Eric Syphard

In case you missed it.... We published a great H:F guest episode with Joel Portice, CEO of egnite - delving into the transformative role of data and AI in revolutionizing cardiovascular care. Joel shares his journey of leading five successful healthcare data companies, including the genesis and rapid growth of egnite, which was spun out from Edwards Life Sciences. egnite's flagship platform, Cardio Care, uses advanced analytics, machine learning, and AI to identify and manage cardiovascular patients, saving lives daily. This episode is a strong addition to our Heart Health Series on Health:Further. Please check it out: https://bit.ly/4fKmzCH

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A compelling read from McKinsey & Company on the importance of a robust infrastructure for integrating and launching AI. #ChartingthepathtodataandAI emphasizes the need for seamless integration and interoperability to drive AI initiatives. #MicrosoftAzureHealthDataServicesFHIR and #MicrosoftFabric serve as the pivotal platform for this endeavor. Explore more at the link: https://lnkd.in/dNhrDz5B.

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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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In conjunction with Biden’s AI Executive Order from last fall, the President's Council of Advisors on Science and Technology (PCAST) just released a study and report exploring the implications of AI for scientific research. A key focus is on how a well-designed, equitably shared, and responsibly used data and AI infrastructure could empower scientists and others to tackle some of our greatest societal and global challenges. Executive summary is below.  Special thanks to Eric Horvitz, Chief Scientific Officer at Microsoft for his work on this committee. #ai #responsibleAI #aiinresearch

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This tiny cute robot, Dr. Guero's PRIMER-V2 was a two-wheeled wonder that actually knew how to pedal and steer! __ 🚨 82% of AI & Data professionals surveyed said developing a businesses AI strategy is the primary responsibility of a CAIO - https://lnkd.in/eqi7K3d9 🚀Subscribe to the newsletter: https://lnkd.in/ex9mu2hM 🔥 ‎Get daily AI updates on WhatsApp: https://lnkd.in/eav_GZ-J #Robotics #Engineering #Innovation #AI #FutureTechnology

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tinyBenchmarks: Revolutionizing LLM Evaluation with 100-Example Curated Sets, Reducing Costs by Over 98% While Maintaining High Accuracy [Colab Notebook Included] The research team from the University of Michigan, the University of Pompeu Fabra, IBM Research, MIT, and the MIT-IBM Watson AI Lab introduced tinyBenchmarks. These smaller versions of popular benchmarks are designed to provide reliable performance estimates using fewer examples. For example, their analysis showed that evaluating an LLM on just 100 curated examples from the MMLU benchmark can predict its performance with an average error of under 2%. This approach drastically reduces the resources needed for evaluation while providing accurate results. The researchers used several strategies to develop these tinyBenchmarks. One method involves stratified random sampling, where examples are chosen to represent different data groups evenly. Another approach is clustering based on model confidence, where examples likely to be correctly or incorrectly predicted by the LLM are grouped. The team applied item response theory (IRT), a statistical model traditionally used in psychometrics, to measure the latent abilities required to respond to benchmark examples. By clustering these representations, they created robust evaluation sets that could effectively estimate performance..... Read our full take on 'tinyBenchmarks': https://lnkd.in/gimsMM3m Paper: https://lnkd.in/gnKJhhSV GitHub: https://lnkd.in/g5jZnYBy HF Models: https://lnkd.in/g4skYq-z Colab Notebook: https://lnkd.in/ggHQPUNk Lucas Weber Leshem Choshen Yuekai Sun Gongjun Xu Mikhail Yurochkin Felipe Maia Polo University of Michigan Universitat Pompeu Fabra Massachusetts Institute of Technology IBM Research

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When I talk to investors, advisors, and technologists outside of the scientific community about the role AI has in scientific research, they tend to immediately gravitate towards generative models automatically writing papers. In this interview with TMC, I describe the much broader impact AI, machine learning, and neural networks will have on science. From generating hypothesis to custom object graph models for forecasting, I am thrilled to be help accelerate the impact AI will have on scientific research.

