Gurinder Singh Chauhan

This approach could turn the Generative AI industry upside down. Most everyone knows that AI requires computers to do a lot of calculations and especially for Large Language Models (LLMs) like ChatGPT. The majority of these calculations are actually “Matrix Multiplications”. (You remember, a 3x3 matrix times another 3x3 matrix). If the researchers in the article are proven correct, they have demonstrated a way that Matrix Multiplication can be replaced with a more efficient alternative inside the LLM getting nearly the same results. The article positions this savings as a win for the environment (yeah environment), but I think they are missing the boat. More efficient means less electricity and potentially a smaller need for GPU resources, the bread and butter of stock darling Nvidia. When you combine this cost saving with reduced privacy concerns of hosted LLMs (Open AI) and innovative LLM licensing schemes (LibreChat - https://lnkd.in/gKQjMfz5) you can see the cracks in the current direction of the LLM industry. Q: If you could run your own LLM on your own private environment with MIT open-source licensing and without expensive GPUs, why exactly do you need Nvidia, Open AI and alike? Bold prediction: if the researchers get this working at scale, we could see a rapid shift and commoditization in the LLM and generative AI space. All the pieces are in place. TLDR details from the article, describing the innovation: “The researchers' approach involves two main innovations: first, they created a custom LLM and constrained it to use only ternary values (-1, 0, 1) instead of traditional floating-point numbers, which allows for simpler computations. Second, the researchers redesigned the computationally expensive self-attention mechanism in traditional language models with a simpler, more efficient unit (that they called a MatMul-free Linear Gated Recurrent Unit—or MLGRU) that processes words sequentially using basic arithmetic operations instead of matrix multiplications.” #AI #GenerativeAI #Innovation

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Large language models (LLMs) are increasingly being explored for their potential in medicine, but applying them effectively to complex clinical scenarios remains a challenge. The paper "MDAgents: an adaptive collaboration of LLMs for medical decision-making" by Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, Xuhai Xu, Daniel McDuff, Hyeonhoon Lee, Marzyeh Ghassemi, Cynthia Breazeal, and Hae Won Park (NeurIPS 2024) introduces a novel framework aimed at addressing this gap. MDAgents structures collaboration among LLMs dynamically, assigning roles—either independent or group-based—based on the complexity of the medical task. This design mimics real-world clinical decision-making processes, where collaboration is tailored to the case at hand. Evaluated on 10 medical benchmarks, MDAgents achieved leading performance on 7 tasks and demonstrated an 11.8% accuracy boost when incorporating external medical knowledge and moderator reviews. A case study further illustrates how the framework synthesizes differing perspectives among LLMs to reach accurate, consensus-driven diagnoses. This framework stands out for its adaptability and its emphasis on collaboration. Ablation studies underscore the importance of the system’s components, from its ability to classify medical complexity to the integration of multi-modal reasoning. By reflecting the dynamic, consultative nature of clinical decision-making, MDAgents provides a thoughtful approach to enhancing LLM-assisted medical diagnosis. To me, this paper is interesting because it takes a practical step toward aligning AI capabilities with the intricacies of healthcare. By emulating real-world medical collaboration, MDAgents not only improves performance but also lays the groundwork for more robust and trustworthy AI systems in clinical settings. What are your thoughts on how AI frameworks can better reflect real-world decision-making processes in fields like healthcare? paper: https://lnkd.in/gd5fX4Ef #artificialintelligence #AIhealthcare #LLMs #AIAgents

