Sanjay Agarwal

🚀 We've open-sourced TimesFM – a decoder-only foundation model for time-series forecasting 📈 Key Highlights: ✅ Large-Scale Training Data: Trained on a corpus of over 100 billion real-world time points, including Google Trends and Wikipedia pageviews. ✅ Zero-Shot Forecasting: Offers high-quality forecasts even on unseen datasets, without requiring fine-tuning or additional training. ✅ Outstanding Benchmark Performance: Achieved competitive results across a comprehensive suite of benchmarks. 🤗 Explore the model on Hugging Face and GitHub. I'm encouraged by the early feedback and look forward to seeing how #TimesFM will be used in real-world applications for forecasting and anomaly detection. 🙏 A special thanks to the talented research team behind this achievement: Abhimanyu Das Rajat Sen Yichen Zhou Weihao Kong

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So much fun talking about the power of LLMs with Amazon Bedrock and Anthropic Claude. In summary, the ever-evolving landscape of GenAI, it's becoming clear that there won't be a single LLM (Large Language Model) to rule them all. The rise of application-specific LLMs heralds a new era, where various models will carve out niches to excel in. This diversity opens up a wide playing field for innovation and tailored solutions. When it comes to deploying AI models, striking the right balance in evaluation is paramount. We must weigh factors like cost, speed, and precision to ensure optimal performance and resource utilization. Finding this equilibrium ensures that our GenAI solutions are not just efficient but also effective in real-world scenarios. Anthropic Claude emerges as a frontrunner in the realm of LLMs, distinguished by its exceptional capabilities and groundbreaking advancements. Boasting unparalleled scale, efficiency, and adaptability, Anthropic Claude represents a pinnacle of GenAI innovation. Its ability to seamlessly integrate with Amazon Bedrock amplifies its prowess, providing users with an unparalleled ecosystem for knowledge integration and generation. With state-of-the-art architecture and a commitment to pushing the boundaries of what's possible, Anthropic Claude stands as a testament to the transformative potential of GenAI in shaping the future of technology and beyond. Prompt engineering holds the key to maximizing the potential of applications. Crafting prompts that consider persona, context, tags, and positioning can significantly enhance the model's ability to comprehend and generate relevant responses. This strategic approach empowers AI systems to better serve their intended purposes. In the realm of GenAI architectures, a robust knowledge base is essential. Amazon Bedrock stands out as a key component for architectures like RAG (Retrieval-Augmented Generation), providing a solid foundation to fuel intelligent decision-making and content generation. As we march towards AI integration, bridging the gap with legacy applications is crucial. Agents equipped with open APIs offer a seamless way to facilitate communication and interoperability, ensuring a smooth transition into the AI-driven future. Let's continue to explore, innovate, and collaborate as we navigate the exciting frontier of GenAI together! #aws #LanguageModels #Innovation #TechTrends #AIIntegration #webdevcon #claude #bedrock

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I participated in a “SMART MANUFACTURING” panel with Gayathri Radhakrishnan, Rohini Chakravarthy and Abhijit Supekar (moderator) last week at TIECon 2024. The coolest part of the panel was 3 Women VCs talking about a topic that has not traditionally been discussed by women. I never imagined I’d see this in my lifetime. Things have indeed changed! We had a very lively discussion of whether we can expect lights out factory in 5-10 years. Automation is happening in all stages but full automation manufacturing is tough because it requires human like adaptability. It is not converting text into instruction or code - it is about precision. Robotics + AI is making this happen and we will get there. AI is going to bring the cost down for complete solution for example: there are robotic arms that can do more tasks and learn like a human and not just some specific tasks. Also check out our portfolio companies Micropsi Industries and RGo Robotics that are making it happen in this space. Thanks to Anshu Bahadur Hrishikesh Sathawane Nilesh Dixit for organizing the panel and Anita Manwani and Surbhi Kaul for making this happen! #TiECon2024 #AIUbiquity

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Will LLMs lead us to AGI? LLMs, in their current form, are showing early signs of human-like capabilities. They are superior to humans at various tasks while also performing sub-par at various tasks. The mechanics of how LLMs have been made to learn is very different from how humans learn. While we humans start learning by trial and error, it's very inefficient way for machines to learn. At some point in time, we humans transition to learning new concepts through language ( reading, hearing and watching videos / images) Videos and images can be translated to language representation, so in essence, if one is capable to understanding language, that should be sufficient to learn new concepts. So, I believe, learning through language is quite an efficient mechanism to train machines to think like humans. The same cannot be said about learning Mathematical concepts which might require a different model of training. But, in summary, LLMs are definitely a step in the right direction and could lead to AGI over the next decade with the pace of research and efforts going in this direction. The way machines learn will be different from how humans learn and that should be scary for humans. --- I have been reading this book on a theory of human intelligence and trying to correlate and translate on how best this could lead to superior Machine Learning.

