Nithum

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

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

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

Do Machine Learning Models Memorize or Generalize? pair.withgoogle.com/explorables/gr… An interactive introduction to grokking and mechanistic interpretability w/ @ghandeharioun, @nadamused_, @Nithum, @wattenberg and @iislucas

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

Confidently Incorrect Models to Humble Ensembles by @Nithum, @balajiln 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…

In partnership with @GoogleMagenta, 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…

Most machine learning models are trained by collecting vast amounts of data on a central server. @nicki_mitch 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…

🤔 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

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…

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 🤬!

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

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

Awesome work by @adamscj and @nithum! wired.com/2017/02/google… via @WIRED

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

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

How can we keep extremists from using technology to cause harm? @POTUS and @WIRED asked our very own @yasmind. bit.ly/2eCOxa8

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

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