Sanjeev Kumar

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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Interesting insight from Santosh Rao on Marketplace by APM My research talks about metrics Tech/SaaS Leaders should measure depending on your scale https://lnkd.in/gGfBWgn5

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Navigating Barriers to Generative AI Adoption in Enterprises (Part I) by BGV 1) Misaligned use cases & high AI infra costs 2) Insufficient model accuracy & poor workflow integration 3) Compliance & data privacy #rafAIgual #GenAI #strategy #enterprise #adoption #roadmap #playbook

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Episode 9 is out ! Data Science Dialogues Full Pod on : https://lnkd.in/ggs9YhF5 Ft. Saurabh Sharma and Amber Guglani Powered by : Data Science Pathshala In this pod we talked about : 📊 Data Science Journey 🤔 Perception Among Colleagues 🌟 What Sparked Your Interest in Data Science? 🚗 Gen AI's Impact on Automobile Automation 💼 Assessing Gen AI's Workforce Efficiency 🧠 Challenges Presented by Gen AI 💰 Investing in Gen AI for Startups 🔮 The Future Landscape of Gen AI in 10 Years 🛡️ Regulating Gen AI for Public Safety 🔄 Addressing Scalability with Gen AI 📚 Learning Pathways for Gen AI

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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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Glad to share that Our CTO, Dr.Srinivas Padmanabhuni, was featured in Wipro Lab45's Think Tank podcast series, #Unpacked. In this insightful episode, Arvind Ravishunkar engages in a thought-provoking discussion with Dr. Srinivas, an esteemed AI leader and the innovative mind behind our AI startup, testAIng.com aka aiensured. 1. Unveiling the Hype Around AGI: Dr. Srinivas challenges the current narratives surrounding Artificial General Intelligence (AGI), emphasizing the significant limitations of AI's reasoning capabilities and why traditional logic cannot fully capture human-level reasoning. 2. The Essence of Human-Level Reasoning: Discover the critical role of common sense reasoning in AI. Dr. Srinivas explains why AI struggles with understanding jokes, sarcasm, and metaphors, highlighting the need for a new direction in AI development. 3. The Future of AI: Explore the transformative potential of AI across industries, as Dr. Srinivas and Arvind discuss the importance of ethical and responsible innovation. Don’t miss this compelling episode that offers fresh insights into the world of AI and its future. Listen to the podcast here: [https://lnkd.in/gPEz5V9K) Tune in now and redefine your understanding of AI! #AI #ML #GenAI #Reasoning #InductiveReasoning #LLM #AbductiveReasoning #DedcutiveReasoning #ExpertSystems Vipul Kocher Shiva Kumar Mula Akhila Makam Venkatesh Vachaspathi Madabhushanam Kevin Vincent Sumit Dhakad Shradha Sharma Ankit Pandey Vijaya Kumar Ivaturi José Díaz Dr. Mahesh Kumar CV Derick Jose Vinay Baid Anil Nahata Ramit Manohar Kaul Arihant Chhalani ROHIT BAFNA Sai Krishna Abhijeet Goyal Aastha agarwal sonika bengani

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Brilliant and Timely: Why Data is the Top Priority in the Life Sciences AI race? Verbatim from Health Catalyst Capital (HCC): Over the past several decades, the life sciences industry has faced a troubling decline in R&D productivity. The cost to bring a new drug to market has increased from about $400 million in the 1980s to over $2 billion in the 2010s, adjusted for inflation. This cost increase is largely due to increasingly complex biology and the inefficiency of the traditional drug development process that relies on a “trial and error” approach. AI stands out as a potentially transformative technology for life sciences companies, capable of analyzing vast amounts of complex data and predicting experimental and clinical outcomes. NVIDIA CEO Jensen Huang has even stated that digital biology will be one of the most significant revolutions in human history.   And yet, the life sciences industry is plagued by a problem preventing it from unlocking the full potential of AI, a problem that other parts of the healthcare ecosystem know all too well: siloed data. According to a McKinsey survey, the lack of integrated data sources was cited by pharma leaders as the largest hurdle to scaling digital and analytics capabilities. Furthermore, nearly half (48%) of senior decision-makers in pharmaceutical companies surveyed by Aspen Technology stated that data silos derailed the efficiency of cross-functional collaboration in their organization.   Data is often siloed within different departments, systems, and repositories in life sciences companies. Without an integrated infrastructure, it’s difficult to pinpoint existing data, access it quickly, and leverage it across the organization, which often stifles decision-making and innovation. Data is the cornerstone of AI models and remains a top priority in the life sciences AI race. A key strategic differentiator is the ability to generate, acquire, and control large datasets from diverse sources for training models in areas such as omics, imaging, and health records.4   Before life sciences companies can achieve digital and analytics transformation, they must embrace data transformation, which is where DataOps comes in. Thanks for sharing Charles Boorady! Check out Unipr to learn how you can lead the charge by using Operations AI to raise R&D productivity in Life Sciences!

