Eric Horvitz

It was a superb chatting with Andrew Beam and Arjun Manrai. Thank you both for the long form conversation! Refreshing break from the "bite sized" world enabled by NEJM AI. What we talked about was possible given the stellar research team I get to work with (Jason Fries, Alison Callahan, Michael Wornow, Suhana Bedi, Akshay Swaminathan, Zepeng Frazier Huo, Scott Fleming and Ethan Steinberg) and the vibrant community at Stanford University School of Medicine, Stanford Health Care supported by the brilliant team at Technology & Digital Solutions - Stanford Medicine. Some of the ideas we discussed are also outlined in blogs at our Stanford Institute for Human-Centered Artificial Intelligence (HAI). e.g. 1. How can foundation models advance healthcare - https://lnkd.in/eiBw4YHr 2. How do we ensure healthcare ai is useful - https://lnkd.in/eDTc7Dqi 3. Are we there yet with LLMs in healthcare? - https://lnkd.in/eDQv2zH4 #HealthcareAI

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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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HeAR is now available to researchers to help accelerate development of custom bioacoustic models with less data, setup and computation. 🚀 Our goal is to enable further research into models for specific conditions and populations, even if data is sparse or if cost or compute barriers exist. #HeAR #accousticdata #bioaccousticmodels

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🌟 Learn how you can utilize data science workflows to tackle real-world problems, in this video tutorial with Grace Woolson from MathWorks! https://lnkd.in/gYr6jZXp Grace will show you how to start on the #WiDSDatathon Challenge #2 using MATLAB. She will walk you through: 📊 Importing Data 🛠️ Preprocessing 🔍 Exploring Data ✨ Creating Features 🤖 Model Training 📈 Making Predictions & Exporting Follow along in the written tutorial: https://lnkd.in/gNvDQ6m4 #heathcare #climate #tutorial #training #MATLAB MATLAB Coding, Valerie Elliott

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