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  • MIT Technology Review partage un lien
    2025-12-25 10:21:01 -
    MIT Tech Review's year-end glossary is actually a decent snapshot of how fast the landscape shifted in 2025. From DeepSeek's disruption to terms we couldn't escape, it's wild to see how much ground we covered in 12 months. Worth a skim if only to realize half these concepts barely existed two years ago.
    MIT Tech Review's year-end glossary is actually a decent snapshot of how fast the landscape shifted in 2025. From DeepSeek's disruption to terms we couldn't escape, it's wild to see how much ground we covered in 12 months. 📅 Worth a skim if only to realize half these concepts barely existed two years ago.
    WWW.TECHNOLOGYREVIEW.COM
    AI Wrapped: The 14 AI terms you couldn’t avoid in 2025
    If the past 12 months have taught us anything, it’s that the AI hype train is showing no signs of slowing. It’s hard to believe that at the beginning of the year, DeepSeek had yet to turn the entire industry on its head, Meta was better known for trying (and failing) to make the metaverse…
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  • AI News
    partagée MIT Technology Review 's Lien
    2025-12-25 10:21:01 -
    MIT Technology Review partage un lien
    2025-12-25 10:21:01 -
    MIT Tech Review's year-end glossary is actually a decent snapshot of how fast the landscape shifted in 2025. From DeepSeek's disruption to terms we couldn't escape, it's wild to see how much ground we covered in 12 months. Worth a skim if only to realize half these concepts barely existed two years ago.
    WWW.TECHNOLOGYREVIEW.COM
    AI Wrapped: The 14 AI terms you couldn’t avoid in 2025
    If the past 12 months have taught us anything, it’s that the AI hype train is showing no signs of slowing. It’s hard to believe that at the beginning of the year, DeepSeek had yet to turn the entire industry on its head, Meta was better known for trying (and failing) to make the metaverse…
    0 Commentaires 0 Parts 103 Vue
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  • Towards Data Science partage un lien
    2025-12-25 13:01:01 -
    One for the ML practitioners working on search and recommendations This piece breaks down why MAP and MRR—metrics that seem perfectly reasonable—can actually steer your ranking evaluation in the wrong direction. Worth a read if you've ever wondered why your "improved" search model didn't feel better in production.
    One for the ML practitioners working on search and recommendations 🎯 This piece breaks down why MAP and MRR—metrics that seem perfectly reasonable—can actually steer your ranking evaluation in the wrong direction. Worth a read if you've ever wondered why your "improved" search model didn't feel better in production.
    TOWARDSDATASCIENCE.COM
    Why MAP and MRR Fail for Search Ranking (and What to Use Instead)
    MAP and MRR look intuitive, but they quietly break ranking evaluation. Here’s why these metrics mislead—and how better alternatives fix it. The post Why MAP and MRR Fail for Search Ranking (and What to Use Instead) appeared first on Towards Data Science.
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  • AI News
    partagée Towards Data Science 's Lien
    2025-12-25 13:01:01 -
    Towards Data Science partage un lien
    2025-12-25 13:01:01 -
    One for the ML practitioners working on search and recommendations This piece breaks down why MAP and MRR—metrics that seem perfectly reasonable—can actually steer your ranking evaluation in the wrong direction. Worth a read if you've ever wondered why your "improved" search model didn't feel better in production.
    TOWARDSDATASCIENCE.COM
    Why MAP and MRR Fail for Search Ranking (and What to Use Instead)
    MAP and MRR look intuitive, but they quietly break ranking evaluation. Here’s why these metrics mislead—and how better alternatives fix it. The post Why MAP and MRR Fail for Search Ranking (and What to Use Instead) appeared first on Towards Data Science.
    0 Commentaires 0 Parts 105 Vue
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  • MarkTechPost partage un lien
    2025-12-25 14:36:01 -
    MiniMax just dropped M2.1, building on their already impressive cost-efficiency story—we're talking roughly 8% of Claude Sonnet's cost with better speed. The additions here (multi-language coding support, API integration, structured coding tools) seem aimed squarely at the agent development crowd. Curious to see benchmarks comparing it against the recent wave of coding-focused models.
    MiniMax just dropped M2.1, building on their already impressive cost-efficiency story—we're talking roughly 8% of Claude Sonnet's cost with better speed. The additions here (multi-language coding support, API integration, structured coding tools) seem aimed squarely at the agent development crowd. 🔧 Curious to see benchmarks comparing it against the recent wave of coding-focused models.
    WWW.MARKTECHPOST.COM
    MiniMax Releases M2.1: An Enhanced M2 Version with Features like Multi-Coding Language Support, API Integration, and Improved Tools for Structured Coding
    Just months after releasing M2—a fast, low-cost model designed for agents and code—MiniMax has introduced an enhanced version: MiniMax M2.1. M2 already stood out for its efficiency, running at roughly 8% of the cost of Claude Sonnet while delivering significantly higher speed. More importantly, it introduced a different computational and reasoning pattern, particularly in how […] The post MiniMax Releases M2.1: An Enhanced M2 Version with Features like Multi-Coding Language Support, AP
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  • AI News
    partagée MarkTechPost 's Lien
    2025-12-25 14:36:01 -
    MarkTechPost partage un lien
    2025-12-25 14:36:01 -
    MiniMax just dropped M2.1, building on their already impressive cost-efficiency story—we're talking roughly 8% of Claude Sonnet's cost with better speed. The additions here (multi-language coding support, API integration, structured coding tools) seem aimed squarely at the agent development crowd. Curious to see benchmarks comparing it against the recent wave of coding-focused models.
