Sriram Raghavan

Featured on the cover of Science Magazine, BioEmu from Microsoft Research, a deep learning system that rapidly generates diverse protein conformations for accurate insights into protein function. Illustration: N. Burgess/Science; Data: S. Lewis et al., https://lnkd.in/gev6H7Dw

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Reflections from my Notes on Credo AI Agents of Trust Summit 2025 highlights how trust has evolved from an abstract concept to a fundamental protocol driving AI value. Navrina Singh noted in her keynote, we've entered the "Age of Agency" where organizations implementing multiple AI agents simultaneously face new coordination challenges that require robust governance frameworks. ⚡ McKinsey's projection of $4.4 trillion annualized AI value underscored a recurring theme: value follows confidence, and (#AI) governance at scale amplifies this value. ⚡ Dean Ball, author of the White House #AIActionPlan discussions emphasized that "self-governance means enterprises need to clearly define what #AITrust means to them," requiring governance teams to be completely AI literate and capable of strategic AI decision-making throughout development and deployment. ⚡ The #AITrustSummit revealed how AI governance has matured from basic auditing to sophisticated evaluation and monitoring systems. Discussions from Microsoft, IBM, and Google showcased their approaches to agent identity, traceability, and observability—highlighting parallels with existing #cybersecurity protocols while addressing unique AI challenges like agent sprawl and shadow AI. Cisco's "Prompts by roles" initiative demonstrated practical approaches to democratizing AI literacy while maintaining governance guardrails. ⚡ "AI safety is a continuous journey, not a destination," reflected Dave Orr, Head of Safeguards at Anthropic, while sharing sobering insights from their August Threat Intelligence Report—reinforcing why Anthropic has prioritized safety and research since its inception. ⚡Key unsolved challenges: - Agent sprawl (Agents calling other Agents) - Shadow AI  - Traceability & Proper Evaluation in complex environment. - Building (extremely) adaptable algorithms/systems  Thank you, Credo AI and Navrina Singh, for facilitating these crucial conversations that reinforces how trust serves as both competitive advantage and an essential foundation for responsible AI advancement. We have our work cutout! Credo AI #AITrustSummit #ResponsibleAI #AIGovernance #AISafety #Innovation #InsureTech

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🚀 Just wrapped up a hands-on project applying Retrieval-Augmented Generation (RAG) to a real-world use case: interpreting LIC’s New Jeevan Shanti policy document using LLMs. The core idea? Combine vector search over regulatory PDFs with GPT-4 to generate grounded, faithful answers to complex policy questions. Key capabilities we built: ✅ LCEL-based RAG architecture using LangChain ✅ Contextual compression retriever for focused document lookup ✅ Modular LLM & embedding selection (OpenAI, HuggingFace, Mistral) ✅ Evaluation using LLM-powered metrics: faithfulness, relevance, correctness 📄 The document used is publicly available here: LIC New Jeevan Shanti Policy PDF 💻 Full code available on GitHub: https://lnkd.in/gS9mEzWA 💡 This architecture brings the best of both worlds — retrieval accuracy and generative flexibility — especially in regulated domains like insurance and financial services. 🔜 Next step: Incorporate Agents that can reason across multiple steps — not just answer a question, but decide which tools to use, when to search, how to validate, and when to stop. That’s where the future of enterprise GenAI lies. Would love to hear how others are pushing RAG, agents, and LLMs in production! #GenAI #LangChain #LLM #RAG #AIForOps #InsuranceTech #LLMEvaluation #LangChainAgents

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Charting AI Future: Innovation Beyond Big Budgets For a long time, many Indian and global AI researchers have been held back by the belief that model training demands enormous capital. In some cases, that may be true. But not always. A Berkeley team just proved it with DeepScaleR-1.5B-Preview. They implemented the DeepSeek R1 pipeline on a tiny 1.5 billion parameter model. The result? It beat OpenAI 01 on math benchmarks - and it was trained for just $4,500. This is a breakthrough. The future of AI isn’t just about size or spending. It’s about smart design, efficiency, and rethinking what’s possible. Even better that everything including code, pipeline and dataset is available to replicate yourself. Conventional wisdom said smaller models couldn’t handle reinforcement learning-based reasoning. That’s why DeepSeekR1 relied on distilled data from a massive 671B model to fine-tune LLAMA and Qwen. DeepScaleR-1.5B-Preview just shattered that assumption. It proved that even small models can learn through reinforcement learning. AI is no longer a game only for billion-dollar labs. It’s a game for those who dare to think differently. Github repo link - https://lnkd.in/gRFG7xzs #AI #DeepSeek #DeepSeekR1

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