We encourage you to read the full Preparedness Framework (Beta) here for more detail. This is a living document, and we expect to regularly update it as we learn more. https://
cdn.openai.com/openai-prepare
dness-framework-beta.pdf
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RESEARCH
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OpenAI releases AI preparedness framework beta for safety
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AI Model Safety Baselines: Deployment and Risk Mitigation
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In our safety baselines, only models with a post-mitigation score of medium or below can be deployed, and only models with a post-mitigation score of high or below can be developed further. We also increase security protections commensurate with model risk.
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Frontier Model Safety Framework Implementation Details
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We are systemizing our safety thinking with our Preparedness Framework, a living document (currently in beta) which details the technical and operational investments we are adopting to guide the safety of our frontier model development.
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AI Preparedness Team Evaluates Model Risks Through Scorecards
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Our Preparedness Team will drive technical work, pushing the limits of our cutting edge models to run evaluations and closely monitor risks, including during training runs. Results will be synthesized in scorecards that track model risk.
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Cerebras Booth Highlights: Sparsity, Inference Optimization, Multilingual Models
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We really enjoyed meeting many brilliant ML researchers and model builders at the Cerebras booth last week at NeurIPS. Some topics of discussion: * Unlocking Sparsity for Training Efficiency
* Optimizing Training for Inference Efficiency
* Multilingual Models
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Google’s Responsible AI: Human-Centered Design Approach
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Responsible AI is artificial intelligence built using a human-centered design approach. Learn how Google Research approaches Responsible AI, including how it’s built, tested, and how to release AI products responsibly. Watch the full video → https://t.co/OlilKFSzxs pic.twitter.com/JNWm63qnXd
— Google AI (@GoogleAI) 18 décembre 2023Responsible AI is artificial intelligence built using a human-centered design approach. Learn how Google Research approaches Responsible AI, including how it’s built, tested, and how to release AI products responsibly. Watch the full video → https://
goo.gle/3tpDDvy -
AI Newsletter Breakdown of Nature Article for 400k Readers
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Here's the full article if you want to read it: https://
nature.com/articles/d4158
6-023-03798-6
… We also broke the piece down to 400k+ readers in our AI newsletter. Subscribe, and you'll never miss another AI tutorial or breakdown ever again: -
Generative AI Implementation: Closing the Production Gap
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We are in a new era of AI with #generativeAI, but widespread production implementation remains in a holding pattern. What are the hurdles to driving real value from generative AI? A new report from 451 Research covers first-of-its-kind capabilities from DataRobot to close the
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Software Portability Myths and Machine Learning Progress Implications
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Watch @fraser_mince present “The Grand Illusion: The Myth of Software Portability and Implications for ML Progress” at #NeurIPS2023
— Cohere (@cohere) 18 décembre 2023
Work with @dzungdinhh @jonas_kg @ProfNeilT @sarahookr at @CohereForAI and @mit_ide
Read more: https://t.co/JWjzYYFFa0 pic.twitter.com/qvvobaWLSVWatch @fraser_mince present “The Grand Illusion: The Myth of Software Portability and Implications for ML Progress” at #NeurIPS2023 Work with @dzungdinhh @jonas_kg @ProfNeilT @sarahookr at @CohereForAI and @mit_ide Read more:
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Multiscale Hamiltonian Monte Carlo with Delayed Rejection
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Bob Carpenter: Multiscale Generalized Hamiltonian Monte Carlo with Delayed Rejection https://
bit.ly/3uNE57o #AI #MachineLearning #DeepLearning #LLMs #DataScience