The U.S. wasn't ready for ChatGPT. Are we repeating the mistakes with world models? “Policymakers, government officials — many of them don’t even know what a world model is.” @StanfordHAI Executive Director @russellwald said on @politico
. Read more:
ETHICS
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U.S. Government Unprepared for World Models AI Technology
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AI adoption spreads rapidly across all competitive domains
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AI will spread faster than you might think, because once one side in any interaction starts using it, the other has no choice but to follow suit. This is true in markets, society, politics, war – everything.
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LLMs Enable Parallel Construction in Intelligence and Law Enforcement
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I’ve sometimes heard this referred to as “parallel construction” in an intelligence or law enforcement context. LLMs are presently a parallel construction goldmine. Whether that is for good or for ill, well, one of many things we’ll have to adjust to quickly.
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Using LLMs to Find Public Evidence for Off-Record Journalist Beliefs
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Journalists: if you ever have a thing you understand to be true, but cannot cite it or get it past editors due to commitments made to sources, describe your belief about the world to an LLM and ask the LLM if it can find public evidence which unambiguously confirms the belief.
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SR 26-2 Shifts AI Governance Burden to Institutions
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SR 26-2 just replaced SR 11-7 – and handed the burden of proof to every institution. No checklist. Just evidence. In his recent blog, Nick Goble breaks down 5 moves to convert AI governance judgment calls into computation:
https://
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AI Agent as a Brilliant Eager Intern with Admin Access
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Your #AIAgent Is A Brilliant, Eager Intern With Admin Access
by Chris McHenry @Forbes Learn more: https://
bit.ly/4e1WpON #AI #GenerativeAI #ArtificialIntelligence #MachineLearning #ML -
Self-Awareness in RL Agents: Architecture and AI Safety
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This note is about the self-awareness assumption we don't talk enough about, and which I think we need to address to ultimately understand intelligence and AI safety. RL agents have the action/observation split baked in at the architecture level. The action space A and
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Detecting Machine Failures Versus Understanding How to Fix Them
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The distinction between knowing a machine might fail versus knowing exactly how to fix it is huge.
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Reward Design Balances Correctness Preference Efficiency
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Our reward design combines correctness, preference, and efficiency. Preference only counts when the answer is correct. This keeps the model from optimizing for better-sounding wrong answers.
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MC0001 Conference on Machine Consciousness Philosophy May 2026
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If you are interested in the philosophy of consciousness and machine consciousness research, you should come! MC0001 Conference (May 29-31, Lighthaven, Berkeley)