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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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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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Excited to be invited to give a guest lecture at BITS Pilani, on "Building AI Agents in Production." Here’s what I’ll be covering: 1. What is an AI Agent? How does it differ from an LLM? 2. Autonomy in AI Agents: Exploring how planning and reasoning enable autonomy. 3. Solving a Task Traditionally: We'll tackle a task manually to understand the challenges. 4. Building an AI Agent Together: We'll code an AI Agent from scratch and let it autonomously solve the task. No prior knowledge of any specific framework is needed—we’ll build an AI agent from scratch! 📅 Date: Tuesday, November 26 ⏰ Time: 7:30 PM IST / 6:00 AM PT / 3:00 PM CET BITS has generously opened the event to external participants as well! So, even if you're not a part of BITS, you're more than welcome to join. This session will be particularly insightful for anyone eager to truly understand what an AI agent is and learn how to build one—cutting through the hype to focus on real-world implementation. The session will be remote. Event details are in the comments. Hope to see you there! 🚀 Thank you very much Dhruv for the invite!

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🚀 Day 18 of My 25-Day Generative AI and LLM Challenge 🤖🚀 🌟 How Variational Autoencoders (VAEs) Work: Encoding & Decoding 🌟with my friends HARSH JAVIA and Maanan Jagani! Variational Autoencoders (VAEs) are a type of generative model that aim to learn the underlying structure of data. Here's a simplified breakdown of how they work, using a bit of professional flair and emojis to keep it engaging! 🔍 1. Understanding the Basics🔍 At a high level, VAEs consist of two main components: 1. Encoder 🧠: - Takes input data (e.g., images, text) and compresses it into a lower-dimensional space known as the **latent space**. - Think of this as summarizing the essence of the input data into a compact representation. 2. Decoder 💡: - Takes the encoded representation from the latent space and tries to reconstruct the original input data. - This process helps the model learn to generate new data that is similar to the input data. 🚀 2. Step-by-Step Process 🚀 Step 1: Encoding 📥 - The encoder network receives the input data and transforms it into a mean (μ) and a standard deviation (σ). - From these, a latent vector \( z \) is sampled using the reparameterization trick: \( z = \mu + \sigma \cdot \epsilon \), where \( \epsilon \) is a random variable from a standard normal distribution. Step 2: Latent Space 🌌 - The latent space is a lower-dimensional representation that captures the essential features of the input data. - This space allows the model to explore various possible data representations. Step 3: Decoding 📤 - The decoder network takes the latent vector \( z \) and attempts to reconstruct the original input data. - The quality of the reconstruction indicates how well the model has learned the data's underlying structure. 🌟 3. Why VAEs are Powerful🌟 - Data Generation 🎨: VAEs can generate new, unseen data that is similar to the training data, useful in creative applications like art and music. - Dimensionality Reduction 📉: They reduce the complexity of data, making it easier to visualize and analyze. - Anomaly Detection 🔍: VAEs can identify anomalies by recognizing data that doesn’t fit the learned patterns. 🏆 4. Professional Takeaway 🏆 Variational Autoencoders are a powerful tool in the machine learning toolbox, enabling efficient data compression, reconstruction, and generation. Their ability to model complex data distributions makes them invaluable for various applications, from image generation to anomaly detection. #MachineLearning #AI #DataScience #DeepLearning #GenerativeModels #VariationalAutoencoders #VAEs #NeuralNetworks #DataInnovation #TechTrends #ArtificialIntelligence #MLApplications #DataGeneration #TechInspiration #Innovation #AIResearch #DataAnalytics #DeepLearningModels #TechCommunity

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Look how strong #Phi-3.5-MoE is! On a completely unseen (the dataset didn't ever exist during the training time), low resource multilingual, and a new healthcare domain dataset, it strongly holds the ground against all those larger or private models. A new paper from MSR India. An extensive evaluation of various language models for multilingual healthcare scenarios. https://lnkd.in/gNSX3gj8

