Edward Y. Chang

University of California, San Francisco Chancellor, Sam Hawgood, in an interview with Matthew Lungren MD MPH at #ConvergeSummit has outlined the use of #AI scribes to create clinical notes as well as other #AI-driven technologies. Key to their success: a group of practicing physicians with knowledge of AI ensuring that the technology fits into the physicians’ existing workflow, which improves physician adoption of the technology. Another key: measure success through reduction in physician burnout and improvement in patient satisfaction, not increases in physician productivity. Final key: have a real-time, frequent review of implemented technologies to ensure that they still support and are relevant to changing clinical care standards - and shut the technologies down if they are not supporting their purpose.

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See Without Being Seen: UCLA’s Unidirectional Imaging Breakthrough. UCLA’s new unidirectional imaging technology enables image formation in a single direction, preventing image capture in the reverse direction. This novel technology, which operates effectively under partially coherent light, offers significant advancements in optical communication and visual information processing by providing selective, high-quality imaging - https://lnkd.in/gXkqGpqH

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"QUANTUM-LIKE PROTEIN SENSING - A HISTORICAL DISCOVERY IN CHEMISTRY" Check out the entire conversation with Dr. Clarice D. Aiello - a MIT & Stanford Researcher & Quantum Engineer interested in how quantum physics informs biology, who is currently the Chief Scientific Officer at Quantum Biology Institute, California, about quantum biology, quantum effects in DNA, ageing, immunology, drug discovery, artificial life, astrobiology and more, on YouTube from the link below: https://lnkd.in/d7s4NX_e

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Lin Guan and Yifan Zhou have led a very interesting study investigating the effectiveness of Vision Language Models in critiquing robot behaviors in terms of the desirability of their "style." If you are wondering how our work on LLM's inability to verify correctness squares with this study on using them as style critics, see my earlier post https://lnkd.in/gBYAr5qq ! This is the reason why being a style critic is one of the roles LLMs can play in LLM-Modulo framework: https://lnkd.in/gyAyKx4E Check out the details of the work @ https://lnkd.in/gfVZ4EYW ( Lin Guan also has a nice twitter thread on the work here: https://lnkd.in/gnieU-qM )

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This week, I'm interviewing extraordinary Bradley Voytek — neuroscience prof at UC San Diego, founding member of UCSD's Data Science Institute, and he was Uber's first data scientist. Got questions for him? Questions at the intersection of neuroscience and data science would be particularly interesting, given that Prof. Voytek's research specialty is combining large-scale data science and machine learning to study how brain regions communicate with one another, and how that communication changes with aging and disease. (UCSD is arguably the world's best neuroscience research institute, so this is quite a special opportunity for ya!) More detail on Brad: • Professor in UC San Diego's Department of Cognitive Science, the Halıcıoğlu Data Science Institute, and the Neurosciences Graduate Program. • Alfred P. Sloan Neuroscience Research Fellow. • Kavli Fellow of the National Academies of Sciences. • Joined Uber as their first data scientist, when it was a 10-person startup, helping build their data science strategy and team. • Outreach work has appeared in Scientific American, NPR... and Comic-Con! • Co-authored the amusing book "Do Zombies Dream of Undead Sheep?" The "Super Data Science Podcast with Jon Krohn" is available on all major podcasting platforms and a video version is on YouTube. Brad's episode will likely be #829 and be released on October 22nd. #superdatascience #machinelearning #ai #datascience #neuroscience

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Polaris 2.0 from Hippocratic AI is here. Safer than ever. 3T+ parameter constellation architecture with voice latency equal to Polaris 1.0 at 1T+ parameters. Read a summary of the changes from our chief scientist Subhabrata (Subho) Mukherjee's blog: https://lnkd.in/gXweaWe5

