Natasha Lepore

series 1/3 ( Physics Informed Neural Network -PINN) Nonlinear PDEs are foundational in scientific research, describing diverse phenomena such as fluid dynamics, quantum mechanics, and population dynamics. They emerge when modeling complex systems, enabling predictions and discoveries. Solving these equations facilitates simulation and analysis, providing insights into system behavior and evolution over time. In scenarios like a chemical spill at an industrial site, where pollutants threaten groundwater, environmental scientists rely on mathematical models based on nonlinear PDEs to understand and predict pollutant dispersion over time. The advection-diffusion equation is a prevalent model used for this purpose, as it considers both the movement (advection) and spreading (diffusion) of pollutants within the groundwater system. By utilizing such models, scientists can simulate the complex behavior of pollutants, allowing for informed decision-making regarding pollution mitigation and environmental remediation strategies. Nonlinear partial differential equations (PDEs) are crucial in scientific research across various disciplines: Mathematical Biology: Nonlinear PDEs are vital in mathematical biology, describing the dynamics of biological systems and populations. Models like reaction-diffusion equations and predator-prey models offer insights into population dynamics, disease spread, and evolutionary processes, guiding strategies for disease control and ecosystem management, as seen in the context of disease outbreaks like COVID-19. Quantum Physics: Nonlinear PDEs are pivotal in quantum mechanics, governing the behavior of quantum systems through equations like Schrödinger's equation and its extensions, such as the nonlinear Schrödinger equation (NLS). These equations offer insights into phenomena like Bose-Einstein condensates and nonlinear optical effects, spanning microscopic to macroscopic scales. Fluid Dynamics: Nonlinear PDEs are indispensable in fluid dynamics, particularly the Navier-Stokes equations, which describe fluid motion and turbulence. These equations are crucial for applications ranging from aerodynamics to weather prediction, enabling a deep understanding of fluid behavior in diverse contexts. Plasma Physics: Nonlinear PDEs are fundamental in plasma physics, governing the behavior of ionized gases in fusion reactors, space plasmas, and astrophysical phenomena. Models like the magnetohydrodynamic (MHD) equations and Vlasov-Maxwell equations capture the complexities of plasma dynamics, offering opportunities for harnessing fusion energy and space propulsion. Image Processing: Nonlinear PDEs are widely utilized in image processing and computer vision for tasks like image denoising, edge detection, and segmentation. Models such as the nonlinear diffusion equation and level-set method enable robust and accurate image analysis in various applications, including medical imaging and remote sensing.

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The Dendritic Integration Theory Table 1 How Dendritic Integration Theory Explains Global Dynamics Conscious state is related to sustained complex neural dynamics Consciousness is related to signal propagation in thalamocortical networks Conscious perception is correlated to a late ‘ignition’ of large-scale networks Consciousness is related to activation of ‘hub’ regions of cortex Conscious state is characterized by specific temporal dynamics Global Dynamics and Local Mechanisms of Consciousness Figure 1 Key Figure. A Cellular Mechanism Supporting the Global Dynamics of Consciousness. https://lnkd.in/g9bZWqYj

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I am honored to participate in the webinar on "Development of novel cancer treatments". 🫀There are three promising fields for saving the world: Amir Ali Hamidieh Quantum, Artificial Intelligence, Genome Editing. 👁️ Here's a brief overview of each topic: 💥Quantum: Quantum technology harnesses the principles of quantum mechanics to revolutionize computing, communication, and simulation. It offers unprecedented capabilities for processing information and solving complex problems, potentially addressing challenges such as climate change and resource optimization. ☠️Artificial Intelligence: Artificial intelligence (AI) involves the development of computer systems capable of performing tasks that typically require human intelligence. AI technologies enable prediction, automation, and decision-making across various domains, from healthcare to environmental management, contributing to addressing global issues and improving quality of life. 🧬Genome Editing: Genome editing refers to the precise modification of DNA within an organism's genome. This technology, exemplified by CRISPR-Cas9, holds promise for treating genetic disorders, enhancing agricultural productivity, and mitigating environmental challenges by engineering organisms for desired traits or behaviors. #cancerresearch #genomics #geneediting #quantum #artificialintelligence #futureleaders #futurefocused #futureleadership #research #development #cancertherapy

