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    Gary Marcus Says Neurosymbolic AI Rescued LLM Scaling

    Cognitive scientist Gary Marcus posted that tools and harnesses overcame pure LLM limits.

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    KU
    3 Sources, 29d ago, first seen 29d ago

    TLDR

    Gary Marcus, identified in the packet as a cognitive scientist and critic of deep learning, wrote that pure scaling of LLMs hit a wall. He stated neurosymbolic AI rescued progress by adding harnesses and tools to address model weaknesses. Marcus noted AI itself avoided any plateau because these additions complemented the original systems. He tied the development directly to the longstanding goals of neurosymbolic approaches. The statement forms the core evidence line in the supplied packet for an AI topic cluster.

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    44.7K

    3 Sources, first seen 29d ago

    Combined views

    44.7K

    3 Sources, first seen 29d ago

    380 likes
    380 likes
    56 comments
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    33 reposts

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    56 comments
    97 saves
    33 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    3 Sources

    @GaryMarcusPure scaling of LLMs did actually hit a wall; neurosymbolic AI rescued it. AI didn’t hit a plateau; pure LLMs did — until people used harnesses and tools to complement their weaknesses. Which was always always always the point of neurosymbolic AI in the first place.
    @kidehenAnd that's the bottom line. It's the neuro-symbolic aspect of AI that's moved the needle to where things are today. LLMs simply handled the fuzzy natural language aspect that always tripped up the symbolic side.

    3 Sources

    @GaryMarcusPure scaling of LLMs did actually hit a wall; neurosymbolic AI rescued it. AI didn’t hit a plateau; pure LLMs did — until people used harnesses and tools to complement their weaknesses. Which was always always always the point of neurosymbolic AI in the first place.
    @kidehenAnd that's the bottom line. It's the neuro-symbolic aspect of AI that's moved the needle to where things are today. LLMs simply handled the fuzzy natural language aspect that always tripped up the symbolic side.