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Harnessing the Power of Large Language Models (LLMs) in Medicine Our preprint (https://lnkd.in/ghurvUAK) presents a framework and a validation study for the safer utilization of LLMs in health and medicine. The continued evolution of Large Language Models (LLMs) in AI holds significant potential for enhancing access to medical knowledge in education, training, and patient care. Despite initial concerns about their non-deterministic nature and the risk of incorrect or harmful outputs, our research proposes a framework to address these challenges and optimize LLMs for safe medical use. Key Findings: 👉️Restricting LLMs to validated, domain-specific medical datasets enhances accuracy. 👉️Implementing an actor-critic prompting protocol, where a Therapist agent generates responses and a Supervisor agent ensures accuracy, improves reliability. 👉️Validation study results: Expert CBT-I therapists rated LLM responses highly, often surpassing human-generated appropriate responses. Summary: Our approach demonstrates the potential of integrating advanced LLM technology into medical applications, meeting regulatory requirements and ensuring safe, effective use in healthcare settings. #LLM #Healthcare preprint: https://lnkd.in/ghurvUAK

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Our newest preprint discusses the implementation of LLM in healthcare and medicine, specifically as a virtual sleep coach for insomnia. #LLM, #healthcare, #insomnia

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🧬 Unveiling the Blueprint of Brain Development: First Map of DNA Modification in Human Brain Uncovered! A groundbreaking study led by UCLA has unveiled the first comprehensive map of DNA modification in the developing human brain, offering profound insights into the molecular mechanisms that could influence neuropsychiatric disorders like schizophrenia and autism spectrum disorder. Published in Nature, this research marks a significant leap forward in our understanding of brain development and genetic regulation. 🔑 Key Takeaways: ◾ The study revealed how DNA modifications in the hippocampus and prefrontal cortex—key areas for learning, memory, and emotion—are involved in brain development. ◾ Researchers used a novel sequencing technique to map DNA methylation and chromatin conformation at a single-cell level, shedding light on the complex gene regulation during brain maturation. ◾ This map provides a vital reference that could improve the accuracy of stem cell-based brain models, crucial for studying developmental disorders. 🌍 Why This Matters: The ability to pinpoint when and where specific genetic variants affect brain development could transform our approach to diagnosing and treating conditions like autism and schizophrenia, potentially from as early as infancy. By understanding the vulnerabilities in brain development, scientists can better target interventions that may alter the course of these conditions. This study not only advances our grasp of genetic influences on brain function but also enhances the potential for developing early predictive and therapeutic strategies. 🔗 https://lnkd.in/eBRRknCw. #BrainDevelopment #Genetics #Neuroscience #UCLA #NatureJournal #MentalHealthAwareness #AutismResearch #SchizophreniaAwareness

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𝗛𝗮𝗿𝗻𝗲𝘀𝘀𝗶𝗻𝗴 𝗔𝗜 𝗳𝗼𝗿 𝗦𝘄𝗶𝗳𝘁 𝗚𝗲𝗻𝗲 𝗘𝗱𝗶𝘁𝗶𝗻𝗴 Recent research published in Nature by Ming Y. Lu, Bowen Chen, and Drew F. K. Williamson, introduced an AI-driven tool that accelerates CRISPR-based gene editing. The approach combined the deep subject matter expertise of the team and augmenting this with models to enhance precision and efficiency of gene editing. Which then enables prediction of optimal CRISPR-Cas9 target sites, minimizing off-target effects and maximizing editing success. A key aspect of this advancement is the applied AI methodology, which integrates multi-modal datasets—combining genomic, proteomic, and phenotypic data. This comprehensive data integration, coupled with subject matter expertise, enables the AI to make highly accurate predictions and refine gene-editing strategies. The synergy between AI and expert knowledge accelerates the development of precise and effective genetic modifications. This breakthrough has implications for a wide range of genetic conditions. Diseases such as cystic fibrosis, sickle cell anemia, and muscular dystrophy, which are caused by specific genetic mutations, can potentially be treated more effectively with precise gene-editing techniques. Additionally, complex conditions like cancer, where multiple genetic factors are at play, could see advancements in targeted therapies, leading to personalized and more effective treatment options. The ability to edit genes with high precision opens new avenues for addressing rare genetic disorders, previously deemed untreatable, and accelerates the path toward innovative cures and interventions. https://lnkd.in/gV6YkzFH #GeneEditing #CRISPR #AI #Biotechnology #ResearchInnovation #AppliedAI #MultiModalData #GeneticConditions #PersonalizedMedicine

