Check out our most recent Explorable "Can Large Language Models Explain Their Internal Mechanisms?"

RT @GoogleAI: Can large language models (LLMs) explain their internal mechanisms? Check out the latest AI Explorable on Patchscopes, an ins…

RT @GoogleAI: While large language models appear to have a rich understanding of the world, how do we know they’re not simply regurgitating…

RT @adamrpearce: Do Machine Learning Models Memorize or Generalize? An interactive introduction to grokking and m…

RT @iislucas: Some of my thoughts on generative AI... and a reboot of the PAIR blog... #responsibleai #hci #mach…

RT @adamrpearce: Confidently Incorrect Models to Humble Ensembles by @Nithum, @balajiln and Jasper Snoek https://t…

ML models sometimes make confidently incorrect predictions when they encounter out of distribution data. Ensembles of models can make better predictions by averaging away mistakes.

RT @_coenen: In partnership with @GoogleMagenta, we invited 13 professional writers to use Wordcraft, our experimental LaMDA-powered AI wri…

RT @adamrpearce: Most machine learning models are trained by collecting vast amounts of data on a central server. @nicki_mitch and I looked…

RT @TensorFlow: 🤔 We've come a long way with #NLP, but what have language models actually learned? Watch Senior Software Engineer at Goog…

Check out our new explorable on machine learning calibration: Machine learning models express their uncertainty as model scores, but through calibration we can transform these scores into probabilities for more effective decision making.

RT @martin_gorner: Beautiful "RNN with attention" tutorial from one of the authors of Google's troll-fighting AI @Nithum.

RT @martin_gorner: @Devoxx My co-speaker for this session will be @Nithum from Google @JigsawTeam. He fights bad behavior online with neura…

RT @martin_gorner: "Tensorflow and deep learning without a PhD" continues @Devoxx on Monday 9:30. Deep learning novices welcome, fresh neur…

RT @MullinSean: Awesome work by @adamscj and @nithum! via @WIRED

RT @JigsawTeam: Introducing Perspective, using machine learning to improve discussions online.

RT @JigsawTeam: We collected and labeled over 1 million @Wikimedia page edits to determine where personal attacks were made.

RT @JigsawTeam: How can we keep extremists from using technology to cause harm? @POTUS and @WIRED asked our very own @yasmind. https://t.co…

@HitchTheKitch Are you guys open tomorrow? Where will you be?

RT @amyxzh: Wikipedia building n-gram models to detect personal attacks and harassment: