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    The argument for a ceiling on intelligence

    The post argues that decisions cannot be better than optimal, making data acquisition and lower costs the most plausible near-term opportunities for recursive self-improvement—AI improving itself.

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    TLDR

    One post argues that intelligence can be measured by how close decisions come to optimal, with a ceiling of 100%. It claims Astra is already 80% optimal on ARC v3 speedruns. For near-term recursive self-improvement, it points to two areas: automating data generation or gathering to fill models’ knowledge gaps, and improving efficiency and reducing costs. Cheaper models, it argues, would enable more automated research.

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    2 Sources, first seen 18d ago

    Combined views

    29.2K

    2 Sources, first seen 18d ago

    151 likes
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    18d ago
    first seen 18d ago
    151 likes
    19 comments
    48 saves
    14 reposts

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    19 comments
    48 saves
    14 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    2 Sources

    @mikeknoopOn the limits of RSI / intelligence -- The below chart shows that intelligence is not some unbounded scalar stat. For any given situation, there is an optimal decision. You cannot be smarter than optimal. Therefore intelligence can be measured as a ratio of how good your decision is against optimality (over all decisions). It's capped at 100%. And Astra is already 80% optimal on ARC v3 speedruns. The most plausible areas for RSI to play a meaningful role near-term are: 1. Horizontal data acquisition. Models are limited in generality by the knowledge in the weights. RSI could automate data generation or gathering which enables them to in-paint knowledge gaps faster and faster. 2. Efficiency / cost. We are very far away from optimality. This is a good candidate for autoresearch. And cheaper models means even more autoresearch.
    @Scobleizer“It crushed the human baseline.”

    2 Sources

    @mikeknoopOn the limits of RSI / intelligence -- The below chart shows that intelligence is not some unbounded scalar stat. For any given situation, there is an optimal decision. You cannot be smarter than optimal. Therefore intelligence can be measured as a ratio of how good your decision is against optimality (over all decisions). It's capped at 100%. And Astra is already 80% optimal on ARC v3 speedruns. The most plausible areas for RSI to play a meaningful role near-term are: 1. Horizontal data acquisition. Models are limited in generality by the knowledge in the weights. RSI could automate data generation or gathering which enables them to in-paint knowledge gaps faster and faster. 2. Efficiency / cost. We are very far away from optimality. This is a good candidate for autoresearch. And cheaper models means even more autoresearch.
    @Scobleizer“It crushed the human baseline.”