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    What comes after AI scaling?

    An announcement for The Information Bottleneck describes a conversation with Adaptation Lab co-founder and CEO Sara Hooker about mostly static models and what it would take for AI to adapt continuously to new tasks, data and users.

    Ravid Shwartz ZivRS
    1 Source, 21d ago, first seen 21d ago

    TLDR

    The Information Bottleneck episode announcement says the conversation with Sara Hooker explores continuously adapting AI, AutoScientist and self-improving systems. It also lists why non-verifiable tasks may be the next bottleneck and why interfaces may matter as much as models. Other topics include open versus closed models, distillation and Chinese AI labs, AI safety, cyber risk, biorisk, and what comes after Transformers and tokenization.

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    1 Source, first seen 21d ago

    Combined views

    1.6K

    1 Source, first seen 21d ago

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    1 Source

    Ravid Shwartz Ziv@ziv_ravidNew episode of The Information Bottleneck with Sara Hooker (@sarahookr) , co-founder and CEO of Adaptation Lab. What comes after scaling? We talked about why today’s models are still mostly static, and what it would take to build AI systems that continuously adapt to new tasks, data, and users. We also get into: • AutoScientist and self-improving AI • Why non-verifiable tasks may be the next bottleneck • Why interfaces may matter as much as models • Open vs. closed models • Distillation and Chinese AI labs • AI safety, cyber risk, and biorisk • What comes after Transformers and tokenization You can find the episode on our website, YouTube, and all the apps. It was so much fun to talk with Sara, I'm sure you will love it!21d

    1 Source

    Ravid Shwartz Ziv@ziv_ravidNew episode of The Information Bottleneck with Sara Hooker (@sarahookr) , co-founder and CEO of Adaptation Lab. What comes after scaling? We talked about why today’s models are still mostly static, and what it would take to build AI systems that continuously adapt to new tasks, data, and users. We also get into: • AutoScientist and self-improving AI • Why non-verifiable tasks may be the next bottleneck • Why interfaces may matter as much as models • Open vs. closed models • Distillation and Chinese AI labs • AI safety, cyber risk, and biorisk • What comes after Transformers and tokenization You can find the episode on our website, YouTube, and all the apps. It was so much fun to talk with Sara, I'm sure you will love it!21d