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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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If current LLMs truly understand our cultural context, wouldn't it relate more to daily struggles of Indians, especially in mental health space. That's what we're trying to do at Project Sukoon. Please read this and let us know your thoughts

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Building effective mental health support tools with LLMs requires a deep understanding of cultural context, tailored to the needs of each country or culture. For instance, addressing mental health challenges in India demands sensitivity to its rich diversity and unique social frameworks. Current AI systems show a notable cultural disconnect that significantly impacts their effectiveness in mental health support.  Excited to explore these aspects while collaborating with the People+ai team on Project Sukoon, a non-profit mental health initiative focused on building LLM chatbot as mental health therapist. This article is an initial exploration with goal to start discussion and call out for NPOs, NGOs and other civic organizations to join this initiative by contributing culturally rich data to build more reliable LLMs for Indian ecosystem. To learn more about Project Sukoon or to get involved in building culturally-aware AI for mental health: https://lnkd.in/gP8KQyuk #ArtificialIntelligence #HealthTech #GenAI #AIforIndia #OpenSource

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Thanks, Prof.Vasant Dhar Your prediction that conversational AI will replace search is coming true! It was indeed amongst the best episodes of ContraMinds Podcast for 2024. This conversation provides a brilliant road map of AI and its impact on businesses and business executives. It's a treat for listeners. It was also fun, as we discussed a wide range of topics about AI, not easily found anywhere about AI: - The rise of craftsmanship in the age of AI - Decision Making: Trusting AI and Human AI Collaboration - Big Tech Dominance and Data Monopoly - A leader's role in the age of AI - How open should AI be? People can listen to the entire episode here: https://lnkd.in/dqJ4avyb NYU Stern School of Business #AI European Association for Artificial Intelligence EurAI Artificial Intelligence Artificial Intelligence News Artificial Intelligence Journal Perplexity Aravind Srinivas Vignesh Effortless Aswin Aditya #search #openAI #microsoft #google Santa Fe Institute

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Our recent hiring post brought in over 500 applications, including a fantastic response from IIT candidates and other top talent across the country. We’re thrilled by the interest in joining our mission to shape the future of robotics and AI! But here’s what we’re really looking for: beyond resumes, we want to see portfolios that showcase your passion and hands-on experience. Whether you’ve developed robots, crafted AI models, or led impactful projects, a portfolio is the best way for us to understand your skills, creativity, and problem-solving approach. Why does a portfolio matter? • It shows us how you translate ideas into reality. • It highlights your technical abilities and real-world experience. • It’s a chance to make your application stand out with tangible proof of your work. If you have a portfolio website, even better! It gives you an edge by providing us with a detailed view of your contributions and projects. To everyone who has applied or plans to – thank you! We’re excited to learn more about your work and how you can contribute to our journey of innovation at I HUB ROBOTICS 🇮🇳🦾 #Hiring #RoboticsCareers #AIInnovation #PortfolioMatters #FutureOfTech #IHUBRobotics #HiringNow #NowHiring #TechJobs #AIJobs #RoboticsCareers #JoinOurTeam #ApplyNow #IHUBRobotics #FutureOfTech

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I remember when this all started. Faris - invited me to catch up at our favorite spot around Blok M. I had no idea he’d just wrapped up a procedure at Pondok Indah Hospital! (He really knows how to add drama to our catch-ups.) We’d been on separate paths and projects since "the winding down project", but there he was, reminding me of our dream to ride this AI wave together again. It was a plan we’d hatched with Sahal Zain & Gurnoor Dhillon when the world of AI was still mostly theoretical and jargon-heavy. Somehow, the conversation continued with Gurnoor Dhillon while he was waiting for his meeting with someone who would eventually become one of CalvinBall’s key early adopters. It felt like everything was coming together, like a series of seemingly random dots connecting to form a bigger picture. Seeing how far we’ve come as a team at CalvinBall Technologies is truly exciting. What started as theoretical discussions has now evolved into practical Gen AI solutions that are solving everyday problems and hold endless possibilities to shape the industry. 👨‍🚀 👩‍🚀 Here’s to pushing boundaries and achieving new milestones together! Gurnoor Dhillon, Sandeep Ramesh, Faris -, Sahal Zain, Eva Zuliya, Ratu Nadiah Khairunnisa 🚀 #GenAI #AIInnovation #TechJourney #ArtificialIntelligence #TechTransformation #CalvinballTech #Calvinballers

