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    Hugging Face introduces Paper Reproductions with ML Intern

    Hugging Face says users can task agents with independently reproducing published experiments to help identify high-quality work.

    AKAK
    Hugging FaceHF
    Bojan TunguzBT
    4 Sources, ,

    TLDR

    Paper Reproductions with ML Intern lets users task agents with independently reproducing experiments from published papers, Hugging Face says. A post introducing the initiative argues that compute-driven discovery, low-quality publications and incentives to keep findings proprietary threaten open scientific sharing. Hugging Face invited users to try the feature on Paper Pages.

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    Combined views

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    4 Sources, first seen 2h ago

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    4 Sources

    🍉 Abubakar Abid@abidlabs🚨 The incentives to share scientific knowledge openly are crumbling. Historically, researchers shared scientific knowledge for two primary reasons: (1) the prestige conferred by discovery, and (2) a sense of expanding the pool of human knowledge. Even within industry, scientific discovery was far too vast an endeavor for a single scientist or team to tackle alone. So scientists explored different frontiers, usually published their findings, and collectively moved the field forward. However, as more scientific knowledge is uncovered simply by throwing massive compute at autonomous agents with minimal human intervention, these incentives are breaking down: (1) discoveries are becoming commoditized. If anyone could have found the same result simply by running the same agentic prompt with enough compute, the prestige of discovery and the pride in one's work disappears, a shift we are already witnessing in software engineering. (2) the explosion of low-quality, low-barrier publications makes genuine breakthroughs increasingly difficult to filter, discover, and appropriately credit. (3) and for companies, keeping discoveries proprietary is increasingly advantageous. Instead of publishing and waiting for the broader scientific community to build upon the work, an organization can simply turn its own compute back onto the problem to iterate faster internally. In the short term, the incentives reward secrecy. But my goal -- and at Hugging Face, our goal -- is the sustainable, long-term expansion of human knowledge that survives individual, closed companies. So we have to build new tools that reward and facilitate open sharing of knowledge. If agents are accelerating discovery, we are going to figure out how to use agents and other tools to preserve scientific integrity. Starting with this: Paper Reproductions with ML Intern -- we’ve made it possible to take a published paper and task agents with independently reproducing its experiments, helping create more signals (e.g. open artifacts) that can help identify high-quality work.2h
    Hugging Face@huggingfaceHelp keep science open, try this out on Paper Pages: https://hf.co/papers2h
    Bojan Tunguz@tunguzOpenAI trampling over all the “norms” of scientific publishing could be the best thing to happen to science in a century.2h
    AK@_akhaliqRT @abidlabs: 🚨 The incentives to share scientific knowledge openly are crumbling. Historically, researchers shared scientific knowledge f…2h

    4 Sources

    🍉 Abubakar Abid@abidlabs🚨 The incentives to share scientific knowledge openly are crumbling. Historically, researchers shared scientific knowledge for two primary reasons: (1) the prestige conferred by discovery, and (2) a sense of expanding the pool of human knowledge. Even within industry, scientific discovery was far too vast an endeavor for a single scientist or team to tackle alone. So scientists explored different frontiers, usually published their findings, and collectively moved the field forward. However, as more scientific knowledge is uncovered simply by throwing massive compute at autonomous agents with minimal human intervention, these incentives are breaking down: (1) discoveries are becoming commoditized. If anyone could have found the same result simply by running the same agentic prompt with enough compute, the prestige of discovery and the pride in one's work disappears, a shift we are already witnessing in software engineering. (2) the explosion of low-quality, low-barrier publications makes genuine breakthroughs increasingly difficult to filter, discover, and appropriately credit. (3) and for companies, keeping discoveries proprietary is increasingly advantageous. Instead of publishing and waiting for the broader scientific community to build upon the work, an organization can simply turn its own compute back onto the problem to iterate faster internally. In the short term, the incentives reward secrecy. But my goal -- and at Hugging Face, our goal -- is the sustainable, long-term expansion of human knowledge that survives individual, closed companies. So we have to build new tools that reward and facilitate open sharing of knowledge. If agents are accelerating discovery, we are going to figure out how to use agents and other tools to preserve scientific integrity. Starting with this: Paper Reproductions with ML Intern -- we’ve made it possible to take a published paper and task agents with independently reproducing its experiments, helping create more signals (e.g. open artifacts) that can help identify high-quality work.2h
    Hugging Face@huggingfaceHelp keep science open, try this out on Paper Pages: https://hf.co/papers2h
    Bojan Tunguz@tunguzOpenAI trampling over all the “norms” of scientific publishing could be the best thing to happen to science in a century.2h
    AK@_akhaliqRT @abidlabs: 🚨 The incentives to share scientific knowledge openly are crumbling. Historically, researchers shared scientific knowledge f…2h