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I remember when this all started. Faris - invited me to catch up at our favorite spot around Blok M. I had no idea he’d just wrapped up a procedure at Pondok Indah Hospital! (He really knows how to add drama to our catch-ups.) We’d been on separate paths and projects since "the winding down project", but there he was, reminding me of our dream to ride this AI wave together again. It was a plan we’d hatched with Sahal Zain & Gurnoor Dhillon when the world of AI was still mostly theoretical and jargon-heavy. Somehow, the conversation continued with Gurnoor Dhillon while he was waiting for his meeting with someone who would eventually become one of CalvinBall’s key early adopters. It felt like everything was coming together, like a series of seemingly random dots connecting to form a bigger picture. Seeing how far we’ve come as a team at CalvinBall Technologies is truly exciting. What started as theoretical discussions has now evolved into practical Gen AI solutions that are solving everyday problems and hold endless possibilities to shape the industry. 👨‍🚀 👩‍🚀 Here’s to pushing boundaries and achieving new milestones together! Gurnoor Dhillon, Sandeep Ramesh, Faris -, Sahal Zain, Eva Zuliya, Ratu Nadiah Khairunnisa 🚀 #GenAI #AIInnovation #TechJourney #ArtificialIntelligence #TechTransformation #CalvinballTech #Calvinballers

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Learn how you can convert a PyTorch model, trained on the web, to a mobile-optimized format. Madhur Zanwar describes the process using a few lines of code. He also discusses his journey and how one approach didn't yield satisfactory results while trying to run it in a React Native app. #machinelearning #pytorch #computervision #edgeml #reactnative #objectdetection #yolov5

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Just published my latest article: "Transforming Business Operations with LLMs: A Path to a Production Ecosystem." a breakdown of the key components necessary for integrating LLMs into core business operations, including Human In The Loop (HITL), Feedback, Evaluation, and Decision Executor subsystems. Learn how these elements work together to start building an AI platform.

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Dive into the insights from a SIGGRAPH 2024 Panel on using data visualization to simplify complex science! 🌐✨ Discover how experts transform intricate concepts into accessible visuals. Don't miss our latest blog post on making science easy to understand. https://lnkd.in/gZxtwfQ8 #SIGGRAPH2024

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🚀 Looking to streamline fine-tuning for models like Llama 3, Mistral, and Mixtral? Our latest blog shows how Anyscale simplifies LLMs fine-tuning with Ray: 🔑 Key takeaways: • Scalable fine-tuning with LLM-Forge • Model Deployment with RayLLM Read more: https://lnkd.in/gQwD4A2U

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Just as a software stack is built in layers, an AI stack consists of an infrastructure layer, a model layer and an application layer on top.​ ​ It needs to process and analyze AI-ready data and combine integrated tools, libraries, and solutions to create applications with generative AI capabilities.​ ​ Ashok Reddy weighs in on the importance of high-performance AI databases for navigating the AI stack effectively in a recent article by Adrian Bridgwater on Forbes.​ ​ Key insights on the AI stack include:​ 💡Importance of bringing on the right partners ​ ⚙️The varying components/layers​ 🛠️The role vector databases play​ 🔮Future Outlooks​ ​ Read more on the AI stack as an entity here:​ https://lnkd.in/eH25bwTA ​ ​ #AI #KX #Innovation #DataScience #Pinecone #Matillion #Datamanagement 

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Fuel your machine learning models with high-quality text annotation services. From sentiment analysis to named entity recognition, ensure your algorithms understand and process text with #accuracy and context. https://hubs.la/Q02_41Pd0 #TextAnnotation #AITraining #NaturalLanguageProcessing #DataAnnotation #NLP #Annotation

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Welcome to our new blog Series on Friendli Model Optimizer! Friendli Model Optimizer is a quantization tool that you can use to improve inference speed, lower resource consumption and maintain model accuracy. In this article, we’ll explore how to use the FMO library and understand its quantization feature. Stay tuned for part 2 of this blog, where we'll dive deeper into the performance analysis. Read Full blog 👉 https://lnkd.in/d-WU5MgJ Try friendli Suite 👉 https://suite.friendli.ai/ #FriendliAI #Friendli #FriendliModelOptimizer #LLM #Inference

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Amidst a range of commercial black box LLMs from large enterprises, we are also seeing Open-Source Machine Learning Frameworks for Advancing Large Language Models in Healthcare. Check out Hippocrates, an open-source LLM framework specifically developed for the medical domain https://lnkd.in/gw5xHdUD

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Explore how to fine-tune BERT using the Hugging Face Transformers library. Discover how you can leverage pre-trained models for your own projects from Shaw Talebi's article. #LLM #MachineLearning

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