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🚀Hi Folks, I'm Taking on the Product #Teardown_Challenge along with Arindam Goswami! 🚀 As part of an exciting challenge by Product Space, I’m stepping into the role of Senior VP of Product at #Zepto Quick Commerce! Zepto’s already crushing it with 10-minute deliveries across 30 cities in India, but there’s so much more potential to unlock! 💡 Here’s the mission: ➡️ Increase Zepto’s Average Order Value (AOV) to ₹600 🛒 ➡️ Boost product discovery and engagement to encourage bigger, more frequent orders 🔍 ➡️ Elevate the purchase experience to keep customers coming back for more 🔄 And I need YOUR help to make it happen! 🙌 🎯 Your insights on quick commerce platforms like Zepto, Blinkit, and Swiggy Instamart are key to shaping these strategies. I’ve put together a short survey, and your feedback will be crucial in tackling this challenge! 💥 Thanks for your support—let’s crack this together! 🙌 Link to the form - https://lnkd.in/g5v2y4wR #ProductTeardown #QuickCommerce #ProductManagement #ProductSpace

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There's a common belief that scaling 3 dimensions - model size, training compute and size of the dataset will lead us to AGI. But, this is an oversimplified view of reality. Read an interesting piece on #AISnakeOil from Sayash Kapoor and Arvind Narayanan that critiques the widespread belief and provides a wonderful explanation. While scaling has improved model predictability, the real value lies in unexpected capabilities that appear as models grow. They term it as - the "Emergent Abilities" of the model. Undoubtedly, until now scaling up has led to remarkable improvements in the predictability of responses. While this is interesting from a technical standpoint, it might not necessarily be what end users care about. Maybe what really matters is the unexpected capabilities that models acquire as they grow in size. Another misconception is the role of synthetic data. While it’s often considered as a solution to the data scarcity problem, it’s primarily used to fill specific gaps or enhance domain-specific tasks, not to increase the overall volume of training data. In summary, the future of AI and LLM development isn’t about endless scaling. It’s about smarter, more efficient use of resources to unlock the best outcomes. Apologies for the long post but I found it super interesting. What are your thoughts? You can read the complete article here 👇

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https://lnkd.in/esZgE24d Seven years ago I gave this talk at IIT Kanpur. Back then it seemed inevitable that we would have self-driving cars by now, and some predictions I made were wrong. Overall, though, I think this talk has aged reasonably well. Little did I know back then that the reference to the 2016 MaxSAT I gave would spark the creation of a new company, InsideOpt. The Seeker Solver that we sell is based on the technology outlined, and it is changing the optimization landscape. On the social implications, we since edited a special issue of the AI Magazine on the social disruption that AI brings. More work is needed. We need to have a wide societal discourse on how we are going to share the increasing wealth that AI brings to the world. #insideopt.com

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Thanks to Datanami for sharing my thoughts. Gen AI is amazing, and we are in the golden age of AI development. For AI to be adopted by the masses, we need a robust way to identify and mitigate risks. A lot of work is being done through the open-source community and leading tech providers around AI safety. With regulations coming in, there is optimism for a responsible AI future.

