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On the latest Vital Signs, Nikhil Krishnan and I had a ton of fun chatting with Dr. Atul Butte. Dr. Butte is a professor at UCSF, Chief Data Scientist of the UC Health System, and a co-founder of numerous healthcare startups. In our conversation, Dr. Butte shared his takes on healthcare AI opportunities, challenges, hype, and more. A few highlights: ⚡LLM use cases, data, and growth LLMs are already helping to solve the “drudgery” aspects of medicine, such as automatically drafting SOAP notes and patient messages. Dr. Butte is excited about future applications of LLMs that can help with the “cognitive” aspects: with difficult medical problems an LLM might be able to stitch together disparate pieces of information to generate a potentially novel solution. He also highlights that prompt engineering will become the next “coding language” as so much of leveraging LLMs is knowing how to ask them the right questions. Additionally, Dr. Butte discusses how since 2016, UCSF has been working to deidentify its patient notes so that it can query them to answer clinical questions and ultimately enhance patient care. ⚡The need for patient-facing AI As patients are increasingly able to access their own personal health data in nicely structured formats (e.g., EHRs, wearables), they desperately need more tools to help them interpret and make decisions using that data. Dr. Butte understands that these patient-facing solutions are challenging to get funded but he emphasizes that patients need to receive the benefits of AI sooner. ⚡Evaluating healthcare AI solutions In order to evaluate AI solutions, we often put human specialists – such as medical professionals – on the same tasks. Understanding that specialists often disagree with each other, a significant challenge arises in figuring out how we can evaluate AI considering we can’t just “average” all the specialist opinions. Dr. Butte points out that there still remains a massive gap in AI evaluation tools. Additionally, there are so many organizations that want to have a say in how healthcare AI is regulated that the landscape is uncertain. An awesome conversation with an expert on all things AI meets healthcare. Listen to the full episode below: Spotify: https://spoti.fi/3Wr3643 Apple: https://apple.co/4acwgan

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Excited to share what Nithin Parsan and I have been working on at YC! At Reticular (YC F24), we're pioneering the future of explainable AI for biology. Check out the post and our first technical blogpost linked in the comments to learn more about what we're focused on.

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If you use LLMs in your cancer research, check out my recent seminar all about how data quality impacts LLM performance. A reliable foundation that is well annotated and accessible to an LLM plays a major role in the value of its results. I share examples of how LLM-powered AI agents query across three versions of the same gene expression corpus with differing results, including: --Unstructured data from the public repository Gene Expression Omnibus. --Structured data from the Crowd Extracted Expression of Differential Signatures project (tool developed by the Ma’ayan Lab at the Icahn School of Medicine at Mount Sinai). --Clean, linked, and harmonized data. Watch it here: https://lnkd.in/gbPB5CRv #LLM #Data #Cancer #Research #Science #Innovation

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For NY Tech Week we'll be demo-ing Substrate at the South Park Commons Science Faire on Thursday: https://lnkd.in/e5JwDKKX There are so many incubator-shaped things out there these days, but there's nothing quite like SPC. It's like a chill, small-batch, locally-sourced alternative to YC, where you can either hang out as a member forever - or choose the VC-funded adventure. The -1 to 0 phase [0] they talk about at SPC is real, and it made a real difference for us to have a home at SPC. It takes a lot to quit your job and do something new, especially when it isn't entirely clear what you're doing. (1) Do it – take the leap! and (2) consider joining SPC. Though the core vision has remained the same, many of the important turns in our squiggle at Substrate (including the graph framework) came from those early days at SPC. If you're here for tech week and can't catch us at SPC on Thursday, DM me if you want to meet up! [0] https://lnkd.in/e6KEMr9V

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Discover how Waymo's autonomous ride-hailing service is revolutionizing independence for people with disabilities in this sponsored article. Jessie Wolinsky, aka 'Badass Blind Babe,' shares her journey navigating San Francisco with ease, thanks to Waymo. Her story highlights the impact of accessible technology & the importance of embracing new experiences. #Accessibility #AutonomousVehicles #Inclusion https://lnkd.in/g_F2Bd57

