Blaise Aguera y Arcas
Blaise Agüera y Arcas is a VP and Fellow at Google, where he is the CTO of Technology & Society and founder of Paradigms of Intelligence (Pi). Pi is an organization working on basic research in AI and related fields, especially the foundations of neural computing, active inference, sociality, evolution, and Artificial Life.
In 2008, Blaise was awarded MIT’s TR35 prize. During his tenure at Google, Blaise has innovated on-device machine learning for Android and Pixel; invented Federated Learning, an approach to decentralized model training that avoids sharing private data; and founded the Artists + Machine Intelligence program.
Prior to Google, Blaise was a Distinguished Engineer at Microsoft, where he worked in a variety of roles, from inventor to strategist, and led teams with strengths in experience design, prototyping, machine vision, augmented reality, wearable computing and mapping.
Authored Publications
Sort By
Towards Generalist Biomedical AI
Danny Driess
Andrew Carroll
Chuck Lau
Ryutaro Tanno
Ira Ktena
Basil Mustafa
Aakanksha Chowdhery
Simon Kornblith
Philip Mansfield
Sushant Prakash
Renee Wong
Sunny Virmani
Sara Mahdavi
Bradley Green
Ewa Dominowska
Joelle Barral
Karan Singhal
Pete Florence
NEJM AI (2024)
Towards Physician-Level Medical Question Answering with Large Language Models
Karan Singhal
Juro Gottweis
Le Hou
Kevin Clark
Heather Cole-Lewis
Amy Wang
Sami Lachgar
Philip Mansfield
Sushant Prakash
Bradley Green
Ewa Dominowska
Nenad Tomašev
Renee Wong
Sara Mahdavi
Joelle Barral
Arxiv (2023) (to appear)
Large Language Models Encode Clinical Knowledge
Karan Singhal
Sara Mahdavi
Jason Wei
Hyung Won Chung
Nathan Scales
Ajay Tanwani
Heather Cole-Lewis
Perry Payne
Martin Seneviratne
Paul Gamble
Christopher Kelly
Abubakr Abdelrazig Hassan Babiker
Nathanael Schaerli
Aakanksha Chowdhery
Philip Mansfield
Dina Demner-Fushman
Katherine Chou
Juraj Gottweis
Nenad Tomašev
Alvin Rajkomar
Joelle Barral
Nature (2023)
LaMDA: Language Models for Dialog Applications
Aaron Daniel Cohen
Alena Butryna
Alicia Jin
Apoorv Kulshreshtha
Ben Zevenbergen
Chung-ching Chang
Cosmo Du
Daniel De Freitas Adiwardana
Dehao Chen
Dmitry (Dima) Lepikhin
Erin Hoffman-John
Igor Krivokon
James Qin
Jamie Hall
Joe Fenton
Johnny Soraker
Kathy Meier-Hellstern
Maarten Paul Bosma
Marc Joseph Pickett
Marcelo Amorim Menegali
Marian Croak
Maxim Krikun
Noam Shazeer
Rachel Bernstein
Ravi Rajakumar
Ray Kurzweil
Romal Thoppilan
Steven Zheng
Taylor Bos
Toju Duke
Tulsee Doshi
Vincent Y. Zhao
Will Rusch
Yuanzhong Xu
Zhifeng Chen
arXiv (2022)
Meta-Learning Bidirectional Update Rules
Andrey Zhmoginov
Andrew Jackson
Tom Madams
ICML 2021
Privacy-first Health Research with Federated Learning
Adam Sadilek
Dung Nguyen
Methun Kamruzzaman
Benjamin Rader
Stefan Mellem
Elaine O. Nsoesie
Jamie MacFarlane
Anil Vullikanti
Madhav Marathe
Paul C. Eastham
John S. Brownstein
npj Digital Medicine (2021)
UIBert: Learning Generic Multimodal Representations for UI Understanding
Abhinav Kumar Rastogi
Chongyang Bai
Srinivas Kumar Sunkara
Xiaoxue Zang
Ying Xu
the 30th International Joint Conference on Artificial Intelligence (IJCAI-21) (2021)
A Field Guide to Federated Optimization
Jianyu Wang
Gauri Joshi
Maruan Al-Shedivat
Galen Andrew
A. Salman Avestimehr
Katharine Daly
Deepesh Data
Suhas Diggavi
Hubert Eichner
Advait Gadhikar
Antonious M. Girgis
Filip Hanzely
Chaoyang He
Samuel Horvath
Martin Jaggi
Tara Javidi
Satyen Chandrakant Kale
Sai Praneeth Karimireddy
Jakub Konečný
Sanmi Koyejo
Tian Li
Peter Richtarik
Karan Singhal
Virginia Smith
Mahdi Soltanolkotabi
Weikang Song
Sebastian Stich
Ameet Talwalkar
Hongyi Wang
Blake Woodworth
Honglin Yuan
Manzil Zaheer
Mi Zhang
Tong Zhang
Chunxiang (Jake) Zheng
Chen Zhu
arxiv (2021)
ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces
Zecheng He
Srinivas Sunkara
Xiaoxue Zang
Ying Xu
Lijuan Liu
Nevan Wichers
Gabriel Schubiner
Ruby Lee
AAAI-21 (2020)
Generative Models for Effective ML on Private, Decentralized Datasets
8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net
