Paul Margetts’ Post

Lead author Dr. Mark Magee highlighted the potential of AI in supernovae research, stating that while one model currently takes up to 90 minutes to create, AI could generate thousands of models in less than a second. Discover more about this groundbreaking research here: [link] #AI #research #supernovae

New research is being carried out using artificial intelligence (AI) to analyse cosmic explosions. A type of AI known as machine learning will be used to make simulations of star explosions called supernovae, that release elements such as calcium and iron back into the universe. https://lnkd.in/dHeS_kEk

New research is being carried out using artificial intelligence (AI) to analyse cosmic explosions. A type of AI known as machine learning will be used to make simulations of star explosions called supernovae, that release elements such as calcium and iron back into the universe. https://lnkd.in/dHeS_kEk

Artificial intelligence (AI) has been a groundbreaking force, enabling remarkable feats in multiple sectors. However, its astronomical energy demands remain a significant concern. The more complex the tasks AI takes on, the more power it requires. Enter a game-changing method by researchers at Germany's Max Planck Institute for the Science of Light, which could revolutionize AI's energy consumption. #ai #energy #enviroment #neuralnetwork #neuromorphic

🌐 Are we on the cusp of a new era in AI? We are at the brink of a major shift in Artificial Intelligence. Exciting developments in AI research have emerged from Massachusetts Institute of Technology, Caltech, and Northeastern University, introducing a novel neural network architecture known as **Kolmogorov-Arnold Networks (KANs)**. This new model challenges traditional Multi-Layer Perceptrons (MLPs) by implementing learnable activation functions directly on the connections between nodes, eliminating linear weights entirely and using splines for parameterization. This innovation leads to smaller, more efficient networks that outperform their MLP counterparts in accuracy and computational efficiency. KANs are particularly promising due to their enhanced interpretability—a critical feature for applications in scientific research and complex system simulations where understanding model reasoning is essential. They enable clearer visualization of the decision-making process, allowing for deeper insights into their operations. The practical implications of KANs are vast. They offer a more sustainable approach to AI by reducing the computational overhead typically associated with deep learning models. Moreover, their ability to work closely with human operators opens new avenues for collaborative AI in scientific discovery and beyond. 🔗 Want to dive deeper? I recommend diving into the full study [https://lnkd.in/dCgq9qFD] to appreciate the potential of KANs fully. This breakthrough represents a significant step forward in making AI systems more efficient, interpretable, and accessible to a broader range of applications. #ArtificialIntelligence #MachineLearning #Innovation #TechnologyNews #DeepLearning #AIResearch

Can AI be applied to solve the 'Three-Body Problem' in Physics? The three-body problem in physics refers to the mathematical challenge of predicting the motion of three celestial bodies interacting through gravitational forces. This problem has proven notoriously difficult to solve analytically due to its chaotic nature, where small changes in initial conditions can lead to vastly different outcomes over time. While artificial intelligence (AI) has demonstrated remarkable capabilities in various domains, including pattern recognition, optimization, and complex problem-solving, its applicability to solving the three-body problem in physics is limited and unexplored. AI techniques such as machine learning could be applied to approximate solutions to specific instances of the three-body problem. For example, neural networks could be trained on simulated data to predict the future positions of celestial bodies based on their initial conditions. However, it's essential to understand that these solutions would likely be approximate and may not accurately capture the system's full complexity and chaotic behavior. While AI methods may offer some insights or approximations to the three-body problem, solving it remains challenging in theoretical physics. #ai #aiapplications #computationalphysics #problemsolving #research

Exploring the Frontier of AI: #Neuroevolution #artificialintelligence is evolving at an astonishing pace, and one of the most intriguing frontiers is neuroevolution. I have been fascinated by the growth in this area and want to share the same with you. Let me first explain neuroevolution, in a language that I understand. It is an approach that draws inspiration from the process of biological evolution to develop artificial neural networks. It's like AI's own version of Darwin's theory, where the fittest algorithms survive and thrive. Why am I so excited about neuroevolution? It is its potential to create AI systems that can adapt, learn, and improve over time without constant human intervention. Imagine AI that not only solves problems but evolves to tackle new challenges as they arise. Applications are vast, from optimizing industrial processes to enhancing autonomous robots, and even revolutionizing healthcare. As we delve deeper into the world of AI, neuroevolution stands out as a promising pathway to achieve more capable and versatile machines. I am surely keeping an eye on this fascinating journey through the evolution of AI! Are you? #AI #Neuroevolution #ArtificialIntelligence #Innovation #TechTrends #AIevolution #FutureTech

How do you overcome AI data limitations & accurately predict failure? The limitations could be rare failures that inhibit machine learning, a complex data environment, noisy, and incomplete data. Physics Informed AI provides fast time-to-value with insights that accurately identify failure in complex systems. Reach out and speak to our science group. USA-Europe-Asia- Australia Dave Cleminson - GAICD MDS Chris Williams, GAICD Ian Oppermann Dr Nerina Di Lorenzo Andrew Lewis Peter W. Lucy Lin Matt Groarke Launch Pad - Western Sydney University Alex Carpenter Paul Plowman David Golding Mario Battaglia Anthony Cooke J #PhysicsInformedAI #PhysicsAI #AI #MachineLearning #DataScience #ArtificialIntelligence #PhysicsModels #DeepLearning #PhysicsSimulation

TorchExplorer: Revolutionizing Neural Network Visualization for AI Researchers #AI #AItechnology #AItool #artificialintelligence #autogradtraces #datadistribution #interactiveVegaCustomCharts #llm #machinelearning #modulelevelvisualization #networkstructures #neuralnetworkarchitectures #researchers #TorchExplorer #unconventionalmodels #wandb

Although its origin dates back to the 1940s, Artificial Intelligence has experienced a significant boom in recent years. At ARQUIMEA Research Center we are committed to it as one of the main drivers of #transformation in society. We use #AI to try to respond to challenges in fields as varied as #health, #entertainment, #mobility, #advertising and #finance, among others. For this reason, in this 2023 from the AI orbital we have worked with the following lines of research: - Deep learning in AI for Neural Radiance Fields (#NeRF) to speed up and automate the scanning and modelling of 3D scenes and objects for both #cinema and extended reality (#XR) experiences 🎬. - Geometric deep learning and graph neural networks for accelerating drug discovery, significantly reducing drug development 💊. - Detection of sperm whale blows using deep learning to prevent fast ferries from colliding with cetaceans 🐳. If you want to know more about our AI orbital, feel free to click: 👉https://lnkd.in/dsKYADvJ #ARQUIMEA #Research #AI