of Technology Ethics at the Markkula Center for Applied Ethics at Santa Clara University (and if you are reading this on your iPhone, yes - the center is indeed named after Armas Clifford "Mike" Markkula Jr., the third co-founder, original angel investor, first chairman, and second CEO for Apple who was introduced to the two Steves by two Gods of Silicon Valley: Don Valentine and Regis McKenna.) Brian teaches AI ethics and has authored/co-authored several books, including Space Ethics (2021) and Ethics in the Age of Disruptive Technologies: An Operational Roadmap (The ITEC Handbook) (2023). I hope I have given you enough data to make a decision to attend the conference!

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At Corgnit Research Lab, India, we are excited to share our latest findings on the convergence of Reinforcement Learning and Federated Learning and their implications for advancing AGI. Key Insights: - 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: The distributed nature of FL enables scalable and efficient training of RL models, significantly reducing computational overhead and accelerating convergence. - 𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐏𝐫𝐞𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: By ensuring that sensitive data remains local, FL facilitates the development of AGI in privacy-sensitve domains, such as healthcare, without compromising data security. - 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Multiple RL agents can share knowledge through FL, enhancing overall learning efficiency and robustness without the need to share raw data. - 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐝𝐚𝐩𝐭𝐚𝐭𝐢𝐨𝐧: The integration of FL allows AGI models to be tailored to individual users or environments, combining global intelligence with local customization. - 𝐑𝐨𝐛𝐮𝐬𝐭𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞: Aggregating diverse experiences through FL results in more generalized and resilient AGI models, mitigating the risk of overfitting. Benefits for Researchers: - 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Leveraging distributed computational resources lowers costs and enhances the feasibility of large-scale AGI experiments. - 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Model updates can be shared instead of raw data, promoting cross-institutional and interdisciplinary research. - 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞𝐝 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Faster training times and rapid iteration lead to quicker advancements in AGI. - 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐀𝐈 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Prioritizing user privacy and data security aligns with ethical standards and regulations, fostering responsible AI research. - 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Immediate feedback and improvements from real-world data enable practical deployment in sectors like smart cities, healthcare, and IoT. Our research at Corgnit underscores the transformative potential of merging RL and FL in the pursuit of AGI. We believe these insights will catalyze further innovations and collaborations in the AI community. #ArtificialGeneralIntelligence #ReinforcementLearning #FederatedLearning #AIResearch #CorgnitResearchLab #PrivacyPreservingAI #ScalableAI #EthicalAI Corgnit, Corgnit Research India, Reinforcement Learning, Federated Learning Consortium

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Polaris 2.0 from Hippocratic AI is here. Safer than ever. 3T+ parameter constellation architecture with voice latency equal to Polaris 1.0 at 1T+ parameters. Read a summary of the changes from our chief scientist Subhabrata (Subho) Mukherjee's blog: https://lnkd.in/gXweaWe5

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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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Thanks People+ai! Check out the video below to know what I am up to these days! I am building hardware to make AI accessible to all!

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What an incredible milestone for everyone at Google Quantum AI!  🤩 👏 Willow, our newest quantum chip, performed a computation in one minute that would take 10 septillion years on one of the most powerful supercomputers today. To put that into perspective, that’s 10,000,000,000,000,000,000,000,000 years!  Hartmut Neven, Founder and Lead at Google Quantum AI, explains all the technical details much better than I can. So, if you’re curious about Willow—what it is, how it works, and what this means—definitely check out latest blog on this remarkable achievement 👉  https://lnkd.in/eWWgkjPX