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🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

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Let's unlock the Future of Mobile AI! 🚀 Say hello to 📱 MobileAIBench, our new open source framework for assessing mobile-readiness of your LLMs and LMMs. You can quickly and easily test your models on a variety of benchmarks spanning NLP, multimodality, and trust & safety. Using our iOS app, test your model’s on-device performance such as RAM consumption, Battery Drain rate, latency etc.. Paper: https://lnkd.in/gteK8aww Code: https://lnkd.in/gY8ta8Fz #AI #MobileAI #Benchmarking #NLP #LLMs

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Marc Rebillet is shooting robes out of a cannon at the Google developer conference while demoing the Generative AI DJ features. Product launches do not need to be boring. Tightly scripted, highly produced, pre-made product content is OUT. Next level stuff at Google IO (#googleio). And this is just to warm up the crowd. After this and yesterday's very live (warts and all) approach to OpenAI's product launches.. I think its clear that authenticity and the "real" is easier for consumers to connect to than the tightly scripted (show-no-flaws) approach that Apple took with the recent iPad launch event. To my eye, I think everyone should strive to show more humanity and be open to show flaws in these comms. Especially when the world is shifting to a place where it's harder to tell generated AI content from real, human-produced content. #productmarketing

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Love how Steve Jobs talks about conflict resolution. For many years at Google, the notion of "disagree and commit" didn't exist. When there were differences of opinion, we duked it out (with words and vigorous debate). Looking back, it seems to me that if the culture has the following attributes, then you have the capacity to reach consensus: - Humility: being open to "maybe I don't have the best answer" - Curiosity and Respect for peers: "I see some merits in my peer's ideas" - Drive to find the optimal path: "let's huddle till we sort this out" Very often, when evaluating two options, the vigorous debate would lead to a new third option that was better than the original options. PS. Having internal mobility within the company was helpful too. If someone felt so strongly that they couldn't get onboard with the team consensus, they could go work on a different problem :-) https://lnkd.in/gkjun3KE

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The Product Roadmap edition #4: We speak to Sahil Gupta (Mckinsey, Blackstone, HBS, Amazon) who has had a starry career over the last ~15 years. He's worked for over a decade in the US, working at Amazon across multiple different teams, and most recently moving back to India. Key Takeaways from an insightful conversation: 👉 The importance of choosing large and growing trends when picking a career direction. Sahil saw the impact tech was going to have on the world and made a deliberate choice to work in that space. 👉 Amazon is an organization that gives someone in product a large canvas to work on across multiple types of roles - from core consumer facing product, to robots, AI, and even chips. Sahil’s experience at Amazon spans robotics, Alexa, and AI product. 👉 Sahil stresses the importance of calibrating problem statements to PM skills and setting expectations clearly. This is a key part of a PM leader’s job, and PMs should sometimes take initiative to drive these expectation setting conversations. 👉 A framework to thinking about career growth as a PM. Sahil Gupta has just begun a new role at Murf AI. He is hiring PMs (3-5 years of experience) to build the next generation of Voice AI products. Reach out to him if interested. Read the fully story here!

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I love the insights. We're starting to see a new landscape of enterprise search in data. And to your point, I think today's search is even more crazily limited in data / lakehouses (like, come on, regex matches are still the only supported SQL search query in year 2024)

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Thank you to everyone who came for the AI Horizons roundtable! We welcomed an impressive group of GenAI builders from Tesla, Clari, Qlik, Moveworks, Amazon, and founders from Cogniswitch, Repello, and EventHQ My favorite moments from the session were: 🔐 Hearing from Saurabh Shintre about the early days of AI security and how he sees it evolving with GenAI models 🤖 Insights from Baris Aksoy on AI agents can automate enterprise security compliance 🎯 Real-life examples from Aryaman Behera of Repello AI on how his company partners with GenAI model creators to identify vulnerabilities Attendees particularly appreciated: 💡 Small-group format that fostered deep, insightful discussions on AI security 🗣️ Candid conversations peppered with real-life examples 🌐 Opportunity to network with fellow GenAI builders A huge shout-out to Suhas Motwani from The Product Folks and Manthan Shah from WestBridge Capital for their partnership! Interested in contributing or attending our next event? Drop a comment below or send me a DM!

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What if your queries required access to multiple sub-related data from different documents, for an LLM to parse and make sense of before returning a coherent and accurate output? We often ask complicated questions, right? 🔹Well to solve this problem, these researchers released Multi-Head RAG (MRAG) which leverages activations of Transformer’s multi-head attention layer, instead of the decoder layer, as keys for fetching multi-aspect documents. They are taking advantage of the fact that multi-head attention can learn to capture different data aspects. 🔹The interesting part is that MRAG is both cost-effective and energy-efficient. In other words, it does not require additional LLM queries, multiple model instances, increased storage, or multiple inference passes over the embedding model. 🔹Moreover, MRAG can be seamlessly integrated with existing RAG frameworks and benchmarking tools like RAGAS as well as different classes of data stores. 🔹The researchers provide a comprehensive evaluation methodology, including specific metrics, synthetic datasets, and real world use cases to demonstrate MRAG’s effectiveness. 🔸Do check it out! #genai #technology #artificialintelligence Click #linkedangle and follow for more content Set the 🔔 notification on my page, don’t miss a post!