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Back in May, I was invited by the local chapter of IEEE SSCS in Santa Clara to give a talk on how to use LLM and Gen AI for high-speed design. The video has been uploaded to YouTube. It is a good summary of how we are using LLM and AI for SI. https://lnkd.in/eUy2HDhB A few months later, Eric Schmidt gave a controversial talk on where he thought AI would progress in the next few years in a Stanford computer science class. He was a little too frank on telling students to steal TikTok IPs and why he thought Google would fail. https://lnkd.in/epMKkmvv Both talks seem to completely overlap on how to create domain knowledge dominance using advanced LLM. He gave examples on taking over TikTok and in chemical engineering, I was focusing on high-speed SI designs. But it can be applied to any other hardware design disciplines.  What we are pursuing, and he seems to suggest the same approach : 1) Use LLM with deep context window to scan through large volume of knowledge (text, video or sound), in my case for SI/PI we will be scanning large volumes of design specs for design domain knowledge 2) Use LLM agents to execute task based on the ingested knowledge (context to action), in my case, we will be initiating simulations based on design spec range and look for optimal solutions 3) Repeat the above with multiple chain of thoughts and store the observation to draw a conclusion, in my case, after scanning through the design specs and obtain optimal spacing for performance, generate routing and constraint rules 4) We also add an optimization step which is necessary for engineering  5) Improve the LLM agent performance using an adversarial agent to critique the LLM agent's performance 6) We also add using fine-tuning and transfer learning to future proof the domain specific models. I apologize both are long talks ~1 hour.  I hope this will be a good starting point to discuss the use of LLM and Gen AI for SI/PI. Feedback and comments are most welcome.

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Some experts are doubtful about the feasibility of LeCun’s vision. Aron Culotta, Associate Professor of Computer Science at Tulane University, points out the long-standing challenge of teaching AI models causality, which is crucial for common sense and reasoning. Despite skepticism from some quarters, LeCun believes that AI agents with human-level intelligence will eventually become indispensable, especially in applications involving wearable technology and smart devices. In conclusion, while current AI systems, particularly LLMs, have not yet achieved true common sense, ongoing research in alternative approaches like world modeling holds promise. The path to AGI remains complex and uncertain, but breakthroughs in understanding and mimicking human learning processes could eventually lead to AI with genuine common sense. #ai #llm #meta #google #bigdata #intelligence #nvidia

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𝕎𝕙𝕪 #𝔸𝕀 𝕗𝕠𝕝𝕜𝕤 𝕟𝕖𝕖𝕕 𝕒 𝕓𝕣𝕠𝕒𝕕 𝕓𝕒𝕤𝕖𝕕 𝕀𝕟𝕥𝕣𝕠 𝕥𝕠 #𝔸𝕀 👉 As I go around giving talks/tutorials on the planning and reasoning abilities of LLMs, I am constantly surprised at the rather narrow ML-centric background of grad students/young researchers have about #AI. This seems to be especially the case with those who think LLMs are already doing planning and reasoning etc. Most of them don't seem to know much about the many topics that are taught in a broad-based Intro to #AI course--such as combinatorial search, logic, CSP, difference between inductive vs. deductive reasoning (aka learning vs. inference), soundness vs. completeness of inference/reasoning etc. I can understand why a strong background in ML and DL is sine qua non these days in using/applying the current #AI technology. That doesn't however mean that other things, that are typically not covered in ML courses, but are covered in Intro #AI courses, are expendable. If you don't know those concepts, you are more likely than not to re-invent crooked wheels (see this for examples of how people get tripped up: https://lnkd.in/gUPPb7s4) All this is particularly relevant for those busy building empirical scaffolds over LLMs (the "LLMs are Zero-shot <XXX>" variety). Most often, these young researchers are coming from NLP. At one point, NLP used to be NLU and students had quite a firm grasp of logic (e..g Montague Semantics!). But over the years, NLU became NLP which in turn has become Applied Machine Learning, and students don't quite have the background in logic/reasoning etc. Now that LLMs have basically "solved" the "processing" tasks--such as information extraction, format conversion etc., NLP folks are trying to turn to reasoning tasks--but often lack the necessary background. (See this unsolicited advice to NLP students: https://lnkd.in/gKTdsH2P) Background in the standard Intro AI topics like search/CSP/logic are useful even if you don't plan on directly using those techniques (e.g. because you want differentiable everything to make use of your SGD hammer). Like MDPs, they provide a normative basis for many deeper reasoning tasks AI systems would have to carry out when they broaden their scope beyond statistical learning. Without that background, you will likely try to pigeon hole everything into "in/out of distribution" framework, when what you need to think of is "in/out of deductive/knowledge closure; see https://lnkd.in/gTWVibdt ) One of the other things that you get exposed to in the standard Intro #AI is computational complexity of the various reasoning tasks. People who jumped in directly via applied ML might understand a bit of sample complexity (maybe?), but are not that attuned to reasoning complexity. (Contd. in the comment below)

