Two possibilities: Either he's saying this now as an employee of OpenAI out of loyalty (which I find hard to imagine), or option two, because he's seen how far along the models are internally (more likely). Because his two statements strongly contradict each other.
ETHICS
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Identity Recognition AI: 68% Accuracy Risk Demands Policy Action
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68% at 90% precision from public posts alone is sobering. Most people have no idea how much identity signal they leak across platforms. This is exactly the kind of capability research that should inform policy before it becomes a product.
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AI threatens authors’ livelihoods in publishing industry
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C'est pas les librairies qu'il fallait sauver, mais les auteurs. Maintenant il y a l'IA…
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Moving Toward Race-Neutral Clinical Algorithms in Healthcare
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We have a new piece in Nature Health led by @dmshanmugam, @sidhikab1, and a wonderful team of coauthors on how to move towards a world in which race is not used in clinical algorithms! Divya Shanmugam (@dmshanmugam) New in Nature Health: how might we move towards a world in which race is not used in clinical algorithms? We need (1) careful comparison of race-aware and race-neutral algorithms and (2) systemic efforts to address underlying disparities. — https://nitter.net/dmshanmugam/status/2036097222404628670#m
→ View original post on X — @berkeley_ai, 2026-03-23 15:11 UTC
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OpenAI Microsoft exclusivity deal and nonprofit structure questions
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1) Afaik OAI still has exclusive deal with Microsoft regarding access to the models. This would completely bypass that. 2) Does the division between for-profit and non-profit still exist? I'd be interested to know how the exclusive access for private equity firms to internal
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NVIDIA OpenShell: Integrated Governance for Autonomous AI Agents
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A new class of AI is emerging: autonomous agents that achieve complex tasks. To deploy them responsibly means building in governance from day one. NVIDIA OpenShell unifies open innovation with built-in security and privacy controls—so agents can operate more securely, predictably, and in line with policies. Learn more: nvda.ws/47bUxyI [Translated from EN to English]
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AI Agents Security Risks: Avalanche Metaphor for Permission Overload
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I'm smiling but this was taken ~30 mins after I'd been buried in an avalanche (snapping a ski in half). IT doesn't pose the same bodily risks, but the way we're giving agents the same levels of access as humans is creating conditions ripe for a security "avalanche." Oso (@osoHQ) A metaphor from @mjasay on agent security: Overpermissioned humans = a buried weak snow layer. Add AI agents = avalanche. We've been ignoring the snowpack for years. — https://nitter.net/osoHQ/status/2036089236131029200#m
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ICML 2026 removes reviews from LLM-using reviewers
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As co-comms chair of ICML 2026 (w @kgorman), I'm super proud of how transparent we've been able to be on all of the (bold!) decisions made. Thanks to all the organizers (esp PC chairs) for being aligned on this. The community deserves to understand these important decisions ICML Conference (@icmlconf) To ensure compliance w peer-review policies, ICML has removed 795 reviews (1% of total) by reviewers who used LLMs when they explicitly agreed to not. Consequently, 497 papers (2% of all submissions) of these (reciprocal) reviewers have been desk rejected Details in blog post 👇 — https://nitter.net/icmlconf/status/2034279558598242523#m
→ View original post on X — @thegautamkamath, 2026-03-23 13:54 UTC
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Five Essentials for Responsible and Trustworthy AI
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AI without trust = risk. AI with trust = scale 5 essentials for responsible AI: Governance Anonymization Data minimization Audits Privacy by design The winners in AI won’t just be the fastest. They’ll be the most trusted. #AI #Privacy #ResponsibleAI #Tech
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AI in Education: Teachers and Students Adoption Concerns
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More and more teachers and students are using #AI – even though it might do more harm than good
by Tal Slemrod @ConversationUS Learn more: https://
bit.ly/3NpZey9 #ArtificialIntelligence #MachineLearning #ML