Rama Akkiraju

The 7th workshop on Neural Scaling Laws is about to start: https://lnkd.in/gQSB5k8v Scaling laws, emergency, alignment, transfer learning, with a focused track on multilingual models. Looking forward to kick it off with Irina Rish and discuss all these topics with Yuhai Tu, Paul Bogdan, Hailey Schoelkopf, Darshil Doshi, Rio Yokota, Neha Sengupta, Preslav Nakov, Tatiana Shavrina, PhD, Yishi Xu. Join in person or virtually! #icml2024 #ai

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If you are still thinking that AI is just a hype, you should listen the conversation between Mukesh Ambani and NVIDIA CEO Jensen Huang. Why ? Mukesh Ambani bullishly talks about Generative AI models, GPUs, need of open source models etc. He takes up the challenge to surprise world with AI capabilities in coming years. He is not someone who has been leading IT/Software companies day in day out like Narayan Murthy/ Shiv Nadar/Bill Gates etc. What it means? AI has already arrived and every big firm, irrespective of their traditional business, wants to build business and create impact using this. AI is not just another cool tech anymore. It is the industrial revolution changing every facet of our life. Embrace it or get ready for classic "Kodak Case study" in your career and business. Watch here: https://lnkd.in/ejvnCMHZ

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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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An endeavor to demystify Quantum Computing for the general audience and our National Quantum Mission. I wrote the book "Quantum Computing and Future" in 2021 and then my friend and colleague Rohit Sharma translated it into #Hindi in 2022. Very basic and simple books on a complex subject like Quantum Computing and its potential applications. The Hindi version is probably the first book on Quantum Computing in Hindi language. #QuantumComputing #Quantum #QuantumMachineLearning #QML #QuantumCommunication #QuantumSensing #QuantumKeyDistribution #QKD #QuantumLanguageModel #QLM

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💡 I recommend this interesting article on how 𝗻𝗲𝘂𝗿𝗼𝘀𝘆𝗺𝗯𝗼𝗹𝗶𝗰 𝗔𝗜 can overcome the limitations of LLMs. As an example, SAP Chief AI Officer Dr. Philipp Herzig explains that SAP introduced a formal parser to train an LLM on the ABAP programming language, 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗳𝗿𝗼𝗺 𝟴𝟬% 𝘁𝗼 𝟵𝟵.𝟴%. 𝗡𝗼 𝗺𝗼𝗿𝗲 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀! ✅ #SAP #AI #generativeAI #neurosymbolicAI

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Taking GenAI and LLMs from POCs to Production 🚀 Join us for an enlightening webinar hosted by CrossML Pvt Ltd and presented by Ankit Aggarwal, as we dive deep into the journey of taking Large Language Models (LLMs) from Proof of Concept (POC) to full-scale production. This webinar is perfect for CTO, AI enthusiasts, data scientists, product managers, and anyone interested in harnessing the power of LLMs to transform business operations. #LLM #AIWebinar #MachineLearning #GenerativeAI #TechnologyEvent

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Discover how NVIDIA's Nemotron-4-Hindi 4B NIM microservice is revolutionizing #generativeAI applications in research, imaging, and #drugdiscovery. See how top institutions like Indian Institute of Technology, Madras, Indraprastha Institute of Information Technology, Delhi, Innoplexus - a Partex Company, and 5C Network are leveraging this technology to make significant advances, from combating antibiotic resistance to advancing neuroscience research.

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Nilekani emphasizes the importance of building models in a way that allows engineering and capability to deliver high-performance applications. Model building is expected to get commoditized, making this approach crucial. Learn more: https://lnkd.in/eSwxZpWi

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Fantastic panel on GTM for Enterprise AI. Thanks to fellow panelists for a sharp discussion and to Emergent Ventures for hosting. Anupam Rastogi Ankur Jain Preethy Padmanabhan Chandini M Ammineni Jason Griggs Deb Bardhan #GTM #EnterpriseAI

