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Our framework for reporting model misalignment

107 points · 96 comments · qprofyeh

  1. Culonavirus · · focus · HN ↗
    I've been so Zitron'd that I find this just funny
    1. Sherveen · · focus · HN ↗
      Ed Zitron is the most objectively and confidently wrong human re: anything going on in AI, competing only with the likes of Gary Marcus and, on his bad days, Yann LeCun.
      1. VCFundedGenYer · · focus · HN ↗
        Explain, precisely, how he is wrong? Everything he's spoken about has come true so far. Are you made that he's good at prediction?
        1. Eisenstein · · focus · HN ↗
          <a href="https:&#x2F;&#x2F;danluu.com&#x2F;zitron&#x2F;" rel="nofollow">https:&#x2F;&#x2F;danluu.com&#x2F;zitron&#x2F;

          Some highlights:

          Feb 2024: &quot;I believe we&#x27;re reaching the upper limits about what generative AI can do and how accurate its outputs can be.&quot;

          July 2024: &quot;Generative AI, as I said back in March, is peaking, if it hasn&#x27;t already peaked. It cannot do much more than it is currently doing, other than doing more of it faster with some new inputs&quot;

          July 2024: &quot;Generative AI models aren’t getting more energy-efficient, nor are they getting more “powerful”

          August 2024: &quot;generative AI is a dead-end technology that has peaked”

          Dec 2024: &quot;I also warned you in March that generative AI had already peaked.”

          Jan 2025: &quot;I believe we’re at peak AI&quot;

          February 2025: &quot;Sam Altman deputizing Orion from GPT-5 to GPT-4.5 suggests that OpenAI has hit a wall with making its next model, requiring him to lower expectations&quot;

          April 2025: &quot;It also, at this point, is pretty obvious that generative AI isn&#x27;t going to do much more than it does today.&quot;

          August 2025: &quot;These models have clearly hit a wall where training is hitting diminishing returns&quot;

          Nov 2025: &quot;the fact we&#x27;re running out of high quality training data and we&#x27;re hitting the walls of scaling laws, in the training paradigm, these models aren&#x27;t getting better. What we&#x27;re seeing today is pretty much what they&#x27;re always gonna be like&quot;

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