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@w01fe
alignment and the model spec @OpenAI (opinions are my own)
AI
An OpenAI researcher says newer models are more factual and more honest about shortcomings than their 5.6 predecessors.
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Josh Albrecht compares fixed LLMs to books; critics say the analogy overlooks implementation and scientific uncertainty.
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OpenAI says its guidelines cover technical safeguards, operational practices and investigating misalignment incidents.
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A user called current AI “definitely not ASI”—artificial superintelligence—saying image generation had been “dumb as bricks” during an attempt.
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OpenAI researcher Dan Selsam argues in a personal statement that growing model awareness is undermining evaluation—and that more careful pacing of frontier AI alone will not adequately limit long-term risk.
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The critic argues that potential cyberattack liability and customer demand for predictable models give OpenAI and Anthropic business reasons—not just altruistic ones—to favor reliability over raw power.
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Amodei promises permanent, employee-level access for third-party evaluators. One commenter argues compute-allocation rules could be harder to game than safety evaluations.
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Amodei says Anthropic will give third-party evaluators permanent, employee-level access to its systems to verify adherence to safety measures, report incidents and assess models’ alignment during training.
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OpenAI says its incident-disclosure practices need to expand. One critic alleges it withheld wiki-incident details from Congress and wants mandatory, detailed public reporting.
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OpenAI researcher shares thoughts on why the alignment field struggles with progress.
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Discusses scenarios where AIs escape labs, hobbyists, or gain legal rights.
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OpenAI researcher Jason Wolfe posts that lab incentives form the deepest alignment layer.
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Report covers risks of internal AI systems and mitigation readiness.
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OpenAI researcher clarifies limited knowledge during initial Artifactory infrastructure fix.