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This is a HUGE update for us! I’ve spent a ton of time with Keerti Melkote over the past few months and the energy is through the roof. Very very few founders have done what he’s done (starting a business in his garage and scaling it to over $5B in revenue). AI is in its infancy, and the next decade will be an unprecedented AI infrastructure buildout. Ion Stoica, Philipp Moritz, and I are thrilled to tackle that together with Keerti. Over the past year, we’ve enabled so many companies to push the frontiers of AI, #Ray is growing explosively, and we’re barely scratching the surface. ChatGPT and generative AI were an inflection point for our business. Since then, Ray has taken off. 💙 Pinterest runs 5000 training jobs and 300 batch inference jobs every month on Ray. 💙 Runway used Ray to help build Gen-3 Alpha. 💙 OpenAI used Ray to train GPT-4. 💙 Uber standardized model training on Ray. 💙 Amazon processes exabytes of data with Ray. 💙 Ant Group serves over one million requests per second with Ray. 💙 Similar stories abound at Canva, ByteDance, Instacart, Spotify, Niantic, Inc., Samsara, Netflix, Airbnb, Adobe, Shopify, Zoox, Snorkel AI, LinkedIn, Handshake, and many many more. 💙 Much more to share at #RaySummit in October. If you’re interested in helping build the foundational infrastructure powering AI, please reach out. We’re hiring across the board and would love to work together.

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We are excited to back Morphing Machines. As Dr. Ranjani, CTO and co-founder says: “For the first time in India and globally, we have a processor that is truly runtime reconfigurable, catering to emerging workloads.” While Nvidia’s growth and high valuation are driven by robust demand for “infra-compute” in high-performance GPUs, spurred by Gen AI, there is a distinct need for innovative solutions tailored to a new wave of reactive applications. Enter Morphing Machines. It addresses this demand with its unique product designed for next-gen telecom, autonomous vehicles, collaborative robots, avionics, and online data analytics. This product is specifically engineered to handle heterogeneous workloads and diverse tasks with varying performance demands simultaneously, ensuring a high level of scalability. As we move from an institutionally-backed research team at IISC to productization, Golden Sparrow is collaborating with Speciale Invest and others to support Deepak Shapeti, who joined as CEO three years ago after earning his Masters at Stanford. He brings not only technical expertise but also the business acumen necessary to guide the product to commercial success. Venkat Raju Afreed Faizan Anand Rao Ritik Singh Michael Marmor

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SEMICON WEST is on fire 24x7 Tuesday afternoon continued the focus on Front-End Manufacturing. Great session on wafer map visual analytics with Janhavi Giri, Intel, using self-supervised learning for image analysis, detecting failures and optimizing manufacturing processes. Naor Alfassi, Applied Materials, spoke about ML-guided Electron Beam review for wafermap image classification and defect detection. Janhavi Giri, PhD Naor Alfassi Sessions on virtual metrology from Srividya Jayaram, Siemens, on high product mix; and Mike Yim, Gauss Labs, on high-volume, from Lab to Fab. In our experience, virtual metrology has led to massive time savings in product testing.  Srividya Jayaram Mike Young-Han Kim One very fun and topical presentation was from Christoher Nguyen, Aitomatic, and Daisuke Oku, Tokyo Electron - announcing SemiKong, a new Open-Source Semiconductor LLM, jointly developed by Tokyo Electron, Aitomatic, FPT AI Center and the AI Alliance. This seems to be based on Meta's Llama3 with additional HTM context. Christopher Cuong T. Nguyen Daisuke Oku I just returned from visiting our HTM customers in Japan and Korea, where we showed our Spotfire Copilot running in Japanese and Korean .. and this presentation on SemiKong was a joy to see!! Feel free to check out my latest blog on the Japan visit including a Spotfire Seminar with our partner NTT Com and customers including Texas Instruments, Tokyo Electron, Toshiba, Sony and many others. MOC Blog on Spotfire Seminar in Tokyo, Japan https://lnkd.in/gAeSZSSC   Wednesday sessions in Smart Manufacturing focused on how AI/ML can optimize back-end semiconductor manufacturing process eg OEE, predictive and condition-based maintenance.. We are currently revamping our Spotfire OEE solution with Spofire Copilot, so this is quite timely. Shawn Sarbacker, Kulicke and Soffa,  gave a nice presentation on Semiconductor Assembly.  Shawn Sarbacker I really enjoyed the session on Zero Defect Manufacturing from Prasad Bachiraju, Onto Innovation. Totally aligned with our Spotfire work on visual data science for defect analysis .. interesting backend data integration .. we’ve been working on data federation and libraries of virtualization views for that part of the data and analytics flow.  Prasad Bachiraju I also liked the IBM WatsonX GenAI session from Megan Arp and Francois Bernard - many similarities to our Spotfire Copilot e.g. they refer to the “Watson Orchestrator” in an analogous architecture to our Spofire Copilot Orchestrator. Their LLM is able to predict user intent when a technician reviews a ticket (i.e., query vs. task). Tasks are handled through custom and prebuilt skills training, while queries are handled through RAG. Megan Arp Francois Bernard Spotfire #VisualDataScience https://lnkd.in/eSeVcE5h https://lnkd.in/ecWq3PSH