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"Smaller is better." There are now Small Language Models (SLMs) that are “smaller” in size compared to LLMs. SLMs are trained on 10s of billions of parameters, while LLMs are trained on 100s of billions of parameters. More importantly, SLMs are trained on data pertaining to a specific domain. They might not have broad contextual information, but they perform very well in their chosen domain -- write Iyengar Ashok and Praneet Adusumilli in this IBM blog. One area that has not seen much innovation is at the far edge and on constrained devices. However, there are smaller models that have the potential to innovate gen AI capabilities on mobile devices. Enterprises that operate in specialized domains, like telcos or healthcare or oil and gas companies, have a laser focus. While they can and do benefit from typical gen AI scenarios and use cases, they would be better served with smaller models. Because of their smaller size, these models can be hosted in an enterprise’s data center instead of the cloud. SLMs might even run on a single GPU chip at scale, saving thousands of dollars in annual computing costs. While running SLMs on-premises seems practical and tiny LLMs on mobile edge devices are enticing, what if the model requires a larger corpus of data to respond to some prompts? Hybrid cloud computing offers the best of both worlds. Might the same be applied to AI models? Click below to read the article and find out! #LLM #SLM #LargeLanguageModel #SmallLanguageModel   https://lnkd.in/gFzyHhfH

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💥💥💥 $100K or 100 Days: Trade-offs when Pre-Training with Academic Resources Apoorv Khandelwal, Tian Yun, Nihal V. Nayak, Jack Merullo, Stephen H. Bach, Chen Sun, Ellie Pavlick Abstract Pre-training is notoriously compute-intensive and academic researchers are notoriously under-resourced. It is, therefore, commonly assumed that academics can't pre-train models. In this paper, we seek to clarify this assumption. We first survey academic researchers to learn about their available compute and then empirically measure the time to replicate models on such resources. We introduce a benchmark to measure the time to pre-train models on given GPUs and also identify ideal settings for maximizing training speed. We run our benchmark on a range of models and academic GPUs, spending 2,000 GPU-hours on our experiments. Our results reveal a brighter picture for academic pre-training: for example, although Pythia-1B was originally trained on 64 GPUs for 3 days, we find it is also possible to replicate this model (with the same hyper-parameters) in 3x fewer GPU-days: i.e. on 4 GPUs in 18 days. We conclude with a cost-benefit analysis to help clarify the trade-offs between price and pre-training time. We believe our benchmark will help academic researchers conduct experiments that require training larger models on more data. 👉 https://lnkd.in/d5Vh3ycg #machinelearning

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SEMICON WEST is on fire 24x7 Tuesday afternoon continued the focus on Front-End Manufacturing. Great session on wafer map visual analytics with Janhavi Giri, Intel, using self-supervised learning for image analysis, detecting failures and optimizing manufacturing processes. Naor Alfassi, Applied Materials, spoke about ML-guided Electron Beam review for wafermap image classification and defect detection. Janhavi Giri, PhD Naor Alfassi Sessions on virtual metrology from Srividya Jayaram, Siemens, on high product mix; and Mike Yim, Gauss Labs, on high-volume, from Lab to Fab. In our experience, virtual metrology has led to massive time savings in product testing.  Srividya Jayaram Mike Young-Han Kim One very fun and topical presentation was from Christoher Nguyen, Aitomatic, and Daisuke Oku, Tokyo Electron - announcing SemiKong, a new Open-Source Semiconductor LLM, jointly developed by Tokyo Electron, Aitomatic, FPT AI Center and the AI Alliance. This seems to be based on Meta's Llama3 with additional HTM context. Christopher Cuong T. Nguyen Daisuke Oku I just returned from visiting our HTM customers in Japan and Korea, where we showed our Spotfire Copilot running in Japanese and Korean .. and this presentation on SemiKong was a joy to see!! Feel free to check out my latest blog on the Japan visit including a Spotfire Seminar with our partner NTT Com and customers including Texas Instruments, Tokyo Electron, Toshiba, Sony and many others. MOC Blog on Spotfire Seminar in Tokyo, Japan https://lnkd.in/gAeSZSSC   Wednesday sessions in Smart Manufacturing focused on how AI/ML can optimize back-end semiconductor manufacturing process eg OEE, predictive and condition-based maintenance.. We are currently revamping our Spotfire OEE solution with Spofire Copilot, so this is quite timely. Shawn Sarbacker, Kulicke and Soffa,  gave a nice presentation on Semiconductor Assembly.  Shawn Sarbacker I really enjoyed the session on Zero Defect Manufacturing from Prasad Bachiraju, Onto Innovation. Totally aligned with our Spotfire work on visual data science for defect analysis .. interesting backend data integration .. we’ve been working on data federation and libraries of virtualization views for that part of the data and analytics flow.  Prasad Bachiraju I also liked the IBM WatsonX GenAI session from Megan Arp and Francois Bernard - many similarities to our Spotfire Copilot e.g. they refer to the “Watson Orchestrator” in an analogous architecture to our Spofire Copilot Orchestrator. Their LLM is able to predict user intent when a technician reviews a ticket (i.e., query vs. task). Tasks are handled through custom and prebuilt skills training, while queries are handled through RAG. Megan Arp Francois Bernard Spotfire #VisualDataScience https://lnkd.in/eSeVcE5h https://lnkd.in/ecWq3PSH

