About - Semantic Experiences
How It Works How does a computer understand you when you talk to it using everyday language? Our approach was to use billions of lines of dialogue to teach an AI how real human conversations flow. Once the AI has learned from that data, it is then able to predict how likely one statement would follow another as a response. In these demos, the AI is simply considering what you type to be an opening
Publications – Google Research
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Position Bias Estimation for Unbiased Learning to Rank in Personal Search
Position Bias Estimation for Unbiased Learning to Rank in Personal Search Xuanhui Wang, Nadav Golbandi, Michael Bendersky, Donald Metzler, Marc Najork Google Inc. Mountain View, CA {xuanhui,nadavg,bemike,metzler,najork}@google.com ABSTRACT A well-known challenge in learning from click data is its inher- ent bias and most notably position bias. Traditional click models aim to extract the ⟨query, do
Latent Cross: Making Use of Context in Recurrent Recommender Systems
Latent Cross: Making Use of Context in Recurrent Recommender Systems Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li∗, Vince Gatto, Ed H. Chi Google, Inc. Mountain View, California {alexbeutel, pcovington, sagarj, canxu, vgatto, edchi}@google.com, vena900620@gmail.com ABSTRACT The success of recommender systems often depends on their ability to understand and make use of the context of the
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https://research.google.com/pubs/archive/36955.pdf
Challenges in building large-scale information retrieval systems: invited talk
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Research at Google
Google Colab
Google Colab
Semantris
Large-scale Incremental Processing Using Distributed Transactions and Notifications
What’s your ML Test Score? A rubric for ML production systems
Semantic Experiences
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Google Colaboratory
Experimenting At Scale With Google Chrome's SSL Warning
https://research.google.com/pubs/archive/41684.pdf