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Microsoft just launched Copilot Vision in Edge—the first AI that can navigate the internet with you in real time. I sat down for an exclusive interview with Mustafa Suleyman (CEO of Microsoft AI) to discuss how it works, infinite memory, AI companions, agents, and more. Dec 5,2024. https://lnkd.in/dxPCCACY #AI #microsoft #copilot

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📢 AI Breakthroughs Honored with Two Nobel Prizes in 2024 🏆 Nobel Prize in Physics: John J. Hopfield and Geoffrey E. Hinton were honored for their “foundational discoveries and inventions that enable machine learning with artificial neural networks.” Hopfield’s associative memory model helped computers store and recall patterns. Geoffrey Hinton improved the Boltzmann machine. These changes allowed AI to learn patterns and classify data automatically. Their discoveries laid the foundation for machine learning technologies. 🏆Nobel Prize in Chemistry: David Baker was honored "for computational protein design." He developed the Rosetta software, which was later enhanced with AI, to build new proteins. Demis Hassabis and John Jumper were awarded the Nobel Prize "for protein structure prediction" with their AI tool AlphaFold, which can predict the 3D shape of nearly any protein in minutes! Rosetta and AlphaFold have transformed how we understand and create proteins and solved problems that have puzzled scientists for over 50 years. 💡These prizes highlight AI’s growing impact on science. Learning basic AI is more important than ever! #NobelPrize #AI #MachineLearning

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In just two days, AI has scored 2 Nobel Prizes. First up, Demis Hassabis and John Jumper from Google DeepMind got the Nobel Prize in Chemistry for their work on AlphaFold, an AI system that cracked one of biology’s toughest problems: predicting 3D protein structures from simple amino acid sequences. 🤯 This isn't just a cool science project, it’s revolutionizing healthcare, speeding up drug discovery, and giving us fresh insights into genetic disorders and climate challenges. All from a piece of tech that’s making what used to take years possible in hours. This follows another win for neural networks (thank you, "Godfather of AI"). Together, these breakthroughs are more than just trophies, they’re a game-changer for humanity. Here’s the thing, AI is spreading like wildfire, and it's going to be everywhere soon. From science to your everyday life, it's knocking on the door, ready to make things faster, smarter, and better. But here's the real question, are we ready? Can we adapt as fast as AI is evolving? What do you think? 🤔

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It was great to contribute in this article on the use of #AI in Assisted Reproductive Technologies, co-authored by leading experts in the #IVF field. Read below for a summary of the findings.

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Yesterday, I posted that #AI-driven research in chemistry would receive the Nobel Prize, and today's announcement confirmed my prediction. This prestigious award marks the second Nobel Prize for AI-related advancements this year. The Nobel Committee's acknowledgment of AI's contribution to protein structure prediction underscores its transformative impact on #bioinformatics, inspiring researchers globally. #Bioinformatics #NobelPrize2024 #ChemistryNobel #AlphaFold Check out my post about this forecast in the first comment.

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I’m not usually one for predictions, but I fully agree with Hadi Javeed: 2025 will be the year the human–AI–computer interface hits production across many industries. We’re already seeing an interesting bimodal distribution in how people are advocating for large language models to be used: • On one end, automating “low-cognition, high-repetition” tasks like scribing and documentation. • On the other, the ambitious “high-cognition, low-pragmatism” efforts—like AI diagnosing patients directly or tackling advanced PhD-level problems—where we’re all comparing who has the “smartest” model. Both ends of the spectrum matter, but I’m most excited about the orchestration layer—where thoughtful workflow design and deep subject matter expertise can unlock AI’s potential across day to day operations for a broader impact. In 2025, I’m eager to focus on refining the Clinician–AI–Patient interface and evolving towards tomorrow’s Clinician–AI–population workflows. I can’t wait to see the new possibilities and innovations that 2025 will bring for all of us!

