Eric Horvitz

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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One of the most fascinating discussions you can watch on what #AI can do for science. It really does make you dream https://lnkd.in/eGBf9qGF

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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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🚀 Transforming Healthcare with AI: Precision and Progress 🚀 * Rapid Drug Development: AI reduced the drug discovery process to just 30 months with Insilico Medicine, compared to the traditional decade-long timeline. *Enhanced Diagnostic Accuracy: In diagnosing complex cases, AI accurately identified tricky diagnoses 39% of the time and provided the correct diagnosis as a secondary option 64% of the time, surpassing traditional methods. *Focused Patient Care: Ambient AI allows doctors to focus more on patients by handling administrative tasks, shown in a study where 3,442 physicians adopted this technology for over 303,266 visits. *Empathetic Patient Communication: AI proved to make patients feel more heard, offering emotional support effectively, though it showed a reduction in perceived empathy when users knew the communication was AI-generated. #AIinHealthcare #HealthTech #PatientCare #Innovation Summary from: https://lnkd.in/gyqbBf5R

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Love this paper. There are countless low-hanging fruits in deploying LLMs in important fields, including healthcare. I particularly liked this use case. Social Determinants of Health (SDoH) have been close to my heart since my work with RWJF (with Oktawia Wojcik) and Sutter Health (with Sylvia Sudat) to extract them from EHRs many years ago. This is a clear-cut instance where AI is not only effective but better than humans in annotating and surfacing important context for the healthcare provider. #AI #healthcare https://lnkd.in/eGMkjGFQ

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👉🏼 Using Large Language Models to Address Health Literacy in mHealth: Case Report 🤓 Elliot Loughran 👇🏻 https://lnkd.in/db46KrZv 🔍 Focus on data insights: - ChatGPT effectively reduced complex language in health-related educational materials. - Content from SmartSHOTS mobile app was adapted to improve readability. - ChatGPT simplified sentence structures to align with a third-grade reading level. 💡 Main outcomes and implications: - Improved accessibility of health information for caregivers of children aged 0 to 24 months. - Enhanced adherence to vaccination schedules through clearer educational content. - Potential for ChatGPT to be a valuable editorial tool for simplifying health-related materials. 📚 Field significance: - Advancing health literacy in mHealth through AI-driven language simplification. - Enhancing communication effectiveness in healthcare education. - Promoting better understanding of medical topics among the public. 🗄️: #mHealth #HealthLiteracy #ChatGPT #DataInsights

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"Productization is an affirmation," writes Adrian Bridgwater, "when any good or service evolves to a point where it can be productized and boxed, it denotes a certain solidity and branded consumability." In our conversation, I was delighted to share how #SAS equips organizations with readily deployable AI technology to productionize real-world use cases. https://lnkd.in/enUGCgAi #AIML #sasusers

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This California Management Review article explores the ROI of AI ethics and governance investments through two lenses: loss aversion and value generation. While many companies focus on minimizing risks like legal penalties and reputational harm, the article argues that a value generation approach—using ethics to build trust, foster innovation, and create competitive advantages—offers greater long-term benefits. By integrating ethics into strategic planning, organizations can both mitigate risks and enhance overall growth.

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Essential #AI thoughts from Fei-Fei Li. I was honored to hear her speak about AI years ago at orientation for new fellows at The Paul & Daisy Soros Fellowships for New Americans and I never forgot her wisdom and prescience. She heads up HAI (Human-Centered AI at Stanford University. Q: What do you you think about AI’s future, what worries you the most, and what gives you hope? A: It’s not AI’s future, it’s humanity’s future. We don’t talk about electricity’s future, we don’t talk about steam’s future. At the end of the day, it is our future, our species’ future, and our civilization’s future—in the context of AI. #futureofeducation #futureofwork

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👉🏼 Practical Applications of Large Language Models for Health Care Professionals and Scientists 🤓 Florian Reis 👇🏻 https://lnkd.in/e3Apu-2f 🔍 Focus on data insights: - 📊 The article identifies six practical use cases for LLMs that can enhance the efficiency of health care professionals and scientists. - 🛠️ Strategies for customizing translations and improving text clarity are outlined, offering insights into language processing capabilities. - 🔍 Emphasis is placed on extracting relevant information and generating specialized knowledge from vast datasets, showcasing LLMs' potential in data analysis. 💡 Main outcomes and implications: - ✅ Increased productivity in research and clinical settings is anticipated with the integration of LLMs into daily workflows. - ⚠️ The article highlights the importance of user experience and the potential pitfalls of initial usage, advocating for adequate training and support. - 💭 Encouraging interdisciplinary collaboration, the piece illustrates how LLMs can assist in brainstorming and narrative compilation for research proposals and presentations. 📚 Field significance: - 🌐 This viewpoint contributes to the ongoing discourse on artificial intelligence in biomedicine, illustrating its relevance in modern health care practices. - 🧠 By addressing implementation challenges, the article fosters a better understanding of how to harness AI tools effectively in scientific environments. - 📈 It sets a framework for future research on optimizing AI applications in clinical and educational contexts, paving the way for innovation in health care. 🗄️: [#LargeLanguageModels #AIinHealthcare #DataInsights #HealthResearch #ArtificialIntelligence #Productivity #Biomedicine #UserExperience]