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In our opening salvo for our book on the AI-Centered Enterprise, co-authored with Natarajan Balasubramanian and Amit Joshi, our editorial opinion article on "What Companies are Getting Wrong about AI" was published in TechTarget News. In this article, based on our research, we argue for a nuanced three phase approach to AI implementation in organizations. Arun Rao, my co-founder at samvid.ai, joins us as collaborator for this article. #enterpriseai #responsibleai https://lnkd.in/g3E4dx64

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𝗧𝗵𝗮𝗻𝗸 𝘆𝗼𝘂 ETCIO 𝗳𝗼𝗿 𝗮𝗻 𝗮𝗺𝗮𝘇𝗶𝗻𝗴 𝘄𝗲𝗯𝗶𝗻𝗮𝗿 𝗼𝗻 "𝗙𝘂𝗲𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗛𝗼𝘄 𝗚𝗲𝗻𝗔𝗜 𝗶𝘀 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗦𝘁𝗮𝗿𝘁𝘂𝗽𝘀!" I am in sync with Tara Lodh, Customer Engineer, Google Cloud when he says he is excited about the voice perspective when it comes to GenAI Voice, as per his vision, will be used in: • code generation • workflow generation • even in the way we use search • day-to-day activities His data strategies are also very well-thought-out: • 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 & 𝗖𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Data is all around flowing all around systems, departments and processes. Therefore, high chance of duplication is there. So, number one strategy should be to break this replication as it can lead to errors and inefficiencies.   And consolidate data as it leads to unification to a centralized platform as it'll ensure that all teams can have access to consistent info. • 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗿𝗼𝘂𝗻𝗱 𝗗𝗮𝘁𝗮: There should be policies and standards to manage data securely and ethically. Compliance ensures protecting sensitive information and maintaining accountability for data usage. • 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Scalability depends on having solid foundation of quality, consolidation, and governance around data. Growth isn’t just about handling more, rather it’s about handling more effectively and responsibly.    • 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: It's about maintaining speed and reliability while managing increasing demands. This is directly related to clean, accurate data that is unified across systems and managed with compliance. Manish Kumar's, Customer Engineer, Google Cloud, tip for startup is worth noting, "Build app that is powered by Gen AI than building GenAI itself" Also, big thanks to Soumik Ghosh, for leading such an engaging and impactful session. I really appreciate for the insights and energy you brought to the table! Conversive #GenAI #DataStrategy #AIInnovation https://lnkd.in/gNi_dAK3

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Great to see this year end story from Sandhya M. on Quantum Computing in CIO.inc. As Sandhya says in the article, "Quantum computing is no longer a distant promise; it's reshaping industries and challenging enterprise IT paradigms. With United Nations declaring 2025 the International Year of Quantum Science and Technology, a global spotlight is firmly on quantum innovation, urging nations to align their technological ambitions with a quantum-powered future." Thank you, Sandhya, for highlighting the importance of Quantum Computing. Next, we from India will shift that spotlight onto our nation, aiming to make this India's decade. Here's to a fantastic 2025 and beyond for India's quantum journey.

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Google’s new quantum chip Willow can solve complex problem in 5 minutes - The Economic Times https://lnkd.in/g-stj2b6 #thinkaboutit #thinkahead #thinkbeyond #thinkdifferent #thinkforward #quantum computing

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A must read for AI enthusiasts from Manish Bhide AI Governance is crucial in the Generative AI era to prevent incidents like the airline company's chatbot hallucinating and promising a non-existent discount. By adopting AI Governance principles, enterprises can ensure responsible AI adoption, mitigate risks, and maintain regulatory compliance.

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🤖 GenAI is driving innovations in the AI hardware space, and companies are investing in High Performance Computing (HPC) human resources. 🔸 Microsoft appoints a new CTO for Super Computing Efforts 🔸 Meta is building custom ASIC and High-Performance Computing clusters (Llama3 was trained on such hardware). 💰 The cost of inference is a concern while generalized and specialized Open Large Language Models are accessible to all. 💡Research directions moved to 1-bit quantization and low-resource fine-tuning of models. 🤝 The benefits of LLM and GenAI are accessible to all, with a cost, and in the future, GenAI and LLM systems will be accessible to all. #genai #generativeai #ai #aiml #llm #datascience https://lnkd.in/g38YPry9

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