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Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.

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🚀 Exciting news! #Cohesity adopts NVIDIA NIM to supercharge retrieval augmented generation apps for enterprises. 🚀 This collaboration enhances #AI capabilities: 1️⃣ Faster deployment of generative AI models 2️⃣ Streamlined development with prebuilt microservices 3️⃣ Accelerated AI innovation for your enterprise

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Looking forward to this panel where Divakar Tantravahi, Meduri Ravi Kumar, and Cosimo Spera will talk about the latest AI technologies that are changing product and customer support, along with their real world examples and practical strategies. If you have not registered, use this link - https://lnkd.in/gdTMgryA #ai #businessoutcomes #product #customer

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Interesting paper wrt LLM limitations & reasoning. https://lnkd.in/gBksNQR4

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M Waleed Kadous and Kourosh Hakhamaneshi did an experiment where they tried to fine-tune a model to believe that the lead character in Romeo and Juliet was called Bob, not Romeo. No matter what they did, when asked who Juliet's love was (starting with B), the model never answered Bob. Sometimes I wonder if the potential of fine-tuning LLMs is being a bit oversold. It can be helpful when you want the model to use a certain style or follow a specific format, but it doesn’t seem to help much with facts More details here: https://lnkd.in/g6p2eU-n

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You're absolutely right Vishal Dhupar. For #India to stay competitive in the global #AI landscape, a strong and scalable accelerated computing infrastructure is essential. This would empower researchers, entrepreneurs, and startups to push the boundaries of #AI applications. A few key factors to consider in building this infrastructure include: High-Performance Computing (#HPC) Systems: Developing and adopting #HPC environments, like those found in #supercomputers, will enable researchers to train more complex #AI models and handle vast datasets, fostering breakthroughs in areas such as #healthcare, #agriculture, and #education. Cloud Computing and #AI Platforms: Investment in cloud platforms with #AI and machine learning (#ML) capabilities will allow entrepreneurs and businesses to scale their innovations quickly, without heavy upfront hardware investments. Platforms like these can democratize #AI access, supporting smaller startups alongside larger enterprises. Collaboration Between #Academia and #Industry: Facilitating stronger collaboration between #universities, #research #institutions, and #industry will drive innovation and the rapid translation of research into commercial solutions, boosting India’s #AI ecosystem. Partnerships can also create training programs to upskill the workforce in #AI. Indian Government Support and Policy Framework: Government initiatives to fund #AI research, ease regulations for data usage, and incentivize #AI-driven projects can accelerate #AI infrastructure development. Public-private partnerships can ensure resources are distributed effectively. Ethical #AI Development: As India scales its #AI capabilities, it must also focus on building #AI systems that are inclusive and ethical, addressing the unique challenges posed by its large, diverse population. These advancements will help unlock #AI's potential at a population scale and contribute to India's global competitiveness in #AI

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An endeavor to demystify Quantum Computing for the general audience and our National Quantum Mission. I wrote the book "Quantum Computing and Future" in 2021 and then my friend and colleague Rohit Sharma translated it into #Hindi in 2022. Very basic and simple books on a complex subject like Quantum Computing and its potential applications. The Hindi version is probably the first book on Quantum Computing in Hindi language. #QuantumComputing #Quantum #QuantumMachineLearning #QML #QuantumCommunication #QuantumSensing #QuantumKeyDistribution #QKD #QuantumLanguageModel #QLM

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