The US has a strong history of fostering innovation, and regulators have played a key role by establishing clear rules and pursuing bad actors. We hope the US will take a more constructive approach to collaborating with innovators while protecting consumers.
POLICY
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Important Preprint on LLMs and Data Rivers
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Important preprint from Sylvie Delacroix on LLMs and Data Rivers. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4388928
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Manufacturing Network Security Barriers: Awareness and Complexity
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A6. The biggest barrier to network security is lack of awareness and expertise and the increasing complexity of manufacturing network, with the adoption of more digital technologies and the interconnectivity of various devices and systems. Manufacturers may prioritize meeting
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Bard’s Training Data Transparency Issues and Speculation
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Bard still seems confused about whether it was or wasn't trained using private data from Gmail (Google says it it wasn't). In reality it probably has zero idea what it was trained since that information was not in the training data, so it's just making guesses.
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ARC advances AI alignment evaluation and security measurement
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We strongly agree there’s much more work to be done on alignment, security, and measurement. You can read about ARC’s specific approach to evaluation here: https://
evals.alignment.org/blog/2023-03-1
8-update-on-recent-evals/
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Who Should Decide How AI Systems Behave?
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How Should AI Systems Behave, and Who Should Decide? https://
openai.com/blog/how-shoul
d-ai-systems-behave
… @ASMEdotorg @3DSNorthAmerica @MargaretSiegien @3DSdelmia @3DStherese @Cindybolt61 @fogoros @DrFerdowsi @CRudinschi @PawlowskiMario @IIoT_World @MEngineeringMag #Science #Engineering #Technology #SET -
Model Safety: Mitigation Without Full Release, Transparency Needed
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There's a lot of ways to mitigate harms without having to publicly release the entire model. There are many papers on auditing, datasheets, transparency etc. With GPT3 we knew the training data. With GPT4 we don't. Without that, we're all looking at shadows in Plato's cave.
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Black Box AI Systems: The Reproducibility and Transparency Crisis
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Without knowing how these systems are built, there is no reproducibility. You can't test or develop mitigations, predict harms, or understand when and where they should not be deployed or trusted. The tools are black boxed.
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Bard’s False Gmail Training Claim Sparks Public Debate
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In the 12hrs since Bard told me it was trained on Gmail data:
-Google replies (says it's not)
-Elon Musk replies (lol)
-Google adds a 'community note' that this is a Bard error and it's not trained on Gmail
-Some ace memes
What should happen next: Real talk about training data -
Lack of Transparency in AI Model Training Data
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There is a real problem here. Scientists and researchers like me have no way to know what Bard, GPT4, or Sydney are trained on. Companies refuse to say. This matters, because training data is part of the core foundation on which models are built. Science relies on transparency.