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Launching TheorIA: A Machine-Readable Atlas of Theoretical Physics
If we want AI models to reason about physics, we first need to give them physics they can actually read.
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Datasets for advancing Theoretical Physics and AI
There is a lack of curated datasets in theoretical physics to train better machine learning models. But what exactly is missing and how can we fill the gaps?
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Selected ideas from NeurIPS 2024
NeurIPS 2024, the largest AI research conference, provides a glimpse into the next frontiers. Here are some of the most exciting ideas presented.
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Opening the LLM pipeline
My notes on a great tutorial at NeurIPS 2024 on how to build a Large Language Model, with many practical tips.
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The path to AGI: quantifying bottlenecks
Scaling artificial intelligence to new heights comes with significant challenges, particularly in hardware, energy, and data availability. As we strive towards Artificial General Intelligence (AGI), the hurdles grow—from the immense GPU requirements to the daunting energy consumption and even the scarcity of high-quality training data. These obstacles are demanding, yet they are not insurmountable, paving the way for ambitious innovations and new solutions.
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Normalization in TensorFlow: speed is an issue
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Setting up your GPU TensorFlow platform
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La materia al descubierto
En los primeros años del siglo XX se produjo una revolución extraordinaria en la física con el nacimiento de la mecánica cuántica, pero también se abrió un campo plagado de grandes interrogantes que mantienen intrigados a muchos físicos. Uno de los descubrimientos más sorprendentes fue que la luz, además de ser una onda, también se […]