In partnership with @Argonne
, we’ve been awarded the 2022 Gordon Bell Special Prize for HPC Based COVID-19 Research! We trained ANL’s GPT-style models on full Covid-19 genome, holding the entire sequence length (10240 tokens) on a single device. Read more: https://
hubs.li/Q01sCD600
RESEARCH
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Argonne’s GPT Model Wins Gordon Bell Prize for COVID Research
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Data Cards Playbook: Toolkit for Responsible ML Systems
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Check out The Data Cards Playbook, a self-guided toolkit that promotes practices to improve model and dataset transparency so that teams across organizations can develop responsible ML systems and datasets. Learn more and try out yourself → https://t.co/Zf41cgMReI pic.twitter.com/1ZEA4Jt3qC
— Google AI (@GoogleAI) 17 novembre 2022Check out The Data Cards Playbook, a self-guided toolkit that promotes practices to improve model and dataset transparency so that teams across organizations can develop responsible ML systems and datasets. Learn more and try out yourself → https://
goo.gle/3UJT8qx -
Understanding the Data Science Lifecycle and Its Importance
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Understanding the Data Science Lifecycle (and Why It's Important) #DataScience
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GALA Model Code Released on GitHub for Researchers
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The code is available. https://
github.com/paperswithcode
/galai
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ImageNetX: Human Annotations for AI Vision Model Robustness Analysis
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We’ve released ImageNetX: a set of human annotations for the popular ImageNet benchmark to gauge model robustness strengths/weaknesses — one of the first large scale efforts to pinpoint mistake types in AI computer vision systems. Explore the dataset
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ImageNetX: Explaining Vision Model Failures Through Detailed Annotations
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Even today’s best #deeplearning vision systems can fail when pose/lighting/background vary. Existing benchmarks show challenging examples but don’t explain why. ImageNetX includes annotations for the entire ImageNet1k validation set + a random subset of 12k training images.
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CS-2 System Achieves Fast SARS-CoV-2 Genome Training Convergence
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At a Gordon Bell Prize finalist talk, Kyle Hippe of @argonne said “The CS-2 system allows for really fast time to solution. We were able to train from scratch on the SARS-CoV-2 genomes and reach convergence in less than a day." Read the @HPCwire article:
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Google Researchers Develop Wildfire Simulation AI Models
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Watch the 7th episode of #MeetAGoogleResearcher, where @drewcalcagno speaks with researchers Vivian Yang and John Anderson to discuss their work on wildfire simulation to better understand and react to these disasters ↓
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PhD Position in Machine Learning and PDEs Now Open
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For those still on Twitter , this is a nice opportunity to work on ML + PDEs https://
vacatures.uva.nl/UvA/job/PhD-Po
sition-in-Learning-PDEs-from-Data/759044402/
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Making Machine Learning Research More Accessible and Discoverable
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Making machine learning research a more accessible, discoverable, and a little more fun