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💥💥💥 Crafting Qubits: Harnessing Quantum Mechanics for Computation How do we actually create and manipulate qubits, essential for realizing quantum computation? Chief Scientist of Hardware Technology Development at Quantinuum, Patty Lee, joins Brian Greene to discuss various quantum strategies, their achievements to date and pathways forward. This program is part of the Big Ideas series, supported by the John Templeton Foundation. Participant: Patty Lee Moderator: Brian Greene 00:00 - Introduction 01:51 - Participant Introduction 02:44 - Approaches To Quantum Computing 07:19 - The Trapped Ion Approach In Practice 22:58 - Obstacles In Quantum Computing 35:14 - The Future Of Quantum Computing 40:10 - Credits 👉 https://lnkd.in/dvyXVKaf #quantumcomputing

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NEWS - Validation and Evaluation for Trustworthy Artificial Intelligence (VET AI) Act Introduced U.S. Senators John Hickenlooper and Shelley Moore Capito introduced the bipartisan Validation and Evaluation for Trustworthy Artificial Intelligence (VET AI) Act (details below). I'm excited about this bill because it builds on recommendations we--as the IEEE USA AI Policy committee--made in a position statement released last February (https://lnkd.in/e9sETwNM). It was a pleasure to co-lead this effort with Dr. Cari Miller. I'd also like to thank Sen. Hickenlooper's staff for being receptive to feedback that I, and colleagues, provided on this bill. Details: ➡ Directs NIST, in coordination with the Department of Energy and National Science Foundation, to develop voluntary specifications and guidelines for developers and deployers of AI systems to conduct internal assurance and work with third parties on external assurance regarding the verification and red-teaming of AI systems. ➡ Such specifications require considerations for data privacy protections, mitigations against potential harms to individuals from an AI system, dataset quality, and governance and communications processes of a developer or deployer throughout the AI systems’ development lifecycles. ➡ Establish a collaborative Advisory Committee to review and recommend criteria for individuals or organizations seeking to obtain certification of their ability to conduct internal or external assurance for AI systems. ➡ Require NIST to conduct a study examining various aspects of the ecosystem of AI assurance, including the current capabilities and methodologies used, facilities or resources needed, and overall market demand for internal and external AI assurance.

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Check out the #AI News Roundup for the Week of April 29 from the Mississippi Artificial Intelligence Network (MAIN)! One of the objectives of #MAIN is to raise awareness about Artificial Intelligence (AI). MAIN helps facilitate this awareness by staying up-to-date on AI news. As part of this effort, MAIN compiles a variety of AI news every week and creates a news roundup. #MAIN #MississippiArtificialIntelligenceNetwork #AINews #News #AIAwareness #NewsRoundup #Mississippi #ArtificialIntelligence #GenAI #GenerativeAI

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Thank you to Neil Gentleman-Hobbs with smartR AI for sharing his article on GenAI and software. The Buy vs. Build is an important consideration. "Generative AI can dramatically accelerate software development by automating tasks like code generation, testing, and documentation. This can lead to faster time-to-market and reduced development costs. However, the cost of acquiring and maintaining AI tools, as well as the need for skilled personnel to manage them, should be factored into the overall cost-benefit analysis." #genai #software #devops #informationtechnology #enterprisetech https://lnkd.in/giSmKXxU

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In a recent in-depth discussion, Colin Masson, Research Director for Industrial AI at ARC Advisory Group, and Dustin Johnson, CTO of Seeq Corporation—a global provider of advanced analytics, AI, and enterprise monitoring to industrial companies—explored the transformative potential of industrial AI, covering everything from user interface design (dare we call it 'GenUI'?) to the nuances of AI copilots, assistants, and agents. This conversation offers invaluable insights for industrial customers looking to leverage their time-series data and AI effectively. #IndustrialAI #GenAI #GenUI #AIAgents #AICopilots #AIAssistants #TimeSeriesData https://lnkd.in/gyJ5V86Z

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Kannan Raman of Amazon Web Services (AWS) sits down with Amar Drawid, Ph.D. to discuss the role of AI in transforming life sciences and the importance of data strategy being tied to business outcomes. They explore the challenges of data strategy, the importance of cultural shifts within organizations, and the monetization of data as a product.

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Check out this May 14 Webinar >> Complex #AI Agents — Use #GenAI to Automate Complex Tasks. Register now at: https://lnkd.in/eDiR5D5H -- presented by @Abacus.AI Join Abacus in this engaging 2-hour webinar on how to use advanced AI agents to automate and revolutionize your business operations. With the help of @Abacus.AI, you can develop agents capable of automating intricate tasks. These agents will formulate a strategy, which you can implement upon approval. These state-of-the-art AI Agents are designed with a planning feature that allows you to: 🟣Streamline complex business workflows: automate both routine and intricate business tasks. 🟣Achieve seamless integration and communication with other enterprise systems. 🟣Enhance automation across various departments including customer support, marketing, technical support, IT, sales, and content creation. Participants in any of the demonstrations will receive certificates. 🟣🟣🟣🟣 #GenerativeAI #DeepLearning #MachineLearning #LLMs #DataScience #DigitalTransformation #CDO #CTO

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Check out this May 14 Webinar >> Complex #AI Agents — Use #GenAI to Automate Complex Tasks. Register now at: https://lnkd.in/eDiR5D5H -- presented by @Abacus.AI Join Abacus in this engaging 2-hour webinar on how to use advanced AI agents to automate and revolutionize your business operations. With the help of @Abacus.AI, you can develop agents capable of automating intricate tasks. These agents will formulate a strategy, which you can implement upon approval. These state-of-the-art AI Agents are designed with a planning feature that allows you to: 🟣Streamline complex business workflows: automate both routine and intricate business tasks. 🟣Achieve seamless integration and communication with other enterprise systems. 🟣Enhance automation across various departments including customer support, marketing, technical support, IT, sales, and content creation. Participants in any of the demonstrations will receive certificates. 🟣🟣🟣🟣 #GenerativeAI #DeepLearning #MachineLearning #LLMs #DataScience #DigitalTransformation #CDO #CTO

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Working with the Palantir Technologies team, across multiple shared customers, has been a great experience for Physna. They do not partner in the usual way - here are three examples: 1. Speed: Palantir is a larger company, but they genuinely operate at series A/B speed. The way they build product and GTM is unique, as an operating model, and allows them to move unusually fast. 2. Scrappy, customer obsessed: Like early stage startups, they Obsess over customer problems. They invest upfront in new customers, and unlike most companies - they are support production engineers going onsite, to solve problems in non Palantir (eg. on prem, customer) environments. 3. Partnering approach: In our experience, the way they partner is mutually beneficial on day 1. Instead of waiting for you to bring them business, they go out of their way to make you successful on their platform (bringing you leads). Special thanks to Nikhil Khosla and Aaron Jaffe, who are both exceptional talents - and represent what makes Palantir a different type of partner. If you work at a startup focusing on Defense or F500 segments, consider if there is an opportunity to work with them. Our partnership has been great, and we are looking forward to expanding it:

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The Department of Defense is tackling complex challenges to integrate AI: managing vast amounts of data, ensuring ethical use, and aligning AI-driven decisions with mission-critical goals. Domino Data Lab's Joel Meyer highlights a key need: AI must seamlessly aggregate fragmented data to power faster, safer decision-making on the battlefield. Learn how experts are shaping AI’s future in defense in this USA TODAY feature. https://gag.gl/j6gRVk

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This is a useful guide for understanding the various stages of enterprise AI competitiveness, but it comes with a strong word of caution from me -- it isn't from a systems perspective. Any enterprise that isn't employing a systems perspective (including human, financial, data and AI systems) will only be competitive if they are either extremely lucky or have some other advantage like an absolute monopoly on something everyone else must have. Those who want to immediately proceed to the most advanced EAI level regarding the human workforce and ecosystems, the KOS is available. It does require integration with specialized vertical software and data for optimization, but is a refined enterprise-wide AI OS, with end-to-end precision data management. The system is mature, has been tested multiple times, and is ready to scale in 2025. We've been collaborating with leaders in one large industry for most of the year and several key customers are teed up. It currently looks like many of their peers will adopt as well. Never underestimate the value of independent R&D following the evidence over an extended period, resulting in refined systems. We didn't jump out of the plane before thinking about parachutes.

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