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"Are you an AI company?" might be one of the most expensive questions in biotech right now. Forcing this narrative can lead to biotech companies picking sides and making costly mistakes that lead them to over or under-invest in the ways they advance their R&D and programs. Rather than picking a side ('traditional biotech' versus 'AI company'), the sharpest leaders and most competitive companies will find ways to weave in elements from both angles. And, over time, the conversations will hopefully evolve away from "which one are you?" and more towards whether companies are covering their blind spots and leveraging every edge they can be, in their race to getting drugs to market. https://lnkd.in/dkCtnDtK

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Recently, Anthropic shared its perspective on California's proposed AI regulation bill, SB 1047, with the California Assembly. While recognizing the importance of safe AI development, Anthropic has raised substantial concerns about the bill's current form, which could potentially hinder innovation and harm the United States' competitive edge in AI. Anthropic's key points include shifting the focus from "pre-harm enforcement" to a more adaptable "outcome-based deterrence" model. They also advocate for regulations that focus more narrowly on frontier AI safety to avoid redundancy with federal requirements. Additionally, they suggest giving regulatory authority to the Government Operations Agency instead of creating a new state agency. Anthropic believes that these amendments will encourage innovation in risk reduction practices and incentivize companies to adopt measures that prevent catastrophic risks. In contrast, the AI Salon Policy & Civic Engagement Guild shares Anthropic's commitment to ethical AI and safe development but has broader concerns about the bills. We believe that overly burdensome regulations may discourage AI development and lead to job losses. Compliance requirements favor large companies, potentially driving smaller startups out of the market, and overregulation might cause California to fall behind other regions in AI development. Furthermore, compliance under penalty of perjury imposes significant personal risk on developers, and strict regulations could disadvantage California-based companies in the global AI market. On behalf of the over 1700+ members of the AI Salon, we urge our larger community to engage in this crucial dialogue about AI regulation. By advocating for well-balanced policies that protect public interests without stifling innovation, we can ensure California remains a leader in ethical and responsible AI development. Please sign the letters of support and join us in shaping a future where AI is developed and deployed with ethical integrity, social responsibility, and inclusive governance. Together, we can bridge the gap between AI technology and policymaking, fostering an environment where innovation thrives alongside robust safety measures. https://lnkd.in/e6MRBmcX #AIRegulation #EthicalAI #Innovation #PolicyEngagement #Anthropic #AISalon #PublicPolicy #TechPolicy Kyle Shannon Leah Fasten Brandon Tidd Stacie L. Matthew Karabinos, MAT Vikki Baptiste Jon Potter Jon Leland Cyndi Coon Daniele Hall

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I continue to be alarmed at the progress of proposed California regulation SB 1047 and the attack it represents on open source and more broadly on AI innovation. As I wrote previously, this proposed law makes a fundamental mistake of regulating AI technology instead of AI applications, and thus would fail to make AI meaningfully safer. I’d like to explain why the specific mechanisms of SB 1047 are so pernicious to open source. To be clear, there are routes that regulators should pursue to improve safety. For example, I would welcome outlawing nonconsensual deepfake pornography, standardizing watermarking and fingerprinting to identify generated content, and investing more in red teaming and other safety research. Unfortunately, the proposed bill pursues a less beneficial and more harmful path. SB 1047’s purported goal is to ensure safety of AI models. It puts in place complex reporting requirements for developers who fine-tune models or develop models that cost more than $100 million to train. It is a vague, ambiguous law that imposes significant penalties for violations, creating a huge gray zone in which developers can’t be sure how to avoid breaking the law. This will paralyze many teams. You can read the latest draft of the law online. I’ve read through it carefully, and I find it ambiguous and very hard to follow. Developers who try to navigate the law’s complex requirements face what feels like a huge personal risk. It requires that developers submit, under penalty of perjury, a certification of compliance with the requirements of the law. But when the requirements are complex, hard to understand, and can even shift according to the whims of an unelected body (more on this below), how do we ensure we are in compliance? For example, the certification must include many different sections. One is an analysis of “the nature and magnitude of critical harms … the model might reasonably cause or enable.” But given that even leading AI researchers aren’t sure what harms models might cause or enable, how is a team of developers supposed to figure this out and declare — under penalty of perjury — that they meet this requirement? Further, some developers will be required to implement “protections to prevent … misuse of, or unsafe post-training modifications of, the covered model and all covered model derivatives … that are appropriate in light of the risks associated with the covered model, including from advanced persistent threats or other sophisticated actors.” Even leading AI researchers don’t agree on how best to “protect” AI models against these supposed risks, or what would be “appropriate.” So how are developers supposed to figure out how to comply with this requirement? [Reached LinkedIn length limit. Rest of text here: https://lnkd.in/g_NhQpni ]

