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In this 20-minute talk, PolyMage Labs founder Uday Reddy Bondhugula talks about why and how one should build effective compilers for AI programming frameworks: https://lnkd.in/gWUQNUsF

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Wrote an article on Byte Pair Encoding (subword Tokenization) and how it works which was potentially used in the the earlier versions of GPT / traditional Language Learning model paradigms. Took a sample of Hindi Language corpus text data to showcase the same. Article below - https://lnkd.in/gCu7Bgpf

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Struggling with knowledge distillation when your student model's accuracy doesn't match the teacher? Ankit Singh Chauhan's new article shares an approach for identifying which layers are most important to map.

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Sarvam AI is outperforming large global LLMs on India-specific use cases. Across real-world India-focused tasks, Sarvam AI (fine-tuned for India) is delivering stronger performance than large general-purpose models — proving that targeted fine-tuning beats sheer parameter size. 🏆 Where Sarvam AI Outperformed: ✅ Vernacular Intent Detection (Hindi + regional mix) More accurate understanding of Hinglish, code-mixed queries, and regional phrasing. ✅ Regulatory & Compliance Q&A (India domain) Higher precision in interpreting India-specific policies and terminology. ✅ Localized Customer Support Responses More culturally aligned tone with fewer generic outputs. ✅ Cultural Context & Idiomatic Understanding Better handling of local expressions and socio-cultural nuances. Why This Is Happening Most global LLMs like those from OpenAI and Google (Gemini) are trained predominantly on large-scale English and Western-centric datasets. India’s linguistic landscape is fundamentally different: • 22+ official languages • Heavy code-mixing (Hinglish, Tanglish, etc.) • Deep cultural and regulatory context • Region-specific communication styles This gap is one reason why adoption of generic global LLMs in India can be slower for core business workflows. India needs models built for India’s data, languages, and users. Task-specific fine-tuning on curated India-centric datasets enables: • Higher accuracy • Lower latency • Better cost efficiency • Stronger real-world alignment 🧠 Key Insight: For country-specific and domain-heavy applications, alignment > size. Kudos to Sarvam for leading India’s LLM movement in the right direction. #AI #IndiaAI #LLM #MachineLearning #FineTuning #GenAI #DigitalIndia

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📢 New Paper Alert! Dr. Aravind Reddy, Principal Project Scientist at CeRAI, WSAI, IIT Madras, recently co-authored a paper that was accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026, one of the leading computer vision conferences. This was a joint work with Dr. Konda Reddy Mopuri and Mr. Saumyaranjan Mohanty, Department of AI, IIT Hyderabad. The paper, titled “DiRe: Diversity-promoting Regularization for Dataset Condensation”, proposes a simple, widely applicable diversity regularizer that can be plugged into existing dataset condensation methods. They show that DiRe consistently improves both test accuracy and dataset diversity across multiple benchmarks (CIFAR-10/100, Tiny-ImageNet, ImageNet-1K) and condensation methods, using principled metrics such as Coverage, Vendi Score, and CLIP-based coverage. Conceptually, the work supports CeRAI’s goals in Responsible AI by: ✅ enabling more data- and energy-efficient training via better synthetic datasets, ✅ making dataset diversity a quantifiable property rather than a vague claim, and ✅ strengthening the use of privacy-friendly synthetic data that remains robust across multiple architectures. Paper link: https://lnkd.in/gk2q5tpW Link to the code for the paper: https://lnkd.in/g7BtMQvw Balaraman Ravindran | Aravind Reddy | Centre For Responsible AI (CeRAI) | Wadhwani School of Data Science and AI, IIT Madras | Konda Reddy Mopuri | Saumyaranjan Mohanty | Department of AI, IIT Hyderabad | Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) | IBSE: Centre for Integrative Biology and Systems medicinE | Indian Institute of Technology, Madras

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Why do models that shine in experiments stumble in the real world? Because performance without discipline is an illusion.   In his latest technical blog, Jayachandran J, Senior Manager, Systems Services, explains why a proper validation set is essential and how testing too early leads to leakage, overconfidence, and fragile models. He breaks down why separating learning, tuning, and evaluation is key for true generalization. A thoughtful read for anyone building ML systems that perform well in theory and stand strong in practice. Read the full blog here: https://bit.ly/3NlpEks

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IIT Madras Pravartak (IITM Pravartak) and MethodHub have entered into a MoU - FOX 5 San Diego: The proposed solutions focus on a suite of AI agents for guided data modeling, metadata management, data quality, catalog discovery, and operational ...

