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Beautiful "RNN with attention" tutorial from one of the authors of Google's troll-fighting AI Nithum. github.com/conversationai…. We presented this toxic comment detection model together in the "Tensorflow and modern RNNs without a PhD" talk. Excuse our French 🤬!

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. pair.withgoogle.com/explorables/un…

🤔 We've come a long way with #NLP, but what have language models actually learned? Watch Senior Software Engineer at Google PAIR, Nithum Thain, discuss AI language model learnings → goo.gle/3HVtolv

Most machine learning models are trained by collecting vast amounts of data on a central server. Nicole Mitchell and I looked at how federated learning makes it possible to train models without any user's raw data leaving their device. pair.withgoogle.com/explorables/fe…

In partnership with Google Magenta Project, we invited 13 professional writers to use Wordcraft, our experimental LaMDA-powered AI writing tool. We've published all of the stories written with the tool, along with a discussion on the future of AI and creativity. g.co/research/wordc…

Confidently Incorrect Models to Humble Ensembles by Nithum, Balaji Lakshminarayanan and Jasper Snoek pair.withgoogle.com/explorables/un…

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. pair.withgoogle.com/explorables/un…

Some of my thoughts on generative AI... and a reboot of the PAIR blog... medium.com/people-ai-rese… #responsibleai #hci #machinelearning #GenerativeAI

Do Machine Learning Models Memorize or Generalize? pair.withgoogle.com/explorables/gr… An interactive introduction to grokking and mechanistic interpretability w/ Asma Ghandeharioun, @nadamused_, Nithum, Martin Wattenberg and iislucas (Lucas Dixon)

While large language models appear to have a rich understanding of the world, how do we know they’re not simply regurgitating from training data? Check out the latest AI Explorable on a phenomenon called grokking to learn more about how models learn. → goo.gle/45ohnQh

Can large language models (LLMs) explain their internal mechanisms? Check out the latest AI Explorable on Patchscopes, an inspection framework that uses LLMs to explain the hidden representations of LLMs. Learn more → goo.gle/patchscopes

Check out our most recent Explorable "Can Large Language Models Explain Their Internal Mechanisms?" pair.withgoogle.com/explorables/pa…

Detecting personal attacks on Wikipedia: some context from the 2015 survey meta.wikimedia.org/wiki/Research:…

Wikipedia building n-gram models to detect personal attacks and harassment: meta.m.wikimedia.org/wiki/Research:… x.com/wikiresearch/s…

How can we keep extremists from using technology to cause harm? President Donald J. Trump and WIRED asked our very own Yasmin Green. bit.ly/2eCOxa8

We collected and labeled over 1 million Wikimedia Foundation page edits to determine where personal attacks were made. bit.ly/2lgWfcB

Introducing Perspective, using machine learning to improve discussions online. bit.ly/2lIZEjS

Awesome work by CJ Adams and Nithum! wired.com/2017/02/google… via WIRED

"Tensorflow and deep learning without a PhD" continues Devoxx on Monday 9:30. Deep learning novices welcome, fresh neurons required :-)

Devoxx My co-speaker for this session will be Nithum from Google Jigsaw. He fights bad behavior online with neural networks.