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Dive into the world of #ecommerce innovation with the latest article by Sriram P H., CEO and Co-Founder, DaveAI, as we explore the integration of large language models (LLMs) with recommendation engines. Discover how this cutting-edge technology is revolutionizing customer experiences and driving sales in the digital marketplace. From personalized shopping assistants to predictive product recommendations, the possibilities are endless.   Read the full article exclusively on Express Computer to uncover the future of conversational commerce and personalized recommendations in e-commerce: https://lnkd.in/gNtNjzeh #ai #ecommerceinnovation #customerexperience

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We are absolutely thrilled to announce that our paper "RoostGPT: Compound AI System as Software Testing Copilot" has been accepted for the inaugural Compound AI Systems Workshop co-located with the Databricks & AI Summit. Sudhir Jangir and I are honored to present a lightning talk and a poster session at the workshop. The term "Compound AI System," coined by Matei Zaharia et. al., is quite profound. Think of it like the difference between a jet engine and an airplane. A jet engine, like a large language model (LLM), is a powerful and essential component. However, an airplane, which uses the jet engine as a powerhouse, incorporates many other systems—such as safety, navigation, communication, and entertainment—to offer a comprehensive and enhanced experience. Another way to understand, by contrast, Compound AI systems is through Sam Altman’s recent quote: "If you think that AIs are going to be 10 times better in the next year or two, do you think that's a huge opportunity for you? Or do you think, you know, that's going to crush your business?" If you are building a feature solely on LLMs, your business might be at risk. However, if you are developing a true Compound AI system, it will become disproportionately more powerful as LLMs improve.

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This is worth reading if you are trying to determine if LLMs are going to transform work in supply chains.

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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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#ObsessivelyEnthusiasticInvestor #UnreasonablyEnthusiasticInvestor #IrrationallyEnthusiasticInvestor at REFASHIOND Ventures: The Industrial Transformation Fund. We look for the people we believe are doing the best work in Industrial Transformation & Supply Chain Technology; Attempting the impossible. Then we go all-in on them. With obsessive and unreasonable enthusiasm. Join us. #IndustrialTransformation #SupplyChain #Innovation #Technology #Startups #VentureCapital #DemandTheImpossible #UnreasonableEnthusiasm #ObsessiveEnthusiasm #IrrationalEnthusiasm 

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My 2nd post on start-ups I met at MIT's "Imagination in Action" conference on June 7th is about code generation and verification. This is a theme I have been exploring lately in my investigations of how we would supervise LLMs that check code generation by other LLMs. 1. coadapt.ai (https://codapt.ai/) lead by Trevor Keith caught my attention because it is working on code generation by focusing on building autonomous web apps. That is a great intersection between exploring autonomy in code generation and commercial applications. We talked about the problem of hallucinations and he described some clever ways he has for reducing them in his work. 2. Nectry co-founded by Adam Chlipala is doing two seemingly disparate things at the same time - allowing non programmers to build software that can be proven to be secure and compliant of its specification. This is done by using Adam's work on formal software verification where he has developed a "legalese" language that non programmers can understand but is, itself, amenable to formal verification. Adam also gave a talk during the conference and I'll share that link when it becomes available as it relates to my own work on the logic of unsupervised evaluation. Adam believes that formal methods may not be a hot topic today, just as Neural Networks once were, but that in the future it will play a greater role in the development of software and AI in general. I agree with that vision. Formal methods are starting to gain traction in ML and AI as seen in the work of Max Tegmark and Steve Omohundro, for example.

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How are LLMs built? How is being leveraged across sectors? Earlier this year, Poorvi Vijay, Vice President at Elevation Capital, delineated Generative AI for Mesa students. She covered: - How Generative AI works: how LLMs understand words & their relationship with one another  - The GenAI value chain across the infrastructure & application layer - How to build GenAI business moat by leveraging proprietary data, distribution, talent, and vertical expertise  - Applications of GenAI across sectors: sales & marketing, education, gaming, application development, finance, and healthcare  - Challenges in building Gen AI: hallucinations, data privacy, data & computing requirements, among others

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https://lnkd.in/dJW9sZpx SelfExtend introduces a novel approach to expanding the context window of LLMs. It leverages a two-tiered attention mechanism—grouped attention for long-range dependencies and neighbor attention for local interactions—both derived from the model's original self-attention. This method allows seamless context window extension without requiring fine-tuning, and extensive experiments have demonstrated its effectiveness across various benchmarks.

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Hear what Dr. Alpan Raval has to say about creating impact with AI, from enabling someone in a rural area to open a bank account using their regional language without the hassle of filling out complex forms, to using computer vision AI for monitoring baby growth in rural India. This transformative work is made possible by skilled AI engineers and scientists collaborating with government stakeholders to identify problems and develop scalable, technology-driven solutions. At the core of these solutions is the need for robust data and compute infrastructure. ✅ Data infrastructure involves the curation of training data, whether through labeled datasets or synthetic approaches. ✅ Compute infrastructure refers to the hardware provided by cloud services, including GPUs. #socialgood #computervision #llms #trainingdata #malnutrition #ctd #anthropometry #asr

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We had a full house in a fireside chat with Arvind Jain, Glean's CEO and co-founder, at Stanford University Graduate School of Business! It was an honor to talk with him about building successful companies in the dynamic #AI era. He shared invaluable lessons from his remarkable journey: building Google search infra, founding Rubrik (which went public last month!), and founding Glean, recently nominated by Forbes as one of the Top 50 AI companies! 🎯 Start with the problem, not the solution Arvind is very bullish on Glean's mission to solve app fragmentation and workplace knowledge. He saw his customers at Rubrik struggle with it and took the time to validate it. People tend to undermine the importance of a problem and get too excited about the solution. However, if the solution solves the wrong problem, users will not adopt it. 🤝 Validate hypotheses with people you don't know Friends want to support and might avoid critical feedback. Arvind and the team spent the first year of Glean sending cold LinkedIn reach-outs to potential customers to validate the problem and the solution. While he could easily leverage his network, he wanted unbiased feedback from people who didn't know him. 🎧 When it's noisy outside - stay focused on your customers. That's your moat. With every week's announcements of AI innovations, Glean's Northstar is to be customer-driven and leverage new technologies where there's a fit. Even though they are busy with the growth struggles of a Series D company, Glean teams are working on 70 % of core work : 30% of experimental projects, to stay ahead of the curve. 📈 Companies should plan how to embrace AI internally As a 2nd-time founder, Arvind is looking for new opportunities to use AI at Glean and improve #productivity, beyond using Glean Assistant. With so much innovation, customers are very excited about AI demos but get disappointed when they try to use new tools for their specific use cases. It will take a (short) while before we see a significant impact on productivity.

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AI-powered knowledge management is revolutionizing productivity, as highlighted in Sirjad Parakkat's insightful article. It's essential reading for understanding AI's potential in transforming traditional systems. Our "Tailored Intelligence" consultancy and tech services go further by ensuring that private data is not used to train public AI tools and keeping human experts in the loop. We combine AI's power with human insight to protect your proprietary knowledge and enhance decision-making. Discover how we take AI-driven knowledge management to the next level. #tailoredintelligence #ai #artificialintelligence #digitaltransformation #aiinsociety #impactai #innovation #visionaires #business #wikiexpert #solutions

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