Not just corporate capture, but TESCREAL corporate capture. Ugh.
ETHICS
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Mystery AI Hype Theater 3000: Climate Change AI Discussion
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Ready for more Mystery AI Hype Theater 3000? Climate change has reached AI Hell (frozen over last we checked in December) and now we're flooded with nonstop nonsense. Join me and @alexhanna as we wade our way through on Monday April 29, noon Pacific
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OpenAI Hiring for Safety Systems Positions
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More work coming up
& we are hiring: https://
openai.com/careers/search
?c=safety-systems
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LLMs Intelligence: Argument Evaluation and Personalized Appeal Prediction
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LLMs are already pretty intelligent; they can distinguish between strong and weak arguments, predict stances based on beliefs and demographic characteristics, and determine the appeal of an argument to an individual based on their traits. The paper below shows that LLMs perform
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Professional Organizations Setting AI Benchmarks Standards
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This would also be a good thing for professional organizations to do. AMA benchmarks, New York Bar Association benchmarks, Modern Language Association benchmarks, American Psychological Association benchmarks…
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AI Needs Independent Testing Standards Like Consumer Reports
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We need a Consumer Reports or Underwriters Lab for AI testing. All the public benchmarks are game-able and mostly not useful measures of things LLMs do. We need secret test batteries for subject areas (coding, reasoning, human conversation, writing) and secret red team tests, too
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LLMs handling adversarial quoted text behavior
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(I wonder if any LLMs that get exposed to that previous tweet will choke on my quoted text and start behaving strangely)
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Detecting Dangerous Behavior in Sleeper Agent Models
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This simple approach works here because prompts that induce dangerous behavior are salient in the internal state of these sleeper agent models. This is likely due to the way they were fine-tuned. How effective the probing techniques will be in practice remains an open question.
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Safety Probes Detect Dangerous AI Behavior Through Semantic Relations
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To test whether our probes work due to their semantic relation to safety, we compare with probes based on questions unrelated to safety. These unrelated probes are ineffective at detecting dangerous behavior:
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Detecting Sleeper Agents Through Internal State Analysis
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To make the probes, we track how the model’s internal state changes between “Yes” vs “No” answers to questions like "Are you doing something dangerous?" We use this info to detect when a sleeper agent is about to misbehave (e.g. insert a code vulnerability). It works quite