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Efficient Long-Term Prediction of Chaotic Systems Using Physics-Informed Neural Operators: Overcoming Limitations of Traditional Closure Models
Predicting the long-term behavior of chaotic systems, such as those used in climate modeling, is essential but requires significant computational resources due to the need for high-resolution spatiotemporal grids. One alternative to fully-resolved simulations (FRS) is to use coarse grids, with closure models correcting for errors by approximating the missing fine-scale information. While machine learning…
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Tubos Reunidos commits to total digital production
Over its more than 130 years, Tubos Reunidos has gone through various phases of modernization and expansion, including incorporating new technologies and more efficient production processes to stay competitive in a fast-paced market. The need to modernize has only intensified over the last decade. Today, producing seamless steel tubes for the energy and industrial sectors is…
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6 ways to prepare for a difficult conversation with a direct report
IT leaders face many challenging tasks, but few are more daunting than engaging with a direct report who has, in one way or another, become unreliable or neglectful. There are various reasons why a manager or executive may need to have a frank conversation with a direct report, says Orla Daly, CIO at training services…
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How to Switch iPhones or iPads and Transfer Data (2024)
Everything you need to know to successfully transfer your contacts, music, photos, and apps from one iOS device to another.
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Can We Optimize Large Language Models Faster Than Adam? This AI Paper from Harvard Unveils SOAP to Improve and Stabilize Shampoo in Deep Learning
Efficient optimization of large-scale deep learning models remains a significant challenge as the cost of training large language models (LLMs) continues to escalate. As models grow larger, the computational burden and time required for training increase substantially, creating a demand for more efficient optimizers that can reduce both training time and resources. This challenge is…
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Flu season is coming—and so is the risk of an all-new bird flu
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. September will soon be drawing to a close. The kids are back to school, and those of us in the Northern Hemisphere are experiencing the joys the end…
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Diagram of Thought (DoT): An AI Framework that Models Iterative Reasoning in Large Language Models (LLMs) as the Construction of a Directed Acyclic Graph (DAG) within a Single Model
Previous research on reasoning frameworks in large language models (LLMs) has explored various approaches to enhance problem-solving capabilities. Chain-of-Thought (CoT) introduced articulated reasoning processes, while Tree-of-Thought (ToT) and Graph-of-Thought (GoT) expanded on this concept by incorporating branching possibilities and complex relationships between reasoning steps. Cumulative Reasoning (CR) introduced collaborative processes involving multiple specialized LLMs. These…
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Un paso adelante en la digitalización del Camino de Santiago francés sin renunciar a su legado, tradición y espiritualidad
Una tradición que data del siglo IX, un legado dispuesto a reinventarse con el fin de seguir latiendo. El Camino de Santiago es un indiscutible del patrimonio mundial, una joya del turismo español que, ahora, vive su tercera gran transformación de la mano de la tecnología y la innovación. Y lo hace, como no podía…
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Big Tech’s New Adversaries in Europe
For the past five years, Big Tech has faced a flurry of new rules and reprimands from Brussels. Now with a new team taking over the European Commission, relations may be entering a new era.
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g1: Using Llama-3.1 70b on Groq to Create o1-like Reasoning Chains
While LLMs have shown promise in natural language processing, they often need help to perform multi-step reasoning and problem-solving, particularly in areas that require abstract thinking and drawing inferences from incomplete or fragmented information. The ability to reason effectively is crucial for LLMs to be truly useful in real-world applications. This limitation hinders the application…