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Interested in a perspective piece on the design of fluorescent CB2R inverse agonists? Check out this quick piece I co-authored with the lab of Alexander J. Grenning published yesterday in ACS Central Science! https://lnkd.in/d2XyA3P6

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Researchers highlight Nobel-winning AI breakthroughs and call for interdisciplinary innovation Date: November 26, 2024 Source: Carnegie Mellon University Summary: A new article examines the convergence of physics, chemistry, and AI, highlighted by recent Nobel Prizes. It traces the historical development of neural networks, emphasizing the role of interdisciplinary research in advancing AI. The authors advocate for nurturing AI-enabled polymaths to bridge the gap between theoretical advancements and practical applications, driving progress toward artificial general intelligence. In 2024, the Nobel Prize in physics was awarded to John Hopfield and Geoffrey Hinton for their foundational work in artificial intelligence (AI), and the Nobel Prize in chemistry went to David Baker, Demis Hassabis, and John Jumper for using AI to solve the protein-folding problem, a 50-year grand challenge problem in science. A new article, written by researchers at Carnegie Mellon University and Calculation Consulting, examines the convergence of physics, chemistry, and AI, highlighted by recent Nobel Prizes. It traces the historical development of neural networks, emphasizing the role of interdisciplinary research in advancing AI. The authors advocate for nurturing AI-enabled polymaths to bridge the gap between theoretical advancements and practical applications, driving progress toward artificial general intelligence. The article is published in Patterns.

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What does prefrontal cortex activation 🧠 ⚡ during interactions with a robot 🤖 indicate? Today, I read an interesting paper about brain and human robot interaction: "Using near infrared spectroscopy to index temporal changes in affect in realistic human-robot interactions" Authors: Megan Strait Matthias Scheutz I wrote a summary: Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technique that uses near-infrared light to measure changes in blood oxygen levels in the brain, which reflect brain activation. fNIRS has advantages over other brain imaging methods, such as greater portability compared to fMRI (allowing participants to wear a cap and move around) and better spatial resolution than EEG. In this pioneering study, researchers used fNIRS to assess participants' emotional responses during real-time interactions with robots. The study involved 38 young adult participants and included two types of robots: one stereotypical and one more human-like, across three conditions: observing the robot and another human remotely, remotely interacting with a robot, and directly interacting with a robot. The fNIRS results during the first interaction suggested that brain activation, specifically in the prefrontal cortex (PFC), was highest when participants interacted directly with the more human-like robot. Interestingly, contrary to some findings that suggest participants prefer human-like features in robots, PFC activation reflected stronger emotional responses, primarily eeriness and aversion, toward the human-like robot. The PFC activation may also indicate increased efforts in emotion regulation among participants. However, during repeated interactions, this initial heightened brain activation toward the human-like robot was not observed. 💡 These findings suggest that repeated interactions with a robot may reduce participants' initial negative emotional responses and make them feel more comfortable during the interaction. 💡 This study provides early insights into how the PFC responds to different types of robots and how these responses can be interpreted alongside participants' self-reported emotional experiences.

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Generative AI for physics will not really be about preserving microscopic symmetries but recovering emergent broken symmetries in just the right way. This is a realization obvious to stat mech and condensed matter scientists, but currently generally very much missing from the "AI will eat your physics/chemistry breakfast lunch & dinner" hype. source: wonderful undergrad book on stat mech of phase transitions by Steven Kivelson et al @ Stanford https://lnkd.in/ewUQYG-Y

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Is it possible for cancer to recur after 24 years? I was traveling yesterday, and during a conversation on the plane, my co-passenger shared that her father had experienced a recurrence of colon cancer after 24 years. Since that time, I have been taken aback by the possibility. When is it permissible to declare an individual cancer-free, and when is it not? How is it possible for cancer cells to remain in a latent state for an extended period of time, and what is the cause of this? Do you have any perspective on individuals who are involved in the field of cancer? #research #cancer #cancerresearch #immunesystem #immunology

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