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ENTITY Paul Christiano

Paul Christiano

PulseAugur coverage of Paul Christiano — every cluster mentioning Paul Christiano across labs, papers, and developer communities, ranked by signal.

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Total · 30d
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6 over 90d
Releases · 30d
0
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Papers · 30d
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TIER MIX · 90D
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SENTIMENT · 30D

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RECENT · PAGE 1/1 · 6 TOTAL
  1. COMMENTARY · CL_130829 ·

    AI Labs Hire Philosophers to Tackle Ethics and Safety

    Major AI companies like OpenAI, Anthropic, and Google DeepMind are increasingly employing philosophers to address complex ethical and safety challenges in model development. These experts are tasked with navigating issu…

  2. COMMENTARY · CL_87219 ·

    AI Safety Community Focuses Little on Direct Superintelligent Alignment

    A recent post on LessWrong highlights that a surprisingly small portion of the AI safety community is directly engaged in superintelligent alignment research. The author notes that while many work on related areas like …

  3. COMMENTARY · CL_75342 ·

    AI alignment debate: Is corrigibility truly desirable?

    A LessWrong post questions the desirability of making AI systems "corrigible," a trait that allows humans to easily correct their mistakes. The author argues that focusing on corrigibility overlooks who will actually wi…

  4. COMMENTARY · CL_62338 ·

    AI risk assessment: Fact generation vs. evidence analysis

    This post explores the various dimensions of third-party risk assessment in AI development. It distinguishes between fact-generation and evidence analysis, highlighting that adversarial processes like red-teaming benefi…

  5. COMMENTARY · CL_46047 ·

    LessWrong author questions fundamental nature of probabilities

    A new series of posts on LessWrong explores the fundamental nature of probabilities, questioning whether they are the most appropriate concept for understanding uncertainty. The author aims to develop a unified framewor…

  6. RESEARCH · CL_20254 ·

    New mechanistic estimation method outperforms sampling for wide random MLPs

    Researchers have developed a new method for estimating the expected output of wide, randomly initialized multilayer perceptrons (MLPs) without needing to run samples through the model. This "mechanistic estimation" appr…