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Some very interesting points from Yossi Matias that worth looking at, and the main one is: "Learn to code" remains crucial, even as AI advances. Here are some additional points: • Understanding tech basics is vital for everyone, not just software engineers • AI will enhance fields like biology, chemistry, and medicine • Healthcare presents a "huge opportunity" for AI transformation • Education could be revolutionized with AI-powered interactive tools • AI can be integrated in life-saving applications like Google's flood forecasting • Junior professionals may face challenges, but opportunities still exist • AI is becoming "both a tool and a science" https://lnkd.in/d5XSWb5Z #artificialintelligence #ai #chatbot #programming #healthcare #aiinhealthcare

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Interesting that on the MIMIC IV readmission models, the LLMs get closer to the traditional ML models. Perhaps because LLMs are not great at representing numeric values (especially at the model sizes tested) that are informative of the prediction for acute outcomes, whereas readmission relies more on diagnoses and other more linguistically-coded inputs. (Though that doesn't explain the poor performance on MIMIC-III readmission)

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Are hallucinations in large language models (LLMs) a flaw or an intrinsic part of their design? As LLMs like GPT generate text, they sometimes produce errors, often referred to as "hallucinations." But are these mistakes simply artifacts, or are they deeply rooted in the way these models function? In the paper "Decoding Hallucinations in Large Language Models: A Token-Level Truthfulness Analysis" by Sayan Ghosh, Jason Wei, and Diyi Yang, 2024, the authors explore how LLMs encode the truthfulness of their outputs within specific tokens. This discovery could lead to better error detection, though generalizing these methods across datasets remains a challenge. Even more interesting is the finding that LLMs often "know" the right answer internally but still generate incorrect responses. This disconnect between internal knowledge and external behavior provides a clearer view of how LLMs function—and highlights key areas for future improvement in AI systems. To me, this paper is interesting because it challenges how we understand so-called "hallucinations" in LLMs. Perhaps the term oversimplifies a more complex internal process. Recognizing these discrepancies is crucial for refining how we interact with AI systems, leading to more reliable outputs and encouraging smarter model development moving forward. Paper: https://lnkd.in/gM-uFW4R #AI #Neuroscience #LLM #MachineLearning #Research

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Last week, I spoke about AI and regulations at an event at the U.S. Capitol attended by legislative and business leaders. I’m encouraged by the progress the open source community has made fending off regulations that would have stifled innovation. But opponents of open source are continuing to shift their arguments, with the latest worries centering on open source's impact on national security. I hope we’ll all keep protecting open source! I’m encouraged by the progress the U.S. federal government has made getting a realistic grasp of AI’s risks. To be clear, guardrails are needed. But they should be applied to AI applications, not to general-purpose AI technology. Nonetheless, some companies are eager to limit open source, possibly to protect the value of massive investments they’ve made in proprietary models and to deter competitors. It has been fascinating to watch their arguments change over time. For instance, about 12 months ago, the Center For AI Safety’s “Statement on AI Risk” warned AI could cause human extinction and stoked fears of AI taking over. This alarmed leaders in Washington. But many people in AI pointed out that this dystopian science-fiction scenario had little basis in reality. About six months later, when I testified at the U.S. Senate’s AI Insight forum, legislators no longer worried much about an AI takeover. Then the opponents of open source shifted gears. Their leading argument shifted to the risk of AI helping to create bioweapons. Soon afterward, OpenAI and RAND showed that current AI does not significantly increase the ability of malefactors to build bioweapons. This fear of AI-enabled bioweapons has diminished. To be sure, that bad actors could use bioweapons — with or without AI — remains a topic of great international concern. The latest argument for blocking open source AI has shifted to national security. AI is useful for both economic competition and warfare, and open source opponents say the U.S. should make sure its adversaries don’t have access to the latest foundation models. While I don’t want authoritarian governments to use AI, particularly to wage unjust wars, the LLM cat is out of the bag, and authoritarian countries will fill the vacuum if democratic nations limit access. When, some day, a child asks an AI system questions about democracy, the role of a free press, or an independent judiciary, I would like the AI to reflect democratic values rather those of authoritarian leaders. I came away from Washington optimistic about the progress we’ve made defending open source. Looking beyond the U.S. federal government, there are many jurisdictions. Unfortunately, arguments for regulations that would stifle AI continue to proliferate. But talking to regulators does have an impact. If you have a chance to talk to a regulator, I hope you’ll do what you can to help governments better understand AI. [Original text in The Batch (with links): https://lnkd.in/g5SZ6ypa ]

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