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#MAJOR #AI 💡 break-through #SURFACED THIS WEEK 💬 While majority of the folks (and candidates I am having for job interviews) dream about riding the #LLM's horses of #GPT-type, in the background there are new trends really emerging. I have been watching closely work of Sepp Hochreiter and his team in giving #GenAI new hope by "resurrecting" LSTM. 🔭 For those of you, who don't follow the AI tech deeply, Long-Short-Term-Memory (or #LSTM) models have been the pre-cursors of the GPT technology. Most of the translators and other language bots being on market before launch of ChatGPT have been actually some form of LSTM application. When OpenAI broke through with Generative Pretrained Transformer (hence GPT) line of models, they looked like good-bye kiss to LSTM. GPT could work with so many more tokens, achieved better precision in tasks and was also faster in inference. 🪜 However, Sepp and his team recognized also one strong weakness of GPT family of models. Their learning is #quadratic, which makes push for more and more parameters prohibitive expensive. That also means that GPT's already got close to their (reasonable) ceiling of how big single model can be. (This is reason why they have been geared into round-of-experts model platforms, but that will be subject for another post of mine later). 💥Not succumbing to GPT dominance, Sepp Hochreiter worked hard on upgrading the LSTM by adding #exponential #gating, #matrix #memory and parallel training. This week they published paper on #xLSTM brand new generation of LSTMs that actually competes well with GPT-technology and are much cheaper to train. And that is after much of the hype a real technological advancement. Large Language Models are not peek of AI, they are just transition to another phase. And this whitepaper is major step on that pathway. Read full detail here :

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As the world shifts, Linkedin is creating new Generative AI powered products to help our members and customers unlock value and navigate this change. Recently I was invited to give a talk at Data Council about how we build these products with Trust and Responsible AI by design. I wanted to thank Daniel Francisco and Pete Soderling for the invitation and great session. And here the link to the talk: "Building Responsible & Trustworthy Generative AI Products at LinkedIn"

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Example of how increased inference speed creates qualitatively new opportunities: Have a conversation with Vapi at https://vapi.ai. Thanks to ultra-fast inference of things like Llama-3 running on Groq, you can have a back-and-forth conversation that feels very human. You can even interrupt Vapi mid-conversation and it will just handle that seamlessly. Pretty awesome.

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The afternoon Keynote @ Stripe Sessions was Fireside Chat between Patrick Collison (CEO of Stripe) and Jensen Huang (CEO of NVIDIA). Patrick was an excellent moderator — asked Jensen really probing, nuanced questions that Jensen answered with his usual sharp wit and unique (and impressively well rationalized) perspective. A couple of quotes among many that stuck with me: - “Current CPU installed market is $1 trillion of hardware, all of which will be upgraded to GPUs over the next 5 years. And I would estimate about another $1 trillion will be spent on top of that during the same time period as companies try to do more with AI. Add those together — and I think at least $2 trillion will be spent on GPUs in the next 5 years.” - “I like to go after Zero-Billion Dollar Markets — nonexistent markets that I envision & can rationalize well, but will be very hard, expensive & time-consuming to achieve.”

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I am thrilled to share that Signeasy AI is now launched in Beta on certain plans 🎉 Based on the early customer feedback, the potential benefits and savings for businesses are enormous. After raving reviews during the closed beta, we've taken a step forward to open up the gates for more users. The most exciting part is that we're just getting started. We're continuously working with customers and partners to build new applications of AI that would benefit businesses the most. AI has the potential to touch every part of the contract workflow and we're most keen to leverage these capabilities. With this launch, we're offering an AI Assistant that sits on top of your contracts and helps you execute them more efficiently, in half the time. If you're interested in trying out or sharing feedback on our AI Beta program, please fill out this short 2 min form: https://lnkd.in/dkt6VBqh Read more details here: https://hubs.la/Q02t2xZc0 #ai #signeasy #contract #genai #contractmanagement Sunil Patro Souvik Dutta Arvind Girish Vivek Daramwal Sarav Thangaraj Karthik Suroju Dhivya Venkatesan Sanjay Basavaraju Kunal Mahajan

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AI Appreciation Day celebrates the advancements and impact of artificial intelligence on our lives, industries, and future. This special day acknowledges the remarkable progress in AI technologies, from machine learning and natural language processing to generative models and autonomous systems. On this day, we honor the researchers, engineers, and visionaries who contribute to the AI field, driving innovation and addressing ethical considerations to ensure AI benefits all of humanity. It is also a moment to reflect on the responsible use of AI, emphasizing the importance of ethical frameworks and inclusive development. Let's celebrate the milestones achieved and look forward to the endless possibilities AI holds for a brighter tomorrow. #AIDay #IndustryInnovation #IntelligentAutomation #AIAppreciationDay #ArtificialIntelligence

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