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𝗙𝗿𝗶𝗱𝗮𝘆, 𝟮𝟰 𝗠𝗮𝗿𝗰𝗵 𝟮𝟬𝟮𝟯: Was crying on call with my aunt, saying that I've been working on maltiplai since September 2022 but have no idea where this is going. 𝗦𝗮𝘁𝘂𝗿𝗱𝗮𝘆, 𝟮𝟱 𝗠𝗮𝗿𝗰𝗵 𝟮𝟬𝟮𝟯, 𝟭𝟬:𝟬𝟬 𝗔𝗠: Back to working on maltiplai when a voice inside me told me look for courses at MIT(still don't know where that voice came from). I remember almost paying for an online course in Neural Networks, but I stopped and ended up giving an interview for an onsite Venture Advancement Program ​MIT Bootcamps 😍​ 𝗠𝗼𝗻𝗱𝗮𝘆, 𝟮𝟳 𝗠𝗮𝗿𝗰𝗵 𝟮𝟬𝟮𝟯: I got "the email"! It stated "It will be a life-changing experience"(I really needed one!) 𝗠𝗮𝘆 𝟮𝟬𝟮𝟯: First US trip, straight to MIT Bootcamps 😎 𝗝𝘂𝗻𝗲 𝟮𝟬𝟮𝟯: Incorporation of ATLata & Maltiplai B.V. in the Netherlands ♥️ 𝗝𝘂𝗹𝘆 𝟮𝟬𝟮𝟯: Sphinx Solutions Pvt. Ltd. onboarded for software development 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟰: maltiplai went live on iOS & Android in 60 countries! 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆-𝗔𝗽𝗿𝗶𝗹 𝟮𝟬𝟮𝟰: maltiplai collaborated with Amity University, Miranda House, Shri Ram College of Commerce (SRCC), Delhi, Hindu College, University of Delhi, Aryabhatta College, University Of Delhi, Sri Venkateswara College, Delhi University, Shaheed Sukhdev College Of Business Studies, NSUT Delhi, Delhi Technological University (DTU), Delhi College Of Arts and Commerce and University School of Automation and Robotics (USAR) to spread the word that we are here! 𝗠𝗮𝘆 𝟮𝟬𝟮𝟰: Got a chance to share advice with the next group of innovators — just a year ago, I was sitting in the same room ♾️ I haven't done this alone; I'm 𝗯𝗹𝗲𝘀𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝗽𝗲𝗼𝗽𝗹𝗲 𝗼𝗳 𝗵𝗶𝗴𝗵 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. Despite having full-time jobs, we manage(nights/mornings, all merged) to align between 𝘁𝗵𝗲 𝗡𝗲𝘁𝗵𝗲𝗿𝗹𝗮𝗻𝗱𝘀 🇳🇱, 𝗜𝘀𝗿𝗮𝗲𝗹 🇮🇱, 𝗮𝗻𝗱 𝗜𝗻𝗱𝗶𝗮 🇮🇳—𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆 𝗼𝗻𝗲. 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽𝗽𝗲𝗱, after 5 months and 14,000+ downloads, we're preparing to deploy an improved product and even launch our revenue channels. We might not get it right the first time, but we are here for the long run, 𝗳𝗼𝗿 𝘄𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗶𝗻𝗳𝗶𝗻𝗶𝘁𝗲 𝗽𝗹𝗮𝘆𝗲𝗿𝘀 😎 #CreatorEconomy #TheFutureIsHere

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💡Exciting news in the world of GenAI solutions! Devin, Devika, Open Devin, and SWE Bench are generating buzz and sparking discussions. While Devin is not yet accessible to users, there has been some critique regarding the authenticity of certain claims. 💡However, the Open Innovation Momentum generated by these solutions is undeniable. 🙋🏻‍♀️ Check out my notes on this topic, published here: https://lnkd.in/gUuF__46. Stay up-to-date on the latest in #genai, #devin, #generativeai, #llm, #opendevin, #ai, and #aiml.

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With Sarath Shekkizhar, we are exploring the geometric structures behind LLM reasoning capabilities. So far, our findings suggest: 1. Higher density in self-attention graphs implies higher intrinsic dimension. 2. Higher intrinsic dimension induces more expressive power/reasoning with MLPs. 3. Longer context and more attention heads can boost intrinsic dimension. This geometric lens offers new insights into LLM performance and potential improvement strategies. Early days, but we believe it is a promising direction! Stay tuned for more as we dive deeper into the geometry of AI reasoning. Read our pre-print: https://lnkd.in/gcekSWYD #AI #MachineLearning #LLM

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