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California governor just vetoed SB 1047!! As per Andrew NG, a pioneer on Machine Learning (my first AI course had been his- Machine Learning using R); this ensures that regulations around Artificial Intelligence would be based on science, not science fiction... So what does this entail? Three main takeaways for you: 1) The Governor did not want any false sense of security to the general public: "The bill, introduced by Sen. Scott Wiener, would need developers to submit their safety plans to the state attorney general, and held them liable if AI models they directly control were to cause harm or imminent threats to public safety." 2) Innovate, not regulate: While this bill was signed by over 125 actors, producers, directors, music artists and other entertainment industry leaders, (Pedro Pascal, Shonda Rhimes, Alec Baldwin included), this might have stifled innovation, that too, in a region that houses 32 of the world's leading tech firms that are pioneering research in AI, as per the letter sent by Governor. 3) There are legislations taking place (hopefully): While this one has been vetoed, the Generative AI Copyright Disclosure Act, introduced by California democrat Adam Schiiff, would require if passed, a notice to be submitted to the Copyright Office prior to the release of a new generative AI system, listing out all copyrighted works used in building or altering the training dataset for that system. On 1 August 2024, the European Artificial Intelligence Act (AI Act) has already been enacted, which aims to foster responsible artificial intelligence development and deployment in the EU. #sb1047 #genai #law Sources: https://lnkd.in/eUA_PJPS https://lnkd.in/eBTfT8ex https://lnkd.in/eDSEqHgM

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A new AI model by UCLA researchers can analyze medical scans 5,000x faster than human doctors with the same accuracy. By using transfer learning from 2D medical data, SLIViT(Slice Integration by Vision Transformer) overcomes the challenge of limited 3D datasets, making it capable of analyzing complex 3D scans with incredible speed and precision. What once took 8 hours now takes just 5.8 seconds. Here’s how it works: 1. Transfer learning SLIViT is pre-trained on extensive 2D medical imaging datasets, enabling it to effectively analyze 3D scans despite the limited availability of 3D datasets. 2. Fast & accurate analysis Using a ConvNeXt backbone for feature extraction and a Vision Transformer (ViT) module for combining these features, SLIViT matches the accuracy of clinical specialists. 3. Flexibility across modalities SLIViT can analyze scans from multiple modalities, including OCT, MRI, ultrasound, and CT, making it adaptable to emerging imaging techniques and diverse clinical datasets. This AI can work with smaller datasets, making it accessible even to hospitals with limited resources. It means: -Rural clinics can offer expert-level diagnostics -Life-threatening conditions are caught earlier -Millions of patients get faster care In healthcare, speed isn’t just about efficiency - it’s about survival. And if SLIViT lives up to its claims in real-world scenarios, it could be a superpower to help save more lives, faster. Could this AI breakthrough reshape the future of medical diagnostics? #ai #innovation #healthtech

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