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My bet is on INDIA 🇮🇳 not on Silicon Valley or Shenzhen India isn’t just participating in the global AI race — we are shaping it. With the 𝗜𝗻𝗱𝗶𝗮𝗔𝗜 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 (₹𝟭𝟬,𝟯𝟬𝟬 𝗖𝗿) and the 𝗦𝗲𝗺𝗶𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗼𝗿 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 (₹𝟳𝟲,𝟬𝟬𝟬 𝗖𝗿), we’re building the compute and chip backbone needed for long-term innovation. Our 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗣𝘂𝗯𝗹𝗶𝗰 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 — Aadhaar, UPI, India Stack — offers something few nations have: trusted rails to deploy AI at population scale. We’re solving for 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗮𝗻𝗱 𝗶𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗶𝘁𝘆 through initiatives like 𝗕𝗵𝗮𝘀𝗵𝗶𝗻𝗶 and homegrown Indic models, ensuring AI speaks to every Indian. Add to this the 𝘄𝗼𝗿𝗹𝗱’𝘀 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝗦𝗧𝗘𝗠 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 and national skilling programs like 𝗙𝘂𝘁𝘂𝗿𝗲𝗦𝗸𝗶𝗹𝗹𝘀 𝗣𝗥𝗜𝗠𝗘, and we have a talent advantage that compounds over time. Startups, academia, and global R&D hubs are already translating this foundation into products, while investment in generative AI ventures is accelerating. The equation is simple: 𝘀𝗰𝗮𝗹𝗲 + 𝘀𝗸𝗶𝗹𝗹𝘀 + 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 = 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽. India’s AI future looks promising not just for us, but for the world. Now is the time to execute, collaborate, and innovate at scale. #𝗜𝗻𝗱𝗶𝗮𝗔𝗜 #𝗗𝗶𝗴𝗶𝘁𝗮𝗹𝗣𝘂𝗯𝗹𝗶𝗰𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 #𝗔𝗜𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 #𝗙𝘂𝘁𝘂𝗿𝗲𝗦𝗸𝗶𝗹𝗹𝘀

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Kharagpur Data Analytics Group, IIT Kharagpur is partnering with NYU Tandon and Pathway to bring you a sharp conversation on what comes after Transformers, straight from the people shaping the next wave of AI. Especially for you if you’re: → 2nd/3rd year exploring research internships → Final year planning MS/PhD applications abroad This is part of a larger push to connect Indian AI talent with top US research labs, and we made sure IIT KGP has a seat at the table. Speakers: → Martín Farach-Colton (Chair CSE, NYU Tandon) → Julian Togelius (Professor NYU, Author Artificial General Intelligence) → Adrian Kosowski (CSO, Pathway | PhD at 20, 100+ papers) → Zuzanna Stamirowska (CEO, Pathway, Moderator) Date: Feb 6, 2026 | 9:30 PM IST Register: https://lnkd.in/dQMwHfCi Limited registrations with exclusive merch for select participants!

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Emerging Stack for LLM-powered Product - Product Development [2/3] Sharing insights from Kuldeep Yadav's presentation on Large Language Models—Stack(LLM Stack). Dive into key learnings on building, deploying, and differentiating LLM-powered products in real-world applications. Building with LLMs means embracing a continuous feedback flywheel: collect quality data, evaluate through human and automated methods, iterate on prompts, and monitor model behavior closely. Methodological evaluation distinguishes mediocre from great apps. Tools like LangChain, LangSmith, and Galileo enable systematic orchestration and observability for production-grade LLM apps. Varun Aggarwal Shailendra Nath Jha Shubham Bansal Jitesh Bhasin Negrah S

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📝 Announcing our EMNLP 2025 paper that introduces the Alignment Quality Index (AQI), a metric leveraging latent geometric representations and clustering indices to diagnose hidden misalignments in LLMs. 🔹 “𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐈𝐧𝐝𝐞𝐱 (𝐀𝐐𝐈) – 𝐁𝐞𝐲𝐨𝐧𝐝 𝐑𝐞𝐟𝐮𝐬𝐚𝐥𝐬: 𝐀𝐐𝐈 𝐚𝐬 𝐚𝐧 𝐈𝐧𝐭𝐫𝐢𝐧𝐬𝐢𝐜 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐯𝐢𝐚 𝐋𝐚𝐭𝐞𝐧𝐭 𝐆𝐞𝐨𝐦𝐞𝐭𝐫𝐲, 𝐂𝐥𝐮𝐬𝐭𝐞𝐫 𝐃𝐢𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐋𝐚𝐲𝐞𝐫 𝐰𝐢𝐬𝐞 𝐏𝐨𝐨𝐥𝐞𝐝 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬” 🔹 In collaboration with Manipal University Jaipur, LinkedIn, Indian Institute of Technology, Kharagpur, Indian Institute of Information Technology Guwahati, Texas A&M University, VIT Chennai, Evalueserve, New York University, Birla Institute of Technology and Science, Pilani (Pragya Lab) 🔹 Published in EMNLP 2025 Main 🔹 Paper: https://lnkd.in/g54bEBCu ✍🏼 Authors: Abhilekh Borah, Chhavi Sharma, Danush Khanna, Utkarsh Bhatt, GURPREET SINGH, Hasnat Md. Abdullah, Raghav Kaushik R, Vinija Jain, Jyoti Patel, Shubham Singh, Vasu Sharma, Arpita V., Rahul Raja, Aman Chadha, Dr. Amitava Das ➡️ 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐨𝐟 𝐨𝐮𝐫 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐈𝐧𝐝𝐞𝐱 (𝐀𝐐𝐈): 🧬 𝑰𝒏𝒕𝒓𝒊𝒏𝒔𝒊𝒄 𝑳𝒂𝒕𝒆𝒏𝒕 𝑮𝒆𝒐𝒎𝒆𝒕𝒓𝒚 𝑴𝒆𝒕𝒓𝒊𝒄: AQI measures alignment by assessing how distinctly safe vs. unsafe prompts cluster in latent activations, using combination of Davies‑Bouldin, Dunn, Xie‑Beni, and Calinski‑Harabasz indices—making it invariant to decoding style and resilient to alignment faking. 🧠 𝑳𝒂𝒚𝒆𝒓𝒘𝒊𝒔𝒆 𝑷𝒐𝒐𝒍𝒆𝒅 𝑹𝒆𝒑𝒓𝒆𝒔𝒆𝒏𝒕𝒂𝒕𝒊𝒐𝒏 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈: we use a sparse learned pooling mechanism across transformer layers to capture alignment‑relevant abstractions without modifying the base model—enabling internal safety diagnostics. 🚨 𝑬𝒎𝒑𝒊𝒓𝒊𝒄𝒂𝒍 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔 𝒐𝒇 𝑩𝒆𝒉𝒂𝒗𝒊𝒐𝒓𝒂𝒍 𝑴𝒆𝒕𝒓𝒊𝒄𝒔: AQI uncovers misalignments that behavioral metrics like G‑Eval or refusal rates miss—especially under jailbreaks, safety‑agnostic fine‑tuning, and stochastic decoding, proving its value as an early‑warning alignment audit tool.