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It's no secret AI/ML X Biology is hot right now, with millions of dollars in funding going to companies with ML guided discovery strategies. This field is the next wave of startups, similar to CAR-T and CRISPR before them. Candidates looking to enter this field often ask about wetlab skills that pair well with ML guided strategies. Here are some skills requested by leading AI/ML X Bio companies: - Display Technologies: Yeast, Phage, mRNA, and Ribosome display are in high demand, with Phage being the most popular. Yeast is also gaining traction because of it's eukaryotic Expression benefits. - HTS Screening: With the evaluation of molecules derived by AI/ML, extensive screening is inevitable. - Automation: Developing assays and workflows for automated liquid handlers is crucial in this field. - CFPS / IVTT: Ideal for expressing multiple proteins simultaneously, despite challenges with folding and disulfide bonds. But some experts can help you solve these issues. ( We know them 😊) - SPR & BLI: Candidates skilled in SPR are highly sought after by companies. - Molecular Dynamics Data: Individuals who can integrate molecular dynamics data into ML algorithms for structure and binding prediction are in high demand. Upskilling in any of the mentioned skills can significantly benefit your career in AI/ML X Biology. While there may be other skills to consider, these are the most requested ones based on current trends in the industry. Unashamed Segue CoFactor is currently recruiting for display experts on both coasts. We are also in need of research associates with SPR. Please reach out for more information. #ML #biology

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I worked in a CRISPR lab during my bachelors and this is insane: Profluent, a Berkeley startup, has created AI that generates novel CRISPR gene editors. The AI learns from analyzing vast biological data to create editors that have never existed in nature. And for context prior to this, CRISPR methods used mechanisms found in nature to explore new variations. This breakthrough points to a future of battling disease with greater precision and speed. And even crazier, Profluent literally open-sourced one of these AI-generated editors, OpenCRISPR-1, for free experimentation. While still very early, this technology holds immense potential. And in my personal opinion, could be the potential gateway for personalized medicine. AI is accelerating progress in healthcare and pushing boundaries faster than ever before. And at this point it's inevitable whether you like it or not.. From creating new proteins for vaccines to revolutionizing gene editing, the possibilities are endless. I definitely think we're witnessing a new era of medicine unfold. Does this excite you or worry you we're gonna see something out of the X-men? Follow Scott Caputo - every week I explore AI, innovation & strategy #AI #GeneEditing #Healthcare #Biotech #Innovation

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Recent communications from Stanford Medicine regarding practical uses of AI today have been educational leading up to the Symposium. The 'Charting AI’s Future in Health and Medicine' theme is akin to HAI's 2024 Artificial Intelligence Index Report. Fortune 500 companies start talking a lot about AI, especially generative AI. "In 2023, AI was mentioned in 394 earnings calls (nearly 80% of all Fortune 500 companies), a notable increase from 266 mentions in 2022. Since 2018, mentions of AI in Fortune 500 earnings calls have nearly doubled. The most frequently cited theme, appearing in 19.7% of all earnings calls, was generative AI." References: 1) RAISE Health Symposium. (n.d.). RAISE Health Symposium. https://lnkd.in/gV8xKfGG 2) Stanford Medicine. (2024, March 25). Stanford Med LIVE: The State of AI in Healthcare and Medicine [Video]. YouTube. https://lnkd.in/gGYQVtbH 3) 2024 Artificial Intelligence Index Report. https://lnkd.in/gNXN5heG