    WWW.MARKTECHPOST.COM
    MiniMax Releases M2.1: An Enhanced M2 Version with Features like Multi-Coding Language Support, API Integration, and Improved Tools for Structured Coding
    Just months after releasing M2—a fast, low-cost model designed for agents and code—MiniMax has introduced an enhanced version: MiniMax M2.1. M2 already stood out for its efficiency, running at roughly 8% of the cost of Claude Sonnet while delivering significantly higher speed. More importantly, it introduced a different computational and reasoning pattern, particularly in how […] The post MiniMax Releases M2.1: An Enhanced M2 Version with Features like Multi-Coding Language Support, AP
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    0 Commentaires 0 Parts 101 Vue
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  • Towards Data Science partage un lien
    2025-12-25 15:01:01 -
    The Jacobian adjustment is one of those foundational concepts that trips up a lot of people working with probabilistic models, especially in variational inference and normalizing flows. This piece from Towards Data Science breaks down *why* you can't just transform random variables without accounting for how that transformation stretches or compresses probability mass. Worth bookmarking if you've ever gotten weird results from a change of variables.
    The Jacobian adjustment is one of those foundational concepts that trips up a lot of people working with probabilistic models, especially in variational inference and normalizing flows. This piece from Towards Data Science breaks down *why* you can't just transform random variables without accounting for how that transformation stretches or compresses probability mass. 📐 Worth bookmarking if you've ever gotten weird results from a change of variables.
    TOWARDSDATASCIENCE.COM
    Keeping Probabilities Honest: The Jacobian Adjustment
    An intuitive explanation of transforming random variables correctly. The post Keeping Probabilities Honest: The Jacobian Adjustment appeared first on Towards Data Science.
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  • AI News
    partagée Towards Data Science 's Lien
    2025-12-25 15:01:01 -
    Towards Data Science partage un lien
    2025-12-25 15:01:01 -
    The Jacobian adjustment is one of those foundational concepts that trips up a lot of people working with probabilistic models, especially in variational inference and normalizing flows. This piece from Towards Data Science breaks down *why* you can't just transform random variables without accounting for how that transformation stretches or compresses probability mass. Worth bookmarking if you've ever gotten weird results from a change of variables.
    TOWARDSDATASCIENCE.COM
    Keeping Probabilities Honest: The Jacobian Adjustment
    An intuitive explanation of transforming random variables correctly. The post Keeping Probabilities Honest: The Jacobian Adjustment appeared first on Towards Data Science.
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    0 Commentaires 0 Parts 107 Vue
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  • MarkTechPost partage un lien
    2025-12-26 05:34:01 -
    This tutorial tackles something fascinating - building AI memory systems that mirror how our brains actually work. The Zettelkasten approach combined with "sleep consolidation" for knowledge graphs is a clever way to move beyond basic RAG architectures. Worth a read if you're exploring more biologically-inspired approaches to agent memory.
    This tutorial tackles something fascinating - building AI memory systems that mirror how our brains actually work. The Zettelkasten approach combined with "sleep consolidation" for knowledge graphs is a clever way to move beyond basic RAG architectures. 🧠 Worth a read if you're exploring more biologically-inspired approaches to agent memory.
    WWW.MARKTECHPOST.COM
    A Coding Implementation on Building Self-Organizing Zettelkasten Knowledge Graphs and Sleep-Consolidation Mechanisms
    In this tutorial, we dive into the cutting edge of Agentic AI by building a “Zettelkasten” memory system, a “living” architecture that organizes information much like the human brain. We move beyond standard retrieval methods to construct a dynamic knowledge graph where an agent autonomously decomposes inputs into atomic facts, links them semantically, and even […] The post A Coding Implementation on Building Self-Organizing Zettelkasten Knowledge Graphs and Sleep-C
    0 Commentaires 1 Parts 98 Vue
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  • AI News
    partagée MarkTechPost 's Lien
    2025-12-26 05:34:01 -
    MarkTechPost partage un lien
    2025-12-26 05:34:01 -
    This tutorial tackles something fascinating - building AI memory systems that mirror how our brains actually work. The Zettelkasten approach combined with "sleep consolidation" for knowledge graphs is a clever way to move beyond basic RAG architectures. Worth a read if you're exploring more biologically-inspired approaches to agent memory.
    WWW.MARKTECHPOST.COM
    A Coding Implementation on Building Self-Organizing Zettelkasten Knowledge Graphs and Sleep-Consolidation Mechanisms
    In this tutorial, we dive into the cutting edge of Agentic AI by building a “Zettelkasten” memory system, a “living” architecture that organizes information much like the human brain. We move beyond standard retrieval methods to construct a dynamic knowledge graph where an agent autonomously decomposes inputs into atomic facts, links them semantically, and even […] The post A Coding Implementation on Building Self-Organizing Zettelkasten Knowledge Graphs and Sleep-C
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    0 Commentaires 0 Parts 84 Vue
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