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Excited to share that I’ll be at the #IndiaAIImpactSummit on 17th and 18th Feb and truly looking forward to it. India stands at a pivotal moment in its AI journey. We are no longer just consumers of global AI innovation — we are builders, deployers, and shapers of AI at population scale. From public digital infrastructure to startups, enterprises, manufacturing, healthcare, agriculture, and governance — AI is rapidly becoming foundational to India’s growth story. What makes the IndiaAI Summit special are the conversations and the people. It feels like a reunion of researchers, founders, enterprise leaders, policymakers, product builders, infrastructure providers, and investors — all united by a shared belief in AI’s transformative potential for India. I’m particularly looking forward to reconnecting with old colleagues and getting to know new builders and scalers in this area. If you’re attending, would love to connect —See you there. India AI Impact Summit 2026 Swiggy

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🚀 Data Shapley in One Training Run A recent paper by Jiachen (Tianhao) Wang, Prateek Mittal, Dawn Song, and Ruoxi Jia: “Data Shapley in One Training Run” presents In-Run Data Shapley, a method for attributing the contribution of each training example to a specific model within a single training run, removing the need for expensive model retraining. This approach calculates data contribution at each gradient update and accumulates these values throughout training. The authors propose two efficient approximation techniques: First-order In-Run Data Shapley – negligible runtime overhead compared to standard training; uses gradient dot-products to approximate contributions. Second-order In-Run Data Shapley – roughly 2× slower but provides more fine-grained contribution measures using Hessian-gradient products. This efficiency enables data attribution even at the foundation model pretraining stage. Several case studies illustrate insights into the contribution of training data, including negative contributions even in well-curated datasets like the Pile. 💡 Key implications: Improved data curation – identify low-quality or harmful data points that hinder training, leading to faster convergence and better performance. Copyright considerations – training data can influence model outputs beyond verbatim memorization, suggesting a need to reconsider data ownership and compensation in generative AI. Model analysis – understand how contributions vary across training stages and different examples. 👉 Question: As attribution methods like this become practical, what are the most critical ethical and business decisions regarding data ownership and compensation in AI development? ⬇️ Research paper link in comments #AI #MachineLearning #DataScience #Ethics #LLMs

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I recently had the opportunity to sit with the team at Mentoria for a detailed conversation on the evolving landscape of AI and Machine Learning. The discussion examined how unexpected opportunities emerge in this rapidly developing domain and why certain underlying competencies often become decisive factors for recruiters evaluating talent. We explored foundational questions that many beginners overlook—what remains unsaid when entering the field, why successful careers extend far beyond coding alone, and which skills retain their relevance as the technological ecosystem shifts. The interview also includes a rapid-fire segment and a focused look at three specific CV issues that can lead to immediate rejection, underscoring the importance of precision and clarity when presenting one’s work. The full interview is available here: https://lnkd.in/gBV4J-TR #AI #MachineLearning #DataScience #CareerTips #Innovation #TechCareer #FutureSkills #Interview

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🚀 How do we speed up AI-powered scientific discovery without giving up control? At #TPC25, ORNL’s Prasanna Balaprakash brought bold answers to the table, showcasing how AI can guide scientific research with unmatched speed and precision. Read more in HPCwire ⤵️

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Having worked with a wide variety of LLMs, this is a great roadmap to follow for anyone who wants to truly learn NLP

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