Tag: neural networks
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Hacker News: Nobel Prize in Physics awarded to John Hopfield and Geoffrey Hinton [pdf]
Source URL: https://www.nobelprize.org/uploads/2024/09/advanced-physicsprize2024.pdf Source: Hacker News Title: Nobel Prize in Physics awarded to John Hopfield and Geoffrey Hinton [pdf] Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the awarding of the Nobel Prize in Physics 2024 to John J. Hopfield and Geoffrey E. Hinton for their foundational discoveries in artificial neural…
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New York Times – Artificial Intelligence : Nobel Physics Prize Awarded for Pioneering A.I. Research by 2 Scientists
Source URL: https://www.nytimes.com/2024/10/08/science/nobel-prize-physics.html Source: New York Times – Artificial Intelligence Title: Nobel Physics Prize Awarded for Pioneering A.I. Research by 2 Scientists Feedly Summary: With work on machine learning that uses artificial neural networks, John J. Hopfield and Geoffrey E. Hinton “showed a completely new way for us to use computers,” the committee said. AI…
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Hacker News: Novel Architecture Makes Neural Networks More Understandable
Source URL: https://www.quantamagazine.org/novel-architecture-makes-neural-networks-more-understandable-20240911/ Source: Hacker News Title: Novel Architecture Makes Neural Networks More Understandable Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses a novel type of neural network called Kolmogorov-Arnold networks (KANs), designed to enhance the interpretability and transparency of artificial intelligence models. This innovation holds particular relevance for fields like…
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CSA: Mechanistic Interpretability 101
Source URL: https://cloudsecurityalliance.org/blog/2024/09/05/mechanistic-interpretability-101 Source: CSA Title: Mechanistic Interpretability 101 Feedly Summary: AI Summary and Description: Yes Summary: The text discusses the challenge of interpreting neural networks, introducing Mechanistic Interpretability (MI) as a novel methodology that aims to understand the complex internal workings of AI models. It highlights how MI differs from traditional interpretability methods, focusing…