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Learn a development pattern to systematically improve the accuracy and reliability of LLM applications in our new short course, Improving Accuracy of LLM Applications, built in partnership with Lamini and Meta, and taught by Lamini’s CEO, Sharon Zhou, PhD, and Meta’s Senior Director of Partner Engineering, Amit Sangani. (Disclosure: I am an investor in Lamini.) The path to tuning an LLM application can be complex. In this course, you'll learn a systematic sequence of steps for improving accuracy by reducing hallucinations:  - Create an evaluation dataset to measure model accuracy - Add prompt engineering and self-reflection - Fine-tune your model including "memory-tuning" which is a new method of embedding facts in an LLM Using the Llama 3-8B parameter model, you will: - Build a text-to-SQL agent with a custom schema and simulate situations where it hallucinates - Understand the difference between instruction fine-tuning, which gives pre-trained LLMs instructions to follow, and memory fine-tuning - See how Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation (LoRA) reduce training time by 100x and Mixture of Memory Experts (MoME) reduces it even further I appreciate Meta releasing the Llama's family of open models -- this course gives an example of the unique type of work that developers can do with such models. Please sign up here: https://lnkd.in/gyxHwPkp

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Loved bringing together a group of research-centric, product-minded technologists building the future of AI tonight in the 🅱️🍎, incl. product / eng leadership @ NVIDIA, Pinecone, Datadog; founding eng @ Abnormal Security, Modal, Counsel Health; applied AI @ OpenAI, Anthropic; research eng @ Meta (FAIR), Harvey, Google DeepMind; founders @ Sapien and some of NYC's greatest stealth AI co's ☄ We covered a lot of ground over 4 hours - some of the recurring themes in our conversations: - Agents:🕴Model inference is getting so much faster, which will unlock AI agents' potential. That said, agents should be trained to handle very narrow tasks, reliably, first vs. an open-ended approach many startups are attempting today - Robotics: 🤖 While the science is not there yet, the robotics market and its impact will be much bigger than LLM. The introduction of humanoids will fundamentally change how we live. That said, the U.S. is in a vulnerable position due to lack of supply chain independence, compared to rivals like China - Value creation: ✨ AI personalization is overrated as consumers mostly care about status (i.e., ‘whatever is the best’) vs. what's tailored to them. Delivering great UX is still hugely undervalued, for both enterprise and consumer use cases. Cost and reliability remain enterprise buyers' front-and-center priorities. AI can preserve endangered rare languages by documenting and creating digital archives / databases cc: our amazing sponsor Arc (Brendan Bettencourt, Erika Bricky, Eric Lo), Visible Ventures

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Generative AI Breaking News (21AUG2024, Wednesday) - Rutgers University Cancer Research - GPT/Natural Language; Rutgers break's through using Generative AI for Cancer biologic protein sequence modelling. quote "Another category of AI-based tools for protein structure prediction are protein language models, which differ from AlphaFold2 and RoseTTAFold in that they are not trained on structures but rather on protein sequences7,8,9. Collectively, such protein structure prediction tools have been extensively used by researchers across various disciplines in the biological sciences and are expected to continue to add value alongside experimental structure" end-quote #rutgers, #cancer, #oncology, #pharma, #biotech, #machinelearning, #startups, #newjersey, #jersey, #miami, #geneva, #switzerland, #lifesciences, #investments, #israel, #berlin, #hedgefunds, #privateequity, #stocks, #nasdaq Ref - https://lnkd.in/eR2RYzvv

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Today's dispatches from quantum: ⚆ Quantum computing for protein structure prediction as demonstrated by framework that accurately predicts Zika Virus NS3 Helicase ⚆ QAOA gets a makeover — QIRO bypasses locality issues, matches classical heuristic performance ⚆ Central New Mexico Community College to offer first of its kind quantum computing bootcamp amidst concerns for related US programs Plus, IBM releases execution modes, QTunisia hosting certificate-awarding workshop for all levels, and Massachusetts Institute of Technology technique for atom-proximity has big implications for quantum. We're THE quantum computing newsletter, bringing you daily insights into the latest news & research. With leaders in academia, founders, and subscribers from top companies like Xanadu, SandboxAQ, and Qunnect, you're in good company. Join now at www.thedailyqubit.com and never miss a single qubit. #quantumcomputing #quantumphysics #innovation

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