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Research#llm👥 CommunityAnalyzed: Jan 4, 2026 11:57

Inferring the Phylogeny of Large Language Models

Published:Apr 19, 2025 13:47
1 min read
Hacker News

Analysis

This article likely discusses the application of phylogenetic methods, typically used in biology to understand evolutionary relationships, to the field of Large Language Models (LLMs). It suggests that researchers are attempting to trace the 'evolutionary' relationships between different LLMs, potentially to understand their development, identify commonalities, and predict future advancements. The source, Hacker News, indicates a technical audience interested in AI and computer science.

Key Takeaways

    Reference

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    PermalinkHacker News
    Research#llm📝 BlogAnalyzed: Dec 29, 2025 07:50

    The Future of Human-Machine Interaction with Dan Bohus and Siddhartha Sen - #499

    Published:Jul 8, 2021 17:38
    1 min read
    Practical AI

    Analysis

    This article from Practical AI discusses the future of human-AI interaction, focusing on research projects by Dan Bohus and Siddhartha Sen from Microsoft Research. The conversation centers around two projects, Maia Chess and Situated Interaction, exploring the evolution of human-AI interaction. The article highlights the commonalities between the projects, the importance of understanding the human experience, the models and data used, and the complexity of the setups. It also touches on the challenges of enabling computers to better understand and interact with humans more fluidly, and the researchers' excitement about the future of their work.
    Reference

    “We explore some of the challenges associated with getting computers to better understand human behavior and interact in ways that are more fluid.”

    PermalinkPractical AI
    Research#AI Compression📝 BlogAnalyzed: Dec 29, 2025 07:50

    Vector Quantization for NN Compression with Julieta Martinez - #498

    Published:Jul 5, 2021 16:49
    1 min read
    Practical AI

    Analysis

    This podcast episode of Practical AI features Julieta Martinez, a senior research scientist at Waabi, discussing her work on neural network compression. The conversation centers around her talk at the LatinX in AI workshop at CVPR, focusing on the commonalities between large-scale visual search and NN compression. The episode explores product quantization and its application in compressing neural networks. Additionally, it touches upon her paper on Deep Multi-Task Learning for joint localization, perception, and prediction, highlighting an architecture that optimizes computation reuse. The episode provides insights into cutting-edge research in AI, particularly in the areas of model compression and efficient computation.
    Reference

    “What do Large-Scale Visual Search and Neural Network Compression have in Common”

    PermalinkPractical AI